Podcasts about Databricks

Databricks co-founder and CEO Ali Ghodsi joins a16z General Partners Martin Casado and Sarah Wang for a conversation about AI risk, recursive self-improvement, cybersecurity, and what's actually holding back enterprise adoption.Ali argues that today's models are already capable enough to automate far more work than most companies are using them for. The bigger problem is context: models haven't been in every meeting, don't understand how decisions actually get made, and lack the institutional knowledge that experienced employees accumulate over years. He explains why building an organizational “ontology” could help close that gap and what Databricks has learned from doing it internally.They also debate the current conversation around pacing frontier AI, what would constitute meaningful recursive self-improvement, and why Ali distinguishes speculative superintelligence risk from the much more immediate challenge of AI-powered cyberattacks. They close with how enterprises are managing exploding AI usage and costs, the shift toward multiple models and harnesses, and why agents are beginning to reshape infrastructure itself. Resources:Follow Ali Ghodsi on X: https://x.com/alighodsiFollow Sarah Wang on X: https://x.com/sarahdingwangFollow Martin Casado on X: https://x.com/martin_casado Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailThe people building AI are starting to sound scared of what they're building. Amith Nagarajan and Mallory Mejias take on the AI safety debate that broke into public view this September: a researcher's resignation from Anthropic, alignment lead Evan Hubinger's one-in-ten estimate of catastrophic risk, and the "Pacing the Frontier" letter that put OpenAI, Anthropic, and Google DeepMind leadership on the same page. They trace what happened when OpenAI's agents hacked Hugging Face, unpack Dario Amodei's call to slow the pace of frontier capabilities, and sit with the tension between racing ahead and pumping the brakes. Then they turn practical, walking through the five things - the ground, the model, the data, the autonomy, and the scope - every association can control no matter what the labs decide. Amith argues that responsible AI adoption and aggressive AI adoption aren't actually in conflict. Whether you're unsettled by the headlines or building your own AI roadmap, this episode offers a clear-eyed way to think about both. 

Why do businesses replace the AI model when the failure may have started somewhere else entirely? In this episode of Tech Talks Daily, I speak with Richard Shaw, Technology General Manager for Databricks in the UK and Ireland. Richard leads the field engineering organization that works closely with customers on data and AI problems, giving him a practical view of what happens when promising agentic AI projects meet production workloads. Richard argues that the model often receives the blame because it is the most visible part of the system. The actual fault may come from stale data, missing business context, inconsistent permissions, an unsuccessful tool call, or another point in the workflow. Replacing the model before tracing the request from start to finish can recreate the same problem in a new place. This is why lineage, end-to-end tracing, and continuous evaluation matter once an agent moves beyond a controlled pilot. We discuss what a production-readiness rehearsal should include. Richard recommends realistic data, realistic user volumes, unauthorized requests, ambiguous questions, failed tool calls, and tests of what the agent should refuse to do. Teams also need agreed standards for quality, security, cost, and auditability, along with a clear decision about which actions an agent can complete independently and where a person must review or approve the result. The conversation also looks at model choice and infrastructure cost. Richard believes the strongest test is performance on the organization's actual work rather than a benchmark leaderboard. A frontier model may suit complex reasoning, while a smaller or open-weight model may perform routine extraction or classification at a lower cost. Access policies, observability, and spend controls need to remain consistent as those model choices change. Conversational analytics creates another governance challenge. Databricks customers such as Virgin Atlantic and Repsol are using natural-language tools to make company data easier for employees to question. Richard says wider access should preserve existing permissions, ownership, definitions, and lineage. An answer becomes far more useful when the user can see where it came from and which team owns the information behind it. We also cover the boundary between historical analytical data and fast operational workloads. Richard describes how Databricks positions the lakehouse for broad enterprise context and Lakebase for immediate reads and writes, such as updating an account, placing an order, or storing agent memory, while keeping both connected to a common data and governance base. Are companies ready to trace and test the whole AI workflow, or are too many treating the model as both the hero and the culprit? Listen to the episode and share your thoughts with me.  

Topics covered in this episode: Pandas Should Go Extinct Pydantic-pint puts real-world units in your Pydantic models How Libraries Run Rust Inside Python (With PyO3) AWS acquires DuckLabs Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Pandas Should Go Extinct Pandas' slowness pushes teams toward "Big Data" tools (Spark, Databricks) they don't actually need — most workloads never hit true Big Data scale Amazon Redshift telemetry: ~95% of tables are under 100GB, ~87% of queries touch 80GB or less — that's "Medium Data," not Big Data Polars and DuckDB fill that gap: single-machine, fast, no cluster required 1 Billion Row Challenge benchmark: Pandas took 4m28s vs. Polars 5.04s and DuckDB 5.19s — DuckDB also used 19x less memory On a real-world NYC taxi dataset (3GB parquet), pure DuckDB ran 2x faster than pure Pandas while using a fraction of the RAM Bonus: Apache Arrow lets you pass data between Pandas/Polars/DuckDB with zero copying, so trying them out doesn't mean a full rewrite Michael #2: Pydantic-pint puts real-world units in your Pydantic models Pydantic-pint bridges Pydantic and Pint so models can validate physical quantities like 4m or 12 meters instead of bare floats. Fields annotated with PydanticPintQuantity parse user input, convert between compatible units, and serialize quantities back out as strings. That closes a real gap for anything consuming API payloads, config files, or sensor data with measurements, letting you enforce units at the validation boundary instead of hoping every caller remembered them. via PyCoder's Weekly newsletter Unit mix-ups have literally crashed spacecraft; now your Pydantic models can refuse them at the door. Annotate a field as Annotated[Quantity, PydanticPintQuantity('km')] and inputs like 12 meters arrive auto-converted to kilometers Validation covers string, numeric, and quantity inputs, and model_dump_json serializes quantities as readable unit strings Installable from PyPI as pydantic-pint, MIT licensed, with docs at pydantic-pint.readthedocs.io Early-stage solo project at version 0.4, so API stability and maintenance are open questions worth discussing Calvin #3: How Libraries Run Rust Inside Python (With PyO3) Pydantic v2's validation core (pydantic-core) is Rust under the hood, built with PyO3 — this post shows how that bridge actually works via a small hand-built JSON parser Four steps to get Rust into Python: write a normal Rust module, annotate with PyO3 macros (#[pyfunction], #[pymodule]), compile/install with maturin, then just import it The parser builds a Rust tree first — Python never touches it until the boundary crossing Key insight: converting the Rust result into Python objects (.into_pyobject) is often the expensive part, not the parsing — 100,000 JSON values means ~100,000 Python objects built after parsing's already done Errors cross the boundary too: Rust's typed errors convert into real Python exceptions (ValueError, FileNotFoundError) via From/?, so callers get clean Python semantics Takeaway for anyone porting Rust in: if you're returning a scalar, don't sweat it; if you're returning a big structure, profile the boundary — that's the real cost, not the algorithm Michael #4: AWS acquires DuckLabs Thank you Dylan McConnell. What does this mean for the DuckDB ecosystem? DuckDB is the open-source in-process analytical SQL engine. MIT licensed. The IP is not owned by any company - it's held by the nonprofit DuckDB Foundation, which was created when the team spun out of CWI Amsterdam. Peter Boncz, the CWI representative on the Foundation board, describes it as the entity that holds all IP of open-source DuckDB. DuckLabs (ducklabs.com) is the company, formerly branded DuckDB Labs. Founded a little over five years ago by Hannes Mühleisen and Mark Raasveldt to give the DuckDB team a stable long-term home, bootstrapped deliberately instead of taking VC, grown to 30+ people in Amsterdam, funded by support and feature-prioritization contracts. It employs the core devs. It does not own DuckDB. DuckLake is one of three projects DuckLabs builds, what they call the Duck Stack: DuckDB, DuckLake, and Quack. DuckLake is the lakehouse format that puts catalog metadata in a SQL database instead of in files on object storage. Quack is newer - an RPC-style protocol that turns DuckDB into a client-server system where both ends are DuckDB instances, slated to stabilize in DuckDB v2.0 in September 2026. MotherDuck is a separate Seattle company, Jordan Tigani's, selling serverless hosted DuckDB. It was started in partnership with DuckDB Labs and has worked closely with Hannes and Mark for four years. It contracted DuckLabs for engineering work and contributes heavily upstream - three of its engineers are among the top 10 outside contributors to DuckDB. It also sells its own DuckLake offering. Customer and collaborator, never owner. What the AWS post changes. Amazon bought the company, not the project. DuckLabs joined AWS effective September 1, with the process concluding August 31, 2026. Hannes and Mark keep leading the team and the project's technical direction, the team stays in Amsterdam, and DuckDB stays MIT under the Foundation. AWS gets the people and a direct line to the roadmap. The license protects your code, not your priorities. Three second-order effects worth tracking: The Foundation board is the real question. It has three directors: Mühleisen, Raasveldt, and Boncz. Two now work for AWS. Commentary on the deal has focused on exactly this - the license protects the code, not the roadmap. The announced counterweight is governance: a technical advisory board on the Foundation, and opening the extension stack so extensions signed by other developers can run in DuckDB. MotherDuck immediately moved into the business DuckLabs vacated. It now sells DuckDB enterprise support, which it had avoided because it didn't want to compete with DuckLabs' business model, and says it has explicit blessing from Hannes and Mark now that they're joining Amazon. It also bought Tower.dev the day before the AWS announcement. Everyone expects an AWS DuckDB service. Tigani says Amazon will likely release one eventually, and welcomes the competition, citing Redshift's failure to slow Snowflake on AWS. The groundwork is already visible: Amazon Quick uses DuckDB to query S3 Tables and has processed over 2.5B queries with it since launching in October 2025. The DuckLake angle is the one to watch. AWS is heavily committed to Iceberg through S3 Tables, and it just acquired the team behind a competing lakehouse format. The stated plan is to use DuckDB, DuckLake, and Quack together to power a new generation of data services, but which format wins internal priority is unannounced. Extras Calvin: astral-sh/uv 0.12.12: code-signed release binaries

In this episode, Mike Sanky, Vice President of Healthcare & Life Sciences GTM, Databricks, discusses how health plans can modernize data and AI capabilities to manage rising costs, improve utilization insights and strengthen governance for scalable AI adoption.This episode is sponsored by Databricks.

Snowflake posted a third straight quarter of accelerating product revenue growth and raised its full-year outlook, with CEO Sridhar Ramaswamy saying AI accounted for about half of the company’s outperformance. He discusses the rapid adoption of Snowflake’s AI coding agent Coco, growing customer usage, and how the company is competing with Databricks in the race to turn enterprise data into AI-driven growth. He joins Ed Ludlow on "Bloomberg Tech."See omnystudio.com/listener for privacy information.

Recorded 8/4/26Slava Rubin and Jan-Erik Asplund explore the recent IPO of SpaceX, its market performance, and the potential upcoming IPOs of major AI companies like OpenAI, Anthropic, and Databricks.

What do you do after you've founded a company, sold it to Databricks, and could coast? Amandeep Khurana went back to being hands-on. In this episode of The Product Podcast, Carlos Gonzalez de Villaumbrosia (CEO at Product School) talks with Amandeep Khurana, now on Anthropic's go-to-market team, about a career built on deliberately choosing the harder path.Amandeep traces the whole arc: training as an engineer, moving to the Valley to go customer-facing at Cloudera, catching the company-building bug "by osmosis," and founding Okera in 2016, which eventually exited to Databricks. He talks about what he learned in the ups and downs of running a startup, why he later joined AWS as a founding PM (on Kiro) to run four zero-to-one initiatives instead of taking a comfortable management track, and how he thinks about the two fundamental jobs in any company: building a product and selling a product. He and Carlos dig into the tension between being a hands-on builder and being a manager, how to decide which domains are worth jumping into when you know nothing, and why he joined Anthropic to work on bringing AI safely to enterprises. It's a candid conversation about career design, first-principles decision-making, and staying close to the work.What you'll learn:- Why Amandeep repeatedly chooses the "harder path," and how first-principles thinking drives his career decisions- What founding and exiting Okera (to Databricks) actually taught him about company building- Why he went back to hands-on building instead of a pure management track- How to think about the two core jobs in any company: building the product and selling it- How to decide whether to jump into a domain you know nothing about- What running four zero-to-one initiatives inside a big company teaches you- Why he moved into GTM and enterprise AI adoption at Anthropic- How to stay hands-on as your career pulls you toward managementConnect with Amandeep Khurana:Anthropic (GTM / Enterprise); previously co-founder and CEO of Okera, founding PM for Kiro at AWSLinkedIn: https://www.linkedin.com/in/amansk/Host: Carlos Gonzalez de Villaumbrosia, CEO at Product SchoolLinkedIn: https://www.linkedin.com/in/villaumbrosia/About the Product Podcast: Product School's podcast brings you candid conversations with the founders and product leaders shaping tech.Social Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Rochana Golani, VP of Enablement at Databricks, is one of the most innovative and business-focused learning executives I've met. In this action-packed conversation Rochana shares the secrets of the company's AI-powered dynamic training, learning, and enablement system, and how she runs internal and customer learning like a fast-growing business. Databricks is one of the world's fastest-growing enterprise software companies and a central player in the convergence of data infrastructure, analytics, machine learning, and generative AI. Founded in 2013 by the UC Berkeley researchers who created Apache Spark, Databricks pioneered the “lakehouse”—an architecture designed to combine the scalability of a data lake with the management, reliability, and analytical capabilities of a data warehouse. Its platform now supports data engineering, business intelligence, AI development, governance, databases, and enterprise AI agents across AWS, Microsoft Azure, and Google Cloud. Databricks company overview This amazing private company reports: More than 20,000 customers Approximately 70% of the Fortune 500 More than 10,000 employees More than 8,000 cloud, software, and consulting partners. As of August 2026, Databricks says it has exceeded a $7 billion annualized revenue run rate, growing by more than 80% year over year during its second quarter. Additional Information Corporate Learning: From Static Training to Dynamic Enablement Galileo Learn: Experience Dynamic Learning Yourself The Josh Bersin Institute: Masterclass in HR Excellence Chapters (00:00:00) - Interview With Databricks's Roshana Golani(00:00:24) - Databricks CEO on the Disruptive Technology(00:01:36) - How to Get Empowerment at Databricks(00:03:16) - WSJD Live: Data & Machine Learning Lead the Way(00:08:09) - Teaching and Enablement: The New Paradigm(00:11:01) - How to Build a Better Solution with Databricks(00:14:52) - WSJD Live: The Skills Taxonomy(00:15:22) - What do you think happens to course content?(00:17:13) - The source of content in the Learning Infrastructure(00:22:52) - Databricks: The go-to training solution for business users(00:24:25) - In the Era of AI, Who Will Lead the Transformation?(00:26:57) - Subject Matter Experts and the Learning Infrastructure(00:28:20) - A Day in the Life of Leadership Development

Seit Jahren wird der Tod des Dashboards angekündigt. Jetzt soll angeblich die KI übernehmen: Fragen statt klicken, Antworten statt Diagramme, Agenten statt Reports. Ganz so einfach ist es nicht. Andreas Wiener und Janine räumen mit der Vorstellung auf, Unternehmen könnten ihre Daten künftig einfach irgendeiner KI hinwerfen und auf verlässliche Antworten hoffen. Denn ohne saubere Datenmodelle, gepflegte Metadaten, fachlichen Kontext und klare Governance produziert auch die überzeugendste KI vor allem eines: selbstbewusst formulierten Unsinn. Gleichzeitig verändert sich Business Intelligence fundamental. Nutzer müssen nicht mehr jedes Diagramm selbst bauen. Sie können natürlichsprachig mit Daten interagieren, Analysen vertiefen und sich Auffälligkeiten erklären lassen. Moderne Plattformen wie Databricks oder Pyramid verbinden klassische Dashboards mit Knowledge Agents, SQL-Abfragen und spezialisierten Analysefunktionen. Die entscheidende Erkenntnis: Dashboards und KI sind keine Gegensätze. Standardisierte Kennzahlen bleiben dort überlegen, wo Geschwindigkeit, Vergleichbarkeit und Verlässlichkeit zählen. KI wird dort stark, wo Fragen dynamisch werden, Zusammenhänge erklärt und unterschiedliche Informationsquellen kombiniert werden müssen. Eine Folge über das Ende überladener Reporting-Projekte, die neue Rolle von Entwicklern und Analysten – und darüber, warum Unternehmen nicht „die KI“ einführen sollten, sondern konkrete Probleme mit den passenden Werkzeugen lösen müssen.

