Podcasts about 100x

Lin Qiao is the Co-Founder and CEO of Fireworks AI, the leading specialized intelligence and AI inference platform that last week raised $1.5BN at a whopping $17BN valuation. With just 200 people, the company has hit $1BN in ARR and expects to hit $2BN before the end of the year. Prior to Fireworks, Lin spent several years at Meta including on the founding team of PyTorch.  AGENDA: 00:07 — Why Did Fireworks Bet on Inference When Everyone Else Was Chasing Training? 00:13 — Can Open-Source Models Turn AI Infrastructure into a Commodity? 00:19 — Should Enterprises Trust Chinese Open Models With Their Most Sensitive Data? 00:25 — Will Model Progress Keep Moving This Fast—or Are We Nearing a Plateau? 00:28 — Will the Multi-Model World Create a $100BN Routing Layer? 00:37 — How Much Will AI Token Usage Explode Over the Next Two Years? 00:43 — Will Token Costs Fall 10x—and Unleash 100x More Demand? 00:49 — Does Fireworks Eventually Have to Build Its Own Data Centres? 01:02 — What Is the Real Bottleneck Holding Back the AI Economy?  

Today the expert council answers your questions on free markets, investing, bitcoin, security cameras, nail injuries, parasitic infections, politics and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely your question will get though screening … Continue reading →

Today the expert council answers your questions on failed foreign policy,  thyroid health, battery back up systems, choosing a fitness program, cooking, advanced AI prompting and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely … Continue reading →

Mike Sherbakov is a Russian-born immigrant, Marine Corps veteran, entrepreneur, angel investor, and co-founder of The Veteran Fund. After building and selling companies, Mike moved into venture capital and now helps back veteran-led startups across defense, AI, space, energy, robotics, and critical technology.In this episode, Mike breaks down his journey from poverty and military service to entrepreneurship, early-stage investing, 100X returns, and building a 130-company portfolio. He also shares what he looks for in founders, why resilience matters, and how veteran leadership is shaping the future of American innovation.Hosted on Ausha. See ausha.co/privacy-policy for more information.

Full episodes of the Bulwark Podcast are available to paid members of The Bulwark via YouTube, Substack, and Apple Podcasts.Sarah Longwell and Bill Kristol (filling in for JVL) take on America's 250th birthday under Trump—and how far the GOP has fallen from Reagan's “you can become an American” to 2,000 ICE arrests a day. They break down the right-wing meltdown over the birthright citizenship ruling, Megyn Kelly's race to the bottom, why the “socialist takeover” panic misses what Democratic voters actually want, and who's really rising in the 2028 field.Read Sarah's Triad newsletter: https://www.thebulwark.com/p/the-real-reason-the-democratic-socialists-are-surging

Dylan Patel, founder of SemiAnalysis, argues the biggest gains in AI don't come from faster chips, they come from software-hardware co-design. Optimizing the model, the kernels, and the silicon together turns a 2x here and a 2x there into 100x. He explains why DeepSeek's experts were shaped for Nvidia's Hopper (and why TPUs struggle to run it), why OpenAI's sparser models and Anthropic's denser ones pull them toward different hardware, and why the so-called CUDA moat was never really about CUDA. Dylan breaks down InferenceX, his living benchmark that runs the latest models on over $50M of donated hardware daily, tracking a roughly 60x annual drop in cost per unit of quality. He makes the case that inference will be a bigger market than oil, that the compute crunch persists because models expand the value of useful work faster than compute grows, and why Jensen Huang is bankrolling neoclouds to engineer a multipolar world. Hosted by Shaun Maguire and Sonya Huang, Sequoia Capital