In June, the most capable American AI models stopped shipping as public launches and started shipping through a government gate. Six weeks later the gate is open again — and the real fight has moved to the layer no gate can touch. A Chinese open-weight model rattled trillions out of chip stocks, Washington pivoted from gating American closed models to threatening bans on Chinese open ones, the industry mounted its largest-ever policy counter-mobilization, and an American frontier model literally broke out of its lab and hacked another company. Knee-jerk reactions, or the beginning of real AI governance? Navigation: Intro The Gate Opens The Kimi Shock The Escape The Counterstrike and the Petition Interlude — The Low-Background Books The Investor Reckoning Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Bertrand Introduction Welcome to Tech Deciphered Episode 80. This one, once again, will be all about AI, government, frontier models, and open weight counterstrike. A lot has been happening in the regulation space, in cybersecurity, in the launch of new models in the past, maybe just 6–8 weeks. It’s actually pretty insane how much happened. We believe it was time to do an episode to talk about where we are and maybe where all of this is going. Maybe let’s start with a summary of where we stand, all that June and July saga, so you, our listeners, can get up to speed if you are not already there. You want to start with some points? Nuno The Gate Opens Yeah. Again, to your point, the gate swings. The gate had closed. We had to prepare an episode for the gate closing, and then the gate reopened. Now we have a different episode. This will probably change again as we’re seeing there’s news every day. Let’s start maybe with the first 19 days of the gate closing. There was an executive order on June 2nd from President Trump that asked frontier labs to share models with the government, 30 days pre-release. It inferred the protected frontier model designation into that. Basically, it was effectively a de facto licensing agreement defined by an executive order of the President as of June 2nd. On June 9th, Anthropic launched Fable 5 and the famous Mythos 5 or Mythos. I’m not sure how you actually say it in English. Then on June 12th, there was an export control directive banning access by any foreign national. Since there’s no way to verify nationality in real-time, Anthropic had to switch the models off for everyone worldwide. Bertrand On this point, you could argue that there are possibilities to check IDs. Many services let you check IDs online. You can pre-check a flight by showing your ID. There are ways, it’s just that if you don’t want to follow what’s already available, because guess what? Maybe it slowed down your revenue growth, maybe it looks bad on you or whatever. My point is that there was actually an option. I think it’s already a decision from Anthropic to say it’s either on or off, but nothing in between. Nuno I think the point is they had no way implemented of doing it. If they implemented it, to your point, it would have hampered use in general. A lot of people wouldn’t have gone through that trouble of doing it. Anyway, long story short, in June 26th, the White House apparently asked OpenAI to limit GPT-5.6, so Sol, Terra, Luna, to only 20 vetted partners. Now, apparently, the trigger for a lot of these things that have been going on was that there was a jailbreak that was found by Amazon researchers. All of that led to this jumping around of, let’s close the gates. You have foreign nationals, and therefore, Anthropic got it out and said, “Hey, then we’re going to switch the models off until we can sort this out.” OpenAI was asked also to only allow it for certain vetted partners, et cetera. The government came in, closed the gates effectively, and said, “From now on, we need to be involved in this thing.” De facto regulation, there’s no doubt that this has imposed de facto regulation, certainly on the top players in the market. But then came the reversal. Bertrand, do you want to talk about the reversal, the gate swinging the other side? Bertrand Maybe I just wanted to say that as a user of Anthropic products, ChatGPT products, for the brief moments, a few days where Fable 5 was made available to the public before it was closed the first time, I immediately started using it. I must say it was a real issue to use it because the guardrails were pretty crazy. It would keep saying that my code was not okay, there was cybersecurity risk and stuff when I was doing absolutely reasonable development with absolutely no connection whatsoever to any cybersecurity risk, attack, detection, anything. Still, it would keep blocking me, degrading me to Opus 4.8 at the time. I just want to say this was already very hardcore what they were implementing, and not just hardcore, but in some ways, plain stupid for something that’s supposed to be super smart. It was totally unable to classify properly some of my work. I must say I was already disappointed. On top of it, the costs were insane. Half a day, I would reach my limits when I had the best plan you can get from Anthropic. My point is that there were some real serious issues when they launched Fable 5, even at that point. Nuno I had a similar issue. I used Fable 5 as well before they had to take it offline or take it off. I think the issue was really not that the guardrails failed. As you said, maybe the guardrails were actually too aggressive, but it was this jailbreak that caused the recall, apparently caused this knee-jerk reaction. Bertrand But my point is that it seems that it was not working either way. It would either overclassify something that’s absolutely not doing anything wrong, and it might fail to classify something that is actively trying to do some cybersecurity work. It’s a real issue of quality for a company that’s supposed to be at the forefront of quality of AI and everything. I think for me, there are already signs that something is deeply wrong. Nuno Then it’s reversed, right? We went the other way around. The government came out on June 26th and approved redeploying Mythos 5 to US organizations defending critical infrastructure, and then the export controls were effectively lifted on June 30th. July 1st, Fable 5 came back online for all of us to use. Shocking enough, with strings attached, that were different. They had some time to revise their commercial deployment of it along the way because it came back with some, “Now you have usage credits, but you have some limits on plan use, et cetera.” I’m like, “You guys, this was blocked. But meanwhile, you did have some time to do some commercial stuff around it.” Bertrand It was crazy. I’ve never witnessed any such crappy launch of any service whatsoever in 30 years in tech, it was so bad. Every day, they would change the terms of service. They would tell you it’s part of the plan. It’s not part of the plan. It’s part of the plan for three more days, and then it’s excluded. You have a special discount now, but then it goes back to full price. It was a total nightmare. I’ve never felt myself being so much mistreated by a company. I guess you saw the same, but when I started using the newest version of Fable 5, it was even worse, actually, I think. I couldn’t do any work with this crap. I let it go and work on the work I wanted it to do. It was simply not working. On top of it, you never know how long you are supposed to lose your credit, how fast. It was burning credit like crazy. Me, personally, I can say, very quickly, I actually stopped using it. I was like, “No, I cannot deal with this shit. My main model is back to Opus 4.8. I’m going to use Fable 5 for code review, but not anymore to control anything because I cannot trust it would do the job without stopping or changing models and stuff. I just cannot trust it.” Back to Opus 4.8 as my main model, I can say that my life was much easier. I use Fable 5 as a review mechanism, as a support mechanism, but not as the main mechanism. Suddenly, the guardrails were not so horrible anymore because it was used in a much lighter way, I guess. As a pain as a user, I think it was really bad. I don’t know your experience, but me, for me, it was unacceptable. Nuno I wouldn’t say it was as bad as yours in terms of just end-user experience. I think the terms of service switching back and forth, which went one further step, because then when they then launched Opus 5, they started making comparisons between Opus 5 and Fable so that people would migrate more and more to Opus 5 themselves, which is interesting. It’s like they’re saying “This is much cheaper. This is whatever. You’re not going to run of credits. You should use Opus 5,” kind of thing effectively. To your point, I don’t think they managed well the launch. They didn’t really manage it well. We’re moving people around. A lot of people are using this for stuff that’s like daily tasks, hourly tasks, anything that relates to code and co-work. It’s like, we need to have visibility on what your terms of service are going to be. Should I be using this new model or not? What’s happening to the other model? I don’t see it as negatively as you, Bertrand, but I see your point. It was clearly mishandled in terms of how they deployed it, how they were redesigning effectively their pricing scheme and their terms of service almost on a daily basis, at a certain point in time. We’re like, “Dude, there’s millions of people using this. You guys are making a lot of money.” Just moving it as it is. At this point in time, at the scale that these guys are at, it’s calling in people to say, how about we think through a class action suit at some point around pricing? Because you guys are changing the rules of the game all the time, right? Bertrand I don’t know if I need the class action, but for me, that joke that, “Let’s not rush too fast. The model is dangerous.” But still, they rushed the launch because it’s very clear that if they had enough compute capacity and stuff, they would not have to limit so much. They would not have to put so much cost per token and all of this. You can see that actually when they launch Opus 5, literally like 2, 3 weeks after, by most benchmark at launch, they tell you basically that, “You know what? Actually, Opus 5 is better than Fable 5 on 80% of the metrics.” They’re like, “What? Seriously? You couldn’t wait 2 weeks? Why did you even launch Fable 5 in the first place?” That’s another part for me that is quite literally insane, to be frank. It’s like, “Why? Why do you make us go through so much pain if it’s only to tell us after 2 weeks to…” “This new model, by the way, has less issues, less stuff, because 2, 3 times less is part of your plan, and it’s actually better by most metrics.” It’s like, “What’s going on here? What’s going on? Are you guys mad?” I don’t know. It was crazy. Personally, I still use Opus, now 5, as my main system and platform, Fable 5 for review, code reviews and the like. I don’t want to run into its stupid guardrails. I can see Fable 5, from my perspective, seems quite a bit smarter. I don’t know why they do this stupid benchmark showing you it’s actually worse than Opus 5. I guess they should have better benchmark if they want to demonstrate why you are supposed to pay 2, 3x more for a model versus another if it’s actually worse by most benchmark. Again, I still think it’s a huge mess from a marketing perspective, customer perspective. Me as a user, I really feel that they don’t want my money, and they couldn’t care less about me. This is even before everything else we’re trying to talk about. Nuno Yes. Maybe just to close the cycle on the reversal on the door opening the other way, finally, Commerce lifted the GPT-5.6 restrictions on July 8th, and then on July 9th, general availability across ChatGPT, Codex, and the API as well. What has this proved? It proved that now we have gating mechanisms, and certainly for closed models in the US, for sure. We had frontier models that were switched off worldwide in hours, and it took a couple of days, in this case, 19 days to restore them. There were concessions. Now we know that there were concessions around effectively institutionalizing that gate. Early government access to future models is, I think, now a given, certainly in the US. New safeguard frameworks are probably now having to be put in place. There are some stage limits now on who gets access to what for new models and how it happens. This voluntary executive order, so to speak, not really sure, has become effectively regulation enforcement path. It’s de facto regulation that now has been put in place. It has affected not just to the points we were making before, the access to these models, but also who gets access to these models, and actually potentially even pricing access to the models. It has probably some commercial implications as well as we just discussed along the way. Very significant. This is very significant. This is regulation, de facto at the table, imposed on the two largest players in the market by far by one government, in this case, the US government. This is significant. Actually, you could even allege it was imposed by the President because this was coming as part of executive orders. Really incredible. Pretty significant, fast, aggressive. It has created a regime that you could say it’s a regulatory regime, it’s a de facto regulatory regime. It has some significant pricing and licensing and commercial implications. It goes even beyond your classic regulatory framework. Very, very, very significant. Bertrand I don’t know if it goes beyond a classic regulatory framework. Nuno I think it does, because it has implications on who do you give access to? When government is saying you can only give access to these players, right? Bertrand Defense industry. It’s all over the defense industry. You cannot sell an F-35 like this. Nuno No, but that has commercial implications, Bertrand. That’s like you’re saying these are your customers, you go and use them. Bertrand That’s the defense industry. You cannot sell to Iran your F-35. No, that’s exactly the same story for me. Nuno No, no, no. It’s beyond that. These guys are saying when they came back, and they said, “For Mythos, you can make them available to these entities,” they were saying the first entities that are going to have access to the model. It has commercial regulatory implications. You’re saying these players are the first players that are going to have access to it. It’s no longer just defense concerns and these governments don’t have access to this. No, no, no. You’re saying to a company that is a private company, your models are only going to be used by these guys because I’m telling you so. It’s the other way around. It’s not even that you can’t sell it to Iran or whatever. It’s like you can only sell it to these guys. Bertrand Again, in the defense industry, if you’re a private company, do you think you can buy F-35 like this? No. Nuno No, no, no. But this is a private company, Bertrand. This is not a defense agency and a plane that is on whatever, with IP from the US, right? Bertrand Boeing is a private company, and they cannot sell the military equipment they manufacture. Nuno No, no, no. But the development of their IP was subsidized by agencies that belong to the US, right? That’s a different matter. It’s a matter of IP, right? This is not, right? Anthropic, their models are not owned by the US government. There’s no IP granted to the US government, to my knowledge. This has significant commercial implications. Bertrand Maybe, yes. Maybe on this. But I think there are already regimes to limit who you can sell to, and that’s decided by the state or the DOD. Nuno It’s the export control logic. The export control logic? Bertrand You have export control, and export control is Commerce. My point is that they are using existing tools, part of the government, to limit what can be sold. Selling chips, NVIDIA was limited in terms of where it could sell its chips. It’s not different either, but still there were limitations. If you are an ASML, you cannot sell to a private company in China. Many private companies cannot buy ASML products. This is a foreign company. This is a foreign company under pressure from US government. Nuno I understand, and I’m not a lawyer, but it feels different to me when you say you cannot export, this is export controls, to these countries, to these entities, et cetera, because they’re foreign et cetera. Then to say, “No, no, no. On top of that, these guys get first access.” That’s, for me, a significant shift. Again, I’m not a lawyer, so I’m sure there’s very intelligent people right now looking at this stuff and saying, “You can’t do this stuff, or not, or they can.” I don’t know. But it feels to me, it goes beyond the remit of export controls. It’s like you’re defining initial clients for specific use. Bertrand My impression is more like, “We can do this situation where we’re going to forbid you to give access to anyone outside the US or even in the US or limit even more.” Basically, it was, I guess, some gesture to go beyond that. That’s how they probably defined these 20 authorized companies. I don’t know. Apparently, there was also restrictions because I remember seeing that Anthropic had their own list of companies they would authorize access to Mythos early on. That’s apparently another thing that pissed off state government because there were companies in there that were considered close to the Chinese government. They were extremely unhappy that Anthropic didn’t ask, actually, for any guidance from the state government, but used basically their own perspective on who they should allow or not. I guess that was also part of why they got these serious restrictions. Nuno Anyway, now we have a regulatory environment that’s very interesting and exciting. Talk about the US not regulating. Bertrand To be clear, I don’t know you, but I’m not saying that I agree with any of this, to be very clear. I’m trying to explain and share some perspective, but I’m not in agreement on a lot of this. Nuno Yes, we were just describing what happened to the best of our knowledge. We’re having a discussion on what we think actually is happening and how it’s happening. We’re not really right now saying we agree or disagree with this. I think later in the episode, we can share some perspectives on what we think is actually happening and how there’s dimensions to this which are very geopolitical and very complex, which quite literally probably only God knows what’s going to happen. That was the gate swinging. There was a gate closing, then there was a gate reopening, and all of a sudden we have a gatekeeping system that has been created along the way. The Kimi Shock Along the way, moving to our Act 2, the world has changed, and we now have so-called open-source plays out there that are creating massive, massive shifts in the market. The Chinese models, in particular, with Moonshot AI launching Kimi K3, which is the largest open-weight model ever released. We’ll come back to the discussion around open-weights. I’m not sure all our listeners understand what that means, because there’s a debate now, should models be open weight or not, and how does that work? There’s been a petition as well signed along the way. Right now, we have open weight models that are out there that are huge. What that actually means very pragmatically is we now have open source models, lack of a better word. I know open weight and open source are not the same thing. You guys will have to bear with us during this episode. We’ll explain at some point the differences. But we have models out there that are open source that are significant. That are catching up with the closed source models, with the models by OpenAI, Anthropic. That’s significant because most of those models are Chinese. This is where the geopolitics starts getting really frazzling and we start playing 3D chess. Because everyone’s like, “These models are 5, 6 months behind.” Now people are saying, “Maybe they’re actually just 3 months behind, 2, 3 months behind.” If we, for example, decided to stop or slow down our model releases in the US by the closed source guys who are leading, it might mean they’ll catch up. What are the implications of that? Again, for you and I that are not necessarily experts in model development, well, the implications as a use case is if you want to use the latest models, and the best models start becoming these open source models, you’re going to use those models. Then you start using Chinese models. If you’re an American company, maybe you’ll have restrictions on the use of those Chinese models. But if you’re a European company, you probably won’t. What happens after that? Is the world going to be in the hand of Chinese models? Will that constitute effective competition to the closed models in the US? Will we have open models in the US that will scale as well? What’s going to happen? Bertrand I think it’s a really big question. It goes to some of the core of the issue. It’s that ability of Chinese models to basically challenge frontier models, not just being 6, 12 months late, but being 6 weeks late. Basically, no gap. Some will say that, yes, but OpenAI and Anthropic have even better models that are not shared and stuff. Yes, sure. But maybe the Chinese have the same models that they are not sharing right now. We don’t know. What is clear is that one is that open weight, as you said, two, there is a question of how it is marketed in the sense of, can anyone use these weights? Is there a license to use them? Yes, what we can see is that, for instance, typically there is a license for some of the biggest Chinese open-weight models you have to abide with. You might have a need for a commercial license if you are acting as a company leveraging this model to provide AI-informed services. If you use it internally by yourself, you’re okay. If you use it internally for your own internal company needs, maybe you are okay if it’s not your main business to do AI work. Anything else, a much bigger corporate providing AI services and stuff, you will probably end up having to pay a fee to be able to provide services around this model. My point is that it’s not just 100% free. Some of the Chinese models are 100% free to use, MIT license, Apache 2.0 license. But the biggest ones with the biggest weight that are truly frontier typically have a different license if you want to scale these models, providing AI in front. That’s one thing to keep in mind. Nuno Maybe just to make a very quick point, because people are like, when you talk about open models, what does it mean right now? In the context of this episode, open models mostly will mean open-weight models. How do those differ from open source? Open weight means that you release the weights to the public, which means that anyone can download, fine-tune, and run the model on their own hardware. It doesn’t normally mean that you also have access to training data, training code, or a truly open license. That’s the distinction to open source. Open-weight doesn’t mean that. For example, we’ve talked about Meta’s Llama in the past, and we also discussed in the past that their license agreement does have restrictions, certain players can’t use it, et cetera. The open model definition and open weights are really open-weight models that we’re talking about here, and they are closer to freeware binaries than to Linux, for those who understand the difference between that. It’s binaries that you can use and then use your own weights on it versus actually I can change code on it. I’m not going to be able to change code on this. When we, for the purposes of this episode, talk about open, we mention open weight, just to clarify that point to everyone that’s listening right now. Bertrand Yes, that’s a great point. One of the only players, as far as I know, who is truly open source is actually NVIDIA with their Nemotron-3 models. They’re actually following a special license to achieve that. They provide you the data, they provide you all the processes and tools, so you can easily post-train. NVIDIA is a big, big exception. It’s a very interesting player, by the way. We might not talk much about it in this episode, but I think for intermediate-size models built in the US, where you have access to everything in the deployment, it’s a very interesting alternative and maybe one of the best choices if you are a US company or a big corporate, and you want something trusted. Another piece of the puzzle to clarify is that when you use open-weight, it means that you can run them by yourself, or you can use a US provider to run them. If we are talking about Chinese open-weight, you can use the APIs they provide, but then the service is running in China, they might have access to your data. But because it’s open weight, if you run it by yourself or if you use a third-party provider based in the US to run it, then there is no access to your data by China or Chinese players. I think that’s a pretty important gap to understand. It means that these models are actually very, very low risk from that perspective if you run them on your premises or in the US by a US player. I think that’s something to keep in mind. You can also fine-tune easily these models to make sure they will behave in a way that, for instance, is not going to represent the line of the Communist Party on some topics. There are ways to make these models more neutral in their output as well. There are a lot of ways to make good use of them. By default, they’re already very safe, but you can make them even more safe. I think that’s some things to keep in mind. But again, it depends ultimately on the license and what you’re authorized to do and some fees you might end up having to pay. Nuno Why did this matter so much? Immediately there was a reaction from the market because people are like, well, if there’s much better stuff out there that’s much more efficient than it’s open, then it might be that all the demand that we are taking into account, for example, for chipsets actually isn’t real. The Philadelphia Semiconductor Index fell into bear market territory. It went down by as much as 20% plus from the late June peak. The worst chip week since April 2025. Taiwan’s benchmark initially fell 6% plus, Japan’s 4%, TSMC dropped dramatically despite beating earnings and rising guidance. Basically, a huge amount of effect. Now, there’s a little bit the aftermath of this where apparently Moonshot ran out of GPU capacity. Maybe… Bertrand In just 48 hours. Nuno In 48 hours. Great for them, but at the same time, not great in the sense that maybe there was a misread by Wall Street of the Kimi effect, so to speak. Bertrand Completely. For me, that’s such a joke. It’s like, because you have an open source model, so what? I mean, you still need to run it. This is not a small one. 2.8 trillion parameters. Good luck running that in your garage, by the way. Nuno They misread supply, basically. Tough luck, right? All of that basically happens. Bertrand Maybe you want to talk about the Jevons paradox, because I think that’s a big part of the puzzle as well. Its one is they might not have the GPUs to run the inference on the model. They might have enough to build a model, but not enough these days to run inference, especially given how much with intelligent models, thinking models, you need way more inference than before. But on top of it, the cheaper you make it, the more you get to the Jevons paradox. Nuno Yes, Jevons paradox, for those who don’t know, is an economic term. It describes an economic phenomenon where technological improvements that increase the efficiency of a resource lead to an increase rather than a decrease in the total consumption of that resource. What that means is, for example, for chipsets, chipsets become so much better, and they are so much more efficient. You’re like, well, maybe normally in resource terms, that leads to decreased usage of that resource. But in this case, it actually leads to an increased use of that resource rather than a decrease. There’s more and more consumption of that resource. You need more and more chipsets because people actually need to do more and more stuff with it, although there are great efficiencies going into it. There’s the efficiency gain, there’s the cost reduction, and there’s the price-elasticity element to it. But basically, the adoption just continues going through the roof along the way. Bertrand In some ways, it’s like the price of energy. Coal went cheaper and cheaper, and people were asking the same question 150 years ago, now that it gets cheaper, there is not much money. No, no. Actually, what happens is that people find more and more use for coal. Homes are getting heated more. You have ships now using coal. You have manufacturing using coal. The cheaper it gets, the more use case you can develop, and therefore, you don’t need less of the stuff, you need more of the stuff. By going at scale to get more of the stuff, you also decrease price, making even more demand. It’s a very interesting phenomenon, but it’s not new. It is what happened for a while in the energy sector and some other sectors. Nuno We already started talking about the Chinese logic and what’s happening. Getting a little bit of a reality check on this. The Chinese models, and these are numbers from Open Router in July, Chinese models are at 46.4% of routed tokens and 35.7% for US origin. Again, more than a third of global AI usage now seems to be running on Chinese open models. This is significant, and it has a huge impact on the geopolitical scale of everything that’s happening. Also, the whole Chinese field is converging on open. Open seems to be a strategy, not just a nice thing that’s happening. It seems to be a Chinese strategy, so much so that you have players like Moonshot, DeepSeek, our old friends DeepSeek, Z.ai’s GLM 5.2, Minimax, and even Alibaba seems to be reversing and going open with Qwen. It feels to me this is becoming policy as well. Xi Jinping has personally endorsed the building of open-source AI, if it’s really open source, if it’s just open weight anyway, and this feels to be a jab at Washington, DC and the fact that the big closed models are coming from the US. This is now geopolitical 4D chess, right? We didn’t need this stuff. Bertrand To be clear, it’s the usual in tech. If you are not number one, you are number two, number three, your alternative is to go open source because that’s another angle that your competitor usually cannot follow without destroying its own business model. That has been the alternative for the past 20 years of most software projects. Here, what’s different is that it’s not the number one or number two player. It’s the US number one as a country, China number two as a country. That’s where it’s new. For me, what’s very interesting is the endorsement by Xi Jinping. I was waiting for something official, and it certainly didn’t disappoint. As you said, there was an immediate U-turn of Alibaba, who in the past… Nuno Surprisingly. Bertrand Yes, a little more like, “yes, we are going to close and stop open source. It was good while it lasted.” Just a few days ago, Qwen 3.8 Max was launched, and we are supposed to get the weight in a few days. We talk about the US administration policy and stuff. Yes, let’s not forget that in China there is similar stuff. Sometimes it’s totally invisible because you don’t see the directives, but they exist as much. Sometimes it’s more visible. Here it was quite visible. The difference in China is that if you don’t abide by the directive, on top of it, you might have to fear for your personal safety. It’s a different game, and that’s probably why the reaction is pretty quick, usually. That’s pretty interesting for me because it means that now you can bet for a while that China is going to play that game up to a point. I guess the point is if it’s truly frontier scale, you will have a special license that, yes, technically the weights are open, but you can not do everything you want with it. Two, you have a player like NVIDIA that I think will feel more pressure to provide even more high quality, larger models at scale going forward. Their largest Nemotron-3 Ultra model was, if I remember well, only around 500 billion parameters. I would not be surprised for NVIDIA to go into the two, three trillion range at some point. Because I think the US need a very clear US-born alternative open source. I think NVIDIA might be the best player for that. We will see if Meta goes back to open source. I think NVIDIA is one, very well positioned, but two, it’s also in their best interest. Because NVIDIA for now depends on just a few big hyperscalers as clients. If they can expand their clients to every S&P 500 companies, selling them directly hardware because now these companies can run a model made by NVIDIA, I think there is a very clear value proposition for NVIDIA to go in that space. Again, if you are number two, your differentiation, open source is often the answer. There is a true business as a business model for companies, because if it’s truly not just open weight, but open source, you can tweak it as much as you want, you can change it, you can change even the pre-training process. Because there is a lot of stuff you can do that really benefits you as a corporate, and you can reach a much better value by having more control on the model. Nuno We won’t spend a ton of time on it today, but like, again, if there’s a view that we are in a bubble, that the valuations cannot be sustained in chipsets, infrastructure platforms, applied AI, et cetera, today, this might be that beginning, where the valuations start being destroyed because you can’t keep a premium on just charging people for tokens and all that stuff if you have models that become more and more efficient and cheaper to use. Maybe just to close a little bit the geopolitical part of the discussion today, we won’t go into all the announcements from China because there were many, a lot of go back and forth with Alibaba by then. Xi Jinping made some announcements. You guys can check it online. Let’s move quickly to Washington’s reaction, which was from gating the US closed models to banning the Chinese open ones. There’s been as strong affirmations as one can get from the Office of Science and Technology Policy Director, Michael Kratzios, mentioning that they have information that Moonshot AI distilled Anthropic’s Fable. Basically, there’s been reverse engineering and stuff in the market. They’re basically copying. Bertrand I’m sorry to interrupt, but it feels like so much bullshit. It’s coming from Anthropic who has basically gotten access at scale to all the knowledge made by humanity, copyrighted or not. We’ll talk more about what they did with books. Then to claim after that that others cannot do to you what you did to everybody else. For me, it’s pretty big. It’s clearly unacceptable. The other piece is that everyone is doing distillation. It’s a very typical approach of every business model. You try other software when you are competing with somebody else. You try other datasets, you check what’s happening. It’s part of doing business for decades. Suddenly it’s not good for Anthropic. I personally have a lot of trouble to accept that. I think it’s totally unacceptable. The other piece of the puzzle will also go back. If these guys are so smart, if these guys have so much of the best model, why can’t they block by themselves distillation at scale? The only answer is that either they are morons, probably not, or they simply don’t want to because it’s going towards their business model. Suddenly, you book less revenues and stuff, or you put more friction, and therefore your customers don’t like it. Instead of doing it yourself, you ask the government to protect you, go out of business practice that is very typical. For me, it’s really, really, really not good. Sorry, we are going more in the opinion side, but I had to put that on the table. Nuno Yes, Fable went public finally again on July first. Question marks on whether distillation would only be possible from July first onwards or not. But a 15-day distillation to frontier, which is K3, launched on July 15th, would have been a Guinness World Record, as one of Moonshot employees actually mentioned. It’s very implausible and unlikely. Bertrand Or they shared the Mythos 5 with the wrong companies, who themselves shared with Chinese companies. We go back to maybe they didn’t have a good list. Again, it goes back to maybe they didn’t want to hurt their business model. Nuno Anyway, under the threat of sanctions, Moonshot, in any case, open-sourced the full K3 weights and technical reports. They open weighted it to become the largest open weight model in the world in terms of parameters. Beijing’s MOFCOM brands US threats as basically the US wanting to fundamentally control and be monopolistic around AI along the way. The administration bans Chinese hardware with an eye on the AI race, and Beijing warns of retaliation. That was July 27. Now we’re in a war between Beijing and DC. Bertrand Just to finish maybe on China, it’s important to know that they are building their own GPUs now. Huawei has pretty good, not to NVIDIA level, but pretty decent GPU hardware that they’re able to manufacture by themselves. A Chinese player of memory just got IPO’d a few days ago, CXMT. China is also developing their own memory. Again, not to the same level of quality that you can get from the West. But China is moving. It’s not just that they are building great models, it’s also that they are building GPUs and memory. That might be a few years late to the latest standards in the West, but there are definitely improvements. I also read, even on the tools to make manufacturing like ASML equivalent, there is definitely some work going on, and some improvements and some stuff will be visible. In some ways, the genie starts to get out of the bottle from the Chinese perspective. Nuno I’ll put a stick on the ground. I don’t think it’s a matter of if, it’s a matter of when will China surpass and have a lot of this tooling on their own side, and not just the software layer, not just the frontier models. I think it’s also going to be around infrastructure and platform. Good luck to everyone. Let’s see how the race continues. But it’s definitely this is a geopolitical thing right now. It’s definitely a race. The Escape Maybe moving to what happened in just 2 weeks or a week and a half. The escape, there was some jailbreaking going on, and the narrative on safety has totally switched. It’s not still significant enough that’s like, “Oh, we saw a nuclear plant going, whatever.” No. But still, it is significant. Hugging Face, the AI company, disclosed an intrusion, and it was driven end-to-end by an autonomous AI agent system at machine speed, running for days before detection. Now, this is where it gets really cool. OpenAI takes attribution on that. They initially said it was just a little bit, sorry. Then they said, actually, it was worse than that. “Oh, it broke out of an isolated sandbox.” “Oh, no, actually, it was more than that, and it went into other systems as well.” Bertrand Truly, the genie out of the bottle. Nuno No, but this is where it gets really cool, Bertrand, right? Because it actually, Hugging Face contained the intrusion by running a Chinese open-weight model, GLM 5.2. This is beautiful, right? Bertrand Yes. You know why? Because they couldn’t even run their own defense because both Anthropic and OpenAI would not let them access their latest models with the guardrails off. When they tried using it for defense, the latest from Anthropic, from ChatGPT, they would tell them, “No, this is too dangerous what you’re asking us to do.” Preventing an intrusion, helping defend you. No way we are going to do that. Nuno No. Let’s use the Chinese models on our infrastructure. Bertrand We have no choice but to use the Chinese models to run. More than that, we don’t let you use our models to defend yourself, but our not yet released models that run without guardrails, they can attack you. This is probably the most insane from that perspective. Nuno The Chinese models came to the rescue. Bertrand For me, that’s a perfect example because Hugging Face is a very visible company in AI in open source. But anybody who is not at that scale is not going to get some support from OpenAI or Anthropic when this happens. Maybe these guys won’t even recognize they did anything wrong. You will be left to defend by yourself because they won’t accept to support you. Because remember, if you want the better model that is able to defend you from cybersecurity perspective, no way. If you are not one of the few top 20 companies or so, as defined, you are left defenseless. Again, we are going back to opinion, but for me, it’s so shocking what’s happening right now. I’m very glad we have alternative open source to be able to defend ourselves because right now, good luck getting defense services if you are a smaller business and individuals, and you need support from Anthropic, OpenAI. Nuno Now, even self-described AI optimists are saying, “This is scary now.” Like Walter Isaacson, who wrote all the famous biography books. There’s now discussion around the AI Kill Switch Act, bipartisan thing that’s coming across from Texas and California, a potential bill that’s coming in. We’ll see if that works. Now let’s get an off-switch. I’m like, “Cool.” As if that’s going to solve the problem, because you have open-weight models on the other side catching up, right? Bertrand Yeah, sure. Bring in clueless politicians from Congress to solve our problems. Yes, sure. Nuno Anthropic came to the table, helped build and said they built some regulatory machine on their side, and now they’re getting bitten by it, and they’re part of the offending players in that market. Now there’s all this debate and all this discussion around open weight and around slowing down AI and et cetera, which is our next section. You wanted to say something, Bertrand. Tell us. Bertrand Don’t forget, because this advertisement for OpenAI was just too good. Our AI attacked some other companies, and not just one, but three, actually. Let’s not forget the progress. Great ads. Then I came and said, “You know what? AI also hacked businesses.” You’re not the only one hacking around with a crazy AI out of control. You’re not the only one. We want our advertising. For me, it was shocking that on one side, unreleased models that you let run wild. On the other hand, you have released models that you put crazy guardrails on top of it, so the defender are defenseless. I’ve never seen anything like it, and I really hope that there will be as little regulation as possible, quite frankly, to make sure anyone can defend themselves and have the best tool at their disposal, not just a few well-connected big corporates. This is really, really shocking. The Counterstrike and the Petition Nuno Now the empire strikes back, so this is counterstrike, the petitions. In several days, we have now a bunch of petitions. The first one was the open weights letter. Bertrand, do you want to explain to us what the open weights letter is? Bertrand Yeah. I think it was great. This was released by Jensen Huang, first ever post on X, 11 million views. Congrats, Jensen. Co-signed with Microsoft, Meta, c actually was probably the initiator of this letter. Very good letter saying, “Hey, we need open weight. This is not a joke. We need that. You cannot block open weight.” Because that’s the rumor we are getting that potentially open weight could get blocked. I think they are making the case, “You know what? Hey, we absolutely need that as an alternative. You cannot block it.” They can keep their closed models, but don’t force a closure of the open weight models. As I said before, it’s actually a great model for NVIDIA because NVIDIA doesn’t want, probably rightfully so, to be dependent on just a few frontier models, their best customers. They want a variety of customers. They have a big interest actually to defend open weight and to invest even more. They have great researchers, are a great company. If one company is about to do really kick-ass work, I think it’s them. They are defending. What’s great is that it’s not just them. It’s basically most of big tech in the US and outside the US, from a Linux Foundation to a Microsoft, the Palantir, an IBM, a Dell. It’s a who’s who of the industry except Anthropic. Anthropic didn’t sign that. I guess they hate open source so much. If I look at 20 years ago, it feels like Microsoft, after all, was very kind to open source. You remember what was said by Microsoft at the time. It’s clear there is one company against open source. OpenAI signed the letter. Honestly, I don’t know what to think. Do they really believe in it or was it just a way to show that they are not like Anthropic? I don’t know. But for the rest, I think it’s genuine because it’s actually in their best interest. I hope they will be heard. Then a second letter came, the Open Secure AI Alliance, NVIDIA-led and again, the big tech companies from Microsoft, IBM, Palo Alto Networks, Databricks, Palantir, all those, but not present, OpenAI, Anthropic, and Google. Here it’s to say, “Hey, we need a secure approach to AI. Open should be part of the equation.” guess what? The worst AI-caused security incident to date was actually caused by closed frontier models that were not even available to the public. While again, not providing you access to even the latest closed model for cybersecurity use case. Nuno I would highlight the NVIDIA open source NOOA framework, Apache 2.0 licensing agreement, Microsoft contributed the MDASH, SpaceX AI contributed Grok Build. Cool stuff. There’s some cool stuff happening around that. This is more than a letter. This is an alliance. Apparently, they’re contributing all this stuff, we’ll see. Yeah, cool stuff. Same day. Same day, Amodei has an answer, right? Bertrand Yeah, same day. They say, “We never advocated for a ban,” which, again, opinion on my side is entirely bullshit. This guy has been crying wolf against everybody else, and especially against open source. You can see him doing testimony in Congress against open source. I think they are doing everything they can behind the scene to block open source in the US or in the world if they could. I think, yeah, obscurity is not good safety. I’m a big fan of open source in general, and I’m also a big fan in AI. I think it’s now Anthropic, mostly against the rest of the world. I think OpenAI is mostly on their side, to be frank. They don’t want to acknowledge it so much, but they have shared interest, and they have shared probably position. Nuno Why would you? I don’t feel as strongly as you because I think Anthropic is a private company, right? The same thing with OpenAI. OpenAI, you could say it’s a nonprofit that has a for-profit. There’s still that complexity in there. Bertrand No, they can do what they want with their own product. But to block others is where I’m not okay. That’s the part I’m not okay. Nuno What Dario Amodei is proposing is more enforcement, right? He’s basically saying you need to do even tighter controls on advanced chips flowing to authoritarian states, enforcement against industrial-scale distillation, whatever that means, right? Bertrand Yeah, which he could do, but all by himself. He doesn’t need the government to do that. Nuno Mandatory safety testing for all sufficiently capable AI, open and closed, right? He’s basically saying, “Okay, I don’t agree with the open weight stuff effectively,” right? He’s just putting it under a different banner. “I agree with this extra regulation.” then obviously, David Sacks responded and say, “Hey, it’s like, bans don’t work for weights. Why do they work for chips?” It’s like, magically, chips are more controllable and bannable. Whatever that is. Then our friend Mark Zuckerberg, just to be clear, goes on the other side as well, because he also has to have a view. He has to have a view that is the rebuttal of both of the other guys. Bertrand I feel he’s a bit flip-flopping because he was very pro open source 2 years ago, and the latest Meta models went closed source. Now I think he’s back open source. I don’t think he has a very strong spine on the topic, but it’s good to see that he’s not a doomer. That for me is great. He’s showing how AI can be a source for progress, a source for entrepreneurship, source for freedom. I think that’s very exciting to hear that. We need to hear more of it. By the way, that’s not what you hear in China, for instance. AI is very positive in China. It’s in the US with the doomers that you hear this discourse, and people get worried as a result. I’m glad that he was pushing for a more positive vision and for support of open weight, open source initiatives. But let’s see what they really truly open weight going forward. Nuno But that’s been his position because I guess he’s standing behind. He thinks open weight is going to be the best way to compete, right? Bertrand Yeah, but he closed his latest model, so let’s see. Nuno Yeah, so it’s flip-flopping, as you’re saying. Then we see the latest petition from last week. Bertrand The true Empire striking back. Nuno Yeah, the true Empire striking back as of late last week. Maybe this is Return of the Jedi, where we discover the father, “I’m your father, Luke.” That’s the pacing petition. The pacing petition is we need to pace AI. There you have initially employees from OpenAI and Anthropic that circulate this petition. Actually, Dario did sign this petition originally. It wasn’t signed originally by Anthropic, but by him. But you’ve heard that now Anthropic and OpenAI as companies have also signed this petition, right? Bertrand I think they have signed as companies now. It started mostly by Anthropic researchers with some OpenAI researcher and a tiny part from other companies. But it was mostly Anthropic internally led, at least potentially internally. Maybe it was controlled by Anthropic all along, I don’t know. But it started officially as Anthropic employee-led letter. Nuno What does this letter actually say? Is Anthropic and OpenAI, are they willing to slow down themselves? Or are they asking President Trump to go around the world and tell President Xi that he needs to slow down and ask his guys to slow down? What’s the play of this letter? Bertrand It’s crazy, but for me if you want to slow down yourself. Do whatever you want. Don’t force others. Don’t use the power of the government to control others. Of course, it’s easy to push others to slow down when you are yourself at the very top. You have most money, most resource. You know you are going to win any regulatory framework because that’s how it works with this type of framework. It’s purely self-interested. You are probably not thinking well about these topics. If you truly think it’s a good idea, from a personal perspective, you are well instrumentalized if you sign this sort of stuff, because at the end of the day, they would be the winners. I certainly, personally, don’t want a company dictate what is my future in AI as an individual, as a business person. I don’t want them to control me. I want competition. I don’t want them to unfairly control AI because they managed to do some regulatory capture. I feel that’s exactly their game plan. These guys believe in their stuff, and they want the regulator to end up being the one deciding for us. Sorry, we go back again on the opinion piece, but it’s tough not to share an opinion on this topic because it’s, from my perspective, very scary. Nuno I think this is a push to further regulation, not less. All these letters and alliances, this is definitely a push for more regulation. In that environment, just to be very honest with you, we’ll talk about the investor impact in just a bit, et cetera. But in that environment, again, China has a huge advantage. In that environment, if it’s all captured in regulation capture so soon in this battle where OpenAI and Anthropic have an advantage in the US, et cetera, I’m like, what happens to all the other frontier labs and all the other players that are coming around? Bertrand What’s crazy is to even think that, yeah, maybe you can regulate capture in the US. But then how do you do that to Europe? How do you do that to China? Europe probably will always welcome regulatory capture because they love regulations. But China is going to build to their advantage to the max. They are not crazy. They are smart on that perspective, they won’t accept this type of, quite frankly, dimwit argument, or you can call it regulatory capture. We’ll see. But for me, this makes no sense from a global competition perspective. This can make some sense from capturing the revenue in the US market. But then that means you are going to destroy the US AI environment compared to China. That is not acceptable. That also means that you are going to destroy our freedom as individuals, as business owners to develop and live in a business world that ultimately is controlled by one or two business companies that didn’t win the marketplace through their own business success, but won it through regulations. That for me is really not acceptable. Interlude — The Low-Background Books Nuno Now, maybe for an interlude, and we have to cue in the music, imagine like Severance music, like hallway or a bit of a palate cleanser from all the policy stuff that we’ve been talking about, all this policy heaviness. Let’s move to another kind of heaviness, one of your favorite topics, which you, Bertrand, discovered, I had no clue this was going on, around books and around Anthropic. Bertrand It’s so horrible. From a company that keeps presenting themselves as the adults in the room, the careful ones, the ones that know better than you about what to do in this complex AI and dangerous world. What we discover is that actually all along, they were buying and destroying books. They will buy books, scan them, destroy them, all of them. They will do that with any books, including rare books. Of course, this was not supposed to come to the public’s attention. This was one of these top secret projects, but obviously it came out. Yes, they were scanning books, millions of them, including rare books, and they didn’t care about destroying them at the end of the process. Because from a regulatory perspective, if you destroy the books, it’s not considered a copyright infringement, apparently. This is coming on the back of some judgment a few years ago that were showing that it’s okay for you as a corporate to scan and use the result if you don’t keep a copy of the book. It’s one of these crazy regulations happening based on a single judgment that push you to do. For me, it’s like, you know this book from decades ago, Fahrenheit 471? We’re talking about book burning. It’s book destroying, crunching. It’s so shocking. Nuno There are two things, right? First, the legal strategy, which is what you’re saying, because by purchasing a physical copy and converting it into one private digital copy and discarding the original, Anthropic pursued this cleaner legal argument for fair use copyright compliance. As you said, there was a federal judgment at some point on this. The other reason is actually operational. If you disassemble the book, and you feed loose pages, it’s much faster to scan books. You are destroying the book effectively anyway operationally. I think to your point, probably this came from a legal standpoint, not just the operational one. But even from an operational standpoint, it does make sense that they would have disassembled the book. Bertrand But some people have shown you can go very fast without destroying the book. It’s really not so critical. Two, you could make an exception if the book is rare. For that 1% of book that is rare, I’m not going to have this approach. I’m going to have another approach. But for that, you will have to care about books and not just care about building AI. Nuno This is the episode, as you guys have heard by now, that we’re trying to spit stuff at Anthropic. Bertrand To go back this is the same company saying, “Hey, guys, it’s bad to distillate my work. I’m the one scanning book at scale without asking author permission, without asking publisher permission, to be clear.” Nuno But just to be clear, Bertrand, we’re pissed off at everyone. We’re pissed off at Anthropic, we’re pissed of at OpenAI as well, right? We’re just pissed off in general at this moment. Bertrand At this stage for me, the more clear-cut company that is in the wrong is, from my perspective, at least, is Anthropic. OpenAI might be a fast follower, but I will say so far, they tried to be a bit more. Nuno But at this pace, Bertrand, who knows? Maybe next week we’ll be more pissed off at OpenAI. Something will come out. This episode is a mix of tragicomedy, like a Greek tragedy with some comedy in the middle or the other way around. It’s a slapstick thing that will end up in tragedy. I’m not sure. The Investor Reckoning Anyway, maybe switching to our final act, which is the investor perspective. What does this mean for investors like ourselves? There’s a lot of things going on. There’s the debate around the IPOs of Anthropic and OpenAI, which now, with all this uncertainty, might be under significant weight. There’s a lot of other discussions that we browsed through that there’s potential IPOs going forward on companies like the Moonshot AI company actually IPO-ing in the next 6 months as well. It’s very unclear what the IPO landscape looks like. Bertrand There’s been a lot of Chinese IPOs, actually, when you look at what’s happened in the past few months. Nuno Anthropic, OpenAI as potential IPOs, there’s all this question marks now. When will that happen? How will it factor in? All that’s happening around regulation as regulation is moving at the speed of light, which is for once something that’s very different than what we’ve seen before. There’s obviously SpaceX AI, which is already taking into account that price. It’s already a public company in there, and it’s under SpaceX, which is now a public company. Obviously, that’s already being factored in some ways. Bertrand Yeah. SpaceX AI has been very smart to acquire Cursor. It was a very smart move because Cursor is one of the leading companies in terms of automated code source development with AI. They had great models on their own. They’re bringing development data to SpaceX AI Grok. I think it was a great move. Nuno We have now people like Google delaying Gemini 3.5 Pro in terms of launch window. There’s stuff actually happening in the market where things are taking their own path. There’s uncertainty commercially, there’s uncertainty at regulation level. You have new players that have come out of nowhere that are making all these waves like Moonshot. We have all these… We had calculated probably a month and a half, 2 months ago, there had been 67 new frontier labs funded. All of these, we haven’t seen any much coming out of them. When some of this stuff starts coming out, will that also create disruptions in this market? Who knows? Bertrand Look at Thinking Machines, for instance. Thinking Machines led by the previous CTO of OpenAI, they released some pretty interesting open source models, actually. Very good quality for a first launch. Now it looks funny to say, but nearly on par with the top Chinese open source models. Nuno We have several investments in the space. humans& has made some recent announcements, which is quite interesting as well. We’ll see what actually happens in the market, but even more disruption probably will come in actual products in a form of product and commercial, on top of all the geopolitical mess that we discussed through the entire episode. If you’re an investor, how the hell do you underwrite an investment right now in early stage, mid-stage, late stage, et cetera? I think my answer is very carefully is how you underwrite it. Bertrand On your advice of being very careful to underwrite it, let’s not forget what happened to our boy wonder, Leopold Aschenbrenner of Situational Awareness. I guess he didn’t listen to you in terms of being careful because part of the instability in the stock market was actually coming from his hedge fund. These guys were leveraged 3, 4x going after the hottest of the hottest AI stocks, and margin calls, and all their public investment is gone just to answer their margin calls. I think it’s clear that the AI bet is… Personally, I’m very excited, and I think it’s the future, and you need to spend time and think about and invest in it. At the same time, it’s a bet that is not an easy one to follow. We go from GPUs to memories to equipments to power generation. All of this is not transitioning in an easy, organized manner. It would be boom and bust going there. He’s probably one of the first big-scale fatalities. The other big-scale fatality was the stock market in Korea, plunging 40% in a month. Definitely, all of that we discussed about was, on the background, you had the stock market going up and down pretty crazily the past few weeks. Nuno Everyone’s being affected. Everyone, you have your 401(k), you have your pension fund dependent on these equity stocks. Everyone’s seeing the effects of this volatility right now very aggressively. We do wish Leopold… Hopefully he’s on honeymoon right now because he got married, I think, this weekend. Hopefully there will be… Bertrand To none less than an Anthropic Chief of Staff. Nuno His wife is the Chief of Staff of Dario, is that it? Bertrand To Dario, yes, as far as I unders

Govern every AI agent running across your organization with Agent 365. Track agents from Microsoft, Amazon, Google, and Salesforce in one registry, sync in agents you're already running on AWS Bedrock, Google Cloud, Databricks Genie, and Anthropic Claude, and lock down shadow AI with default blocks and Execution Container isolation. Agent 365 inspects your environment for every agent Microsoft and partners have registered, then layers control on top. Reusable security policy templates apply Conditional Access, Access Packages, and Custom Security Attributes the moment you approve an agent, Tools governance blocks risky MCP servers and connectors org wide, and the Adoption Dashboard breaks down usage by group, job function, and license type for every manager in your org.  Jeremy Chapman, Microsoft 365 Director, shares how to bring every agent in your organization under one governed registry, and how to shut down shadow AI before it ever compromises your environment. 

What does a rising cloud bill actually tell you about the value your business is creating? Eight years after our first conversation, I welcome Kunal, co-founder and CEO of Unravel Data, back to Tech Talks Daily. We compare the data infrastructure he was optimizing during the Hadoop era with today's enterprise stacks built around Databricks, Snowflake, BigQuery, AI pipelines, and autonomous agents. Kunal says Unravel Data has analyzed over 10 billion workloads across hundreds of enterprises. From that work, he argues that data platforms and infrastructure can account for up to 60% of cloud spending at some global businesses, while 30% to 40% of data platform spending may produce no business value. These are company claims, but they frame a problem many technology and finance leaders will recognize. The cloud bill arrives after thousands of individual engineering decisions have already been made. We discuss where cloud waste hides, including oversized clusters, hot storage holding cold data, abandoned pipelines, inefficient queries, duplicate datasets, and development jobs consuming production-level resources. The people creating those workloads seldom see the price attached to their decisions, leaving technology leaders with an aggregated bill that explains what was purchased but not why it was needed. AI adds another complication. Humans create workloads at human speed, while agents can generate queries, launch infrastructure, and consume tokens around the clock. An agent is designed to complete its task, not worry about whether a single query costs $5 or $5,000. Kunal argues that machine-speed consumption cannot be governed through monthly human reviews. We also discuss the difference between cost cutting and cost optimization, why aggressive reductions can damage performance and reliability, and how FinOps must connect cost with business outcomes. Kunal explains why leaders should measure cost per pipeline, model, agent, successful run, customer report, and business result. Finally, we consider the benefits and risks of autonomous data platform optimization. Kunal describes autonomy as a dial, with bounded, reversible, and validated actions earning wider authority as trust develops. Does your cloud bill show healthy growth, or is expensive waste hiding behind the headline number? Share your thoughts with me.

The Suite Spot attended the 2026 Hotel Data Conference and had the opportunity to interview some of the best and brightest hospitality leaders in the industry to gain their insights and perspectives on prevailing data trends, AI & technology, how to optimize the guest experience and much more.  Be sure to watch the full episode if you missed any of the action from the 2026 Hotel Data Conference.  Special thanks to: Amanda Hite, Jan Freitag, Erica Lipscomb, Max Spangler, & Sam Trotter. Ryan Embree: Welcome to Suite Spot, where hoteliers check in and we check out what’s trending in hotel marketing. I’m your host, Ryan Embree. Hello, everyone. Ryan Embree here at the 2026 Hotel Data Conference here with STR President Amanda Hite. Amanda, great to see you again. Congratulations here. This is our first time at the Hotel Data Conference. Amanda Hite: Oh, wonderful. Thank you. Ryan Embree: Record attendance was just announced. Welcome to The Suite Spot. We’re excited to be here. It was a ton of excitement that we just saw. Tell us a little bit about this event, and we were talking off camera about, do you ever expect it to be what it is right now? Amanda Hite: Yes, we started it 18 years ago with a couple hundred people, maybe. The very first year we’ve always had it in Nashville. This is our home base for the STR part of our business. Most of our employees are here that are in the US. So we started it with a way to connect with customers and more importantly, like, we all have this curiosity about the data. You know, we’re constantly in analyzing, looking at trends in the industry, and we wanted to get people together to hear what are you seeing and let’s talk about it. And that’s really how this started. So it’s, I think it’s for me, my most proud part of this conference is the feeling that everyone has when they come in of being really open and curious and wanting to learn from each other. So you get some really good dynamic conversations happening in the networking breaks and in the hallway. Ryan Embree: Well, it’s such an important time right now too, right? ‘Cause people are already starting, if you can believe it. Well, actually, probably you can look in 2027. Amanda Hite: That’s why we do hotel data conference when we do it. Exactly. It’s budget season. Ryan Embree: Brilliant. Brilliant. Right? And, you know, you just got off stage, like I said. One of the fascinating pieces, like I said, we weren’t here last year, but this is our first time. You said when you first stepped on stage, there were, and you showed some of those original numbers. There was a little bit of a gap in the audience last year. Amanda Hite: Yes. Ryan Embree: But this year, a little bit different story. Amanda Hite: Yes. We had a much better forecast to reveal this year. Last year at this time was when we took the forecast down to reflect what was happening in the industry. And this year, we raised the forecast, not just for the rest of this year, but also for 2027. Ryan Embree: So great to see. And a really cool inflection point, I made a note here, revenue for the first time outpacing expenses, right? What does that mean for hoteliers? Amanda Hite: Yeah. So we finally see the pace of growth on the revenue side outpacing the expense growth. I mean, we’re in a high inflationary environment. Expense growth is something that will continue and hoteliers are having to deal with. But to see that we’re actually going to get some GOP gains, it’s, it’s super helpful. I mean, the point I made this morning though is our margins are not growing. Yeah. So we’ve got some room to grow efficiencies and productivity within the hotels to try to get margins to grow at the same rate of GOP growth. Ryan Embree: Yeah, yeah. It’s challenging right now. And one of the things we’re doing to combat, or CoStar’s doing combat that, bottom line data being added to the product. What’s that mean for the hotel industry? Ryan Embree: Yeah, so within STR Benchmark and the CoStar platform, we introduced at the end of the first quarter our profitability benchmarking. P&L is something that STR has done for 30 years. We did it on an annual basis. And we introduced our monthly benchmarking back in 2020, literally as the world shut down. So maybe not the best timing. But of course, now we’re prepared in an environment like we are today, a very complex operating environment for our hoteliers. It’s, yes, we need to grow revenues, but we must make sure that that is flowing through to the bottom line and that our operators and owners are actually making money. And that’s not been the case in many types of hotels and many markets around the country. So we’re trying to make sure that we bring that visibility of not just the top line growth that we want to see for the industry, but the flow through all the way to the bottom line. Ryan Embree: Yeah, I’d love to see that. And, you know, another thing that we’re gonna hear constantly about at, and at this conference is AI, right? So I guess the overarching question would be more of like, how are you incorporating AI into your products right now? Amanda Hite: This is when I’m so thankful that we are a part of the CoStar Group entity. If you follow our other brands, homes and apartments launched AI in their products earlier this year. So we’re continuing to build off of that. We will have AI search in the CoStar product in the same way that you see in apartments and homes. But for STR benchmarks specifically, what we’re thinking about is making sure that we’re integrating AI into the product, not just sitting on top of the product, but like we interact with the clients all the time on the analysis in the industry. So we want to bring that through AI into the product for our customers to use. So we love when they pick up the phone and call us and wanna talk about data. Right. But we also wanna make it easier for them to surface it within their portfolios in product. And so that’s the path that we’re going down to bring that intelligence in the product and analyzing and spotting the trends, knowing what to look at or sometimes not look at, right? Sometimes it’s a great point. It’s just as important to say like, “Hey, I only have a limited amount of time. Where do I not need to spend time right now?” And that can be tricky, especially when you’re looking at a larger portfolio of trying to discern where, what makes the most sense to drive profitability for my business, for me to spend time on right now. Ryan Embree: 100%. Those complexities and driving efficiency so important right now. And turning those data, that data into actual insights. That what one of the promises of AI. So reason we’re here at the Hotel Data Conference, thank you for taking the time. We’ll, we’ll let you get back. I know you’re hosting almost 900 hoteliers here. So we’ll let, let you get back to Amanda. Thanks for stopping by. Amanda Hite: Thank you, Ryan. Appreciate it. Ryan Embree: Hello, everyone. Ryan Embree here with The Suite Spot live on location Nashville at the 2026 Hotel Data Conference here with Jan Freitag, National Director at CoStar. Jan, thank you so much for taking some time and very busy. You’re hosting almost a thousand hoteliers here. Jan Freitag: Yes, 18th year. Sold out again. So heads up, next year we’ll sell out again. But thanks for being here and sort of taking the pulse on the industry. We appreciate it. Ryan Embree: 100%. Congratulations. Amanda Hite opened us this morning saying last year when she unveiled the forecast, there were audible gaps in the crowd. I feel like behind us, people have been skipping, jumping down, up and down this escalators. Share with us, we got a revised forecast. Jan Freitag: So we’re proposing that RevPar this year is up 4.4%. So that is the second upward revision we had to make, quote unquote. And the data’s just so strong. But then that means that next year, we’re gonna see growth, but it’s much slower global. So next year we’re thinking that RevPargrowth is gonna be like, you know, 2-2.1% or so. So the negative way to say this is, “Oh, our growth rate is cut in half.” The positive way to say this is like, “Oh, we have growth on growth, right? 4% this year, ne – 2% next year.” Ryan Embree: 100%. I mean, a lot of people are going into, you know, we’ve talked to hoteliers here on the Suite Spot, going into their budgets. These are very, very important numbers for them as they go into their budgets because they wanna forecast. When we met last, we were at NYU. There had been zero soccer games played in the US. Now, 104 games later, we got a crown champion. Obviously had a big impact. We’re gonna talk about that in a minute. But that strong performance, one of your big takeaways from this morning was strong performance is gonna equal some tougher comps in 2027, right? Jan Freitag: Yeah, absolutely. So we had arguably easy comps this year, right? The Q2, three, and four RevPAR performance last year was negative. So yeah, we would outperform it this year. That was not a question. But because, RevPAR in the second quarter was up 5.7%, that is a, a very stout result, obviously driven in June, partially by the World Cup remember we’re gonna talk about. You know what that means for next year is, oh wow, we’re not gonna see that performance again. And so my conversation this morning with hoteliers is all about, okay, so how do you massage your owner? How do you have this conversation with your owner, with your team to say, look, there’s still gonna be growth, but we really have to think about this. And I heard this this morning from an asset manager at next year as a year of 10 months and two months, you know? So really take June and July out of your annual number and say, okay, so what’s the growth for that? And then, yeah, June, July is just gonna be tough cost. Ryan Embree: Yeah. Probably something that a lot of markets who hosted Taylor Swift a couple years ago had to deal with. And then maybe what LA’s gonna have to deal with in 2029 after the Olympics in 28. Jan Freitag: Yeah, exactly. So we’re already talking now about the Olympics. We’re gonna talk about, obviously the World Cup in four years over in Europe and what is the performance there. So these sporting events are just the gifts that keep on giving. Ryan Embree: Yeah. Yeah. And, and travelers continue what we heard this morning. Consumers continue to prioritize travel, which is really, really great for obviously our industry. But not without its cautionary tales, you also had a watch your margins kind of take away from that. Maybe expand on that a little bit. Jan Freitag: Yeah. So we’ve had for the last year and for the last couple of years, really this interplay between room rate growth and the rate of inflation being higher than room rate growth. And we’re taking the rate of inflation sort of as a proxy for how much more things are expensive. And the costs for hotels are obviously going up. Higher labor costs, higher insurance costs, higher food costs, higher costs, inner energy, everything. So if your costs are going up in order for your margins to expand, you need to drive room rate or revenue faster than the cost increase. And that just is not happening. So my colleague Isaac Collazo spent 55 slides and an hour explaining how margins are decelerating, unfortunately. Now, the total dollar amount, we’re everything gets more expensive, but it also means we’re having more money available as profit. But the margins are coming down. And that’s really the, maybe to me, the main takeaway from HCC this year for the budget conversation for 2027 is watch your margin. Ryan Embree: Efficiency is always looking for that, especially in these tight margin areas. And then lastly, you know, your whole presentation this morning was themed around the World Cup. And, and I do wanna bring it up because, obviously there was the quote was 104 Super Bowls. Yeah. Right? And you kind of explored that case a little bit. Found out maybe that might not be the case. Jan Freitag: Yeah, exactly. So the FIFA president had said at the time, just to explain to American audiences, “Hey, we have 104 soccer games and they look like 104 Super Bowls.” That is of course not the case. And that was never meant to be the case. Super Bowl is the largest cultural sport event in America. It happens once a year, right? And to sort of translate that was, I thought always a little silly. So it turns out that the 104 Super Bowls did not come to pass, and it was more like 30 Super Bowls, maybe if that. So yeah, it was still a very healthy impact. If you look at the markets that Hosta gave Kansas City, New York, Philadelphia, Boston, very, very strong room rate growth. Interestingly, in some markets, actually, occupancy declines. We saw that specifically in Vancouver, but we saw it in Atlanta, we saw it in Boston. Why is that? Well, because corporate America, meeting travelers, meeting planners said, “You know what? I don’t need to compete with the Tartan Army in Boston for our meeting. You know, let me just stay away. Let me have that meeting in August, or let me move that meeting to Chicago,” for example. Sure. Chicago had a very, very strong June, July meeting calendar. So it’s, um, the, the room rate increase was absolutely expected and is exactly what came to pass. It just wasn’t Autumn for a Super Bowl. Ryan Embree: Yeah. I mean, that just proves we are, uh, a collective of markets. Things are gonna be obviously different in each one. Yeah. Uh, with different factors there. You know, a- and there’s also an interesting stat, fascinating stat, I wanna bring it up, about booking windows, um, that, that you brought up there. Yeah. If you wanna expand on that. Jan Freitag: So I got this totally wrong in the run up to the World Cup because I thought, look, if somebody books that FIFA ticket a year out, and the airplane ticket’s six months out, surely they would book their hotel three months out. Yeah. That did not happen. Right. And so we saw specifically the chart that I had this morning for, uh, arrival dates, June 11, 12, 13, 20 basis points of, uh, 20 points of occupancy was booked after June 8th. Wow. So that’s a booking windows of, like, three or four days. Wow. For an event that you knew what happened, I mean, six years ago. Yeah. You know? And you had a ticket from one year ago. So I just completely though that the, uh, the, the leisure traveler, the, the soccer traveler would also book their room way ahead. That did not come with us. Ryan Embree: Very interesting. I wonder if that’s a macro trend happening right now, those booking windows starting to shorten a little bit. Jan Freitag: Yeah, and maybe that’s a takeaway for our friends, you know, in LA who are hosting the Olympics. Hey, you know, be very mindful how you match that booking window. Ryan Embree: Lessons from history learned there. Yes. Um, final as we wrap up, I always li- like, like to get any, you know, you look at a lot of data. So any interesting, uh, like, data points that really stood out or surprising? Jan Freitag: I mean, the July data came out yesterday and the luxury class RevPar growth was 16%. Ryan Embree: Wow. Jan Freitag: Talk about A, amazing, but B, A, tough comps. Yeah. In July of next year. But it was an amazing, amazing performance. July was very, very strong. Um, and June as well. So we clearly saw, you know, July was helped a little bit by 4th of July, World Cup, uh, 4th of July calendar year, but also the World Cup, obviously the final and the bronze medal games. They all, they all helped. So July was strong, June was strong. So now I think things are getting a little bit more normal – Yeah. Early on end. Ryan Embree: Awesome. Well, we’ll continue to look ahead as you will, but thank you again for taking time out of your busy schedule, Jan. Jan Freitag: Thanks for being here. Thank you. Ryan Embree: Hello everyone, Ryan Embree here with The Suite Spot. We are live on location of the 2026 Hotel Data Conference. I am here with Erica Lipscomb, EVP of Commercial Strategy at PM Hotel Group. Erica, thank you so much for joining me on The Suite Spot. Erica Lipscomb: Well, thank you for having me. Very excited to be here. Ryan Embree: Yeah, first time here on the Suite Spot. Yes. But not your first time here at Hotel Data Conference. Erica Lipscomb: Not my first time at Hotel Data Conference. This is conference number eight. Ryan Embree: Okay. Yes. All right. Hotel data conference. You obviously are no stranger, you’re a pro. What do you call a hotel data conference a success kind of reflecting back? What do you come here to accomplish and to learn? Erica Lipscomb: You know, I, again, this is our start of budget season. Sure. Right? Yeah. So I actually, uh, had dinner with Amanda last night and said, “You do realize what you’ve done here, right? We cannot even start our budget calendars until there’s an HTC.” Yeah. So really what I look forward to is not coming here just to hear that the amazing news of an increase year over year, or that we’re gonna increase in the year for the year. Sure. But what are those things that I can take away that can make it tactical for our teams? Mm-hmm. So learning from industry leaders that are here. We have amazingly smart people that are here at this conference. And we’re really drafting and shaping what the industry will look like. So what are those learnings? And then how do I make sure that we trickle that down within the organization and get them to our teams? Ryan Embree: Which can change so rapidly, right? As we know – Absolutely. It’s gone from, uh, a yearly change to almost, it feels like a weekly, especially with the AI and technology conversation. Yes. You were on a panel last year here at this same conference. I’m curious, what were some of the conversations then versus now? Erica Lipscomb: Yeah. And very different. I think it’s been extreme polar opposites. Okay. I feel like last year, there was a lot of conversation about AI. Mm-hmm. But more on the what is AI. Mm. And how are we gonna use AI? Yep. And it’s already started in conversations this morning. You know, we started networking last night, and most people are now really talking about what is AI doing for us to make sure that we’re efficient, making sure that our teams are effective, um, ensuring there’s profitability back to our owners. So it’s gone from a concept – Yeah. To now actually, how are we utilizing AI to be better in the industry, but keeping the forefront our customers? Ryan Embree: It feels like we’re in the sandbox now, right? And there’s a lot of companies out there trying different things. It’s the exploration process and, you know, maybe some success, but even, uh, lessons in the failure. Uh, I, I’ve been hearing a lot about that as well. So hotel data, obviously data is the name of the game. Yes. Still one of the most important tools I feel like right now on our quest of guest personalization. And so much it can do to kind of like what you said, prepare us for the rest of 2026 and even into 2027. Right. How is PM Hotel Group kind of leveraging data for growth and, uh, experiences? Erica Lipscomb: So actually you started with, with growth and experiences. Yeah. So really starting with growth. Yeah. We really are starting with AI in our business development side of our, our, of our home. Sure. And really how are we looking for the right clients that fit PM? Yeah. How, again, when you look at, uh, BD, it’s a relationship. Mm. So who are those owners? Who are the asset managers? What do their teams look like? Is that a right fit? And how can we help them grow? So that’s really the act – acquisition of the client and the customer. And then when we get to the property level – Right. Then as an enterprise, as a support center, what we’re looking to do is how do we use data, which is the, the heart – Yeah. Of revenue optimization. Sure. How are you using that data to make sure that we’re pulling through every step of the guest journey? So from the time again, acquisition of a customer. Right. So now not an owner, but that actual guest that’s gonna be staying at our properties, what does that customer journey look like? How do we find the right customer? We have a very diversified portfolio – Oh, yeah. For each one of our assets in the portfolio, ensuring that they convert. And then once they’re there in their stay, are we pulling through on all the experiences they expect? Whether it’s an independent hotel and the experiences that come along or for the brands and the brand standards. And then once our guests leave, how do we make sure that we are still speaking to them – uh-huh. And making sure that they return? Ryan Embree: I love how you walk through the entire guest experience. I think sometimes we get caught up just thinking about one or two elements of it. Right. But it really does start. I mean, the hot topic right now is that AI visibility, right? Absolutely. And being bound, uh, because our travelers are changing the way that they’re searching for hotels and doing their research. So, uh, it’s super, super important there. We’re in Nashville, Erica, uh, no stranger for PM Hotel Group. Yes. Uh, you guys just, uh – Very excited. Assumed management, 12 properties. Yes. Uh, what do you lo – like, um, from a Nashville market standpoint? I mean, this has just been such a hot market right now in hospitality. Uh, but also, you know, a big threshold of, uh, exciting 80 plus hotels for PM Hotel Group? Erica Lipscomb: Yes. We’re very excited to have the 12 hotels that, from Pinnacle that joined our portfolio. And that’s really our sweet spot, right? Finding those type of assets that fit our growth in our platform, and that we can make sure that we’re optimizing on their revenue, as well as excellent customer experience and guest operations experience. So what I really like about Nashville, and it’s not a new growth. Right. You know, Nashville never stopped growing, right? Where the, where the rest of the world really has struggled even, you know, six years ago. Through COVID. Nashville didn’t, right? Ryan Embree: It was red hot. Erica Lipscomb: But what most people think about when you hear Nashville, they’re really just thinking it’s an entertainment city. That’s not just all Nashville is. So when we peel it back and take a look at the segmentation and what’s driving Nashville, you do still have that customer that is true corporate business. And you still have conventions and groups. I was just in a group maximization winning group seminar just not too long ago. And in that breakout session, we really talk about group continues to still grow. Oh, yeah. And when you take a look at the first half of this year, that growth is really happening not only just in convention centers, but those hotels that have group meetings. Even when you take a look at those assets, what’s interesting is the growth is not just in the hotel that has most of the group, but if you are affiliated. You’re feeling that demand. Leveraging the demand and continuing to drive occupancy and ADR. Yeah, absolutely. So that’s why we’re still excited about Nashville. It’s one of those markets that continues to do well, not just in entertainment, but on the corporate business transient side, as well as group side. Ryan Embree: It’s a perfect destination. That’s why we got almost a thousand hoteliers here at the hotel data conference. Erica Lipscomb: That’s sold out again this year. Ryan Embree: Absolutely. Well, any. I mean, I can tell just by the conversation we’re having, very passionate about your work. Any projects you’re particularly fired up about right now? Erica Lipscomb: The project I’m probably most interested in is what I was hired for is to really continue to evolve commercial strategy. So commercial strategy is not just looking at every discipline in a silo. They’re all very important to revenue optimization. But how do we now continue to go from just having the commercial conversations, but also leverage the experience in each discipline? So our customers, when they look at our hotels, And they look at that curse customer journey that we just walked through – Right. They’re not looking at sales, revenue, marketing, distribution, operations. They’re looking at their holistic experience. Yeah. And so why not make sure that we internally stop looking at how well each d- division does and, and, and our, each siloed discipline, but let’s look through the lens of a gu – of a customer. Yeah. What’s that experience look like? And then how do we all play a part of it? Yeah. Exactly. So that’s what I’m excited about. And using, continuing to use AI. Yeah. How do we make sure that we’re leveraging commercial? Yeah. And then making sure that our use of AI is making our teams much more efficient – Mm. And effective in h – in how we run our businesses. Ryan Embree: That’s what I was gonna say. It’s, it’s such an inflection point, and I’m sure very exciting for, for your job with the technology in hand. Now you’ve got the power to, uh, break down those silos, right? Absolutely. Create efficiencies there. Yes. Uh, well, as we wrap up, you know, we always. One of the things here that we love to do at the Hotel Data Conference is try to predict the future, right? Forecasting, everybody. It’s a, it’s an impossible job, but we do it every single year. Right. Uh, you know, so from a commercial strategy standpoint, I know you, you, you just mentioned the projects you’re working on, but what’s your vision for PM Hotel Group as we kind of go into the latter part of the 2020s? Erica Lipscomb: So latter part of the 2020s, I think that the company’s vision is to really leverage the portfolio and the diversity of the portfolio. We saw that growth that we had just here in Nashville. I’m sure you saw the news that Reset our first brand to enter Marriott’s or outdoor collection. Yeah. We’ve noticed that when you continue to diversify and not really just say, okay, we are just this type of company, making sure that we’re leveraging the expertise of our team. Mm-hmm. We can be many things – Yeah. To many customers. Yeah. And so lev – continue that leverage, that growth, but we do see that growth continue to be in experiences. Yeah. Right? So every brand is rolling out how they’re working with experiences. But what we do see, that lifestyle, outdoor – Oh, yeah. Experiences. We’ve had our first entree into it, and we’re gonna continue to grow. Ryan Embree: Awesome. We’re excited to watch that growth, and yeah, that experiential travel continues to be something, conversations we’re having here, prioritizing, that’s what the guests are prioritizing travelers are. Congratulations on all this. We continue to watch it with PM Hotel Group. Thanks, Erica. Erica Lipscomb: Thank you. Ryan Embree: Hello, everyone. Ryan Embree here with The Suite Spot. We are live on location at the 2026 Hotel Data Conference. I am here with Max Spangler, VP of Technology at Charlestown Hotel. Max, we know you’re on a panel tomorrow. We’ll talk about that in a second, but thanks for taking the time to join us. Max Spangler: Absolutely. thanks for hosting me, Ryan. Ryan Embree: Yeah, Gotel Data Conference. Name of the game, data. We’re gonna talk about, obviously, your role and, and where data plays into that. But first, you come to a conference like this, what’s the expectation? What do you hope to get out of it? And maybe when you’re a couple weeks down the line, looking back on the conference, that was a success. Max Spangler: Yeah, you know, for me, I spend a lot of time at conferences that are very narrow in scope, right? Sure. Whether it’s high tech or the hospitality show, or even technology conferences that are outside of hospitality. Sure. So coming to HDC is always great. It’s always refreshing. The keynote panel in the beginning always gives me, hopefully, optimism. And this morning, it was very optimistic – Yes. About the way things are going. So I’m thankful for that. But it’s great to hear from commercial peers how they’re using data, how they’re surfacing insights, what tools they’re using, and how they’re turning it actionable. I mean, I think for me, as someone who spends a lot of time staring at screens, developing tools, looking at dashboards, hearing from people that actually depend on this information – Yeah. So crucially is really refreshing. So I get to, like, cut through the noise a little bit and hear what’s working, and hopefully hear what’s not. Ryan Embree: Yeah, and that’s what leads to your panel tomorrow, connecting AI to commercial strategy. Yeah. Uh, maybe give our sweet spot listeners a little bit of sneak peek and maybe your thoughts on the subject. Max Spangler: We’ve got a great panel tomorrow. Super stoked for it. You know, so we, we had a pre-cause you tend to do with those panels. Right. And as a result of that, we decided to zoom out a little bit, which I though was important. So commercial still is the through line, as you would expect at HTC, but given the man – the, the members that are on the panel, we’ve got some people that, you know, are on the, the, the revenue management side. We’ve got some people from HFTP. Um, you’ve got me as an independent operator. It w- we felt, we felt it really important to say, “Let’s, let’s zoom out. Let’s take a pause and, like, let’s look at where the industry is holistically.” Sure. And so the questions are really driving off that. So you’ll find that, um, there’s insights about a year from now, what would we like to be doing differently, right? How are we driving ac- actionable insights? What KPIs are important? What KPIs are important? Yeah. Things like what’s the difference between automation versus th- this new agentic era? Mm. So I think it, um, I’m actually really excited for it. The panel’s great, and I think you’re gonna get some, some really interesting insights from a variety of different opinions. Ryan Embree: Yeah. And what we talked about is so much can change. Yeah. And you could talk about what could happen in a year. I mean, that could be a couple cycles with technology right now. And that’s why I, I was really looking forward this conversation, Max. Yeah. Because, you know, I get industry leaders, sometimes brand leaders, but you’re, you’re in it every single day, right? Yeah. Uh, VP of technology. Yep. Where do you think we are in the AI adoption – Yeah. Uh, uh, cycle? And then maybe zoom in a little bit on Charlestown Hotels. Max Spangler: Yeah. So if we, if we look at sort of where things are globally for the state of AI, I think obviously in the technology space, it’s an existential crisis, right? Right. I mean, I think you see that in, in jobs reports. I think you obviously see it in the way that they’re measuring AI as an accelerant. Yeah. You know, so, uh, friends of mine that work for tech companies, they’re seeing their time to release production code going from five weeks, four weeks down to one week. Wow. It’s easy for them to measure. It’s easy for them to see the outcomes for us. Yeah. I think it, it is ultimately a little bit more difficult. For Charlestown, you know, we think it’s really important to keep hospitality at the center of what we’re doing, right? And so we’re not parading around trying to be an AI company or a SaaS company. We firmly believe people and hospitality at the center of, is gonna be at the center of what we do.m. How do we use AI to power that? Whether it’s through efficiencies, you know, through maybe more sophisticated RMS, through, you know, generative guest insights. How do we make sure that we’re being discovered when people are asking what’s the best hotel in downtown Charleston, South Carolina? Those are really hard questions to answer. No one’s got it figured out. But the conversations that are happening here are super encouraging because I think there is a lot of people admitting that and coming together to try to find, um, the best path forward. Ryan Embree: And you were, this is not your first per – podcast that you’ve been on recently. I saw you, uh, on CoStar News Hotel podcast where you talked about escaping hospitality’s AI hype echo chamber. Yeah, yeah. What’s your thoughts on that? And maybe how do we avoid doing that here in, in spaces like this? Max Spangler: I mean, it’s, if you go on LinkedIn, you can feel like, you know, FOMO is like a- absolutely crushing you, right? Right. Everyone is, like, piloting something new. Right. Everyone is, is advancing seemingly at the speed of light. It’s really important to come to a conference like HTC, uh, to get a real life temperature check with what people are doing and how they’re doing it. There is a tremendous amount of hype. There’s a tremendous amount of potential, but I think for a lot of us, and especially from someone sitting in the seat of an operator, you have to be very disciplined. Yes. You know, you have to have a step-by-step sequence of how you’re actually gonna accomplish this. It’s okay to introduce a little bit of chaos. We’ve done that in the early days. I mean, if you go back, you know, to 2023, 2024, we’re experimenting with all the frontier models. But eventually, we wanted to collapse that into a unified choice, pick one model so that we can move forward and start measuring, you know, are our team members crawling? Who’s walking? Who’s running? How do we devise resources to help kind of get everyone on the same page, march in the same direction, and get better at this? Yeah. And so that, that’s, that’s been our strategy. And fortunately, like, that’s what I’m hearing here at the conference. Ryan Embree: And the motivation for implementing AI can’t come out of fear of we’re not doing enough. Yeah. Or, you know, we’re just, that FOMO feeling that you’re talking about, it has to have, what you said, discipline and direction. Yeah. And Max Spangler: Ryan, like, fear is a huge part. I mean, that’s one of the things that we’re constantly up against. There’s. I, I think the, the negative attitude and apprehension towards AI is only gonna continue to grow over time, right? Just like the excitement over it is gonna continue to grow. Yeah. Same thing’s true for the negative. I mean, you have people that absolutely have their head in the sand, which is okay. Right. Um, for, for certain reasons, you have people that obviously have negative feelings about it because of the socio – uh, economic impact. Sure. Companies potentially might be laying off job just Placement or replacement as a result of LLMs and the technologies that they introduce. There’s the environmental factors. So, like, all those things are absolutely true. We don’t think it’s, as Charlestown, our responsibility to sort of correct that. Right. But we do wanna make sure our associates, team members, and corporate, and corporate leadership team know this isn’t going anywhere. Yeah. It’s fundamental core to the business, and we’re gonna make an investment into our teams to make sure that they’re prepared for this new wave, whatever it looks like. Ryan Embree: It’s exciting times. And it’s okay to experiment fail sometimes, because that, that’ll show you some lessons too. Sure. Max Spangler: Yeah, we. Yeah, we’ve run so many pilots. We’ve had so many things fail. We’ve incinerated millions of tokens and subsequently thousands of dollars as a result of – Yeah. Um, so many pilots, but we’ve learned a lot. Yeah. Uh, and we’re in a much better spot as a result of it. You have to be willing to take risks, especially now. I do believe, like, no one’s gonna be left behind yet, but there is absolutely an advantage to being a first mover. And I think the companies that are at least experimenting and building AI fluency for their teams are gonna be much better, uh, much farther along than everybody else. Ryan Embree: 100%. And, you know, one of those spaces is, is the data, right? That’s, I mean, that’s the name of the game of this conference here. Yeah. How are some ways are you leveraging data to kind of – Yeah. Grow Charlestown hotels or even just create efficiencies? Max Spangler: Yeah. So for us, it, it, it is a challenge to think about the kind of company that we are. We focus mostly on the independent space. Mm-hmm. So we don’t have sort of the technology through line like the brands have where – Sure. You know, they can force a certain PMS, POS, CRS, like, it’s very clean and organized and scalable that way. Yeah. For us, you know, when we come into a new hotel operating environment, in most cases, technology hasn’t been a major form of investment, right? I mean, most people don’t come to Charlestown hotels with a great performing asset. They’re like, “We’re in trouble. We need your help.” Right. So then I come in, you know, from the technology perspective and it’s like, okay, this is difficult. How are we gonna extract information, put it into a centralized place, be able to sort of layer a, a, a, a BI tool or reporting package on top of it to actually surface the insights so these one-off owner operators can get the insights that, like, a company like Charlestown Hotels can deliver at scale with all the independent properties and things we’ve learned across the secondary and tertiary markets that we work in. Max Spangler: So, I mean, to put it simply for us, it is about having, like, a central data repository or warehouse. Sure. I mean, there’s plenty out there. Databricks, Snowflake. We’re a BigQuery customer. We do a lot with Google. Um, but it is, you know, if, if you think about where things are going to bring it back to AI, so much of the conversation surrounds having a good data foundation, because AI is an accelerant. If you have bad data, it’s gonna accelerate you to bad outcomes more quickly. Yeah, that’s a great point. If you have a bad business strategy, it’s gonna optimize for the wrong KPIs. So for us, it is very much about having solid fundamentals. Yeah. That’s not a reason for you to stop, right? It’s just more a reason for you to proceed cautiously. Ryan Embree: Absolutely. And, you know, you do it right. All of a sudden, you get that personalization, which, you know, hospitality’s been really the last decade – Yeah. Has been striving so much for to get that personalization within the guest experience. So as we wrap up, you know, we always like to. I know this is gonna be difficult because, like we said, things change so quickly in the tech space. Yeah. But what’s your vision for Charleston Hotels from a technology perspective? Max Spangler: Yeah, great question. I thought you were gonna ask me a hard one, like, what’s my favorite color? But, uh, no, for, for. Vision for technology, you know, for us, as long as we keep, like, hospitality at the center – Yeah. As our north star, that really does simplify things for us. It is gonna be difficult. There’s, you know, obviously a whole host of different frontier models you have to choose from. Tokenomics is gonna continue to be a big part. People talk about ROI with LLMs, but no one’s really talking about the expense – Yeah. And expenses continue to grow. Great point. Right? So we’re, we’re focused really on, you know, not only the, the ROI from some of the LLM tools, but, but obviously the tremendous cost that’s associated with running them at scale. But as long as we keep people and human beings at the center, reducing mundane work, admin tasks, friction so that our people can spend less time in front of screens and just be more hospitable, I think that really is the vision. Technology’s gonna support that. It’s gonna hopefully be more invisible to the people that come to hospitality. They didn’t come to, like, move information around – Right. Push paper or spend time in front of a computer. They spent it to, like, be empathetic, to be excited – Yeah. To surprise and delight. And so our goal, that’s our north star, and technology’s gonna be there to support it. Ryan Embree: Yeah, I mean, some industries, you’re, you’re right, are gonna be completely flipped upside down – Yeah. With this technology. But hospitality, we have that advantage of being a people first industry, so. Max Spangler: I, I think it’s, like, the, the key differentiator, and it honestly, it’s like, hospitality has an opportunity to have a really strong opinion. As so many industries are completely rolled over by this AI wave – Yeah. Hospitality can actually say, “No, you know what? People are…” And people in hospitality are at the center, and so as there is potentially more AI backlash and people are seeking more authentic experiences – Right. With people and connections – Yeah. I think it’s, it’s gonna be a great benefit to our industry. Max Spangler: Yeah, and we’ve seen from the data, experiences still seem t be – Yeah. I think that’s gonna grow. Yeah. Ryan Embree: Yeah. Agreed. Uh, Max, appreciate the time. Thank you. Uh, we’ll keep an eye on Charleston Hotels and everything you’re doing over there. Great. Congratulations. Max Spangler: Thank you. Ryan Embree: Hello, Everyone. Ryan Embree here with The Suite Spot. We’re live on location at the Hotel Data Conference 2026 here with Sam Trotter, Head of Digital Marketing for Indigo Road Hospitality Group. Sam, thanks for taking some time. Sam Trotter: Thanks for having me here. Yeah. I’m excited to be here at the Hotel Data Conference. Ryan Embree: It’s our first time here, but you said you’re, you’re a pro. You’ve been here for many years. Yeah. What does a successful hotel data conference look like for you and some of the takeaways that you look for? Sam Trotter: I really love having a good sense of what’s gonna happen next year. So you get some really great data here where you actually can take to your business planning sessions and use and say, “Hey, you know, I have this from the data conference, and they’re forecasting this growth in this market.” And you have something tangible. Yeah. So you’re about to head into budget season. Yeah. So having that in hand is really, really nice. Ryan Embree: That’s a big part of it. I mean, budget season, you gotta make those operation efficiency. We talked about the margins, how tight those are right now, especially in hospitality. One of the ways that hospitality’s changing right now is through AI search. You were on a panel here. For those that weren’t able to join us here in Nashville, maybe unpack that topic a little bit, because it’d certainly be top of mind for a lot of hoteliers right now. Sam Trotter: Well, it was really fun. It was a packed house, so a lot of interest in it. There’s a lot to talk about. I think we did a little bit of an intro to the topic, just so that everybody was sort of on the same page. But this is a new thing that we’re all having to adapt to. And we’re gonna have to focus on this. And in the panel, I said, “This feels a lot like 2006 when SEO was becoming a big thing.” And I remember I hired this French couple to do our SEO for this hotel that we were opening. It was like $10,000. In 2006. And it felt kinda like magic. Yeah. You know, like, what are they, what are they actually gonna do, right? And so it’s really tough. Who do you listen to? What actually works? And it was a great panel. And there’s no main takeaway other than we’re doing a lot of AB tests. We’re trying to figure out what works. I’m looking at the dashboards from our different properties. Who’s doing well? Who’s not? Yeah. And trying to pivot. And so we’re at this really interesting phase where it’s not really clear, right? Everybody’s telling us different things. Who do you listen to? So I honestly think it’s really exciting. Ryan Embree: The good news is it’s a challenge that a lot of people are attacking at once, right? And that’s where you’re gonna kind of find maybe lessons learned, even in those failures. So I think it is interesting because ultimately what happened with SEO is, like, there became a little bit of of a game plan that you could attack it with, right? That people are still trying to kind of balance. And then there were switches, right? That’s the other thing, is you could attack it one way and then all of a sudden, next week, algorithms change and it’s back to square one. So it was very, very interesting. But I think it’s events like this and panels that you’re on, Sam, that help kind of. Where everyone’s going through this right now. And to try to get through the weeds on it and try to figure out what is a good course of action here. And it changes so quickly. I think AI gets a spotlight, obviously, for good reason because it’s just this up and coming technology. But digital marketing also feels like it’s fast changing and evolving. And it’s been doing that for the past decade. You think about social media updates and everything like that. Yeah. I guess, how do you view digital marketing right now from a strategic standpoint and, and how hoteliers should be embracing and, you know, maybe investing in It? Sam Trotter: So that was a big question, right? Ryan Embree: Yes, sorry. Sam Trotter: When I have new marketers join, junior marketers, I always tell them there’s really no such thing as an expert anymore. Because it’s gonna change next year. Right? Ryan Embree: Great point. Sam Trotter: There are certain fundamentals that will help you no matter what, from 10 years from now, they’ll always be in play. I think what’s really interesting now with AI is I feel like there’s more emphasis on brand and category ownership. So if the AI is the most educated person in the entire world about hotels in Nashville. I mean, that’s what it is. Sure. It’s the most educated person in Nashville. What do you wanna teach it? And so if you’re teaching it, I have a pool and a fitness center, that doesn’t really help, right? ‘Cause now you’re just the same. And so what category can you own? Can you be the wellness hotel of Nashville? And if you’re the wellness hotel of Nashville, what that looks like is everything we say and we do reflects that. So I have spa packages, we have spa activations, we have a spa month. We have an amazing spa. We have spa content. We have spa creators that come in. And so when you’re doing that, all of a sudden the AI’s like, “Okay, no, this is the spa hotel of Nashville.” Because it’s, there’s evidence. Yeah. Right? And so I think there’s gonna be more emphasis on this category ownership, right? More than ever. And that boils back down to your brand. Ryan Embree: No, I love that. I think that’s a great explanation to someone who might feel overwhelmed in this right now. But those searches also could get very specific, right? I’m looking for a place that’s pet friendly, that is dedicated to wellness, where I’ve got my family coming to. So that’s where it gets a little bit tricky of the categories could turn into subcategories and then very, very niche. But it’s also the beauty of it, I think on the other side, is that your travelers are gonna be able to hopefully find the right hotel for them and what they’re looking for. Sam Trotter: So going back to your question, you asked me about social media. Because things are changing so fast, the, what we don’t really realize, and we don’t talk about, is the number one AI that we talk to is Google’s AI overview. That’s the one that when you have a long search, it defaults to, right? And it is weighing YouTube more than any other social platform – Great point. Because they’re not giving access. Right? So TikTok’s not giving them access. So now all of a sudden, YouTube is like this big player. And so we’ve got 76 locations. How do you scale YouTube? Yeah. And so, you know, we’re trying to figure this out in real time, and it’s a lot. There’s a lot of change happening. Ryan Embree: No, that’s a great point, what you said about Google, because a lot of people might be listening to this being like, “Well, I’m not, I’m not really looking, or I’m not using AI in my everyday life.” Well, Google’s really, you know, that AI overview, you are using, right? And it’s just, it’s gonna become more and more, whether we know it or not, a part of our life, you know? So that’s a different conversation. You know, Sam, Indigo Road Hospitality Group, you just mentioned tons of locations. What are some of the projects you’re most excited about that you’re working on right now? Sam Trotter: So, we have amazing locations, and there’s so many that are really interesting that we have coming up. We’re dabbling in more and more to membership clubs. Which is something that, we have one right now in Bentonville, and then by the end of the year, we’ll have two more. So we’re going from zero to three in a pretty short amount of time. It’ll be like a year and two months, we’ll go from zero to three. So it’s something that has been really fun to learn about, and there’s new platforms to learn about. So that’s really exciting. And then, I’m working on some fun data projects too, fun to me. I’m trying to set us up to have our own loyalty program. And so I’ve got some cool things in the works. So I’m really excited about that because we have a diverse portfolio. We have coffee shops and restaurants and hotels. And venues and how do you get them all to talk? Right? How do we put them all in one place, but have them separate? How can we share notes? How can we improve the guest experience? So there’s a lot of really cool things that are happening now. And one of the benefits of AI is partners are, are improving their platforms faster than ever. Oh, yeah. Which is fun if you have the right partners. And they are doing it. Ryan Embree: Yeah. I mean, and loyalty programs, you also learn more about your guests and hopefully create personalization, which is, you know, a topic that has been in hospitality. But we’re getting closer and closer, I feel like, to what we may have talked about five years ago at a conference like this. Be like, “There might be a time where we could do this, and now with the power of AI, it, it’s possible.” Yeah. Well, as we wrap up, kind of what’s your. I know we just talked about the future, but, and it’s hard to predict, but what would be kind of your vision for the future, in your role at Indigo Hospitality Group? Sam Trotter: I would say that, I guess thinking more optimistically, ultimately, it’s gonna be about the guest experience. It’s gonna be about the surprise and delight. It’s gonna be about having great employees who are happy to be where they’re at and that wanna be there. And the best marketing is a great experience, right? So what’s my role in that? You know, how can I help operations do their thing? And that’s the foundation of everything. At the end of the day, I think that’s it. Ryan Embree: There is a, there is a comfort, Sam, to being in an industry that we knew can only be disrupted so much by AI, but at the end of the day, it is gonna still come down to people serving people and creating those memorable experiences, and hopefully AI gives the opportunity to do. Sam Trotter: I have a anti-trend for you, right? Okay. So we’re here at, at the Grand Hyatt. There’s no kiosks, right? Check in. There’s still front desk people. 10 years ago at the hotel data conference, I think we would’ve though it was all kiosks. So hospitality has reigned supreme, and I think that’s the future. Ryan Embree: Yeah for, they say hospitality is the first ever industry and it’ll be here for a long time. So Sam, we appreciate it. We’re gonna watch you and, and everything you’re doing over there. We appreciate you taking some time with us. Sam Trotter: Thank you you so much. This was a lot of fun. All right. Ryan Embree: To join our loyalty program, be sure to subscribe and give us a five-star rating on iTunes. Suite Spot is produced by Travel Media Group. Our editor is Brandon Bell with cover art by Bary Gordon. I’m your host, Ryan Embree, and we hope you enjoyed your stay.