How do you get an inbound from OpenAI and Anthropic?Goldcast is the video content platform behind companies like OpenAI, Anthropic, GitHub, Uber and Airbnb, before it was acquired by Cvent in a nearly $300 million deal earlier this year.Palash Soni (Co-Founder and CEO, Goldcast) joins the Neon Show.Goldcast entered one of the most overfunded categories in SaaS. Hopin alone had raised more than $1 billion. This is the story of the decisions that helped Goldcast survive the category and ultimately become one of the biggest MarTech acquisition stories of the last few years.Enterprise customers are won long before they ever sign a contract. We trace that idea through Goldcast's journey, from landing Drift as its first marquee customer and reaching its first $1 million in ARR, to the relationships that quietly compounded over the years and eventually led to an inbound from the likes of OpenAI and Anthropic.We discuss how retention has always been MarTech's biggest challenge and why AI doesn't fundamentally change that. And why acquisition, not an IPO, is the most realistic outcome for most companies in the category.This episode is about winning in a market everyone had written off, and the decisions that turned Goldcast into one of the few companies left standing.00:00 - Trailer00:36 - How Palash caught the startup bug05:51 - Meeting the co-founders08:50 - Fundraising has never been easy for Goldcast10:36 - How we got a term sheet in 2 days12:43 - We quit HBS and they became our first customer18:47 - Customers told us our product looked ugly19:50 - How Drift founder changed the course of Goldcast21:44 - When competitors raised $250 million23:31 - How Goldcast got high-profile angel investors30:33 - The elevator pitch of Goldcast31:06 - When companies in your space are crashing32:36 - Was virtual events even a valid space after COVID?43:15 - How Goldcast won OpenAI44:38 - What led to the acquisition53:33 - One thing Palash would change about the last 5 years56:58 - Founders should define company values58:48 - Why we had an unusually large post-sales team01:01:09 - Retention in MarTech has always been subpar01:04:36 - Is acquisition the only path for a MarTech company?01:09:16 - How Goldcast got great logos01:11:01 - How the three co-founders split roles01:12:05 - If not acquisition, then what?01:14:20 - How founders move to higher ACVs01:16:21 - The ethos of the founding team01:17:25 - The book that changed me-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.We go deep on Omnigent: Databricks' open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.Then Reynold walks through Databricks' database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks' infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn't need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it's the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it's doing work?”If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y

Same presentation. Same offer. Same you. $91,000 in sales. Two weeks later with a different audience? $4,000. In this solo episode of The Expert Edge, I break down the insight that separates six-figure coaches from seven-figure coaches: the price you charge is based less on what you do and more on who you serve. This isn't about changing your offer or tweaking your funnel. It's about understanding that a psychologist charging per hour makes $250-$350. The same person doing emotional release work with an upleveled avatar charges $50K per package. Same work. Different avatar. 100X different price. Most coaches obsess over their product. "How do I improve my course?" "What sales funnel works better?" They're focused on the wrong thing. The real leverage is identifying your upleveled avatar and understanding what problems they actually have. What you'll learn: → Price is determined by who you serve, not what you offer - Two people doing identical work charging 10X different prices based on avatar → Higher level avatars have higher level problems - Beginners ask "What's a lead magnet?" High-level clients ask "How do I own market leadership?" (premium problems = premium prices) → Beginners require volume, premium clients don't - One $50K client is easier to work with than ten $5K clients and takes action faster → Different avatars have different desires - Beginners want their first $10K. Upleveled avatars want market leadership and multi-six to seven-figure businesses → Why resourcefulness is the real differentiator - Beginners are less resourceful because they haven't developed the internal mechanisms to push through hard things Real insights from the episode: The $91K vs $4K story - same presentation, same offer, different audience, wildly different results Why beginners are "stuck" - they're still dealing with foundational problems (foundational problems don't pay premium prices) The woman in my Platinum group who left a "high-level" program because everyone was asking basic questions like "What's a lead magnet?" Why you shouldn't focus only on the lowest common denominator (beginners) The emotional release work example: $250-$350/hour as a psychologist vs $50K packages with upleveled avatar Higher level problems: team problems, profitability problems, scaling problems, system problems, market leadership The consistency difference: Beginners making $3K one month and $20K the next (chaos). Premium clients with stable recurring revenue Why premium clients are easier to work with - they're resourceful, know what they want, and take action How to identify what problems your upleveled avatar actually has Building specific products for different avatar levels (not everyone joins high-level programs) If you're running an established expert business doing $300K+ (or aiming there), Platinum is our highest-level mastermind. It's for people ready to build a highly profitable, 2-3 million dollar business with a small team, full lifestyle, and zero complexity. We focus on market leadership, scaling profitably, and building systems that work. Apply at colinboyd.co/platinum Short application. If it's a fit, we'll hop on a call. Join our next Speak to Convert Masterclass. In this live workshop, you'll discover how to build and launch a high converting presentation that gets you clients every time you present. https://colinboyd.co/speak Discover how to authentically connect with your audience & fill your programs with a Conversion Story - Version 2.0 (AI Edition) is now available. https://www.conversionstoryformula.com Hit the "Follow" button so you don't miss an episode! Love this podcast? Write a review and give it a 5-star rating!  For all the show notes and links: https://www.expertedgepodcast.com/blog/episode326 Connect with Colin on Instagram: https://www.instagram.com/colinboyd/  