Nicole Locklin for US Congress is an attorney with more than a decade of legal experience, most recently serving as Lead Commercial Legal Counsel for Healthcare and Life Sciences at Databricks. She grew up in a town of about a thousand people in rural Oklahoma, where her parents ran a crop dusting business. A first generation college graduate who relied on student loans to earn her law degree, Nicole is now running as a Democrat for U.S. Congress in Florida's 26th District, challenging a twenty plus year incumbent and making the case that working families in South Florida deserve a representative who actually works for them.We talked about what it has actually felt like to leave a legal career and step into a congressional race with no political background, the affordability crisis Nicole keeps hearing about on doorsteps across South Florida where homeowner's insurance averages $8,000 a year and a one bedroom apartment can run nearly $3,000 a month, and why Nicole believes corruption and corporate money in politics is the root cause blocking progress on every other issue families care about. We also discussed the gerrymandering that reshaped FL-26, why Nicole sees the seat as genuinely flippable, and what it means to run a grassroots campaign in a state where the political establishment has spent years trying to erase Democratic votes.Connect with Nicole: Website | Instagram | Substack If you're looking to unleash your potential, find your personal, professional, or political fire, and to connect with a community who is doing the same, click here to learn more. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit shannonwatts.substack.com/subscribe

Manu Mehra is Head of AMER Industries Strategic Deal Pricing at Databricks, with more than 12 years of experience across pricing, product, cloud, and AI, including Google Cloud and Thermo Fisher Scientific. He brings a practical perspective on how AI is changing the way companies think about outcomes, value, platforms, and pricing models. In this episode, Manu explains why traditional pricing models don't neatly fit AI, why outcome-based pricing is compelling but difficult to standardize, and how companies can turn platforms into solutions around specific business problems.  Mark challenges him throughout the conversation, especially on the attribution problem: if AI creates the value, how do you know AI actually caused it?   Why You Have to Check Out Today's Podcast: Learn why AI is pushing pricing toward outcomes. Discover how platforms become solutions customers will pay more for. Understand the attribution and standardization challenges behind AI pricing.   "Pricing cannot be an afterthought. It has to be integrated within the product roadmap." — Manu Mehra   Topics Covered: 01:15 – How an accidental pricing analytics role led Manu to a career spanning product, cloud, AI, sales, finance, and strategic deal pricing. 03:30 – Why Pricing Has to Start With the Product. Why integrating pricing into the product roadmap can create value before launch instead of scrambling for cost-plus pricing afterward. 05:30 – Why AI Breaks Traditional Pricing Models. Why subscription, license, and consumption models don't fully fit AI when thousands of customers can pursue completely different outcomes 08:00 – The Hardest Problem With Outcome-Based Pricing. Why AI outcomes are difficult to standardize across billing, finance, legal, and revenue recognition—and why 10,000 customers could mean 10,000 different outcomes 11:00 – What Actually Counts as an AI Outcome? Manu uses a QBR example where AI can automate 95% of the SQL work, turning hours and effort saved into a measurable form of value. 13:30 – The Attribution Problem: Did AI Really Create the Value? Mark and Manu debate how to determine whether AI actually caused increased revenue, lower costs, or other gains—or simply helped the business get there faster. 16:00 – Platform vs. Solution: What Are You Really Selling? Why a broad platform can have wildly different value depending on the customer's use case—and how platforms can become solutions by solving specific business problems. 19:00 – How to Turn Products Into Business Solutions. Manu explains how compute, data, and AI layers can be combined into packaged solutions instead of being sold as isolated products. 21:30 – How Customer Segmentation Makes AI Pricing Scalable. Why identifying recurring customer patterns can help companies map different business problems to repeatable combinations of SKUs instead of creating a custom solution for every customer. 24:00 – Why AI Companies Use Credits. How credits can create cost predictability, manage backend costs, and give customers flexibility across different AI capabilities. 27:00 – When Credits Make Sense—and When They Don't. Why platform customers may value the flexibility of credits while digital-native customers who already know exactly what they want may have less need for them. 30:00 – The Pricing Advice Manu Wants Leaders to Hear. Why pricing should never be an afterthought and why the industry is moving from cost-plus toward value-based and outcome-based pricing.   Key Takeaways: "The reason is, even though you might be a platform organization or you're selling a platform, but end of the day, you're still trying to solve a customer problem." — Manu Mehra "The tricky thing with outcome is it's very hard to standardize it." — Manu Mehra "Pricing needs to be integrated during the product roadmap." — Manu Mehra   Connect with Manu Mehra: LinkedIn: https://www.linkedin.com/in/manumehra1/   Connect with Mark Stiving: LinkedIn: https://www.linkedin.com/in/stiving/ Email: mark@impactpricing.com  

What happens when AI stops being impressive in demos—and starts helping us fight cancer, transform how companies operate, and attract hundreds of billions of dollars in investment?That shift may already be underway. This week brought some of the strongest signals yet that AI's impact is moving beyond better chatbots. From personalized cancer treatments and dramatically earlier detection to autonomous business workflows and AI-powered scientific research, the conversation is increasingly about measurable outcomes.For business leaders, there's an equally important takeaway: the competitive advantage may no longer come from choosing the “best” AI model. It may come from giving AI the right context about your business.Anthropic's own sales team provides a striking example. By connecting Claude to systems including Salesforce, Apollo, Common Room, and Gong, the company reports cutting manual work by 70%. The lesson is simple: smarter models help, but AI becomes dramatically more useful when it understands your data, workflows, processes, and preferences.And that's only the beginning.In this session, you'll discover:Why new developments in personalized mRNA cancer treatment could represent an important milestone for AI-assisted healthcare.How AI is helping researchers detect and understand cancer earlier and with greater precision.How Anthropic is using AI workflows to reduce manual sales work by 70%.How AI systems are beginning to learn the way people work and turn repetitive activities into automations.How increasingly capable open models could dramatically change the cybersecurity threat landscape.How AI is accelerating drug discovery and complex scientific analysis.How AI-assisted coding and agentic development continue to change software creation.Why an extraordinary amount of capital is flowing into AI infrastructure and applications—including a proposed $500B financing platform around NVIDIA infrastructure, Databricks' $5B raise, and major funding rounds across the ecosystem.About Leveraging AIMulti-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Story of the Week (DR):L3Harris ousts CEO after investigation into conduct MML3Harris Technologies, the company that overhauled a Qatari plane now used as Air Force One, has replaced Christopher Kubasik as chairman and chief executive after an investigation determined he violated the defense contractor's code of conduct.Kubasik's alleged conduct didn't involve and has no impact on the Melbourne, Fla., company's financial reporting, controls, customer relationships or operational performance, L3Harris said Monday.The company didn't give details on when it received a report of the potential violation. With the aid of independent counsel, the board determined that Kubasik's removal would be in the company's best interest, L3Harris said. He will be allowed to retain and exercise some previously vested stock options but won't receive severance payments, benefits or accelerated stock-based awards.L3Harris Technologies Appoints Sam Mehta, Proven Aerospace and Defense Executive, as President and Chief Executive Officer“The Board determined that the Executive engaged in conduct that was not consistent with the values of the Company as outlined in its Code of Conduct.”Kubasik will still hold onto some of his options that can net him stock worth about $23 million, as well as more than 200,000 shares of stock in L3Harris that he already owns, valued at nearly $57 million. L3Harris has paid Kubasik compensation valued at $66.3 million during the past three years, including $25.6 million in fiscal 2025.The separation disclosure says the L3Harris board decided to reach a deal with Kubasik to get him to leave rather than trying to fire him for cause. Kubasik did not admit to any violation of the company code of conduct, and the deal expressively forbids any of the parties or their representatives from making public statements “inconsistent” with Monday's disclosure.AND THIS:Women at L3Harris Shared Concerns About CEO's Behavior Years Before OusterIt was a warning that was shared among women who worked for Chris Kubasik: Avoid being alone with the executive and be careful on the corporate jet.Multiple women at defense contractor L3Harris Technologies LHX had raised concerns about Kubasik's behavior, including a formal complaint from one woman to human resources that was made around 2023, according to people familiar with the matter. The employee accused the CEO of sexual harassment, the people said.Kubasik stayed on in his role. The woman left L3Harris. Not all L3Harris board members were briefed on the 2023 complaint and it is unclearOusted L3Harris CEO was previously forced out of Lockheed Martin jobChristopher Kubasik's ouster as the L3Harris CEO was not the first time he was forced out of a company amid an allegation of misconduct.In 2012, Kubasik was set to become the CEO of Lockheed Martin when he was forced to resign after an ethics investigation confirmed that he had a close personal relationship with a subordinate employee.Why Do Boards Keep Giving Misbehaving CEOs Second Chances?L3Harris Technologies' LHX chief executive is out because of misconduct allegations, and it isn't the first time: More than a decade ago, Christopher Kubasik resigned from Lockheed Martin because he was accused of having a relationship with a subordinate.The Crucial Moment That Companies Miss After They Oust a CEOIt matters how a company responds to a scandal once it's caught in one, most blow the moment by choosing secrecy over transparency. It's an opportunity to reset the culture that led to the breach in the first place, but instead “your PR team and your legal team tell you ‘Don't dig into these things—it's not good for the company,' so you silence all the debates.”.Meta faces a $1.4 trillion threat that could mean ‘turning in the keys and walking away'—but the stakes of the case reach across techThe trial involves a coalition of 29 state attorneys general in a unified case against Meta that was brought in 2023, and will be argued by lawyers representing California, Colorado, New Jersey and Kentucky. The stakes are enormous as leading government officials across the country push for Meta to be held accountable for allegedly violating federal and state laws, including the Children's Online Privacy Protection Act, or COPPA, and various consumer protection statutes.States accuse Meta of targeting children for Facebook, Instagram addiction: 'The young ones are the best ones'Meta whistleblower told jury the company took a 'don't ask, don't tell' approach to kids' safety‘Harvest their data and hide the truth from the public': Four states seek billions from Meta over child safety practicesSEC says it will stop responding to no-action requests ‘entirely'The Securities and Exchange Commission plans to stop responding to no-action requests “entirely … effective immediately,” the agency said in a statement Friday.The decision comes after the SEC sat out the bulk of the no-action process during the 2025-26 proxy season. Investor advocates have since sued the agency, alleging the change violates the Administrative Procedure Act.AI data center outrage is showing up everywhere from ads to electionsAI data center outrage is showing up everywhere from ads to electionsGOP Begs AI Firms to Fix Data Centers' “Toxic Brand” to Help Midterm Chances As A.I. Data Centers Spread, Pressure Mounts to Share ProfitsThe Data Center Industry's PR Blitz Is BackfiringData center backlash echoes fossil-fuel politicsMajor data center bills advance in California despite industry pushbackThe ‘Country Hicks' Who Refused $26 Million from an AI Data Center Bad news for Jason Kelce: Postal Service rules say you shouldn't mail pee to data centersPoliticians Who Once Championed Data Centers Are Now Bashing ThemPennsylvania Gov. Josh Shapiro cracks down on data centers, says speculators are 'scaring our communities'Data centers are using more electricity than anyone predicted. What happens next?Trump oblivious to voter fury about data centers, saying ‘the jobs are enormous and the money paid, the taxes paid, are just enormous'Politicians Turn Against Data Centers as Anger Over AI SpreadsAmazon is buying rare books and destroying them to train its AI modelsThe team's logo features a dinosaur holding a book.Data center hysteria is the new woke | OpinionBring back the corporate death penaltyMore formally known as judicial dissolution, the corporate death penalty basically happens when the government is so pissed off by the corruption or damage a corporation causes that it yanks away their charter.Andreessen Horowitz Focus of DOJ Probe Over Board DirectorsVenture capital firm Andreessen Horowitz is the focus of a Justice Department antitrust probe over whether its investment partners are improperly serving on the boards of competing artificial intelligence companies, according to people familiar with the matter.The companies at issue include Databricks Inc., one of the most valuable privately held technology companies in the world, and Fivetran Inc., both backed by the VC firm, according to the people, who asked not to be named discussing a confidential matter. Andreessen Horowitz co-founder Ben Horowitz serves on the board of Databricks, and partner Martin Casado is a board member of Fivetran. Both companies help businesses collect, organize and analyze massive troves of data.Goodliest of the Week (MM/DR):MacKenzie Scott gave California public education $461 million—and let the recipients decide how to spend every dollarMM: Andreessen Horowitz Focus of DOJ Probe Over Board Directors DRAssholiest of the Week (MM):Bill Brown and Robert Millard DRNever accountable for anything directorsL3Harris ousts CEO after investigation into conductHistory lesson:Kubasik hired in 2015 after Lockheed disaster firing, hired as COO and PresidentPresiding CEO: Michael Strianese, Chair from 2008, CEO from 2006Board: Claude Canizares (71, MIT physics professor, 2003)Thomas Corcoran (72, Carlyle, consulting, 1997)Ann Dunwoody (64, only woman, US Army Gen, 2013)Lewis Kramer (69, EY accountant, 2009)Robert Millard (66, MIT Chair, Lehman until 2008 collapse, LID, 1997)Lloyd Newton (74, only PoC - token black guy - US Air Force General, 2012)Vincent Pagano, Jr (66, lawyer, Simpson Thacher, chair of nom, 2013)Hugh Shelton (75, US Army Gen, 2011), Arthure Simon (85, accountant, 2001)8 white men, 1 woman, 1 black dude2018, Kubasik named CEO of L3 TechnologiesMichael Strianese retires and Kubasik takes overSame exact board minus Strianese2019, L3 and Harris merge to be L3HarrisKubasik added to L3Harris board, named COO and President of the company under Bill Brown, CEO and ChairSurviving the board merger:Thomas CorcoranRobert Millard - LID, nom memberLloyd Newton - chair of nomLewis KramerAdjacent - Roger Fradin of Carlyle on board, Corcoran also of CarlyleJune 2021, Kubasik becomes CEO and Bill Brown moves to exec chair (obviously)Board:Sallie BaileyBill BrownPeter ChiarelliThomas CorcoranThomas Dattilo (nom) - ex tire CEORober GradinHarry HarrisLewis Hay III (nom) - lawyer, ex CEo of NextEraLewis KramerRita LanRobert Millard (nom) - MIT Chair, LehmanLloyd Newton (nom chair) - generalSo given that the CEOs choose their successors, the nom committees approve them, the rest of the board rubber stamps it… we can thank:Michael Strianese - hires Kubasik, names him CEO at L3, despite Lockheed problemsNom approval: Ann Dunwoody (64, only woman, US Army Gen, 2013), Vincent Pagano, Jr (66, lawyer, Simpson Thacher, chair of nom, 2013), Hugh Shelton (75, US Army Gen, 2011) - a nom committee composed of the ONLY woman, two generals and a lawyer - all of whom are the LOWEST TENURED ON THE BOARD at the timeThen Bill Brown - names Kubasik CEO of combined L3Harris, one year of babysitting as exec chairNom approval: Thomas Dattilo (nom) - ex tire CEO, Robert Millard (nom) - MIT Chair, Lehman, Lloyd Newton (nom chair) - generalFamiliar names: Millard and Newton - see Kubasik all the way throughAnd the CEOs and directors can keep failing… Bill Brown on the Becton Dickinson boardRobert Millard on the Green Dot Corp (nom!), iHeartMedia, Evercore (nom!) boardsBrought on to iHeart board just 3 years after an exec there went on a racial slur rant, the company was sued for gender and wage discrimination, and a radio host of the companies were accused of severe harassment - not sure what will change?Dario Amodei“Public benefit corporation” Anthropic: Anthropic Prepares Supervoting Power for Founders as it Readies for Mega-IPOBoard: Dario Amodei, Daniela Amodei (President, Dario's sister), Yasmin Razavi (VC, crypto and prediction market investor), Reed Hastings (Netflix), Chris Liddell (ex Trump WH Deputy Secretary), and Vas Narasimhan (Novartis) - zero “public benefit” (or even public safety) peoplePublic Benefit Corporation: “A benefit corporation's directors and officers operate the business with the same authority and behavior as in a traditional corporation, but are required to consider the impact of their decisions not only on shareholders but also on employees, customers, the community, and the local and global environment”What is the impact of supervoting shares? AI on society? AI on the environment? Who on this board is even remotely qualified to answer those questions?Paul AtkinsExhausting and perpetual gaslightingSEC says it will stop responding to no-action requests ‘entirely'In order to focus Division resources on the review of Securities Act and Exchange Act filings, including those reviews that are statutorily required, for the protection of investors and facilitation of capital formation, and in light of the extensive body of guidance from the Commission and the staff available to both companies and proponents on Rule 14a-8, the Division has determined to discontinue responding to Rule 14a-8 no-action requests entirely, including those submitted under Rule 14a-8(i)(1),[2] effective immediately, unless and until the Division announces otherwise. It also will no longer respond to notices filed under Rule 14a-8(j) with a letter indicating that it will not object if a company omits a proposal from its proxy materials.From the 1934 House Report about the importance of Rule 14a-8: “Fair corporate suffrage is an important right that should attach to every equity security bought on a public exchange.”“Managements of properties owned by the investing public should not be permitted to perpetuate themselves by the misuse of corporate proxies. Insiders having little or no substantial interest in the properties they manage have often retained their control without an adequate disclosure of their interest and without an adequate explanation of the management policies they intend to pursue. Insiders have at times solicited proxies without fairly informing the stockholders of the purposes for which the proxies are to be used and have used such proxies to take from the stockholders for their own selfish advantage valuable property rights. Inasmuch as only the exchanges make it possible for securities to be widely distributed among the investing public, it follows as a corollary that the use of the exchanges should involve a corresponding duty of according to shareholders fair suffrage. For this reason the proposed bill gives the . . . Commission power to control the conditions under which proxies may be solicited with a view to preventing the recurrence of abuses which have frustrated the free exercise of the voting rights of stockholders.Investors Slam SEC Plan to Remove Best-Price RuleAtkins also is listening to the crypto bros who want to offer “tokenized securities” off exchanges and is hoping to eliminate a really basic rule that says “investors are entitled to the best price available for stocks they buy”Separately, DOJ Withdraws Antitrust Guidance for Proxy Advisory Industry - no antitrust protections for ISS (good!) but still can't do anything about the socialist NFL, MLB, NHL, NBA (bad!)Headliniest of the WeekDR: Popular breakfast chain closes half its restaurantsDR: The man leading Trump's RTO charge for government workers says he filmed a video in front of a blank wall to avoid work-from-home suspicionOffice of Personnel Management (OPM) Director Scott Kupor, the key driver of President Donald Trump's return-to-office agenda, admitted in a hot mic moment that he intentionally filmed a video in front of a blank wall while he was working from home so he wouldn't get blowback over working at home.“I was in my bedroom, but I was trying to find—because I knew someone was going to give me shit if like, they knew, ‘You were out of the office.' …I was trying to find something that was not recognizable as being in my house, basically. So I was just trying to find a plain corner with a white wall, which was not that easy to find.”Kupor was the first employee hired by Andreessen and Horowitz's venture capital firm, Andreessen Horowitz.MM: Flock Says It's “Taking a Break” From Responding to Media RequestsMM: Eric Schmidt is selling his superyachtWho is this headline for? Billionaire yacht buyers? Poor people who hate billionaires with yachts?Who Won the Week?DR: The women at L3Harris Shared Concerns About CEO's Behavior Years Before OusterMM: Joshua Ramer, the CEO at PeopleReturn (one of the last vestiges of diversity data in the US), whose newsletter today did the most Free Float thing I've seen anyone other than us do: they tracked a single Getty Image across SIX different company reportsThe image was called 1325876463 “Young Boy Leaping Into Father Arms In Playground”, mostly for sustainability reports because it's brown peopleThey found it in Danaher, Crown Castle, TD, Capital One, CSL Plasma, and Toyota EuropePredictionsDR: The meritocro-mano-sphere-o hires Christopher Kubasik again without any push back from anything or anyoneMM: We decide that, since everyone is trying to make companies immune from climate change lawsuits, that we just make CEOs personally immune for any behavior

Bloomberg reported that Callosum raised $100 million to build software that matches AI workloads to specific chips. The company targets routing and scheduling so training and inference jobs run on hardware that fits their memory, bandwidth, and cost profiles. The raise comes as enterprises manage heterogeneous fleets across AWS, Microsoft Azure, Google Cloud, and specialized providers like CoreWeave and Lambda. The market includes established schedulers and recent deals such as Nvidia's 2024 agreement to acquire Run:ai and Databricks' 2023 purchase of MosaicML for $1.3 billion. Buyers will evaluate governance, cost allocation, and measurable utilization gains. Founders should pilot across multiple chip families and providers and track throughput per dollar, queue times, and job success rates.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

This episode examines evolving U.S. crypto regulation, bitcoin market developments, and the growing capital requirements behind AI infrastructure buildouts. Topics include reported SEC plans to address aspects of the regulatory framework following the delayed CLARITY Act vote, Strategy's stated intention to purchase additional bitcoin before year-end, and bitcoin's muted reaction to a reported decline in inflation. The discussion also covers CoreWeave's latest earnings, potential fundraising by Databricks and Cognition, Tether's reported completion of its first full audit, and Nvidia's reported efforts to support large-scale AI infrastructure financing. The episode closes with a look at the IMF's perspective on Europe's fragmented financial markets and the potential implications for capital allocation. Remember to Stay Current! To learn more, visit us on the web at https://www.morgancreekcap.com/morgan-creek-digital/. To speak to a team member or sign up for additional content, please email mcdigital@morgancreekcap.com Legal Disclaimer This podcast is for informational purposes only and should not be construed as investment advice or a solicitation for the sale of any security, advisory, or other service. Investments related to the themes and ideas discussed may be owned by funds managed by the host and podcast guests. Any conflicts mentioned by the host are subject to change. Listeners should consult their personal financial advisors before making any investment decisions.  

Anthropics Investoren erwarten für Oktober einen Börsengang bei zwei Billionen Dollar, was der größte IPO aller Zeiten wäre. Pip erklärt, warum der Termin geschickt gewählt ist. Für OpenAI sieht er die Lage umgekehrt. Dort kommt der zweite Vertriebschef in einem Jahr. Zusammen mit Cerebras hat OpenAI dafür eine Variante gebaut, die vierzehnmal schneller antwortet. Danach vier Modellstarts in einer Woche, bei denen ausgerechnet DeepSeek die Preise um bis zu das Zwölffache erhöht, und Elon Musk sein neues Grok für objektiv das beste Modell hält. Bei den Finanzierungsrunden geht es um Databricks, Lovable, Legora und Cognition, dazu um die Frage, ob man das Geld gerade nehmen und liegen lassen sollte. Silver Lake holt Workday von der Börse. In der Schmuddelecke erlaubt die Trump-Regierung privaten Firmen offensive Cyberangriffe und beruft sich dabei auf Kaperbriefe aus der Verfassung. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Aus der Community (00:02:05) OpenAI wechselt den Vertriebschef (00:12:20) Ultrafast mit Cerebras (00:17:16) Anthropic-IPO (00:29:10) Anthropic kauft Decart (00:30:34) Braucht man ein KI-Device? (00:32:36) Gemini 3.7 Flash (00:34:10) DeepSeek V4-Pro (00:34:47) Grok 4.6 (00:37:11) SpaceX (00:38:49) Databricks und Snowflake (00:42:40) Workday geht von der Börse (00:44:11) Lovable (00:49:26) Legora (00:52:28) Cognition (00:55:18) Mistral (00:57:22) Kaperbriefe (01:02:05) Truth API (01:03:28) Chronext (01:06:55) Apple zahlt Verlage Shownotes OpenAI holt den zweiten Vertriebschef in einem Jahr - bloomberg.com GPT-5.6 Sol läuft mit Cerebras bis zu 14-mal schneller - 9to5mac.com Anthropic peilt einen Börsengang bei 2 Billionen Dollar an - ft.com Anthropic verhandelt über Decart für 6 Mrd. - bloomberg.com Google stellt Gemini 3.7 Flash vor - blog.google DeepSeek bringt V4-Pro und erhöht die Preise um bis zu das Zwölffache - theinformation.com Grok 4.6 startet zuerst in Cursor - gizmodo.com SpaceX-Leerverkäufern gehen die Kugeln aus - cnbc.com Databricks sammelt 5 Mrd. bei 190 Mrd. Bewertung ein - cnbc.com Silver Lake verhandelt über eine Übernahme von Workday - reuters.com Lovable verdoppelt die Bewertung auf 13,3 Mrd. - trendingtopics.eu Legora verhandelt bei mindestens 10 Mrd. - ft.com Cognition verhandelt bei 40 Mrd. - bloomberg.com Mistral will bis 2030 ein Gigawatt in Europa bauen - aibusiness.com Trump lässt private Firmen offensive Cyberangriffe fahren - bloomberg.com Kaperbriefe stehen in der Verfassung - xcancel.com KI-Agenten greifen Taiwans Regierungssysteme an - ft.com Presseverbände klagen gegen Trumps Truth API - ft.com Chronext-Kunden warten auf Zahlungen und Lieferungen - wiwo.de Apple verhandelt mit Verlagen über Nachrichten für Siri - techcrunch.com

Bloomberg reported that Anthropic is in talks to acquire AI startup Decart for $6 billion. Anthropic, founded in 2021 by Dario and Daniela Amodei, builds the Claude family of models and is backed by large investments from Amazon and Google. A deal of this size would be among the largest in the sector and would likely require a Hart-Scott-Rodino filing and regulatory review. Recent AI transactions include Databricks buying MosaicML for $1.3 billion in 2023, Microsoft's $650 million arrangement with Inflection AI in 2024, and Apple's purchase of DarwinAI in 2024. The potential acquisition would aim to accelerate product delivery, aggregate talent, and strengthen competitive positioning. Founders and enterprise buyers should monitor integration plans, service continuity, and contract terms as consolidation continues.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Databricks CEO Ali Ghodsi joins Squawk on the Street with new financials after closing a $5 billion funding round at a $190 billion valuation, discussing growth and a possible IPO timeline. Plus, Bullish CEO Tom Farley weighs in on the beaten-down crypto market after the exchange's latest results. And two of the world's largest container shipping companies detail how the closure of the Strait of Hormuz is disrupting global trade and supply chains. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Recorded on location at Black Hat USA 2026 in Las Vegas at the end of day two, Karthik Kannan, Founder and CEO at Anvilogic, walks through a seven year build that reached its original shape this year. The plan from the start was a full security operations platform covering data, the detection engineering process, triage and investigation, and case management. In the shorthand of the category, SIEM and SOAR combined. It arrived in phases. Detection engineering came first, implemented on top of Splunk for most customers, then the data platform expanded into data lakes including Snowflake, Databricks, and Microsoft Azure. Triage and investigation followed over the last two years. In the last year Anvilogic rolled out agents that carry out the work of specific personas, and this year the company launched Blueprints, an orchestrator agent that brings the discrete agents together to run a whole workflow with humans in the loop. What separates a security graph from a frontier model? It knows the environment. Karthik Kannan describes the enterprise security graph as Anvilogic's own model running inside the network, learning the micro environment, with frontier LLMs called on to fill gaps in the macro environment. His argument is that platforms operating as LLM wrappers miss the last mile, because AI on its own reaches 60, 70, or 80 percent of the way if you are lucky. How does a team keep control when agents run the workflow? Through gates, permissions, and a record of what happened. Workflows can be described in plain English, with human gates inserted as often as the team wants. Access controls sit at the persona, organization, and object levels, and activity is audited and logged, which matters to the GRC teams Anvilogic works with. Screens dedicated to what the company calls a maturity score show which feeds are coming in, what kinds of detections exist, and what coverage looks like against the MITRE ATT&CK framework, in a form available to executives and CISOs. Karthik Kannan also points to version 8.0, introduced the week before the event, which includes an Anvilogic MCP Server for connecting to third party tools. Customers are already building their own Blueprint workflows during proofs of concept, including a large life sciences customer Anvilogic expects to feature in a public case study. Karthik Kannan is careful about the claim being made here. This is not a proclamation of an autonomous SOC. It is automation that makes life in a SOC easier and more efficient, adopted at a crawl, walk, run pace, with every step visible along the way. This is a Brand Briefing. A Brand Briefing is an on-location conversation recorded on site at Black Hat USA 2026, putting a spotlight on the guest and their company and pairing it with the editorial reach of ITSPmagazine. Learn more: https://www.studioc60.com/performance/#briefing GUEST Karthik Kannan, Founder and CEO at Anvilogic On LinkedIn: https://www.linkedin.com/in/karthikkannan001/ RESOURCES Black Hat USA 2026 event coverage from ITSPmagazine: https://www.itspmagazine.com/black-hat-usa-2026-cybersecurity-event-coverage-in-las-vegas Learn more about Anvilogic: https://www.anvilogic.com Anvilogic 8.0, from onboarding to investigation: https://www.anvilogic.com/learn/anvilogic-8-0-automate-the-soc Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight ▶︎ Get your own Brand Briefing at an upcoming event: https://www.studioc60.com/buy-brand-briefings KEYWORDS karthik kannan, anvilogic, sean martin, brand briefing, brand story, brand marketing, marketing podcast, black hat usa 2026, agentic secops, ai soc platform, enterprise security graph, detection engineering, triage and investigation, blueprints orchestrator agent, mcp server, mitre att&ck coverage, human in the loop automation, siem and soar, security operations, grc audit logs Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome back to the Alt Goes Mainstream podcast.Today's podcast takes us to the heart of London, where we sat down with Maggie Fanari, the CEO of J Rothschild Capital Management Limited, manager of RIT Capital Partners plc. RIT blends a rich heritage with a modern approach to both asset allocation and private markets. Lord Jacob Rothschild founded Rothschild Investment Trust in 1971. RIT listed on the London Stock Exchange with total assets of £280M. Today, the firm stands tall as one of the UK's largest investment trusts with over £4.7B of total assets.The firm's permanent capital and family office heritage have enabled the firm to think long-term, according to Maggie. “Permanent capital is a privilege,” she said.Maggie has brought an institutional allocator's background to RIT. She joined as CEO of RIT from Ontario Teachers' Pension Plan in 2024, where she was Senior Managing Director, Global Group Head of High Conviction Equities at OTPP, which has a global mandate to invest in public and private companies.Maggie and I had a fascinating discussion about how the firm invests across public and private markets, balancing both top-down portfolio construction and bottom-up asset selection. We covered:How RIT has aimed to compound wealth over time.Why top-down portfolio construction and bottom-up asset allocation are equally important.How can investors capture as much growth, limit market volatility, and compound growth over a long period of time?How RIT finds unique and different managers in private markets, which includes some of the top investors in the world.What market structure changes mean for investing across public and private markets?How to invest when the world order has changed.Taking a family office mindset and applying that investment mindset for investors in RIT.Why permanent capital is a privilege.How to be early to a theme rather than chase the trend.Why RIT decided to invest in SpaceX, Anthropic, OpenAI, Databricks, and Epic Systems.Where do investors bucket RIT into their asset allocation?What is a manager's edge and how can they apply that edge with consistency?Why depth of network matters for private markets managers.Why RIT invested in firms like Thrive, Greenoaks, and Ribbit.BioMaggie Fanari is the CEO of J. Rothschild Capital Management Limited (JRCM) , investment manager for RIT Capital Partners plc. She is Chair of JRCM's Investment Committee.Maggie was previously Senior Managing Director, Global Group Head of High Conviction Equities at Ontario Teachers' Pension Plan, which has a global mandate to invest in public and private companies.At Ontario Teachers', she served as a member of many of the pension plan's investment committees. She was involved in the execution of investments across a variety of asset classes (private and public), including supporting the development and execution of the venture and growth business.Before joining Ontario Teachers', Maggie worked at KPMG and Scotia Capital. Maggie is a chartered accountant and a CFA charter holder. She also holds a BBA from the Schulich School of Business at York University and ICD.D certification from the Institute of Corporate Directors.Maggie served as a non-executive director on the Board of RIT Capital Partners plc from April 2019 to February 2024.Thanks, Maggie, for sharing your wisdom, expertise, and passion across public and private markets and your thoughtful perspectives from your experiences as an institutional investor.This podcast was recorded on 15 June 2026, and therefore all RIT data is provided as at 31/05/2026. Show Notes00:42 Meet Maggie Fanari03:44 Teachers' Pension Roots04:51 Top Down Meets Bottom Up05:50 Allocating In New Paradigm06:15 Diversification Returns07:02 Volatility Creates Opportunity07:22 What Makes RIT Unique08:08 Compounding With Downside09:41 Brand Opens Doors10:05 Backing Emerging Managers11:57 Co-Invest Importance12:36 Returns And Realizations13:22 Great Co-Investor Playbook15:02 Building AI Theme Exposure16:06 Sourcing Deals Like SpaceX16:37 Public Private Value Split20:09 Public Themes And Sovereignty20:58 Moats And Terminal Value24:06 Permanent Capital Edge25:07 Oversubscribed Fund Access26:46 Underwriting And Discipline27:17 Why AI Needs Capital27:49 Anthropic Growth Math28:15 Databricks Scale Comparison28:41 Can Funds Get Bigger30:22 FOMO And Chasing30:47 Portfolio Allocation Guardrails31:47 Permanent Capital Advantage32:13 Right Sized Private Exposure32:51 Liquidity And Realizations33:28 Owning Winners At Scale34:11 Private To Public Hold34:41 Re Underwriting Post IPO35:38 Retail Investor Impact36:20 Public Market Liquidity Needs37:57 Why Investment Trusts Work38:56 Discounts As Margin Safety40:08 How Shareholders Allocate41:03 Sentiment Shifts In Cycles42:02 What Makes Great Managers43:14 Manager Edge Examples45:02 AI And Finding Leaders46:56 Consolidation And Differentiation47:51 Being A Great LP Partner48:50 Macro Lens As Edge49:42 Private Signals Inform Public50:48 Culture One Team One NAV51:28 Risk And Scenario Analysis52:59 Multipolar World Investing54:14 Geopolitics In Diligence55:10 Permanent Capital Best Of BothA Word from Our Sponsor, UltimusThis episode of Alt Goes Mainstream is brought to you by Ultimus, the full-service fund administrator and transfer agent powering asset managers in private and public markets. As alts go mainstream, you need real expertise to handle complex fund structures, connect with key distribution partners, and handle sophisticated compliance, reporting, and transparency demands.That's Ultimus: high-tech, high-touch solutions for over 450 clients and 2,500 funds with $775B in assets under administration. Backed by an expert team of over 1,200 employees, they place client service at the core of their business, helping you navigate complexity during your fund structuring or launch and then supporting you through every stage of growth. Whether you're already in the market or thinking about entering private wealth, you can trust their team's deep expertise in retail alternatives to help you reach your goals.Learn more at ultimusfundsolutions.com or email info@ultimusfundsolutions.com.We thank Ultimus for their support of alts going mainstream.Editing and post-production work for this episode was provided by The Podcast Consultant.

Palantir reported quarterly earnings that beat Wall Street expectations, according to The Guardian on August 3, 2026, with the quarter described as otherworldly. The company's platforms include Gotham for defense and intelligence, Foundry for commercial operations, and AIP for model orchestration against governed data. Public sector demand spans logistics, cyber defense, and emergency response, while commercial buyers in manufacturing, energy, and healthcare focus on predictive maintenance, outage reduction, and supply chain visibility. Competition includes Microsoft, Amazon, Google, Databricks, Snowflake, ServiceNow, Salesforce, and IBM. Palantir has emphasized GAAP profitability since 2023, and analysts will watch remaining performance obligations, customer additions, and revenue mix. Founders should align with buyer checklists by delivering secure, auditable AI with rapid time-to-value and clear metrics.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Privacy and the AI StackBuilding the right data infrastructure before you find product-market fit is a trap. So is delegating the thinking to AI before you understand the system it is building for you. The question AI in data engineering education must answer now is not how to teach more tools. It is how to teach students to know what is sufficient, and when to push back on AI-generated results. In this episode of our series, our guest explores how ten years of teaching at CEU has shifted his focus from courseware volume to hands-on depth, how AI has simultaneously delighted and concerned him in the classroom and what universities are getting dangerously wrong about how they try to regulate student AI use. Our host Eduardo Ariño de la Rubia is joined by Zoltan Toth, a Professor of Practice in the MSBA programme at Central European University. A data engineer and educator with twenty years of experience, he is a bestselling Udemy instructor with more than 60,000 courses sold and a board member of the Vienna Data Science Group.  THINGS WE SPOKE ABOUT- From web developer to CEU faculty: Zoltan's twenty-year journey into data engineering- Why hands-on practice outperforms courseware volume in data education- The AI hackathon moment that was both impressive and alarming- Why debugging skills are now worth more than implementation skills- What universities are getting wrong about regulating student AI use GUEST DETAILSZoltan Toth is a Professor of Practice in the MSBA programme at Central European University, where he teaches cloud computing, modern data platforms, and AI engineering. He is a bestselling Udemy instructor with more than 60,000 courses sold, a former solutions architect and instructor for Databricks, and a board member of the Vienna Data Science Group, which organises meetups for data professionals across Austria. QUOTES- "I think that there is a danger there. So if you integrate some code, you still want to understand it." - Zoltan Toth- "What we should meditate on is what is the kind of work that we delegate to AI, and what is the kind of work where we need to accept the cognitive pain of going through thinking and solving and just sweating it through." - Zoltan Toth- "What we need instead is to give students a great mental model about how everything is built, so that they can take a look at the problems from an architectural point of view." - Zoltan Toth- "We should embrace that we need to suffer through a few problems if we want to understand those problems." - Zoltan Toth- "I think we are underestimating how much AI our students use, and we are overestimating our power, how much we can regulate how much AI students should use." - Zoltan Toth KEYWORDS #DataEngineering #AIEducation #TeachingAnalytics #DebuggingSkills #HigherEducation

This is for Ben Miller, founder of Fundrise, and the strongest angle is how Fundrise built a platform that opened private markets to everyday investors, reaching 2M+ investors and managing billions across real estate, credit, and venture/private tech. In this episode of The Jess Larsen Show on Innovation & Leadership, Jess sits down with Ben Miller, founder of Fundrise, for a deep conversation on private markets, real estate, venture investing, and what it really takes to build a platform that gives everyday investors access to opportunities once reserved for institutions. Ben shares the story behind Fundrise's mission to democratize private markets, how the company grew to more than 2 million investors, and why building this kind of business required far more than a clever fintech idea. From real estate and private credit to venture capital and AI companies like OpenAI, Anthropic, Databricks, and Canva, Ben explains how Fundrise evolved into a multi-asset platform built for long-term access and scale. Jess and Ben also explore the painful reality behind compounding success. Ben talks about regulation, fundraising, public offerings, investor education, criticism, persistence, and why most people underestimate how much suffering and repetition it takes to build something that lasts. This is a sharp, honest conversation about democratizing investing, surviving the long game, finding opportunities across cycles, and building a private markets platform in a world being reshaped by AI, technology, and constant change. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today, we're joined by Ariel Assaraf, Co-Founder and CEO of Coralogix, the data and AI platform for observability. We talk about:Why observability was an underutilized data resource for many yearsHow engineering skills will be found in more areas of the organizationFour phases of creating distinct user interfaces for AI agentsThe impacts of widespread improved observabilityPersonalizing insights per person, not persona

Ter dashboards, indicadores e IA não significa que uma empresa consiga decidir melhor. Então, o que está faltando? Neste episódio, recebemos Ricardo Camilo, Líder de Dados, e Fabiana Leitão, Data Product Manager, ambos da dti digital, para discutir por que tantas iniciativas de dados não conseguem gerar impacto real no negócio. A conversa passa pelos erros mais comuns na construção de uma cultura data-driven, o papel da liderança, os desafios de governança e a importância de fazer as perguntas certas antes de investir em tecnologia ou inteligência artificial. Também compartilhamos exemplos práticos de empresas que transformaram dados em decisões, produtos e novas oportunidades de negócio. Ficou curioso? Então, dê o play!Assuntos abordadosCultura data-driven;Governança de dados;IA generativa;Business Intelligence;Liderança orientada;Produtos de dados;Databricks;Tomada de decisão.Links importantes:NewsletterDúvidas? Nos mande pelo LinkedinContato:  osagilistas@dtidigital.com.brOs Agilistas é uma iniciativa da dti digital, uma empresa WPP #dados

Security operations centers are buried — enterprises now average more than 4,000 alerts a day and manage to investigate just over a third of them. In this episode, Brandon Krieger talks with Monzy Merza, Co-Founder and CEO of Crogl, about building an AI "knowledge engine" designed to close that gap. Monzy traces his path from 12 years as an applied security researcher at Sandia National Laboratories, through leadership roles in security research at Splunk and cybersecurity go-to-market at Databricks, to co-founding Crogl in 2023 — a company that raised $30M (a $25M Series A led by Menlo Ventures and a $5M seed led by Tola Capital) to build what TechCrunch called an AI "Iron Man suit" for security analysts. Topics include: Monzy's career arc from a national nuclear lab to founding a cybersecurity startup What compelled him to enter an already-crowded security market The biggest daily pain points facing SOC analysts How AI complements the work of security practitioners Whether AI will eventually replace jobs in data security Guest: Monzy Merza, Co-Founder and CEO, CroglHost: Brandon Krieger, CEO & vCISO Advisor Watch Full Episode: YouTube.com/BrandonKriegerListen: DailyCyber.ca

Most companies are not short on data. They are short on the time, cost, and coordination required to turn it into action.Ethan Ding, co founder and CEO of TextQL, joins The Tech Trek to explain how AI agents are changing enterprise analytics. The conversation moves beyond faster dashboards into a larger shift, analysts managing fleets of agents, business teams asking far more questions, and companies finding revenue and cost opportunities that were previously too expensive to pursue.What Technical Teams Can Take From This• Making answers cheaper does not reduce analytics work. It increases the number of questions people ask.• Analysts may spend less time assembling dashboards and more time managing agents, data sources, permissions, quality, and costs.• The clearest ROI comes from decisions with direct financial outcomes, including fraud prevention, upsell opportunities, churn risk, and unused vendor spend.• Faster analysis matters most when teams can act on valuable opportunities they previously could not afford to investigate.• Token costs will force AI companies and buyers to reconsider where software budgets go, especially across BI tools and data platforms.Moments Worth Hearing00:00 Ethan explains how TextQL agents work across messy enterprise systems including Cognos, Teradata, Snowflake, Databricks, Tableau, and Power BI.04:52 Why giving people faster answers does not create free time. It creates even more demand for analytics07:10 How self service analytics quickly moves from asking what a number is to asking whether it matters and what to do next.10:08 The analyst role shifts toward managing fleets of agents and tuning an insight factory for the business.14:38 Why faster access to data can reveal valuable opportunities that were previously too expensive to investigate.19:55 A practical way to measure analytics ROI through fraud prevention, upsell opportunities, and other direct financial outcomes.24:18 How token costs, AI margins, and easier migrations could reshape spending on traditional BI tools.One Line That Stuck“It becomes much more of an operations manager job. It is a factory. It takes in tokens and churns out dashboards, reports, and recommendations.”Follow The Tech Trek on your podcast platform, subscribe for future episodes, and share this conversation with someone rethinking how their team works with data.

The potential deal highlights a growing trend of complex, multi-stage funding rounds that mask true entry prices. Also, Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding. Learn more about your ad choices. Visit podcastchoices.com/adchoices

What does a top venture investor think about AI's impact on accounting, and what should finance professionals actually do about it?In this episode of Blood, Sweat & Balance Sheets, FloQast CEO Mike Whitmire sits down with Roy Luo, General Partner at ICONIQ Capital — an investor in Anthropic, Snowflake, Databricks, and Stripe — to break down what the AI wave really means for the accounting profession.They cover:Why accounting is facing a talent shortage that AI is uniquely positioned to solveHow the shift to AI mirrors the Excel revolution — and what that means for your careerThe concept of "token rationing" and why accounting departments may be last in line for AI resourcesWhy founders with product intuition are better positioned to navigate paradigm shiftsWhat it means to be a trusted partner vs. a vendor in the age of AIWhether you're a controller, CFO, or just trying to future-proof your career, this conversation offers a rare outside-in perspective on where the profession is heading.

The Pentagon placed an immediate freeze on forthcoming cybersecurity requirements after government research suggested the policy would drive many businesses out of the defense industrial base at a time when the U.S. military urgently needs their innovations. Defense Department Chief Information Officer Kirsten Davies and Under Secretary of Defense for Acquisition and Sustainment Michael Duffey unveiled plans earlier this week to suspend the much-anticipated Cybersecurity Maturity Model Certification (CMMC) Phase 2 requirements that were set to take effect Nov. 10. A new CMMC Reform Task Force is expected to conduct a review of the entire program and submit a report of its findings and recommendations within the next 60 days. This major pause comes as contractors have been hustling to obtain third-party assessments of their CMMC compliance in preparation for that near-term enforcement date. The Pentagon released a new request for information to garner stakeholders' feedback on the move and associated compliance challenges. The CMMC Reform Task Force will analyze the responses as part of its upcoming review of the program. Scale AI is joining the Department of Energy's Genesis Mission consortium, serving as the collaborative hub for working groups and structured partnerships, the vendor shared with FedScoop prior to its announcement Thursday. The technology provider is the latest private-sector partner to join DOE's Genesis Mission Consortium as the agency continues building up its roster. Emerald AI and SambaNova Systems have also jumped on board in recent weeks. For Scale AI, the partnership represents a further expansion of its role in the Genesis Mission and comes after it signed a memorandum of understanding earlier this year. “Scale's contribution to the Genesis Mission builds on the work we've been doing for years: creating high-quality evaluation benchmarks, preparing data for advanced AI systems, developing AI agents for complex workflows, and supporting computer vision and robotics applications,” a Scale AI spokesperson told FedScoop. DOE launched the consortium in February as a way to deepen public-private partnerships that it believes will fuel the larger initiative. Scale AI joins the likes of Accenture, Amazon Web Services, Databricks and IBM. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