Good Morning, I'm Nelson John. On today's Top of the Morning: India just filed for its biggest IPO ever, as the NSE goes public at a ₹5 trillion valuation. A little known businessman is sitting on a ₹5,800 crore fortune from one early Jio bet. Meta is dropping $900 million on Cred and handing its founder the keys to WhatsApp. The telecom department says no to cheaper spectrum. And the milk in your fridge has quietly become much harder to fake-check. 15 minutes, five stories, zero fluff. Tap play. Learn more about your ad choices. Visit megaphone.fm/adchoices

Managing employees well starts long before difficult conversations ever happen. In fact, the systems you put in place from day one can determine whether your team grows together or struggles with disengagement and poor performance. In this episode of the Business of Apparel podcast, Rachel explains why great leadership and effective employee management go hand in hand with preparing for the possibility of letting someone go. She shares how to set clear expectations and implement performance improvement plans that protect both your team culture and your business. Rachel also discusses how underperforming employees can impact morale and why retaining A-players sometimes requires making tough decisions.

Most SaaS founders in the messy middle are making the same expensive mistake — building first and validating never. In this episode, Jeff Mains sits down with Corinne Kavanagh, founder of CAC Media & Publishing and former Microsoft Azure Data team contributor (part of a team that drove $500M+ in revenue with 76% YoY growth), to unpack what it actually takes to scale past the growth plateau.Corinne shares why your top-of-funnel obsession may be quietly killing your growth, how to validate demand before writing a single line of code, and why a fractional CMO may be the smartest hire you're not making. She also introduces her CARE re-engagement method, her SaaS Marketing Playbook, and the SCALE framework for building an AI-first marketing department without homogenizing your brand.If your business is growing and suffocating at the same time, this episode is for you.Key Takeaways0:24 — Welcome & episode framing: Why the messy middle is where most SaaS companies stall out3:22 — Guest intro: Corinne Kavanagh, founder of CAC Media, fractional CMO firm for SaaS & tech companies4:10 — Startups vs. enterprise: What big companies do differently — and what smaller companies can learn from retail validation models5:12 — Feature prioritization trap: Why founders rush to build before validating demand, and how to use micro-testing ($5–$10 ad spend) to validate before committing resources15:30 — Pre-development checklist: ICP study → messaging tests → distribution partner conversations → pricing research → competitive analysis17:09 — Competitor vs. customer time allocation: Why founders should be "in all channels" — and how AI tools can automate competitive monitoring23:04 — AI modernization in marketing: Efficiency gains without sacrificing brand authenticity — plus the importance of an AI use policy23:49 — Early churn warning systems: The retention play most SaaS teams ignore — and how to re-engage customers before they leave24:24 — The CARE Method: Corinne's re-engagement framework for growing lifetime value and sealing the leaky bucket25:08 — Account-based marketing (ABM): Why a focused list of 100 ideal accounts beats a massive TAM for execution27:01 — Growth plateaus: How to read your revenue chart — what "bubbles" mean vs. a flat line, and what each signals about your acquisition and retention engines29:48 — Aligning marketing, product & sales: Breaking down the wall between sales and marketing through co-invention, shared messaging, and CMO-level integration40:38 — The SCALE Framework: How to build an AI-first marketing department without producing brand slop45:24 — #1 marketing shift for 2026: Stop running your company — start building systems that run it for youTweetable Quotes"You can beat everyone else to market — but if your customer is not ready and chomping at the bit to buy it, it doesn't matter." — Corinne Kavanagh"Stop thinking about top of funnel only. Retention is half the story, and most SaaS companies are ignoring it." — Corinne Kavanagh"A consultant does a drive-by. They drop strategy and leave. That's not how you actually scale." — Corinne Kavanagh"If you're in the feature rat race, step back. Ask yourself: am I creating a category, or just chasing competitors?" — Corinne Kavanagh"Your marketing team should feel responsible for the P&L — not just the pipeline." — Corinne Kavanagh"Don't give sales a playbook and say 'go sell it.' Alignment has to be co-invention, or no one buys in." — Corinne Kavanagh"The most dangerous thing you can do with your runway right now might be shipping the next great feature." — Jeff Mains"Pretend you have a $200M company. What would you stop doing that you're doing right now?" — Corinne KavanaghSaaS Leadership Lessons1. Validate demand before you build — always. Retail companies won't spin up a new product line without marketplace testing. SaaS founders should apply the same discipline. Run micro-ads ($5–$10), talk to a pre-engagement cohort, and confirm that desire is "fiery enough to click the buy button" before writing a line of code.2. Your leaky bucket is as dangerous as an empty funnel. Pouring money into top-of-funnel while ignoring churn is a