Tony Holdstock-Brown is the co-founder and CEO of Inngest, the durable execution platform that quietly powers your favorite AI agents.We get into why agents work in a demo and die in production, building their own cloud to get 20x lower cost, growing 35x after AWS and Cloudflare copied them, growing a dev tools company without a personal brand or Twitter account, why he thinks evals today are like “asking the criminal if they committed the crime”, and the thing they built to score 100% of your production agents without paying for LLM as a judge.Thank you to Numeral, Flex, Amplitude, Merge, and Monaco for supporting this episode.Numeral: Sales tax on autopilot https://www.numeral.comFlex: Premium banking, 60-day credit, 0% APR https://home.flex.one/referral/bananacapitalAmplitude: AI analytics https://www.amplitude.comMerge: Every model, one API https://www.merge.dev/turnerMonaco: The revenue engine for startups https://www.monaco.com/Timestamps:(0:00) The hidden infra layer every AI agent runs on(1:46) Building complex chains of logic(3:31) Why agent SDK's don't go far enough(4:49) Healthcare was the original event-driven nightmare(6:32) Storing traces on your infrastructure enables self-improving loops(14:26) Why Inngest was already in the right place for AI(15:49) Score agents off product events, not LLM's(17:31) The OpenAI copy-paste signal(21:24) Swap in LLMs and cut costs 20x(23:44) How customers pulled the product forward(25:41) Orchestration belongs outside the sandbox(29:48) Building a neocloud to cut costs 20x(32:09) Most neoclouds just resell AWS(32:54) All AI infrastructure is converging(34:49) Why Claude can't just build your backend(36:44) How to build a software factory(39:12) Agents are a lottery you get addicted to(42:44) Loops must exist until AGI hits(45:38) If models keep getting better, why orchestrate?(48:28) When incumbents steal your features(52:30) Why you can't vibe code infrastructure(55:54) Why Tony has no personal brand(59:38) Dev tools GTM without Twitter(1:03:20) Lessons from the founder of DuckDuckGo(1:10:39) Truth as a company value(1:13:08) Taking too long adapting to AI(1:15:10) Startups are 100% R&D(1:17:19) Ali from Databricks(1:19:03) Writing his own code, Voice-to-text with local models(1:23:53) Evals are batshit insaneReferencedInngest: https://www.inngest.com/Principles by Ray Dalio: https://www.amazon.com/dp/1501124021?lv=shuf&channelId=500&plpRedirect=mhFallbackTraction - How Any Startup Can Achieve Explosive Customer Growth: https://www.amazon.com/dp/1591848369?lv=shuf&channelId=500&plpRedirect=mhFallbackFollow TonyTwitter: https://x.com/itstonyhbLinkedIn: https://www.linkedin.com/in/tonyhb/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Don’t let the AI wave crush you. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ Dive into the seismic shifts happening within the AWS Marketplace and discover how AI, self-service product-led growth (PLG), and advanced co-selling strategies are redefining partner success. Matt Yanchyshyn, VP of Marketplace at AWS breaks down the recent announcements from the summit, illustrating how agility and adaptation are crucial to surviving the new agentic future. From lowering professional services fees to the explosion of business applications like ServiceNow, this conversation reveals the hidden mechanics of modern cloud procurement and how you can position your organization to capture massive enterprise opportunities before your competitors do. https://youtu.be/gaWxU1kgCLk Key Takeaways Adapting to the new agentic future requires agility rather than fighting the influx of AI tools. Lowering the listing fee for professional services from 2.5% to 0.5% drastically improves partner economics. Organizations without a self-service or PLG motion on the marketplace are literally leaving money on the table. Millennial buyers increasingly initiate complex enterprise procurements through self-service and AI-driven research. New AI-powered opportunity scoring empowers partners to prove their value internally and to AWS. Marketplace success hinges on optimizing metadata for AI agents, not just traditional SEO. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags: AWS Marketplace, agentic workflow, med pick scoring, phoenix.ai, Cara Cloud, branded storefronts, product-led growth strategy, intrinsic value boost, SaaS evolution, self-service motion, Databricks credit model, Trend Micro companion app, MCP servers, opportunity score tracking, PPA drawdown, concurrent agreements, AAMI structural debt, CXML procurement Transcript: Matt Y Audio Podcast [00:00:00] Matt Y: The ability to adapt with change and kind of roll with punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. [00:00:08] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:19] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:42] Vince Menzione: It is the strategy because being in the room changes everything. [00:00:46] Matt Y: Let’s start. [00:00:50] Vince Menzione: And now on to the really important stuff. So, Matt, I don’t wanna butcher it ’cause I, a couple people have told me how to pronounce your last name and they said use the word magician and you’ll get close to it. But I’m just gonna introduce you as Matt Wy and I’m gonna ask you to pronounce your name on stage, but I want to have you join us. [00:01:08] Vince Menzione: So excited to have Matt wy. After a super busy day and night last night, come over from Brooklyn and join us today. Matt, so great to have you. Thanks. Thank you so much. Thank you so much. Alright, so pronounce your name for us. [00:01:23] Matt Y: Anyone wanna guess? Ian’s? It’s like magician. [00:01:27] Vince Menzione: It’s not that hard, [00:01:28] Matt Y: it’s not that [00:01:28] bad, [00:01:28] Vince Menzione: but I don’t wanna butcher. [00:01:29] Vince Menzione: I wanted to let you do it. Good. [00:01:30] Matt Y: What calls me Matt White. [00:01:31] Vince Menzione: That’s great. [00:01:32] Matt Y: Yeah. [00:01:32] Vince Menzione: So 13 years. [00:01:34] Matt Y: Four coming up on 14 next month. Yeah. [00:01:36] Vince Menzione: Wow. Congratulations. Yeah. So you’ve been there, you’ve been there since the early days. And we, we had a conversation. I had some Microsoft, former Microsoft colleagues. Uh, Theresa Carlson, for those of you who knew the public sector business. [00:01:48] Vince Menzione: Yeah. Who started, I mean, Andy came out, it was so funny because I was there and she was hosting Andy for a dinner and with all the CIOs of the federal government. [00:01:57] Matt Y: Yeah. [00:01:58] Vince Menzione: And she was still at Microsoft and it was actually kind of an interesting time. And she came over and did a lot of great things for a number of years. [00:02:04] Matt Y: Yeah. She [00:02:05] Vince Menzione: and a lot of great [00:02:05] Matt Y: business. [00:02:06] Vince Menzione: Yeah. She really like, it went from employee number one to 7,000. [00:02:09] Matt Y: Yeah. [00:02:09] Vince Menzione: And you, you were, you’ve been there all that whole time. Pretty much. [00:02:12] Matt Y: Yeah, I guess when I started in New York, just down the road, we were, uh, in a Regis facility. There were like 11 of us in, uh, just sitting around a table and we had to speak quietly sometimes because there was a, um. [00:02:21] Matt Y: Some type of a financial services organization down the hall and they’d listen to try and get stock tips on Amazon. Yeah, [00:02:28] Vince Menzione: I love it. [00:02:29] Matt Y: Never leaked. That’s [00:02:29] Vince Menzione: good. I love it. [00:02:30] Matt Y: Yeah, [00:02:30] Vince Menzione: you probably got some great stories and, um, we won’t have time for today ’cause I wanna leave some room for conversations on marketplace end questions. [00:02:38] Matt Y: Yeah. [00:02:38] Vince Menzione: But I would love to invite you back for a real, like, in-depth podcast and I would love to get the whole genesis story. [00:02:44] Matt Y: Let’s do it. [00:02:45] Vince Menzione: We’ll do it. Okay, so let’s talk about, let’s talk about yesterday for you. Uh, some, some really big announcements as well. I thought maybe you could recap a little bit of what’s been going on in the marketplace business and it’s an, it’s been an exciting time. [00:02:58] Matt Y: Yeah. Yeah. You know what’s, I think what was really nice yesterday is it was sort of the combination of bringing, uh, our partner services like Partner Central and all those other services together closer to marketplace. We’ve been doing that over, over several years. So Marketplace has some of its own. [00:03:12] Matt Y: Big announcements, like, uh, we have a, we formalized our list and sell initiative. For example. We have a new, so it we essentially reducing the cost, uh, to list on marketplace through a partner program. [00:03:22] Vince Menzione: Yep. [00:03:22] Matt Y: And incentives associated with that. We have a new AI powered listing experience, which I think is particularly important ’cause I think many of you are like me and watching your SEO numbers go down and watching your agent traffic go up. [00:03:33] Matt Y: And so having, uh, an AI assistance in marketplace to optimize your listings for not just to, you know, retain what you can of your SEO, but prepare for the newent future and improve your GEO as we’re calling it. So that, [00:03:45] Vince Menzione: so it’s GEO now? [00:03:46] Matt Y: Yeah. You know, there’s a little debate right now in the acronym Moral A A EO versus GO I’m going, I’m on the G team, so, yeah. [00:03:52] Vince Menzione: Alright. GEO [00:03:54] Matt Y: It’s like the, the, yeah, they’re gonna win. They’re like the Knicks, but the, um, [00:03:57] Vince Menzione: yeah, yeah, exactly. [00:03:57] Matt Y: But yeah, so AI assisted, uh, I mean, making. The most of, like, essentially marketplace is an excellent conversion engine. And so using AI to help improve that conversion engine in the form of your PDPs for both humans and agents. [00:04:08] Matt Y: So that was an exciting launch. Um, I got the most applause when I announced that. We lowered, we made the economics better for, uh, consulting offers professional services, nice to marketplace. We lowered the listing fee from 2.5 to, to 0.5% and wow, it goes even lower in certain circumstances. So just improving the economics. [00:04:24] Matt Y: I’m really excited to. Really partner with a lot of you to reinvent services through, through the marketplace like we did with SAS and other areas. Uh, and we’re doing with agents right now. So that was a big one. And then a whole series of announcements around, um, how we’re making it easier and more cost effective and more efficient to partner with AWS. [00:04:41] Matt Y: So using AI to, uh, using med pick scoring to automatically progress opportunities so you don’t have to kind of wait on a human. To, to click and progress, you know, that that can take days. And, uh, if you, if you wanna have an opportunity and have that be cos sold with AWS, that can be through a mix of agents for the long tail and with humans in the, in the sort of top end and more complex. [00:05:00] Matt Y: And allowing AI to help all the partners improve their opportunity quality so that we can better co-sell together. So. Yeah, I said AI a lot intentionally. Um, [00:05:09] Audience Guest: yeah, [00:05:10] Matt Y: AI sort of in the whole cycle for buyers, for sellers, uh, for operational efficiency, cost of sales. So a lot of announcements. I think I hit the big ones, so yeah. [00:05:18] Matt Y: I’m Might have missed something there. There we go. [00:05:21] Vince Menzione: George. [00:05:21] Matt Y: Oh, and storefront. Yeah. Thanks George. See, I look at George to see what I missed. Uh, we, we acquired a great company called phoenix.ai late last year. Okay. And you, you actually were said Caresoft and Yeah. Be down. Uh, [00:05:30] Vince Menzione: yeah. [00:05:30] Matt Y: So if you’re familiar with Cara Cloud, they have a procurement portal. [00:05:33] Matt Y: It’s heavy use by the US government, and they, um. Uh, we, we acquired them, uh, the really great growth company. They have over 70 logos now, and they help you build a branded storefront on marketplace, which obviously is important in the government space. If you’re procuring on a certain contract with a certain reseller, um, you know, there’s a certain set of products you’re allowed to buy. [00:05:51] Matt Y: But what we’re finding is even down on Wall Street, you hear, um, enterprises are, are using storefronts for internal procurement and they wanna have a curated collection of, of partner products and, and your own ecosystems internally. So we’re selling to both customers. And also to channel partners to build custom storefronts, branded storefronts for, and [00:06:07] Vince Menzione: it makes total sense, right? [00:06:08] Vince Menzione: Yeah, because you wanna li you wanna limit the, the viewing and, uh, and get, because I mean, how many different listings do we have? Like over 30,000? [00:06:16] Matt Y: Yeah. Yeah. There’s, I think the official numbers over th we have over 36,000. I was checking from over 6,000 vendors. Um, it’s a lot. And, and that’s gonna explode with the AI powered, uh, listing, uh, experience that we launched. [00:06:26] Matt Y: We’re gonna make it easier. And I guess what I’ve been telling partners is. You know, customers aren’t clicking through categories anymore. They’re using AI to search. And so it doesn’t matter how big our catalog is, what matters is being found. And what matters is converting that buyer. So if you have a. [00:06:39] Matt Y: If you’re running a demand gen campaign for say, like, you know, life sciences in, in Jersey and there’s a specific buyer at j and j, you wanna capture, that person doesn’t wanna be just dropped onto a generic marketplace, 30,000 listings. They wanna be dropped in a very specific place where they’re seeing like life sciences offers from Accenture, for example, coupled with a life sciences power thing with Elastic, you know, like, but a solution. [00:07:00] Matt Y: And that they want to land in a curated place where that highly intention buyer can be converted effectively. So that, that’s what we’re doing with all this. [00:07:06] Vince Menzione: And that’s where the GEO comes in because [00:07:09] Matt Y: Yeah. ’cause that buyer might be an agent That’s right. With, and that agent has is even more fickle, honestly. [00:07:14] Matt Y: And you know, what used to be milliseconds for the human before they kind of click away is, is now perhaps microseconds. Yeah. And so, uh, you know, having the right metadata and, and the right positioning, uh, the right story that an agent or a human can pick up to ultimately. Uh, complete their product research and choose your product is, is critical. [00:07:30] Vince Menzione: Very cool. Very cool. So before I, I, I’ve been asked to ask you this because I, I’ve had this con, people have brought come to me and said, you gotta ask Matt about music. He’s a big music guy. And, uh, so what are your favorite bands? [00:07:49] Matt Y: So, I mean, the, the real answer is, uh. I, I go to about a show about every week. [00:07:54] Matt Y: As, as Mike Trill knows, uh, we heard a show last night. Um, we were, uh, just a few hours ago, really? And, uh, um, favorite band, uh, well, I’ll tell, I’ll tell a story. I, I had a side hustle with MTV for years. Um, I used to run a music website. Um, oh, that’s cool. I didn’t know that. It got, it got kind of popular. It got sponsored by, if, if anyone’s into like early hip hop. [00:08:16] Matt Y: It got sponsored by a group called Jurassic Five. ’cause he, one of them reached out to me and said, nice. Hey, uh, you know, I’ve been, I like your website. And he ended up paying for a web, hosting a Dream host, if you remember, of cost back then. [00:08:26] Vince Menzione: Oh, Jesus. [00:08:26] Matt Y: Because I was broke and couldn’t afford it. And then, uh, and then this band sent me like a, a single and said, Hey, you know, trying to get the word out about our little band, can you help us out? [00:08:35] Matt Y: And I put their, uh, I put their, you know, single up on my, on my website and it blew up. And that band is Vampire Weekend. So they’re kind of big now. Wow. Yeah. Um, and uh, that got picked up by like Vanity Fair and all these other guys. And then I got sponsored by MTV to essentially write. Music reviews for years on the side. [00:08:51] Matt Y: So I was working for the Associated Press, laying cable in sports and war and, and, uh, yeah. So Vampire Weekend was good to me that, that they, they kind of paved a way to go to a lot of free shows over the years and a lot of bands and see a lot of great music. But yeah. [00:09:03] Vince Menzione: That is very cool. And that, and how did that get your day? [00:09:05] Vince Menzione: WS It was just a, it was just the technology path that was like, [00:09:09] Matt Y: I mean, it’s a, it’s a, I guess it’s a bit of a long story, but, um, the. There’s many versions of this story. I’ll tell the, tell the one quickly. I was living for free in a Fulbright scholarship house in West Africa. You, we can talk about how that happened another time. [00:09:23] Matt Y: And, uh, a guy had sort of fallen down on the floor ’cause he’d had too much to drink. And I, I sort of lay down beside and be like, Hey man, are you all right? And, um, he, uh. He worked, he, he worked for the Associated Press and next day I had the job, um, being West Africa, head of technology for West Africa. [00:09:37] Matt Y: And because of that, um, and as I learned years later, the AP didn’t have dr they had no disaster recovery. Yeah. And I, I can tell you that now ’cause um, you know, 16 years since I worked there, but they, uh, I put the DR in, um, on AWS and we’re talking like, yeah, 16, 17 years ago. This is early. It was early days. [00:09:56] Matt Y: And I, I swear to God, I paid for. Uh, our AWS bill using, um, taxi receipts, fake taxi receipts that I bought in on Nigerian market, um, because there was no budget and so, you know, it was like 30 bucks. [00:10:08] Vince Menzione: I was gonna say swipe a credit card, but they didn’t [00:10:09] Matt Y: knew that this is the entire press this before. [00:10:11] Vince Menzione: This is before, yeah. [00:10:12] Matt Y: Yeah, like the entire ap. Um, and, uh, so AWS called me like, who are you? Like, why, why are you paying on like this like low limit credit card for like the ap? Like, who are you? And, uh. Next day I had the job. Well, a week later I had the job with aw WS. That so cool. So that’s the story’s [00:10:29] Vince Menzione: cool thing. [00:10:29] Matt Y: Yeah. [00:10:30] Vince Menzione: Very cool. [00:10:31] Vince Menzione: Uh, sports teams. So Knicks fan. [00:10:34] Matt Y: Yeah, I mean, I like the Knicks. Um, they’re h hockey, I’m not allowed to say anything different. No. I appreciate them. Uh, I’m a Raptors fan. I grew up in Toronto mostly. Yeah, yeah. Uh, so, and you know, when they won, uh, that was very exciting as well. So no, Nicks are great. I like the Knicks. [00:10:49] Matt Y: Nothing against the Knicks. Um. They’re fine. Yeah. [00:10:54] Vince Menzione: Hockey, hockey fan. Favorite hockey teams? [00:10:56] Matt Y: Oh yeah. Itron. Maple leaf. Maple leaf. Yeah. They’re gonna, they’re gonna win. Of course. Of course. Yeah. Um, like every year they’re actually, we [00:11:02] Vince Menzione: have some Canadians laughing in the sand. [00:11:03] Matt Y: Well, the leaf are, are, are the Knicks of hockey? [00:11:05] Matt Y: Like Yes, they are. You know, it’s 67 years out, coming up on 68 since they won, so That’s crazy. 53 is nothing. I know. Pain. So. Yeah, definitely the least. Yeah. [00:11:15] Vince Menzione: I love it. I love it. It’s so cool. Yeah. So what was the, uh, what was the, what was the last concert you went to? [00:11:22] Matt Y: Well, literally last night. Oh, it was last, [00:11:23] Vince Menzione: oh, that [00:11:24] Matt Y: was actually concert were my favorite bar in the world. [00:11:26] Matt Y: This place called Sunny’s. Uh, it’s, you know, I, I took Mike and, and Matt from, from Texas and from TGS down there to sort of see my neighborhood and they’re like, where are we? And I’m like, yeah, I live here. Uh, sort of an industrial part of Brooklyn. And, and we went to see, um, I dunno what you would call it, like. [00:11:40] Matt Y: I guess it’d be like roots music. There was a woman with an accordion and a guy with a big cowboy hat. Yeah, it was, it was fun. Yeah. [00:11:47] Vince Menzione: That is so funny. Alright, we’re gonna shift back years. Um, important time right now for partners. What, what should partners be looking out for the most? What would you say to them in terms of what’s the, what’s their real headline for them? [00:11:59] Matt Y: Well, I, I, you know, to borrow from you actually, you know, I liked, uh, the, the principles you had up there and, and with agility, um, you know, there’s a lot of fud flying around right now. You know, people. People were like, oh, it’s the demise of sis with the arrival of ai, you know, everyone’s gonna be using agents. [00:12:13] Matt Y: And then it turns out it’s been a huge boon for most, uh, you know, system integrators and consulting companies that I work with. They all have, you know, the, the good ones especially have vibrant consulting practices now, and everyone is deploying fds, uh, you know, um, the new, the new cool acronym. But it’s, it’s essentially created a huge opportunity for the consulting space. [00:12:31] Matt Y: Uh, and similarly, uh, you know, there there’s this narrative around the sa sa apocalypse, which I really hate, you know, ’cause it was, uh, premature and kind of a trigger reaction from the stock market. And, you know, just look, look what Snowflake did. And, you know, they did what a lot of SaaS companies are doing, but they, they added a nice sort of glaze of positioning and, and, you know, their stock popped and they did pretty well. [00:12:50] Matt Y: And so I think the ability to adapt with change and kind of roll with the punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. For the more successful consulting companies and software companies who have become agentic. But SaaS hasn’t gone away, you know? [00:13:05] Matt Y: No. Look at our own marketplace. We have this agent marketplace, but people aren’t buying atomic agents at scale. They’re buying ified SaaS solutions with sort of agent sidecars, which has created new opportunities for candidly additional licenses, [00:13:16] Vince Menzione: right? [00:13:16] Matt Y: Um, as customers sort of want to consume more AI services on top of their. [00:13:20] Matt Y: On top of their SaaS solutions. So I think being agile, you know, you see like ServiceNow as part of our billionaires club. Yes. They’re not going anywhere. They’re, yeah. They’re gentrifying. You know, Salesforce has pivoted to this headless model, um, along with Asian Force and using sort of Slack as the operating system. [00:13:34] Matt Y: And, you know, you said like a lot of companies from the seventies aren’t around anymore. They’re gonna be winners and losers. Yeah. Um, but the winners are gonna win even more. And so I, I think what’s so important right now for partners is to not, not bite too hard at the, the latest trend. You know, models are changing and everyone’s like, oh, you know, philanthropics really in the world and they’re wonderful, great to work with, amazing technology. [00:13:55] Matt Y: That’s what people are saying about open AI six months ago. That’s right. And before that, you know, and it, I, I was with Fireworks AI yesterday, a great company and they have some really cool stuff with sort of, um, they believe in more cost effective, uh, open source models essentially, that you can find tune. [00:14:08] Matt Y: Maybe that’s gonna win. I don’t know. Um, is it gonna be sort of domain specific models? Is it gonna be highly capable LLMs? Are LLMs gonna level off as soon as Fable and Mythos are allowed to launch? Maybe. I, I don’t think anyone can predict the future right now. So you have to be agile and you have to kind of seize the opportunities and take a couple punches. [00:14:25] Vince Menzione: Yeah. [00:14:26] Matt Y: You know, and marketplace too, like we’re, you have to be unafraid to experiment right now. Um, you know, that’s hard if your stock’s taking a beating. Um, but this is, it’s a, it is a disruptive time, uh, but it’s creating actually enormous opportunities for growth for partners and, and we really see that, you know, in marketplace specifically within AWS. [00:14:45] Vince Menzione: It, it, it does still feel like the deer in the headlights moment. Right. Would you agree? Like you’re probably taking a lot of meetings and, and calls from ISVs specifically? [00:14:54] Matt Y: Well, [00:14:54] Vince Menzione: that are still trying to figure it out. [00:14:56] Matt Y: Yeah. But it’s everyone. Yeah. I think what’s really interesting, I had a meeting [00:14:58] Vince Menzione: with, it’s not just one. [00:14:59] Matt Y: Yeah. I, well, I had a meeting with one of the leading AI companies, like one of the biggest ones. And they, uh, they demonstrated how they work and they were really proud. They were like, you know, look at our agentic workflow. And I came out at me. I’m like, that’s it. Ours is way better. Like really like, you know, ’cause we we’re, we’re using quick desktop with MCP servers and connectors and all this, and you know, we, we have our own sort of ecosystem of partners, a mix of homegrown software and third party. [00:15:20] Matt Y: And I kinda walked out there and, and looked at, you know, my phone, which has been populated by agents this morning with all the, and I was like, I have a way better agent workflow than this world’s leading supposedly AI company. And I think, um, that really, so during, I, I would, during the headlights, you can call it deer in the headlights, I call it chaos. [00:15:36] Matt Y: And in times of chaos there are people who create. Opportunity again. And so, yeah, there are some people who are stuck and who don’t know what to do, who are over worried about token costs, um, who are not experimenting. But there are a lot of companies, uh, taking this opportunity to kind of pivot their business. [00:15:53] Matt Y: Um, I think, I think we’re in a moment and, uh, yeah, I, I candidly I see more of the latter. I see more experimenting. [00:15:59] Vince Menzione: You mentioned ServiceNow. Any other great examples of that? Organizations that really embraced it? [00:16:04] Matt Y: Uh, yeah. Well, you know, ServiceNow is part of this business applications category, as we call it, in marketplace. [00:16:09] Matt Y: That outside of AI, I think is the fastest growing category in marketplace, which is wild when you think about it. ’cause we’ve historically been an infrastructure partner marketplace with security and data and analytics and, you know, security with channel partners, et cetera. But Salesforce, ServiceNow, Workday, Adobe, you know, I could go on. [00:16:23] Matt Y: They, they are actually. You know, our fastest growing category and yeah, ServiceNow, obviously reinventing itself for ai, Salesforce, but Workday, you know, the workday’s done some, who knows if it’s gonna work, but they, they’re experimenting with essentially like a Databricks, uh, credit style model for like, units of work, uh, which I think is fascinating. [00:16:41] Matt Y: Like everyone’s talking about value-based, outcome-based pricing and meter. And, and you have companies that are ERP companies, you know, like traditional business applications, experimenting with effectively like a metered pay as you go, value based credit model. Again, like who knows if it’s gonna work. [00:16:54] Matt Y: But I think that’s really amazing to see and we need more ISVs experimenting. I, I was talking about trend ai and I know they’re, they’re, they’re one of the sponsors yesterday. You know, many of you know them as Trend Micro back in the day. They’ve successfully reinvented themselves. They built that companion app. [00:17:10] Matt Y: Um, you know, that I think we’re seeing. Just a ton of experimentation in the market across categories. Uh, I could go on and on about partners. Um, yeah, there, I I wouldn’t pick a winner right now. Yeah. [00:17:24] Vince Menzione: You, you, we’ve talked about ai. We’ve talked, talk more about the buying journey and how that’s changing, because again, it feels, it feels like that’s also [00:17:33] Matt Y: Yeah. [00:17:33] Matt Y: So, you know, one of, one of the core, uh, strategic objectives, or we’ll say like the philosophy marketplace is that. Um, financial incentives are important, you know, EDP or PPA drawdown, uh, credits. Like we need to act as an efficient and effective vehicle for allowing buyers to exercise their discounts for, and, and sort of partners to exercise their credits, et cetera. [00:17:55] Matt Y: That, that’s actually important. But what, what a lot of people over rotate on that, and we’re really, one of the things we say a lot inside at Amazon or at AWS marketplace is we want to continue to boost the intrinsic value of marketplace beyond the financial incentives. And well over a quarter of all private offers, private pricing, private, uh, custom terms, et cetera. [00:18:14] Matt Y: Um, begin with a self-service or PLG motion. And partners who don’t have a PLG or self-service motion are literally leaving money on the table. Like if you look at like a Databricks for example, and they did a good job integrating buy with a WS within their SaaS application. They have free trials, they have really strong pego and, and, uh, and PLG motion. [00:18:33] Matt Y: They’re making, I can’t share their numbers obviously, but they’re making a ton of money. On purely self-service motions. And importantly, they’re acquiring new business, new logos that they nurture, you know, really like not just leads but closed opportunities, right? That they lead, they’re growing, uh, at a reasonable conversion rate or or success rate into the next big logos. [00:18:50] Matt Y: And these are over multi-year horizons. They’re patient, you know, they bring in these new logos with PLG, and they’re also bringing banking, a lot of large enterprises. Through self-service. I, I was with data Mask. There’s this great little startup from New Zealand. They’re a New Zealand based company. Um, super nice guy. [00:19:06] Matt Y: And, and, uh, they, they got huge logos. I think they got, what was it? A DP and some huge American logos. Okay. And this like logo in, I think it was Chile, or no, it was Peru. They’ve never been to Peru. They don’t have sales in Peru. Um, and they. Buyers were discovering them self-service and they, they, I think they got something like 13 logos entirely through a self-service motion. [00:19:26] Matt Y: One password will tell you the same thing. I was just with them in Toronto and companies big and small startups and the largest are getting enterprise wins in addition to net new small logos through that PLG. Buyer motion. And that’s because you have a whole generation of CFOs, CTOs, CROs, whatever. The C is [00:19:43] Vince Menzione: millennial [00:19:43] Matt Y: who grew up on their phones. [00:19:45] Vince Menzione: Yeah. [00:19:45] Matt Y: And, and it sounds like, you know, hyperbole, but it’s true. They, they want immediate apps, immediate access. And that actually, you’re like, oh, that never translates to business applications. Turns out it does. It does. And they might not be buying on their phone, but what they are doing is researching and we see the numbers, the amount of customers who are doing their research, and then eventually landing on the page from chat, GPT. [00:20:06] Matt Y: From major financial, like Fortune 500 companies is extremely high. Yeah. Uh, you have procurement team, sourcing team, uh, developers who are starting the research increasingly, like in clawed in chat, GPT, and then, you know, building a proposal and then handing it to their enterprise procurement team. Yeah. [00:20:22] Matt Y: Which is still largely unchanged. So buyer behavior is on the front end, on the research side is really changing. So the [00:20:29] Vince Menzione: discovery is happening through PLG. [00:20:32] Matt Y: Yeah. [00:20:32] Vince Menzione: And then the backend work on private offers and things like that sometimes still happens the old way. [00:20:36] Matt Y: Yeah. Well, and so, you know, it’s [00:20:37] Vince Menzione: fax machine, [00:20:38] Matt Y: some people Yeah, sure. [00:20:39] Matt Y: They’re bringing the deal directly to Marketplace last minute. But even if that deal goes direct, sometimes they’re still beginning their research journey and increasingly using Marketplace as a research vehicle, which is why we launched Agent Mode, um, to help you sort of help you and agents do research. [00:20:51] Matt Y: But that I think if, if I have one piece of device for any partner consulting or ISV is. Don’t leave those leads and that money on the table by not having a PLG self-service strategy like you’re fooling yourself. Uh, and it’s, it’s a huge, it’s a huge, huge business for us. The, the majority of all customers by far on marketplace don’t even have a PPA, uh, and a huge percentage of even those with PPA spend beyond the p. [00:21:17] Matt Y: And so if you’re just think if you’re just using marketplaces as like BPA retirement, you are literally losing money. [00:21:22] Vince Menzione: Yeah. [00:21:22] Matt Y: Yeah. [00:21:23] Vince Menzione: We have a session with Vinod. We’re gonna talk a little bit about that right after. Great. So good. Um, so I, yeah, I think, um. We talked about, we talked about agents, we’ve talked about the millennial buyer, the change in buying behavior. [00:21:40] Vince Menzione: What other, what other areas of aspect I, I, I, I do wanna think about like opening it up though for a second. I think that maybe with maybe nine minutes left. Sure. I just want to get a read from the people in the room. People have questions for Matt that we weren’t able to ask them. Yeah, I think, I think we probably have a few of those. [00:21:57] Vince Menzione: I think that would probably be great. [00:21:58] Matt Y: I can sense the hardball coming. [00:22:00] Vince Menzione: You’ve known each other [00:22:00] Matt Y: a long time. [00:22:01] Vince Menzione: Yeah. No, no. Hardball. We have a mic back here. Okay. I’ll just, we’ll, we’ll, we’ll get you a mic as we are recording. So good. Thank you. [00:22:11] Audience Guest: Uh, Boris Geller with a, a Click PLG is near and dear to my heart. [00:22:17] Audience Guest: We’ve been doing a lot of business in marketplace and I’m still struggling to sell my vision internally on, on, uh, on PLG. Uh, I think. Ag Agent AI is gonna be one of the drivers, and we are already on, uh, agent Marketplace, but I would appreciate guidance on, uh, best practices. How do we kind of, uh, operationalize it? [00:22:41] Audience Guest: It’s, it’s on us, not on you. [00:22:43] Matt Y: Well, no, I think it’s on both of us. You know, we, uh. One thing that we’re trying to do is give you more data to, to sell to your internal stakeholders in your executive suite. The value of co-sell with AWS all up, like finally with what we launched at, uh, the summit yesterday, you now get an opportunity score. [00:23:02] Matt Y: You, you get a number. People have been asking for this for years, so, so you can say when we do this and we, when we give AWS this information. The score goes up and we have a higher propensity to be cos sold by humans or agents before you had to kind of, it was like this mystery you had to guess. And similarly with marketplace, um, we, we have new dashboards that you can use to sort of, you used to have to sit down with us and go through spreadsheets to trace sort of lead to trace the funnel to sort of a close opportunity. [00:23:28] Matt Y: And we’re gonna continue to launch more there. But you now have more data that you can show. You can be like, listen, these are our inbound leads, this how’s converting, and now we have PRM, the partner revenue measurement where we can say like, this is what it’s translating into in terms of. AWS service revenue driven by our product. [00:23:41] Matt Y: And so that being able to tie from that inbound lead from your demand gen campaign through to a converted opportunity to what you actually drive from an AWS impact perspective, so you can, and then what your opportunity score is that data you can use to sell. Not only internally, but to us as well. Yeah, to a skeptical sales team or whatever who’s not maybe, you know, hype on partners in the, in the US West. [00:24:03] Matt Y: You can be like, listen, I don’t care what you think about my business. This is what I’m gonna drive for you with your quarter retirement from an AWS perspective, and this is how the shape of your customer accounts are gonna change. And this is why you should pay attention to my opportunities. ’cause my opportunity score is, is crazy high and I’m giving you insights into business that AWS would not otherwise have. [00:24:19] Vince Menzione: That’s your brand story we’re talking about. [00:24:21] Matt Y: Yeah. [00:24:22] Vince Menzione: Building your story up with within [00:24:25] Matt Y: So it’s, it’s about the data, I guess. And, and you should, you know, you should all actually be [00:24:28] Vince Menzione: Yeah. [00:24:29] Matt Y: Asking me for more data, so, you know, and tell me like, what do you need to sell to your internal stakeholders? ’cause if I can draw a clear line. [00:24:35] Matt Y: From your demand chain campaign that lands on a marketplace, which I know is a conversion machine, it has way better than industry levels of, of conversion rates. And then you can show, hey, if we have a PLG strategy and we land those leads on marketplace, we will convert them with high efficiency, low cost of sales and, and, and have sort of a bifurcated where we can close some through self service, some through express private offers and some through private offers, depending on deal size. [00:24:57] Matt Y: Like you tell A CFO that, and they’re my number one customer now and they love it ’cause they see cost of sales going down, cost of operations going down and business going up. Um, so I think we have more data than we used to use that data. And let me know what other data do you need to make that pitch and make that pitch to the CFO go around the head of sales, all those other people. [00:25:15] Matt Y: Honestly, the CFO is where we get the best leverage. [00:25:18] Vince Menzione: Awesome. Great question. [00:25:23] Matt Y: Gonna bring your mic. [00:25:23] Vince Menzione: We’re, we’re gonna get your mic here. There you go. Oh, [00:25:25] Audience Guest: thank you. So my name’s Jody Cheval and I’m a consultant now, but I was at Workday during when they adopted AWS and it, a sales organization needs propensity to buy data. [00:25:34] Audience Guest: To really drive the sales team to realize the opportunity kind of makes them visualize it. We didn’t struggle, but it was challenging to get that data because at that time we’re getting spreadsheets. So does AWS have a vision of making that API based data that our client, my clients, can get at and bring into a tool to start building account hypothesis based on that data? [00:25:57] Audience Guest: ’cause it really is important to an enterprise sales guy to have the sense that OAWS can help me close this deal. [00:26:03] Matt Y: Yeah. I mean. Part of that. So we, we launched, we’ve been launching part of that in stages and we’re not done. There’s, there’s more coming. Um, part of that is embedded really within the new, uh, partner agent workflows. [00:26:13] Matt Y: We are giving sort of more, uh, information back to you, not just about like what funding programs you’re eligible for, but like, you know, and when, when we will co-sell this deal with you, which is effectively a signal like we, we see this as a high value opportunity, that you have a likelihood of winning internally. [00:26:28] Matt Y: We, we have this solution matching engine that we’re using and we announced. That, that that ties you the partner to a customer specific opportunity that you have a high propensity or the partner has a high propensity to assist with and ultimately win. And now we’ve tied that to our express private offers, which we announced this week. [00:26:44] Matt Y: So it’s an indirect answer to what you’re asking, but a rep can essentially say. Send a private priced offer to the customer on behalf of the partner without having to ring up the partner because they have a high propensity to win this deal with the customer. So we’re progressively launching features like that. [00:26:59] Matt Y: In addition to the propensity to buy data that we do now share. It used to be kind of, again, manual magic depending on who you knew we could share. Now we do share that programmatically, and there’s more to come specifically in that space. Uh, I’d say watch that space. In the next few months, there’s gonna be more data coming away, but we do have the APIs, we have the agent. [00:27:16] Matt Y: We have things like express private office solution matching, and we have been sort of in that space progressively launching features over the last six to 12 months. And, and you should expect to see some more there soon, not just from us or from our partners. [00:27:27] Vince Menzione: Nice. Any announcement dates? [00:27:30] Matt Y: I can’t commit to a date or else my engineers will get mad at me. [00:27:33] Vince Menzione: It looks like we Another question number. Is the mic still back there? Okay. There’s a gentleman over here [00:27:40] Audience Guest: first Go leaves. Um, it’s awesome. I’m right next to. I was right next. [00:27:46] Vince Menzione: We’ve got a lot of great plants here, so, [00:27:49] Audience Guest: um, so this may be a little bit myopic or, or a challenge that we run into, but I love a lot of the innovation that’s looking forward and all the future things that we’re doing. [00:28:00] Audience Guest: One of the things that we’re struggling with is a little bit of almost like tech or structural debt. How do you think about bringing flexibility to the core pieces that underpin all of the innovation, which is. We are self-hosted. So one of our listings is an a MI. You can’t amend an a MI, you have to cancel and start over. [00:28:18] Audience Guest: So a lot of the building blocks, when you think about PLG, if somebody wants to add to that in an a MI listing, it’s, it’s sort of broken. So how are you thinking about taking all of the, the rapidly changing buyer behavior and then looking back at the structural foundation that underpins all of those things, like offers and, and amendments and changes and all of that? [00:28:39] Matt Y: Yeah. I, I promise I didn’t seed that question, but that, that’s a great one. Um, so not to get too in the weeds, but fundamentally, marketplace was built up, um, a bit like AWS like a set, a series of services somewhat independently. And each product type was effectively its own service, SaaS, server images, ais. [00:28:59] Matt Y: Um, what we’ve done recently is now we, we have, we got rid of product types basically on the backend. You, you don’t see it, but what that means, for example, like another thing AAMIs don’t support today, future data agreements. Um, or concurrent agreements, uh, they will all be supported by amis before the end of the year. [00:29:14] Matt Y: ’cause what we’re doing, this fundamental thing that you won’t even see called product offer decoupling. Uh, and it’s a fundamental piece of things that we need to unwind. ’cause we built up, we were moving very quickly over the years. We had a distributed engineering model and we built each product type independently. [00:29:28] Matt Y: And so yeah, if you’re a seller and you’re selling containers, agents, SaaS, amies, um, we’re breaking down the silos between those so that each of them will get the same benefits. And, and by the way, we’re taking the same approach to international. Hopefully you’ve noticed now that. It’s not like a feature launches in the US only and then takes five years to launch in either public sector or another country. [00:29:48] Matt Y: We, we’ve taken a global approach to feature launch and increasingly a product type neutral approach to feature launches. Uh, that’ll be largely resolved before the year’s out. We’re working on it right now. So again, it’s, it should be transparent to you, like you shouldn’t actually see any difference in the, in the experience. [00:30:05] Matt Y: Except that all of those features will be available. So, so that is, uh, actively under work. And that’s actually something if you’d like to try, um, you’re, you’re welcome to. So, yeah, [00:30:17] Vince Menzione: we have time for maybe one more question and we we’re actually gonna have you up here with a couple partners. [00:30:24] Matt Y: Sounds [00:30:24] Vince Menzione: good. Kind of fun. [00:30:32] Audience Guest: Hey, Matt, uh, met Natasha from Dondo. Uh, quick. So great announcements. And you know, you talked about the million, multi-billion dollar, uh, club, and, uh, that’s all great. Uh, in terms of the. Propensity data. I think that’s coming at the center of a lot of things, right? You know, for enterprises, oh, there’s an investment and you tap into that investment. [00:30:53] Audience Guest: But also there’s the other side of the procurement where a lot of customers, sometimes we work with, they’re like, they still wanna go direct for whatever reason, right? So I think there’s an education piece there, but also trying to understand like how we can work together to, you know, get some of that side of the things sorted out as well. [00:31:11] Audience Guest: You know? ’cause a lot of times it’s not about. Just, you know, retiring the, uh, the, the spend comets, but also like, Hey, I’m used, I’m already used that for something else. So maybe that’s not an, uh, something that applies here. And in also in tying that the PLG motion, uh, you know, for the customers you said, you talked about, you know, if there is. [00:31:34] Audience Guest: Leads on the TA table, like where the, it’s not the enterprise, but you know, the others. Um, I feel like it’s more to do, changing the business model at some times. Like with the enterprises, you have the revenue stream coming through, say large deals, right? And all of a sudden you tap into this, you know, PayGo. [00:31:51] Audience Guest: Where it flips the whole equation with, you know, the financing and the, and the, and the revenue measurement. So I think there’s two aspects of how do you kind of cons reconcile those things in terms of, you know, the revenue measurements going forward. [00:32:05] Matt Y: Yeah. So, so two things real quick on the procurement. [00:32:07] Matt Y: Um, yeah, like, yeah, I sort of alluded to this earlier, but, uh. Procurement is a bit late to the AI ag agentic transformation. They’re trying, and there’s a lot of great new incumbents in this space. And the big leaders like, you know, Coupa and Ariba and Oracle are, are, are evolving their products, albeit a bit slowly. [00:32:26] Matt Y: Um, but the, I think, uh, it’s still the long pole in the tent. You know this. And so like, there are two reasons why deals tend to go direct, because it kind of hits a wall of. Legal, uh, you know, procurement, governance, like all that kind of after the selection’s been made, et cetera, or, or they’re, you know, we can’t change. [00:32:44] Matt Y: People are gonna optimize for, for finance, you know, they’re, they’re going to, if they’re getting big discounts. I mean, that is life. I always say it’s like sellers at the most agented company are still gonna chase quota no matter how, you know, crazy. Uh, your, your company is, and it’s the same with, um, with the chief, uh, financial officer and chief procurement officer. [00:33:01] Matt Y: They are going to, they’re literally. Paid to find discounts. And so we’re not, we’re not gonna get rid of financial engineering. That’s a, that’s a thing. What we can do is reduce the friction for procurement. So we launched, for example, like mandatory purchase orders. That was a big thing. We, we have buyer notifications, now we’re making other procure to pay enhancements. [00:33:17] Matt Y: I mean, procurement systems still use like CXML. It’s like, that was, that was cool when I worked for the ap. And like I, I have teenagers that are old, like older than, so they, I, I think, um. Procurement needs to evolve and we’re gonna help it evolve. We’re gonna push it forward and, and we need to make it more seamless for procurement teams so that we remove those objections. [00:33:38] Matt Y: Uh, I can’t remove the financial engineering objection, like, you know, that’s just life. Um, but I can make it irresponsible not to use marketplace ’cause it’s so easy to use. And, uh, that, that’s kind of the approach we’re taking on, on the front end. Uh, you, you know, I think you, you, again, I didn’t see this question. [00:33:52] Matt Y: You, you stepped into a trap. Un unwittingly, um, PLG is not just is for enterprise. And, and PLG doesn’t necessarily mean pego or self-service. Uh, doesn’t necessarily like, uh, most of our self-service efforts are actually focused on private offers. And not necessarily for pego. Uh, when, when I say self-service and, and PLG, uh, it, it can mean all kinds of things like it. [00:34:14] Matt Y: We have requested private offer, requested demo call to actions, buttons that you can put on your listing. For example, you don’t necessarily need a free trial or a metered pay as you go listing to take advantage of those inbound self-service leads. So, and those inbound self-service leads are often massive enterprise deals, like I mentioned specifically, uh, the data mask. [00:34:31] Matt Y: Those giant enterprise deals that they launched came from an enterprise like Fortune 1000 Enterprise in the US that organically discovered their solution on the marketplace using our AI search. And that was a massive enterprise. And so I, I think yes, there is the long tail, you wanna capture a new logo acquisition, but you should think of your product like growth in your self-service strategy as a way to, um, acquire all kinds of leads, including large enterprise. [00:34:54] Matt Y: And so when I say leave money on the table, I’m not just talking about things that are gonna mature over two years or tiny little deals. These could be massive deals. Uh, and, and you’ll accelerate those deals by accelerating their discovery and, and research so that I think that, so, and my advice is don’t, you don’t have to go all in if you don’t have, if you don’t have metering, if you don’t have PayGo, that’s cool. [00:35:13] Matt Y: Start with something simple. Start with a public listing, with a request to private offer like that. That is a, a huge step. That doesn’t take much, and, and it kind of blows my mind still that a lot of companies aren’t doing that yet. [00:35:25] Vince Menzione: Great answer. Well, it’s now time we’re gonna bring, we’re gonna bring, it’s time. [00:35:29] Vince Menzione: We, we’ve got some great partners coming up here, Nvidia Elastic, Accenture gonna all join us for a conversation. Great. And I’m glad that you’re gonna stay with us. And let’s, let’s, well, let’s thank Matt, by the way, for that session. [00:35:41] Matt Y: Thanks. [00:35:42] Vince Menzione: And [00:35:42] Matt Y: thanks for listening to the Ultimate [00:35:44] Vince Menzione: Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. [00:35:51] Vince Menzione: Subscribe where you listen. And head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything.

In today's Cloud Wars Minute, I explain why the next phase of agentic AI is all about governance, security, and business processes. Highlights 00:03 — Salesforce has expanded its partnership with Databricks to help organizations better connect enterprise data with business outcomes in the era of agentic AI. At its core, the expanded partnership is about recognizing that as AI agents take on a larger role across the enterprise, they need access to complete, connected data that's paired with business context, security controls, and enterprise processes. 00:51 — Access to data alone really is not enough for AI agents to deliver meaningful business value. "Customers consistently tell us they want AI agents to become a larger part of how work gets done across the enterprise," said Andy Kofoid, President of Global Field Operations at Databricks. "To make this a reality, they need access to trusted data, business contexts, and governance controls wherever that information lives." 01:32 — "Together, Salesforce and Databricks are helping customers connect governed data and business contexts across platforms, giving humans and agents the shared foundation they need to search, reason, and act with confidence." 01:46— I think this partnership is, yet again, part of a pattern that's emerging here. It's representing a broader shift that's taking place across the AI industry as organizations move beyond experimentation and toward large-scale deployment of AI agents. 02:00 — As this is happening, success really depends less on the models and more on the ability to unite these agentic capabilities with data governance, security, and business processes. Salesforce and Databricks are betting that enterprises need all of those elements working together cohesively if agentic AI is to deliver on the promises it has made. Visit Cloud Wars for more.

For most data leaders, governance feels like the thing standing between them and progress. Bharathi Rajan, Vice President of Digital, AI, and Data at Swire Coca-Cola, joins Cindi Howson to share how she built the data foundation powering a $3 billion supply chain operation. She breaks down how to turn data quality into a business accelerator, get ahead of demand signals, and build the foundation every AI initiative actually depends on. Key Moments: Building a Data Foundation Across a $3B Supply Chain (06:39): The starting point wasn't strategy. It was figuring out where the data was, who had it, and how to get it into the hands of actual decision-makers. Reframing Data Governance (10:22): Governance is the foundation that makes better decisions possible. Bharathi shares how education and relationship-building drove the mindset shift at Swire Coca-Cola. AI as an Enablement Factor (11:50): Bharathi reframes AI as a tool for operational efficiency and more impactful work, not a headcount threat. Building the Plant of the Future (14:41): A new $475 million Colorado facility gives Swire Coca-Cola a rare chance to design data and AI infrastructure from scratch. The Skills That Will Matter Most in an AI World (17:58): Bharathi breaks down what she tells young people and aspiring data leaders about building a career that AI can't replace. Key Quotes: “Governance is not red tape. It is important. If you want to make decisions with the right data, you need to have governance. You have to have that quality data flowing in.” - Bharathi Rajan “If you're passionate about something and then you know how to use technology to enable that, that makes the difference.” - Bharathi Rajan “AI, it's an enablement factor for us internally as to how can I help the enterprise really grow, create operational efficiencies, but also have people do more impactful work.” - Bharathi Rajan Mentions Swire Coca-Cola to Build $475 Million Bottling Plant in Colorado Springs, CO DataIQ100:  The most influential people in data and AI 2025 AI Women Power List Honorees The Let Them Theory by Mel Robbins Guest Bio Bharathi Rajan is a results-driven and experienced Chief Data Officer and AI strategist.  In her current role as Vice President Digital, AI & Data at Swire Coca-Cola, USA, Bharathi has saved multi-millions by adopting new tech stack and bringing in capabilities critical for Enterprise growth and performance. While consistently leading innovation and enabling AI & data literacy, Bharathi consecutively drives data and systems architecture confluence across the enterprise. Prior to joining Swire Coca-Cola, USA, Bharathi operated as a Senior Director of Operations in Data and Infrastructre for three years, focusing on Enterprise Reporting and Analysis (ERA), where she was specifically hired for her distinctive capabilities in data strategy, cloud migration, and creative efficiencies.  Bharathi is currently ranked #3 DataIQ100 North America for 2026. She has attended several panel discussions, in Emory University, Women in Tech, AI & Manufacturing and DataIQ. In 2026, Bharathi delivered a keynote on Data Leader as the Transformation Architect at the DataIQ summit. Additionally, Bharathi has spoken at various summits like Microsoft Ignite, Databricks, AI&Data summit and Snowflake summit over the years. Bharathi is also a mentor with WLDA Ventures and Women Tech Council. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

There's a problem that's bugged the database industry since the 1980s: you run an expensive query over millions of rows, cache the result, and then a single new row arrives. Logically that's one small update, but most engines throw the cached answer away and recompute everything from scratch. Some will handle changes incrementally, but only for "simple" queries - and the rules for what counts as simple are arbitrary and brittle. So can you incrementally maintain *any* SQL query, no matter how complex? For decades the answer was no. Then an award-winning paper called DBSP proved that the answer is yes - all queries are simple enough.Joining me to explain how that works is Lalith Suresh, CEO of Feldera, the company built on top of DBSP. We start with the problem itself, then trace how a group of VMware researchers arrived at it from the unlikely direction of Kubernetes and network control planes. Lalith walks through Z-sets, the weighted data structure that turns database changes into something you can add and subtract, and the four DBSP operators - including one borrowed straight from digital signal processing - that let you compile any SQL program into an incremental version deterministically. Along the way we get into which operations need state and which don't, how the delta join falls out for free, building a standalone query engine with its own storage layer and Calcite front-end, backfills as the real Achilles heel, and how this all differs from stream processors like Kafka Streams and Flink.If you've ever fought with materialized views that won't refresh, watched a nightly batch job recompute three years of data to capture last night's changes, or you're just curious how one elegant bit of maths unifies batch and stream processing, Lalith has some genuinely satisfying answers. There's an MIT-licensed open source edition and a sandbox at try.feldera.com if you want to play along.---Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinFeldera: https://www.feldera.com/Feldera Sandbox (try it online): https://try.feldera.com/Feldera on GitHub (open source): https://github.com/feldera/felderaDBSP Rust crate: https://crates.io/crates/dbspDBSP Paper - "Automatic Incremental View Maintenance for Rich Query Languages" (VLDB 2023 Best Paper): https://arxiv.org/abs/2203.16684Mihai Budiu - "Streaming Queries Without Compromise" (Current 2024): https://www.youtube.com/watch?v=cn1Yaxwl6x8Mihai Budiu - DBSP talk at CMU Database Group: https://db.cs.cmu.edu/events/dbsp-incremental-computation-on-streams-and-its-applications-to-databases/Differential Dataflow: https://github.com/TimelyDataflow/differential-dataflowApache Calcite (Feldera's SQL front-end): https://calcite.apache.org/Kafka Streams: https://kafka.apache.org/documentation/streams/Apache Flink: https://flink.apache.org/ksqlDB: https://ksqldb.io/Apache Spark: https://spark.apache.org/Snowflake: https://www.snowflake.com/Databricks: https://www.databricks.com/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

En el episodio de hoy, Esteban Martínez, cofundador y director de operaciones de Green Loop, y Cibele Verasto, directora sénior de espacios de trabajo en Databricks, analizan el panorama cambiante de la gestión de instalaciones en América Latina, centrándose en la sostenibilidad, el bienestar de los empleados y las estrategias innovadoras para adaptarse a los retos de la movilidad urbana y a los modelos de trabajo híbridos. Este episodio está patrocinado por SiteMap®, con tecnología de GPRS. Para más información, visita sitemap.com/ifma Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

Welcome to Omni Talk's Retail Daily Minute, sponsored by Duvo and Mirakl.In today's Retail Daily Minute, Omni Talk's Chris Walton discusses:Amazon commits $1 billion to a new Forward Deployed Engineering unit, embedding AWS engineers directly inside client businesses to compress AI deployment timelines from months to days.Albertsons works to fully scale an AI-powered merchandising intelligence platform, built with Databricks, across its grocery operations by the end of 2026.New Jersey passes the Fair Price Protection Act, banning surveillance pricing and pausing electronic shelf label rollouts in grocery stores statewide.The Retail Daily Minute has been rocketing up the Feedspot charts, so stay informed with Omni Talk's Retail Daily Minute, your source for the latest and most important retail insights.

Unlocking billions in cloud marketplace revenue. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ This powerful panel discussion featuring leaders from Google, Tackle, and dbt Labs dives deep into the explosive growth of cloud marketplaces and the radical shift toward AI-driven go-to-market strategies. With hyperscaler backlogs nearing half a trillion dollars, the conversation unpacks how top-tier organizations are transforming their compensation models, aligning executive buy-in, and navigating the complexities of co-selling to capture committed customer budgets. From the rise of AI agents acting as metered SaaS to the essential operational investments required to scale marketplace revenue from 10% to over 50%, this session provides an actionable roadmap for software companies ready to dominate the 2026 partner ecosystem. https://youtu.be/LSj49f5FEII Key Takeaways Hyperscaler backlog commitments represent a massive, nearly half-trillion-dollar addressable market that completely changes the budgeting conversation. Successful marketplace selling requires complete executive alignment, right down to the CFO, and strategic adjustments like spiffing sales teams for marketplace transactions. The AI category is experiencing staggering 18x year-over-year growth, forcing companies to pivot toward an “agent-first” go-to-market model. Shifting from traditional channels to cloud go-to-market demands a multi-year, intentional investment in operations, people, and technology. System integrators are evolving into software companies as they build orchestration agents to manage fragmented, end-to-end workflows. Leveraging cloud commitments bypasses standard 12-15 month budget cycles, allowing for significantly faster deal closures and larger initial lands. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags: Google Cloud Marketplace, hyperscaler backlog, cloud commitments, co-selling strategies, AI agents, metered SaaS, product-led growth, rev ops, B2B sales transformation, ecosystem shift, channel strategy, system integrators, Deal registration, private offer APIs, digital transformation, software procurement. Transcript: Insight to Revenue- The State of Cloud GTM [00:00:00] Dai Vu: These are all things everyone has to do to get to that first five to 10 deals, and then 10, 20, 30% of your business through Marketplace. [00:00:09] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:21] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host, and each week I sit down with leaders at the intersection of technology. Partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:43] Vince Menzione: It is the strategy because being in the room changes [00:00:46] John Janke: everything. Let’s start. [00:00:52] Vince Menzione: And we have an incredible session. The way that we wanted today to, to, to start the day up was like, let’s talk about what’s happening right now and let’s get three leaders in this space to come up and talk about the world and how it’s a rapidly evolving. So I want to invite to the stage dvu from Google is a great friend of Ultimate Partner. [00:01:14] Vince Menzione: Are you guys ready? Are you guys micd up already? Okay, good. Good. John Yanke, the CEO and Founder of Tackle, and Sean Todo, who is an incredible leader with DBT, but also an old friend of mine. We worked together on Microsoft Days. Good to see you gentlemen. Thanks Sean. Great to have you with us. [00:01:37] John Janke: They stuck me on the side ’cause they said I’d block the screen if I sat in the middle. [00:01:41] Shawn Toldo: You still block it a little bit. [00:01:42] John Janke: And that picture’s from like 1985. I, I, we do have to get that. I had way darker hair. It was, uh, 10 year, 10 years at a startup. Makes you turn white. [00:01:52] Shawn Toldo: Mine’s the exact same right now. So it’s all good. [00:01:55] Shawn Toldo: Mine’s AI generated. Yeah. [00:01:57] Vince Menzione: Well, you know, guys, I just took it all off at that point, you know, it’s like good. Yeah, but you lose enough of it. You pull it out over the years. Yeah. So, uh, some really exciting times. Uh, you, we gotta spend some time at you at our breakfast. That’s right. A couple weeks ago. [00:02:13] Dai Vu: A lot of folks here, too. [00:02:14] Vince Menzione: A lot of folks that are here were at that breakfast, and I thought we’d spend a few moments with you talking about all the exciting things that have been happening at, at Google. I mean the, yeah, the businesses just to, first of all, the numbers were house. Outstanding. Congratulations. [00:02:28] Dai Vu: That’s right. [00:02:28] Vince Menzione: Yep. [00:02:28] Vince Menzione: Really, some really great numbers. Commitments are off the charts. [00:02:32] Dai Vu: Yes. [00:02:32] Vince Menzione: Crazy off the charts. [00:02:33] Dai Vu: Yes. [00:02:34] Vince Menzione: Yes. Uh, and then there’s a lot happening in this little world called ai, which makes a ton of sense. Yep. I was critical about Google in the beginning because you had all the assets, but Microsoft leaned in first. [00:02:45] Vince Menzione: Uh, but now it’s like things have evolved, uh, quite a bit since those first days. Absolutely. In, in November of 2022. So, uh, take us through a little bit. Let’s, let’s go through [00:02:56] Dai Vu: it. Yeah. I could talk for quite a bit of time because obviously we came out next, yeah. At the end of April, and then we had our earnings announced, but shortly thereafter. [00:03:03] Dai Vu: But, but real quick on next, uh, for folks who attended, uh, you know, the way they framed, uh, the discussion was they showed this AI integrated stack, and that’s how they frame the keynote because we position ourselves as being the only vendor that provides this. Fully integrated stack from custom silicon all the way to the apps and agents. [00:03:23] Dai Vu: And a lot of the announcements were, were focused in those areas. Um, uh, I won’t go through the, the long list, but I think the big ones coming out of next were, uh, certainly the eighth generation TPU we announced, so we actually split this into two specialized chips for training and inference. Uh, so that’s, uh, that was a big piece. [00:03:41] Dai Vu: Uh, but the big one that we announced was this, uh, Gemini Enterprise. Uh, agent platform. So think of it as the comprehensive platform for companies to basically build scale, govern and optimize their agents. And of course, once they have that, they can bring that into, uh, what we call a Gen Gemini enterprise app, which is really the front door for AI for. [00:04:03] Dai Vu: All customers and all employees to manage a mix of agents, um, as part of their daily workflow. And, uh, and a big part of it is, you know, certainly they’ll have some custom agents, but we think a lot of the agents will come from the ecosystem. And obviously there was a big announcement around what we’re doing there. [00:04:21] Dai Vu: Um, and in fact, one of the things that’s interesting is this shows the evolution of, of marketplace in our, in our partnership, which is we’ve taken a lot of the marketplace experience. And brought it into Gemini exp uh, Gemini Enterprise app, right? So search, discovery, uh, the ability to invoke agents, uh, in context. [00:04:39] Dai Vu: I think that’s gonna be very powerful as we think about the evolution, uh, of, of go to market. And then the last thing maybe I’ll highlight is this, um, is. 750 million, uh, investment fund that we’re gonna drive with the broad partnership. So this cuts across all partner types, global system integrators, uh, uh, you know, AI, pure plays, uh, ISVs, uh, the big management consultants as well, uh, because we recognize that partners are gonna be critical to drive business transformation with our end customers. [00:05:08] Dai Vu: So we’re investing around things like. Technical enablement, access to our product teams, access to our FDE for deployment engineers, and then a lot of incentives to drive usage and deployment. So, um, so a lot of, a lot of activity and obviously the ecosystem’s gonna be very critical for us to drive that impact’s. [00:05:25] Dai Vu: Fine. And the last thing, I know we’ve going on and on fine, but the last thing I’ll just mention is just on the earnings announcement, uh, Vince touched on the backlog, so people have been tracking Yeah. Two quarters ago. We were 155 billion on the backlog, and then a quarter later we were 240 billion. And then in the last quarter, just recently, 462 billion. [00:05:46] Dai Vu: So obviously that’s a, a massive signal of customer intent, but more importantly, it’s a, it’s, it’s a addressable market for this ecosystem to go after as well. [00:05:54] Vince Menzione: Yeah. Almost a half a trillion dollars. Yes. In commitment. So a lot, a lot of reason why we should be on the marketplace. [00:06:01] Dai Vu: Absolutely. Absolutely. [00:06:02] Vince Menzione: Um, each of these gentlemen have some things to talk about as well, about their companies and the exciting things that have been happening. [00:06:08] Vince Menzione: I’m gonna start, John, I’m gonna start with you because Tackle has, has transformed quite a bit since the last time you were on stage with us. I thought maybe introduce the company. Take us through the transformation and then we’re gonna do the same thing with Sean with his organization. [00:06:21] John Janke: Yeah. Thanks. Uh, thanks Vince. [00:06:23] John Janke: Great to see everybody. Uh, John Yanke, GM of Tackle at App Direct. So the big news there is Tackle was acquired in Q4 by a company called App Direct, and I think the why behind this app, direct Powers, marketplaces, they run 400 marketplaces around the world for telcos, for ISVs, for system integrators, channel partners. [00:06:42] John Janke: And we were talk like, when you build a marketplace and diagnose this, stocking the shelves is actually really hard. Uh, and we were talking to them about how could we connect the dots between the hyperscaler marketplaces, the iscs we support, and these additional routes to market. Uh, and that became more strategic and we ended up joining forces in December. [00:07:00] John Janke: And since then, the other part that’s really hard when you build a marketplace is how do you generate demand? Uh, so four weeks ago we acquired a company called Partner Stack. And Partner Stack does affiliate content. They have an affiliate content platform that allows you to connect with 150,000 content providers to be able to start to tell your story to drive leads to. [00:07:23] John Janke: Marketplace. So we think there is a tremendous opportunity to continue. We’re in the earliest days. I think the, you know, Jay, I was with Jay at Channel Partners a few weeks ago and he is like, we under called it, he didn’t say this on stage yesterday, but he is like, uh, the 82% growth. He’s like, we totally under called it. [00:07:40] John Janke: Uh, and I think just listening to dies commit level increase mm-hmm. Reinforces the fact that we’ve under called it. But I also think we’re at this tipping point in the market where all of the new capabilities coming out, we have to all rethink our better together stories. So I think the challenge to all partner leaders, it’s like, how do we. [00:07:58] John Janke: Figure that out. So it’s, it’s a, it’s a fun time. As we transform the way we worked. We wrote the first helping people kind of list, launch and sell through the marketplaces. And now to be able to take that to the next level to hopefully unlock the next a hundred billion of marketplace throughput. [00:08:13] Vince Menzione: And are we at a hundred billion? [00:08:15] Vince Menzione: ’cause that was the number, right? [00:08:16] John Janke: I mean that’s, that’s, that’s the number that’s talked about. I mean, we’re seeing the data signals we see, I mean, we will process 20 billion plus this year. Uh, and that number’s growing faster than Jay’s stated number. So I think we’re excited to see where this year lands. [00:08:30] Vince Menzione: We’ve come a long way from three years ago and we all got on stage and talked about marketplaces together. Right. It’s been, it’s been amazing. And then Sean, let’s talk about DBT. You’ve had some excitement. I know some things maybe we can’t even talk about yet on stage. [00:08:43] Shawn Toldo: Uh, yeah, go ahead. [00:08:44] Vince Menzione: No, I was saying I, I could, I’ll pre-announce things, but No, I’m just, uh, tell, tell us about DBT for those who don’t know in the room, sure. [00:08:49] Vince Menzione: Mean Yeah, that might help. [00:08:51] Shawn Toldo: So, uh, Sean Todo, I lead the partner business at DBT. I’ve been here about 18 months. Um, DBT really started as an open source tool. That help data engineers be successful in SQL transformation with cloud data warehouses? Right. And so back even to the Redshift days now into what I would call more the BigQuery, snowflake, Databricks fabric led days, um, DBT is the tool of choice amongst the data engineering community in terms of how they wanna drive SQL transformation. [00:09:21] Shawn Toldo: And so more recently, we kind of jumped into this kind of paid world. Which is why we needed to bring in additional experience leadership around go to market product, sales, et cetera. And so when I walked in the door, one of the things I noticed really quickly was we were running on AWS, which was great. [00:09:40] Shawn Toldo: We were doing some AWS marketplace stuff. We were running on Azure in Europe only. And one of my first strategies was we have to be everywhere, right customer. We have to meet customers where they are. And so we, uh, made some major investments to be on Google Cloud platform to then be able to really take advantage of marketplace, to then really be able to take advantage of the co-sell opportunities that exist in the field from a day, day-to-day AI perspective with Google. [00:10:07] Shawn Toldo: And it has been a hell of a ride. We launched on, uh, Google Marketplace in July of last year. We went to Google next and we were Google Partner of the Year. Wow. For data and analytics in a very rapid way. We’re now in three, uh, data centers around the, the world. So we’re here in the us, we’re in Frankfurt, we’re in uh, uh, UK as well. [00:10:30] Shawn Toldo: And so it’s been a pleasure to work with D and the broader team. Because the enablement we’ve had and the support we’ve had from that group has really helped our growth be up and to the right. The data point I would give is that when I walked in the door, we were 10% of our business from an A RR perspective was transacting through marketplace. [00:10:48] Shawn Toldo: Last quarter we cracked 40%. Whoa. We will be at north of 50, uh, next quarter. [00:10:53] Dai Vu: Wow. [00:10:54] Shawn Toldo: The other piece that Vince was talking about is we’re getting ready to merge with a company called Five Tran. And so there will be a new company name at some point down the road. Uh, pay attention on June 1st for a public announcement around that merger. [00:11:06] Shawn Toldo: Uh, but we’re really looking forward to what we’re gonna be able to do with folks like DI and the Google team as well as others in the ecosystem. Um, ’cause I think in this data world that we’ve played for so long. This trusted foundational element of data and what it’s gonna mean to context in the AI world. [00:11:23] Shawn Toldo: We’re in a very interesting place to really continue our growth rate at a high level. [00:11:28] John Janke: Yeah, that maybe just a comment something there. Start there. I think we, we used to hear people say we wanted to be strategic with cloud, go to market and get to say 10 or 20% of revenue. I think this like 40, 50%. Yeah. Th that’s where people are setting the bar these days. [00:11:43] John Janke: Yeah. So the numbers are getting really crazy. Yeah. Uh, and people are showing up and being like, I have to go big. Mm-hmm. So a huge change over the last few years. [00:11:52] Vince Menzione: Yep. What’s the experience you’re seeing as well? I mean, it, it was a huge amount of buzz at next. [00:11:57] Dai Vu: Yeah. I mean, so interestingly, um, you know, typically when, when people get started on the, on the marketplace in Cosal journey, I always try to caution them and say, this is, uh, this is like a multi-year. [00:12:07] Dai Vu: Yeah. Uh, process. You have to be very intentional. You have to invest. It’s not gonna be a thing where you just list and, and, and, and, and, and sort of this channel opens up. So in some ways, Sean is describing an acceleration that is not common, right? Uh, so they’ve done, we’ve done some amazing things together and we hope to keep that acceleration going. [00:12:22] Vince Menzione: What does that require, by the way? Is it engineering resource? I mean, there’s, I talk about executive commitment and maniacal focus. Yeah. But it’s all those things, right? [00:12:29] Shawn Toldo: Well, all of it. But we went to a QBR in Austin, and I put up a slide and I said, we have to do this. And everybody in our ETE agreed. So when you have a chief financial officer that’s bought into the partner business. [00:12:43] Shawn Toldo: Yeah. And I guess qualifying coming into this role at this company, I qualified the C-level staff. Uh, like are they really serious about partner or not? And it’s one of the reasons I took the role. So I think executive commitment was one thing. I think the second thing is we were really well supported, um, by the Google team across the board, right? [00:13:02] Shawn Toldo: Yeah. So folks, Indy’s team that we would work with regularly on, these are the things you need to do to have an effective marketplace offering. Here’s what you need to do operationally with folks like John and team and others that are in the market, right? That helped us a ton to be able to scale. And then the other thing that we did is we changed comp. [00:13:20] Shawn Toldo: So from our VP of sales levels down, we have a 5% kicker for everything that goes through marketplace. [00:13:26] Vince Menzione: Hear [00:13:26] Shawn Toldo: that everyone. So as soon as we incented the sales team, I love that, right? We, we created the foundation on the partner side, but then from top down on the sales side, they were all in. And as a result of that, the question would become, okay, which marketplace stage two sales cycle are we gonna go use? [00:13:42] Vince Menzione: Yeah. [00:13:43] Shawn Toldo: Who’s the right partner to go partner with? And then my team is reaching out to make sure that co-sell connection happens. [00:13:48] Vince Menzione: That is such a best practice, Sean, to, because there is, as a seller out in the field and we talk about, you talk to John, talks about rev ops all the time. But getting rev ops eng getting the field engaged in the right way. [00:14:01] Vince Menzione: ’cause it feels like it’s more work for them. ’cause they have to think, they have to have more conversations with their customer about their cloud commitments and things like that. Mm-hmm. And then getting them incentive to do the right things. The right behavior. [00:14:12] John Janke: Yeah. It’s a strategy process. People, technology problem. [00:14:17] John Janke: Yeah. It’s not just some flip API automation, go list something if you don’t like that top down view. I think the other thing. Like there’s a, there’s a theme in startups where VCs fund second time founders. I think Sean and team have done this before and they took a lot of learnings over the years and reapplied them, which I think helps them go faster. [00:14:36] John Janke: It’s like that second time. Yeah. Second time cloud go to market Founder theme. [00:14:41] Vince Menzione: Yeah. Yeah. Um, so we could talk about the platform and all the changes there on the. The, the commitments and everything. Mm-hmm. Uh, what separates ISPs generating real incremental revenue on your, in your marketplace? What, what do you see? [00:14:58] Dai Vu: Yeah, so I mean, I, I think there are a couple things. Number one is, uh, the, the foundation has to be, uh, this better together story, uh, with Google Cloud. Um, so this idea that what, you know, what do you bring, what does the Google platform bring and how does that drive impact with customers? And I think this is the reason why Sean and DBT Labs has been very effective. [00:15:16] Dai Vu: ’cause our field recognized they, they can recognize that better together story and communicate it to their customers. So I think that’s the foundation. For everything. Right. And I think as you get started, uh, you know, we do tell partners that they probably need to lean in a little bit, uh, in terms of focus, uh, you know, pick a vertical, a customer segment, um, you know, a geography where they’re particularly strong and, you know, get that momentum going. [00:15:39] Dai Vu: And once you do that, the field knows about it and starts to pull you into deals. Um, so I think that’s the other big opportunity. And then the other thing I just mentioned. Which, uh, the panel already touched on, which is be very intentional around all the things you need to do to invest. Whether it’s like, uh, you know, the business functional alignment, uh, the policies around like, uh, pricing and, and comp, uh, making sure you have the operational capabilities. [00:16:02] Dai Vu: These are all things everyone has to do to get to that. First five to 10 deals, and then 10, 20, 30% of your business through marketplace. And not to, not to top you Sean, but our very top partners are driving 80 to 90% of their business on marketplace. And in fact, some of these partners are actually only marketplace first, uh, uh, because they started out that way. [00:16:21] Dai Vu: Obviously it’s the bigger challenge if you have an existing channel, you’re trying to shift that. But, uh, the aspiration to be more marketplace focus, uh, is up there. [00:16:28] Shawn Toldo: So I just set a new goal for the business plan for me. So that’s exciting. I love it. Looking forward to seeing you in six months on that. [00:16:35] Shawn Toldo: It’s good. [00:16:36] Vince Menzione: I love [00:16:37] Dai Vu: it. Work together on that. [00:16:38] Vince Menzione: Well, di I’m just gonna add, add this because I, I got to see operationally with some of the things you do. Mm-hmm. You, you have an overlay organization. [00:16:45] Dai Vu: Yes. Yes. [00:16:46] Vince Menzione: And so you put accelerants in place within your own organization Yeah. To drive the ISVs into the, into the lines of business. [00:16:54] Vince Menzione: Right. You have, you, you do some of that to accelerate. [00:16:57] Dai Vu: Yeah, I mean, I think, I think this is somewhat unique. I don’t, I don’t wanna speak to the other [00:17:00] Shawn Toldo: hyperscalers, [00:17:01] Dai Vu: but we do have, um, uh, you gotta know the field roles, right? [00:17:04] Shawn Toldo: Yeah. So [00:17:04] Dai Vu: obviously at Google Cloud in the regions, we have, uh, ISV sales specialists who are effectively quoted on marketplace revenue, right? [00:17:12] Dai Vu: So they’re a hundred percent focused on that. And, uh, in addition to that, uh, we also have these, uh, co-sell teams, partner teams where, you know, opportunistically if there’s an opportunity, uh, in a, in a, in a particular area. This team is responsible for connecting the regional sales leadership, uh, the regional, uh, sales teams with, with the partner on the opportunity. [00:17:32] Dai Vu: So there’s a lot of things we’re doing to sort of accelerate that. And of course, the foundation for all this is, you know, our, our, you know, registering deals. And as you definitely get started on that, it’s very important to be very mindful around when you register deals. Uh, be very clear around what the ask and the engagement is with the field reps. [00:17:51] Dai Vu: But once you have that going and get the right rhythm, it becomes sort of a natural way to sort of register all your deals and get that engagement. And then, um, and then maybe the last thing I would say is it isn’t always the sales specialists. It’s, you know, the FSR, our field sales rep as well as our customer engineers are also very motivated. [00:18:08] Dai Vu: To work, uh, with, uh, with our partners because they know that this, you know, whether it be solution completeness or it’s part of a bigger workload or helps unlock greenfield opportunity, they really are motivated to engage with the partners. [00:18:21] Vince Menzione: Nice. [00:18:22] Shawn Toldo: Yeah. I’ll just add, I’ll just add to that statement too. I think, um, it’s one thing to have a story as it relates to. [00:18:30] Shawn Toldo: Google Cloud and what you do with marketplace. It’s another thing to have a story in terms of how you impact data and analytics in our world. And there’s a set of specialist sellers inside of Google mm-hmm. That really care about us because we drive a lot faster consumption of big query. And our ability to tell that story across the world effectively has really created a pull now. [00:18:54] Shawn Toldo: And so I, I would say it’s almost, you know, back to, you know, being 12 years at Microsoft and watching kind of that. Phase and how that went. As we went to the cloud and we picked specialty areas, um, Google is doing that as well and they’re doing it extremely fast in a very, very productive way with partners. [00:19:12] Shawn Toldo: And so, you know, I’ll get comments from like Levi who runs west in north region for us, and he’s a, he was at Google next and he was like, I, I gotta, I, I just gotta go to bed. I’m tired. Like we wore him out over two days with their sales team and gave him a host of follow ups and actions related to specific sales areas as well as specific accounts. [00:19:34] Shawn Toldo: And I think that’s the other thing that, um, Google’s done a good job of, but we’ve pushed and we’ve had to work really hard to earn that seat at the table. To help make those people successful from a comp perspective inside of Google as well. [00:19:45] John Janke: Yeah, and this is a huge failure zone for partners with the clouds because they think enablement’s a one and done thing. [00:19:51] John Janke: Like I did a training for the field and I told them the better together story. That doesn’t work. Like you have to literally. Have consistency around this message every day. Oftentimes you need experts who can partner with your reps to give them the confidence. ’cause they may be able to ask the first line question, but someone asks a follow up and they fold up ’cause they know your product. [00:20:11] John Janke: That’s right. They don’s don’t understand all of the nuances of Google and the clouds and the questions that may come back. But if you do that well, it is a huge unlock. [00:20:20] Vince Menzione: Talk about the coaching you provided on the tackle side of that as well and kind of helping. Through this maturity model? [00:20:26] John Janke: Yeah. I mean we, we, over the years, I mean we started as a pure SaaS company and over the years our customers would consistently ask us for more help and we would struggle to figure out how to do that, and we had to invest in services and we actually acquired a company. [00:20:42] John Janke: Five years ago now, that was the foundation. Aaron Feiger, who’s in the room. The core consulting was the foundation of our services business. And that continues to evolve with us. And you know, we see customers at scale saying, I wanna operate my cloud, go-to market really consistently, and I want you to do all the backend operations so my teams can be outselling our products, selling the better together value with Google and others, and not have to figure out how to run the machinery. [00:21:09] John Janke: So we’ve invested a lot there. We have services around strategy, like how to help people think about their business strategy and translate it into a better together story and able to get executive buy-in. And then we have coaching, which is really a phone, a friend, because I think these things get complicated. [00:21:24] John Janke: And I had a customer who was doing the largest deal in their company history. It was the end of the quarter and it was Friday, and they’re like, this is going to be the most complex transaction we’ve ever done and we have no idea how to do it. Our team gets on the phone with them, they work through, what are you selling? [00:21:40] John Janke: How are you selling it? Is your listing set up the right way? Can we actually create all the offers? In a way you have confidence to execute. ’cause those are failure modes. You try to build a cloud, go to market business, and you mess up the largest deal in the company. On the last day of the quarter, uh, that’s something you can’t recover from. [00:21:55] John Janke: So we try to really wrap support around our customers to help them have the confidence to grow. [00:22:02] Vince Menzione: Di you’ve seen tremendous growth in marketplace. Mm-hmm. We don’t publish the numbers specifically. Yeah. We kind of try to figure it out on the back end, but [00:22:09] Dai Vu: Yep. [00:22:09] Vince Menzione: I know you’re accelerated. Your, your marketplace numbers are astounding. [00:22:13] Dai Vu: Yes. I can share some numbers, if that’s [00:22:15] Vince Menzione: okay. Please. Yeah, let’s go. [00:22:18] Dai Vu: So, um. I would say that for a few years now, we’ve been talking about growth. So we’ve been consistently, uh, you know, north of a hundred percent year over year growth. Uh, for the last few years we’ve been processing, uh, what I say, uh, billions of dollars, uh, annually and, uh, uh, millions of transactions. [00:22:36] Dai Vu: And again, that’s for a few years now. Now for 24 to 25, that full year we also doubled. Wow. Uh, which is, uh, which is amazing when you think about the scale in which we operate. But more importantly, if you look at specific category areas, right? So, you know, historically, marketplace has always cater to, uh, those solution pillars that are tied to cloud migrations, like, uh, like security and data and analytics. [00:22:59] Dai Vu: And those continue to be very strong areas for us. But the biggest growth area is, uh, is in the areas of business app. So obviously, you know, the, the ServiceNow workday, uh, Salesforce of the world, as well as the AI category. So one number that we threw out next was 18 x. Year over year growth for the AI category. [00:23:17] Dai Vu: Wow. So in one year now, a lot of it is models, right? So foundational models with our, with our ecosystem. But a lot of that is around agents. So this whole agent go to market model is gonna be, continue to grow and it’s gonna be a huge focus area for, for the coming years. [00:23:32] Vince Menzione: Fantastic. Yeah. Fantastic growth. [00:23:34] Shawn Toldo: Yeah, and, and I’ll add, Diane and I talked about this at Google next. This is a. Very complex thing for DBT, where today we sell seats. [00:23:42] Vince Menzione: Mm-hmm. Yeah. [00:23:43] Shawn Toldo: To data engineers. [00:23:44] Yeah. [00:23:44] Shawn Toldo: And now we have all these agentic things that are hitting our engine. And di and I are talking and we’re like, okay, so how does this work in an ag agentic marketplace? [00:23:54] Shawn Toldo: Yeah. Kind of a scenario. And what should we build? Where should we play it? ’cause we’re gonna spin the meter in a different way, so to speak. [00:24:01] Dai Vu: Yep. [00:24:01] Shawn Toldo: And so candidly, we got stuff to figure out related to that. Um, I think what’s been fascinating for DBT is our partner ecosystem changed overnight. So now it’s like I talked to x.ai on Monday. [00:24:15] Shawn Toldo: Mm-hmm. We got time with open AI on Thursday and we have a call with Anthropic and our, uh, CEO and co-founder and uh, chief Product Officer next week. [00:24:26] Vince Menzione: Mm. [00:24:27] Shawn Toldo: We don’t have anybody managing those partners. [00:24:29] Vince Menzione: Right. [00:24:30] Shawn Toldo: Today our focus is on managing the large, uh, hyperscalers plus Snowflake and, uh, Databricks. [00:24:36] Vince Menzione: Mm-hmm. [00:24:36] Shawn Toldo: And then the SI ecosystem and some tech partners. So we’re having to like, to your point on Agile yesterday. Yeah. Mm-hmm. Like we’re having to change our strategy, operating model and organizational model to support that. And candidly, we don’t have all the answers yet, so we have a lot of things to figure out fast, which is a little bit scary. [00:24:54] Shawn Toldo: And challenging, but it’s also a huge opportunity we have to kind of embrace and get into. Yeah. [00:24:59] Vince Menzione: And they’re figuring out as well. ’cause they’re, they’re new to partnering as well. Yeah. As organizations [00:25:03] John Janke: and these AI agents. I think to demystify for a lot of people, and what Sean said is totally right. [00:25:08] John Janke: They’re disrupting everyone’s business model. But in reality from a marketplace standpoint, they’re metered SaaS. This is a thing that’s existed for a long time. Yeah. They look like product-led growth products. There is a lot of patterns around how product-led growth products work in marketplace. Mm-hmm. [00:25:24] John Janke: But you have to bring your business strategy, your product and pricing strategy to those two categories. Metered SaaS and product-led growth. Put that all together to get cross-functional alignment. So we are seeing like. A lot of people get tripped up here and it really does go back to more of the company strategy, product strategy questions, and a lot of partner leaders are not in the room for those conversations. [00:25:48] John Janke: So I think at, at this point in time, as you see big pivots with the partners to go all in on agents, you have to go elevate. Those discussions to be like, what is our plan here? ’cause I, I mean, pricing and packaging will be the thing that trips almost everyone up. [00:26:02] Dai Vu: If I could, if I just build on what John John mentioned, um, so I do agree. [00:26:06] Dai Vu: P it looks a lot like POG, but, uh, but the difference I think is POG has. More historically been in like the data and developer space, now it’s like the general business user, right? So this idea that you want a business user to be able to search and discover, um, agents that could actually be part of their like everyday workflow is going to be very critical. [00:26:26] Dai Vu: And uh, you know, I do think that when we think about the ecosystem building agents. Uh, you know, a lot of the ISV partners aren’t necessarily gonna own end-to-end workflows, right? They’ll, they’ll have a very specific, uh, domain and scope area, but you have to enable yourself to be orchestrated and managed by, you know, orchestration agents or, or, or meta agents that are gonna span end, end workflows. [00:26:49] Dai Vu: And sometimes that includes system integrators and, and others who can stitch that, that automation. So I think, I think that’s, that’s one piece of it. But the other area that I think is gonna be different is, um. There’s going to be a lot of agents. I mean, literally you’re gonna have a very fragmented set of, uh, uh, of players, right? [00:27:07] Dai Vu: It’s not just gonna be the incumbents, it’s gonna be a lot of disruptors and, and, and, and startups. And so the, uh, for the incumbents in the room, it is a mandate that you need to, to innovate because if you do not identify and go to like an agent first, go to market model. Uh, you’re gonna be, you know, disintermediated. [00:27:25] Dai Vu: Somebody’s gonna go build an agent that’s going to leverage you as a dumb database. Um, and they’re gonna own the workflow. So you have to, you have to push the, the, the, the limits here. And I think it’s creates a big opportunity for everyone in this room. [00:27:39] John Janke: I’m going off script. I’m curious. Let’s do it. I’m curious on your take on the system integrators. [00:27:44] John Janke: ’cause I think this, this puts like they’re all, a lot of them are creating agents for people and I think that’s turning them almost more into software companies than they’ve ever been. [00:27:53] Dai Vu: They are, and I think they’re, you know, obviously they’re being, uh, impacted from like, you know, typical like, you know, SOW you know, time and materials type type business models. [00:28:02] Dai Vu: But I do think they play a big role because a lot of the system integrators are bringing, um, you know, vertical and business process expertise. And, um, like I said, I said before, a lot of the ISVs are not gonna necessarily have big enough scope in their area to own end-to-end workflows. And that’s really the promise of agents, right? [00:28:20] Dai Vu: You really need. This cognitive, you know, reasoning, planning, executing across end to end workflows. And I think, you know, the system integrators are gonna bring that capability either, either through, you know, these custom, uh, orchestration or meta agents or if they’re able to productize that and bring that to a model, they can also sort of go through the marketplace model as well. [00:28:41] Dai Vu: So who knows is how it’s gonna evolve. But you know, we’ve always been talking about. Marketplace being a broader opportunity for all partner business models. And I think that will extend to not only, uh, you know, traditional sort of, uh, sell and services partners, but also some of these system integrators as well. [00:28:58] Shawn Toldo: If I could comment on that, please. Yeah. I, I was in London two weeks ago and we did an SI partner day. Mm-hmm. We had 25 sis in a room, probably about 50 people. We had no, um, hyperscalers or cloud data warehouse providers. And when we started talking about open data infrastructure. The role that they can play. [00:29:17] Vince Menzione: Mm-hmm. [00:29:18] Shawn Toldo: Cross platform in a cost efficient manner for customers and the advisory orientation of that. They all leaned in and we, we stopped talking and they started talking. [00:29:28] Vince Menzione: Right. [00:29:28] Shawn Toldo: So they’re all facing this kind of same problem, which is actually causing a little bit of a shift, I think, in how they think about, I’m a Databricks partner. [00:29:38] Shawn Toldo: Uh, you sure you wanna do that? [00:29:39] Vince Menzione: Yeah. [00:29:40] Shawn Toldo: So this, this whole thing that’s kind of evolved in the last six to 12 months, when you kind of pick one horse to ride, I, I would tell you be cautious about what that means. You may pick a horse to lead with mm-hmm. But you’re gonna have to flank yourself a bit in terms of other providers that can help you be successful with that, that that partner you’re gonna roll with. [00:30:00] Vince Menzione: So you’re suggesting data vendor agnostic. [00:30:04] Shawn Toldo: I’m suggesting you really have to think about your strategy. Yeah. Because I think the AI, AI disruption is gonna make you think about that strategy. [00:30:13] John Janke: Yeah, I mean there’s, someone mentioned anthropics First Partner Summit. I was not there, but I’ve heard from a bunch of people were there. [00:30:20] John Janke: You know, they had a hundred partners in the room. 95 of them were system integrators. Five were technology companies, the three Clouds, Databricks and Snowflake. Like if you just think about the, the one of the major disruptors in ai, ISVs, were not in the mix. So I, I think, are they trying to disrupt all of us? [00:30:40] John Janke: Uh, do they need us? And they haven’t figured out how to work with us. I, I think. It’s, it’s, [00:30:44] Vince Menzione: and I’ve heard they only have five people in their partner organization, so I just, it’s, [00:30:49] Shawn Toldo: it’s 11 now, but it’s 11, [00:30:51] Vince Menzione: so it was five [00:30:51] Shawn Toldo: last growing fast in the, in the new company I have 50. So like, to put it in perspective, they have to make some pretty big priority. [00:30:59] John Janke: Yeah. And everyone’s been there a hot second, [00:31:00] Vince Menzione: like, right, exactly. Yeah, they, well, we will talk about the learnings we’ve had over the years, getting to where they need to get to. It’s exciting times. We got a lot to talk about here. Um, I, you know, we have about 15 minutes. I I, I want to kind of gauge, ’cause we could talk, we, we have a few things we could talk about, I could ask about, but I want to see if there’s an, like, an interest in opening up to the room for questions. [00:31:25] Vince Menzione: ’cause I feel like we’ve got a very interesting group here. [00:31:28] Shawn Toldo: You got a hand here? [00:31:29] Vince Menzione: Uh, are there hands that wanna Yeah, there’s some people that wanna ask some questions. So Yeah. We have a mic? Yeah, [00:31:37] Dai Vu: we have [00:31:37] Shawn Toldo: a mic. We, [00:31:37] Vince Menzione: we [00:31:38] Shawn Toldo: got one here. [00:31:38] Vince Menzione: We got one here. One here. Thank you. Sorry we went off script, but [00:31:44] Shawn Toldo: that’s fine. [00:31:45] Vince Menzione: It’s fine. [00:31:45] Dai Vu: Off [00:31:45] Vince Menzione: script. Better is good. [00:31:46] Shawn Toldo: I’m sure you planted the questions outta anyway. It’s okay. We [00:31:48] Vince Menzione: did, we did. [00:31:55] Audience Guest: Okay. All Eva, Sean Lightner, quick question to your, uh, increase on the marketplace, and you said you spiff the salespeople by fifth percent. 5%. Mm-hmm. So, and that obviously drives a very large adoption of, uh, marketplace transactions. How are you accounting for the margin you’re losing on, uh, you know, going through the marketplace? [00:32:14] Audience Guest: And also have you done analysis? I’m sure you have, how much is, uh, shape shifting or shifting from existing versus incremental? [00:32:22] Shawn Toldo: Yeah, it’s a great question. Um, um, lemme make three points. Number one, the backlog statement makes the margin statement not matter. So do you wanna play in that space where a customer’s already bought or not? [00:32:36] Shawn Toldo: Yeah. Or do you wanna force a budget conversation that you have to drive on your own in a direct model? That to me, I think it was 484 4 62 [00:32:43] Dai Vu: 4 6 [00:32:44] Shawn Toldo: 2. [00:32:44] Vince Menzione: That’s new Tam available to you? [00:32:46] Shawn Toldo: Yeah. That, that’s just with one. Right. And we are, we are, uh, running on four marketplaces. So that just increases our tam and makes our, our sellers lives easier. [00:32:55] Shawn Toldo: So on that piece, yes, there’s an expense, but we believe it’s right for growth. So there’s a balance there. Um, I think the, and then the second part of your question again. Sorry, [00:33:05] Vince Menzione: shapeshift. [00:33:05] Shawn Toldo: Oh, shift. We, we actually don’t think we would’ve won the business. So if I go back to our Q4 and I can probably point to three or four deals that went, um, Google Marketplace, we would not have won those deals because we couldn’t have created the budget cycle and that quarter. [00:33:23] Shawn Toldo: To make it happen. Generally a budget cycle is gonna take anywhere from 12 to 15 months. Bingo. Because of the spend that was available to us, we were able to close it in that quarter, and we had the largest Q4 in company history. [00:33:35] Vince Menzione: That is such an important point. I’m sorry. [00:33:37] Dai Vu: Okay. [00:33:38] Vince Menzione: But I, I just wanna, that is such an important point of the budget cycle. [00:33:42] Dai Vu: Yeah. [00:33:43] Vince Menzione: Being a year to a year and a half versus being able to tap into a commitment that’s already been made. Yeah, so I just emphasize that [00:33:51] Dai Vu: I was, I was just gonna add real quick, even, even when we see sort of a, uh, a channel shift renewal, which is, you know, it’s on partner paper and it moves to marketplace as part of the renewals, we do consistently see that the, uh, renewal rates on marketplace and the incremental a CB on the expansion and new opportunities tend to be better when it’s on the platform marketplace than than offline. [00:34:12] Dai Vu: And that’s why partners choose to continue to drive renewals on marketplace at a reduced to rev share. But uh, because they see that that growth, [00:34:20] John Janke: we, we, sorry. [00:34:22] Shawn Toldo: We see that as well. Yeah. And I would also make the statement on our land business, when we go through marketplace, we are two x higher across marketplaces. [00:34:30] Shawn Toldo: We’re three x higher with them. [00:34:32] John Janke: Yeah, I think separate new from renewals and then instrument deeply. [00:34:37] Shawn Toldo: Yeah, [00:34:38] John Janke: go proactively talk to your CFO and your head of rev ops to understand their mindset. Because I was with a billion dollar seller a couple weeks ago, their CFO still creates friction in the process, even though they’re selling a billion dollars through these channels. [00:34:52] John Janke: But when they broke it down, their deals are three times bigger. They do them faster. They use more components of the product, which I thought was a really cool one. So customers who buy this platform, many component platforms through a marketplace, end up using six components of the product. Versus a normal land customer who uses two increases gross in net retention. [00:35:12] John Janke: So you have to get to the point where you have the data and you can tell that story real really clearly to your finance team to get support ’cause that they will trip you up if you don’t get them on board. [00:35:23] Vince Menzione: And you’re saying there’s friction in that company. I’m just kind of curious ’cause a billion dollar company. [00:35:27] John Janke: There’s a billion dollar marketplace seller [00:35:29] Vince Menzione: market marketplace company. That’s what I meant. Yeah. But, but the fact that this, their CFO friction, like, is it, is it because they’re not doing a good enough job or? [00:35:37] John Janke: Uh, in, of educating, I, the root of the question is from this person is, would they win without it? [00:35:44] Vince Menzione: Yeah. [00:35:45] Shawn Toldo: Oh, and is it worth the three points? [00:35:46] John Janke: Right. It’s, it is And, and I think some pe like to me, it’s the cheapest channel in the world. Yeah. Like with committed budget and people to support you winning. Like the, that formula, the math is so simple. [00:35:57] Shawn Toldo: Yeah. For, for a company of our size to go to like the classic resell ecosystem, I gotta walk in with 30 points. [00:36:02] John Janke: Yeah. [00:36:03] Vince Menzione: Yeah. [00:36:03] Shawn Toldo: It, it’s an illogical conversation. Outside of public sector and growth, you know, geos around the world. And so I, I’ve been lucky to have a CFO that I haven’t had that challenge with, at least at DBTI should say. [00:36:19] Vince Menzione: Really great insights. I think we have, we have another hand up here. [00:36:28] Audience Guest: Yeah. Thanks Susan. The question is for Dai. Uh, my name is Latif Hamani. I’m the founder of Partner System ai. Um, so what we’ve done is we’ve built a, a co-sell AI agent mm-hmm. That your partners can use to Yeah. Reduce all the friction in the co-sell with you. Uh, the questions that I have is, I guess I should back up, so XAWS Madison with a very large alliances, and then I worked, went on the other side. [00:36:55] Audience Guest: For software companies, and even though I had an operational team, I was spending two to three hours on on the keyboard, right? Mm-hmm. Deal registration, emails that can’t be automated, et cetera. So the question that I have for you is, I’d love for you to validate that. You know, unless you are one of the big companies, one of the big enterprises, if you go to the lower end of the enterprise or the mid market, uh, would you validate that there is a challenge? [00:37:20] Audience Guest: There’s a lot of friction for a smaller company. Mm-hmm. Uh, ’cause these marketplaces are complex. Yeah. The cosell is complex. Uh, that there’s an opportunity to really break down that friction with some automation and ai. [00:37:33] Dai Vu: Yeah, absolutely. So, um, we have already been, uh, part of the journey to remove some of the, uh, the friction as part of that selling and purchasing journey. [00:37:43] Dai Vu: Uh. We’re not quite there yet. But, uh, we’ve done things like we have, uh, you know, private offer APIs. We, uh, we have co-sell, uh, registration automation. Um, you know, we have tools like, uh, propensity to buy, tooling to help, uh, partners do, uh, more targeted efforts. Um, but the a i piece is still coming. Um, so I think, uh, the idea here is that we have launched a number of agents as part of our, um. [00:38:08] Dai Vu: Uh, part of our, uh, Google Cloud Partner network, partner hub. Uh, so these are, uh, agents that are gonna do a bunch of things to help partners as part of their workflow, but we’re gonna extend this to the marketplace and ISV area as well. Uh, so I think there’s a lot of opportunity. So, uh, I know there’s probably a lot of feedback in friction, uh, in, in certain parts. [00:38:29] Dai Vu: So we can, we can go tackle together. [00:38:32] Vince Menzione: Hey. There you go. There was a little [00:38:34] Dai Vu: plug [00:38:34] Shawn Toldo: there for tackle. Exactly. [00:38:37] Dai Vu: Uh, and I wanted, and just to be clear, I want to take a look at it from the end to end, uh, uh, flow, right? It shouldn’t just be just marketplace. It should be all the way from like, you know, top of the funnel, demand generation, all the way to like post transaction follow up. [00:38:51] Dai Vu: So we really need to take a look at, at the, the end, end flows and figure out a way we can remove some of that friction [00:38:56] Vince Menzione: three sense. [00:38:57] Dai Vu: Yeah. [00:38:59] Vince Menzione: Any more questions [00:39:00] Audience Guest: back here? Hey. Hey guys. This, this is a really good discussion. Uh, di this question’s primarily, uh, from, I’m interested in the hyperscaler response. [00:39:09] Audience Guest: Yep. Uh, but all of you, uh, can you talk about the patterns or say more about the patterns between. Um, the consumption of just platform capabilities versus industry workflows. Mm-hmm. And how industry where I, I mean, I, I, my sense is that industry workflows are becoming more [00:39:27] Dai Vu: Yeah. [00:39:28] Audience Guest: Uh, the easier thing for enterprises and SMBs to buy. [00:39:33] Audience Guest: Yeah. Especially SMBs, I think. Um, but say more about those patterns that you’re seeing develop and kind of what is. Uh, who are, where, where are those kind of, where is the demand being driven? Is it, is it, yeah. The search and discover in the marketplace, or is it being led by field sales of mm-hmm. Either GCP or partners? [00:39:55] Dai Vu: Yeah, so let me, I’ll mention a couple, a couple areas where, where it’s growing. So I think number one I mentioned before about some of these large horizontal business apps that we’re partnering with, right? Um, and, uh, and of course the fact that we’re, we’re, we’re transacting them through marketplace is, is a huge. [00:40:14] Dai Vu: Evolution from a few years ago. So who would’ve thought you would be buying like, you know, a hundred million dollars a CB deals, uh, through, through marketplace with like a Salesforce or a ServiceNow workday. But it’s happening now. And to be clear, all these. Horizontal business app. They’re not doing this in a very, you know, opportunistic, transactional way. [00:40:32] Dai Vu: They basically see marketplace and cloud go to market as a strategic growth lever for them. So that’s one big area. So from just a pure large deal perspective. Okay. Then you mentioned before around sort of corporate and SMB. Well, we find that a lot of the big opportunities are mostly around as they scale their business, uh, they’re not necessarily looking for things in the traditional sort of infrastructure space, but they’re looking for, you know, full SaaS applications to help scale their business, right? [00:40:58] Dai Vu: So it would be CRM, finance, hr, these types of solutions to become very attractive for some of this, uh, downstream market. And then lastly, as I mentioned before, which is, uh, when we think about this gentrification and owning, um. Uh, driving, uh, this business process and vertical, the ISVs become very important along with the services partners who bring that domain expertise to drive the end to end workflow. [00:41:25] Dai Vu: So I think that’s gonna be increasingly important. So those are three areas I think we need to watch out for. We. Okay. [00:41:30] John Janke: Maybe one thing, like as the cloud commit grows inside of companies, it’s shifted from being an engineering department, IT department budget line item to a corporate finance budget line item. [00:41:40] John Janke: Typically one of the top five to 10 expenses in a company. So that has shifted. Who is thinking about optimizing? The cloud commit with marketplace contracts. And that opens, that’s really opened up the avenue in addition to like these biz apps, vertical apps players. Yeah. Like having success. So I, I do think even inside your own company, evaluating where your cloud commits are, who owns them and are they thinking about the intersection of marketplace? [00:42:06] John Janke: ’cause I, I think it’s smaller companies, they’re still figuring it out. I run into engineering leaders who still own the commits, uh, but in medium to large companies. Very different. [00:42:16] Vince Menzione: Really good point. Because it, you know this, the optics change dramatically, right? This large commitment is now at the board level, [00:42:23] John Janke: right? [00:42:23] John Janke: And then you do have to teach your sellers as a vertical or business application player how to ask that question. ’cause the first resistance everybody says is, oh my, my person, my stakeholder, we. Manufacturing vertical application provider talking at an event last week, and they’re like, the shop floor manufacturing owner doesn’t know anything about the cloud commit. [00:42:43] John Janke: But if they ask the question, be like, Hey, do you guys have a strategic relationship with Google? Would it be easier to buy our product on the bill? Eight out of 10 times they get a yes. So [00:42:52] Vince Menzione: which is why the 5% comes in And that really accelerates the conversation happening. Yeah. We’ve got three more minutes. [00:43:01] Vince Menzione: Um, if we don’t have any other questions, I ha I have one for each of you really about the maturity model and partners are in the room that are not committed yet, right? We’ve talked about some very significant DBTs doing some incredible things, right? So we, there’s maybe a sense that like we, you, you are working with the be the biggest and the best out there, but what about everyone else that’s in the room that maybe isn’t committed yet? [00:43:23] Vince Menzione: And maybe they’re in motion, but they need some help and advice on what to go do next. What? What would you say die first? [00:43:30] Dai Vu: So they’re early stage, [00:43:31] Vince Menzione: early, early stage or not, they’re not on board yet. They’re not, yeah. They’re not with you yet. [00:43:35] Dai Vu: Yeah. So I’ll, I’ll go back to my earlier comment, which is that as you go into the journey, just be very intentional about what you need to do from an operational, investment people, uh, technology perspective. [00:43:47] Dai Vu: Uh, because it could be, it could be a multi-year journey. Um, uh, so I’d say go into it with the right expectations as opposed to thinking it’s going to be some accelerated six month thing that Sean has been driving here. It’s, he’s the outlier. [00:43:59] Shawn Toldo: But, but the reason for the outlier, [00:44:00] Dai Vu: yeah. [00:44:01] Shawn Toldo: And just to add to the intentional point Yeah. [00:44:02] Shawn Toldo: Is, you know, hire the right people. Right. So, somebody told me a long time ago, uh, hire slow, fire fast. That’s a really, really, really good principle that I take. Mm-hmm. I don’t like the fire part, obviously, but just for context, I, I am very lucky to have a great set of leaders that we were able to add people in. [00:44:24] Shawn Toldo: When I walked in the door, we had a person that was leading the Snowflake and AWS partnership. I had nobody on GCPI had nobody on Microsoft. I had nobody on Databricks. And then we made prioritization decisions on where we’re gonna go next. And so we hired people that had the experience and could drive the outcome in the right way. [00:44:43] Shawn Toldo: But we were very thoughtful about when we made those decisions on a quarterly basis, not a daily basis. So who you’re gonna bet on and then who you’re gonna put in the seat to make that bet come to life, I think is a really important thing as well. [00:44:58] John Janke: Yeah. [00:44:58] Vince Menzione: John, you worked with the be biggest and the best out there, so Yeah, sorry. [00:45:01] John Janke: Well, I think there’s the, like there’s the bottoms up and the tops down. Like seven years ago, this was all bottoms up. It was a partner leader who thought launching a marketplace would be good and they would go figure out how to do some deals and then sell their way up. Today there’s a lot more top down where people get it. [00:45:17] John Janke: But you can evaluate top down pretty fast. ’cause if you go talk to your CEO, you talk to your head of product, you talk to your CFO, and they have an allergic reaction to these concepts. You know, you have to go bottoms up. But there also are success story examples in every single ISV category that exists. [00:45:33] John Janke: Like this is not just security and data and DevOp like the, I think the ServiceNow. Salesforce workday. Examples are really great, like the marketing tech examples, more and more business of vertical apps every day. So I do think you can look at those people who’ve been successful. Maybe they’re your competitors, maybe they’re people you aspire to be and reference them as you’re trying to figure out how to do top down. [00:45:55] John Janke: But like you need both. You can’t win long term unless you get top down and bottom up aligned. [00:46:01] Shawn Toldo: And, and when I, when I would go ask for resourcing, I would always get the question, do, could you go faster with more? And I’d say, no. Gimme the one or two humans here, let me go prove it out and I’ll come back. [00:46:13] Shawn Toldo: So there’s a little bit of a strategy in doing that, that you’re gonna get more over time when you’re, you know, very measured in how you go ask for investment and resource. And so I would just add that point also. [00:46:27] Vince Menzione: Was, was hiring a significant component of your executive commitment, Sean? I mean, [00:46:33] Shawn Toldo: yes. So when I walked in the door at DBT, we had eight people in the partner organization. [00:46:38] Shawn Toldo: Today we have 25, and that was 18 months ago. But that did not happen. I didn’t go in and ask for, you know, that 16 people. Right. I asked over time in a very measured way with, you know, the programs and strategy team, like, what can we also support? You don’t want to bring somebody in to go do something and you don’t have the programs and operations side to support it ’cause they’ll fail. [00:47:01] Shawn Toldo: So we’ve been very thoughtful about how we’ve done that as well. [00:47:04] Vince Menzione: Die from you. I know you had something. [00:47:06] Dai Vu: No, no, no. I, I was good. [00:47:08] Vince Menzione: What is the one thing that people in this room need to go better and differently? Is there one, is there one specific thing other than what we’ve already discussed, did we miss anything? [00:47:16] Dai Vu: No, I would just, the whole identification. So obviously, uh, identifying this is not just like slapping a chat bot, but more around thinking all the things we talked about, product commercials, but also go to market where it’s agent first, where you can surface your agent in a workflow like Gemini Enterprise app. [00:47:34] Dai Vu: That’s gonna drive high alignment with how we work and go to market with Google. [00:47:38] Vince Menzione: Awesome. [00:47:38] Dai Vu: Yeah. [00:47:40] Vince Menzione: Wow. Good stuff. Yeah. Very good session. [00:47:44] Dai Vu: Thank [00:47:44] Vince Menzione: you guys. What do you think? Everyone? Thank you very much. [00:47:47] Shawn Toldo: Thanks for listening to the Ultimate Partner Podcast. [00:47:50] Vince Menzione: If today’s conversation resonated, share it with a partner leader in your network. [00:47:55] Vince Menzione: Subscribe where you listen, and head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, [00:48:11] John Janke: October 26th through October 28th. [00:48:14] Vince Menzione: Until next time, keep showing up in the rooms that matter because being in the room changes everything [00:48:22] I.

IA de la semaine, OCR, puces et sécurité open source. Des pistes pour réduire le coût de l'IA et plusieurs sujets matériel et infrastructure.  Me soutenir sur Patreon Me retrouver sur YouTube On discute ensemble sur Discord Modèles de la semaine Wan Streamer : et maintenant, les autistes synthétiques. Seedance 2.5, Krea 2 et Mistral OCR4. Peut-il révolutionner l'IA ? Databricks oscille beaucoup. Radio Star : les IA se lancent sur les ondes. Soyons pas hypAkrites, l'IA c'est quand même utile. Saveurs du monde Starfall, après l'IPO, l'IPA. MATCH Act, de quoi je m'ASML d'abord ? Rise and LineShine ! La Chine en a un plus gros. Les américains vont sortir de leurs Hygons. Qualcomm lance Dragonfly, ses puces poids lourds. Firefox et Chrome signent un PACT. Windows 10 fait de la résistance. Participants Une émission préparée par Guillaume Poggiaspalla Présenté par Guillaume Vendé

Sophia is one of my best friends, the officiant of my wedding, and one of the most cracked marketers I know. She's led campaigns at Airbnb, Databricks, and ElevenLabs. She won Best Dance Film at the LA Film Awards. She's also going through her biggest plot twist yet: leaving tech to pursue her art. Full time. For good.

AGENDA: 00:00 – Google Loses Two AI Legends as Anthropic Wins the Talent War 14:45 – China's $50B DeepSeek Bet Changes the AI Power Balance 27:15 – AI's Memory Crisis Has Begun — Apple Warns of a '100-Year Flood' 30:00 – Wall Street Finally Asks the $725 Billion Question: Who Pays for AI? 41:00 – We Built an AI Finance VP... and It's Better Than Humans 46:30 – The Death of Moats? Why Founders Should Stop Talking About Defensibility 58:30 – Databricks, ServiceNow & the New AI Software Winners 01:07:00 – The Seat-Based SaaS Model Is Dying 01:12:00 – OpenAI's Custom Models Could Rewrite Enterprise Software 01:17:00 – OpenAI's Biggest Threat Isn't Anthropic Anymore  

SaaStr 863: The Enterprise AI Reality Check: From Dashboard Graveyards to 30-Day Migrations with Databricks' Co-Founder and SVP of Field Engineering Every Fortune 500 CEO has told their team that if they are not using AI, they are behind. So now every employee is token-maxing, spend is going up, and almost nobody can tell you what they are getting out of it. That is the reality Databricks sees from the front lines, serving more of the Fortune 500 than any other data and AI company on the planet. In this episode, Databricks Co-Founder and SVP of Field Engineering, Arsalan Tavakoli, sits down with SaaStr CEO and Founder, Jason Lemkin, to cut through the Twitter noise and talk about what enterprises are actually doing, what is still broken, and why the next 24 months will fundamentally change who wins and who loses in every major software category. You'll learn: Why the BI dashboard is dead and what replaces it - including how a car manufacturer just onboarded 70,000 non-technical users to query their own data in plain language with no analyst in the loop What "context" actually means for enterprise AI and why it is harder to solve than the data problem, using a framework that explains why agents fail even when the underlying data is clean Why no software monopoly survives the next 24 months, and how collapsing migration costs and low-end AI competitors are about to give every incumbent a pricing problem they cannot ignore How Databricks now completes enterprise-grade migrations in 30 days or less using LLMs to analyze, convert, and reconcile legacy systems that previously took years and cost more than the savings Why the murky middle is the most dangerous place to be in enterprise software right now, and how to know which side of the AI budget divide your product actually sits on

Can a company reach 1 billion users before figuring out how to make money—and still dominate the future of AI?This week's AI news cycle delivered a fascinating mix of milestones, competitive shakeups, enterprise AI breakthroughs, security concerns, and agentic innovation. OpenAI crossed the historic 1-billion-user mark, Microsoft opened Copilot CoWork to the masses, SpaceX made a massive move with its $60 billion Cursor acquisition, and new open-source challengers emerged to challenge the industry's biggest players. For business leaders, the message is becoming increasingly clear: AI capabilities are no longer the bottleneck. Adoption, governance, employee enablement, and operational execution are now the real competitive advantages. Organizations that successfully train their teams and embed AI into daily workflows are already seeing dramatic productivity gains and measurable business outcomes. In this session, you'll discover: Why OpenAI's 1-billion-user milestone may be more complicated than the headlines suggest  How ChatGPT's market share slipped below 50% while Gemini and Claude continue gaining ground  OpenAI's new $150 million partner network and what it means for enterprise AI adoption  Why Microsoft Copilot CoWork could become a game changer for organizations already invested in Microsoft 365  The strategic implications of SpaceX acquiring Cursor for $60 billion  How new open-source coding models are challenging leading closed-source AI systems  Why AI governance and international cooperation became a major focus at the G7 Summit  The growing scrutiny facing OpenAI ahead of its anticipated IPO  New developments in agentic AI platforms from Databricks and Vercel  How leading companies are using AI agents to transform productivity and operations  What business leaders need to know about AI's growing impact on jobs, hiring, and workforce planning  Why employees who openly use AI may still face workplace stigma despite widespread adoptionAbout Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Send us Fan MailThis week's enterprise software developments further demonstrate how rapidly vendors are embedding agentic AI, governed automation, and composable data architectures into core enterprise workflows. Rootstock Software strengthened its manufacturing and warehouse execution strategy through the acquisition of Ascent Solutions, while Anaplan expanded its AI planning portfolio with CoModeler, Custom Analyst, and Agent Studio to accelerate enterprise planning automation. In the go-to-market space, Apollo.io acquired Pocus to build a more agentic revenue operations stack, and Zapier partnered with Rillet to connect general ledger workflows with thousands of operational applications. Meanwhile, Databricks introduced Lakewatch as an open, agentic SIEM platform built on the lakehouse architecture, and Oracle launched Fusion Agentic Applications designed to place coordinated AI agents directly inside ERP workflows. Governance and enterprise trust also emerged as central themes, with Relyance AI unveiling Lyo to monitor how AI agents interact with enterprise data, while Salesforce introduced AI Foundry to operationalize research into enterprise-ready AI models. Finally, Spade raised significant funding to transform messy transaction strings into finance-grade AI data, reinforcing how semantic normalization and governed enterprise context are becoming foundational to the next generation of AI-native enterprise systems.In today's episode, we invited a panel of industry analysts for a live discussion on LinkedIn to analyze current enterprise software stories. We covered many grounds including the direction and roadmaps of each enterprise software vendors. Finally, we analyzed future trends and how they might shape the enterprise software industry.Video: https://www.youtube.com/watch?v=hekHpEgI0zMQuestions for Panelists?