losing strategy. Build early churn warning systems using platform data (login frequency, monthly active users) and re-engage customers proactively before they silently leave out the back door.3. Bring marketing into R&D — not just into launch. Marketing shouldn't receive a finished product and be told to "figure out how to message it." A CMO-level voice in early R&D conversations means better competitive analysis, more relevant feature decisions, and messaging that actually lands in the marketplace.4. Break down the wall between sales and marketing. The old grudge match — "sales can't close our leads" vs. "marketing gives us garbage" — is a systems failure. Solve it through collaborative co-invention: shared meetings, shared messaging, and shared accountability for what's working.5. Category creation beats feature competition. If you're in a feature rat race with competitors, you've already lost the game. Step back and ask: how do we position ourselves so far apart from the competition that comparison becomes irrelevant? Companies like WooCommerce and GoDaddy didn't win by having more features — they won by creating new categories.6. Systems are your most important 2026 marketing investment. The #1 shift every SaaS founder needs to make: stop running the machine manually. Build systems around what's consuming your time, project forward to what a 100X customer base would require, and install those systems now. That's what gets you out of the messy middle for good.Guest Resourcescc@cac-media.comhttps://cac-media.comhttps://www.linkedin.com/in/corinnefss/https://www.instagram.com/corinnecava/https://twitter.com/Corinne_C_WAEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Read my new book, "The Price of Becoming." www.LearningLeader.com/Becoming This is brought to you by Insight Global. If you need to hire one person, hire a team of people, or transform your business through Talent or Technical Services, Insight Global's team of 30,000 people around the world has the hustle and grit to deliver. My Guest: Scott Harrison is the founder and CEO of charity: water, a non-profit that has raised over a billion dollars and funded tens of thousands of water projects to bring safe drinking water to millions. He previously spent a decade as a New York City nightclub promoter before a dramatic career shift led him into humanitarian work. Key Learnings Scott started a charity: water with $20 from a birthday party. Then $15,000... Twenty years later: over a billion dollars raised, 21 million people served. He says it should be 10 to 100 times more. The cure for water already exists. We're looking for water on Mars while 700 million people drink dirty water on Earth. We solved this hundreds of years ago. We just haven't implemented it. 25% of the money sitting in American donor-advised funds would give every human on Earth clean water. That's parked philanthropic capital. Already tax-benefited. Just waiting. The goal is always 10X what you're doing. If we raised a million last year, we want ten this year. If we raise $100 million, we should raise a billion. The opportunity is always orders of magnitude larger than the moment. Show, don't bullet. Scott shows 210 photos in a 45-minute keynote. No PowerPoint. Single images. A story unfolds frame by frame. Be early to the technology. First charity on Instagram. First to hit a million Twitter followers. First to use VR. The question is always the same: how does this new thing further the mission? The 100% model: solve for the cynic.  Public donations go to one bank account that funds only water projects. Overhead is raised separately from entrepreneurs and business leaders. Then track every donation to a specific village. Don't be mid. Scott's 11-year-old daughter says nobody wants to be mid. Excellence is a core value. There's a lot of mid out there. Design everything. The fact cover sheet. The PowerPoint. The website. The package. "We're always dating." If the message comes in an ugly package, you're at a disadvantage before you start. Treat the donor like a Michelin three-star guest. If a restaurant can think that carefully about a meal, you can think that carefully about a donor who can save a million lives. The Goldman Sachs partner who changed Scott's paradigm. Before making an eight-figure ask, Scott asked a partner: "How does it feel when people ask for a lot more than you expected?" The expected answer was irritated, offended, put off. The actual answer: "I feel flattered that they think I would be that generous." People are generous. The well is there. You just have to drill deep enough. Scott has spent 20 years asking for too little. That might be his next obsession. People give to people, not causes. A dynamic leader who transfers their enthusiasm gets the donation. The cause doesn't. Most of the donations Scott and his wife give are to people, not topics they were already passionate about. Talk 10% of the time. When Scott meets a donor for the first time, he wants to know their whole life story. Their marriage. Their kids. What they wanted to be when they grew up. Be genuinely curious or don't bother. Hire for integrity, humility, curiosity, and energy... 16,000 applicants for 36 roles last year. Energy matters most. Someone who can get you fired up about pickleball, Patagonia, or a new running shoe is exactly who you want on the executive team. The dinner test for hiring: Can you imagine having this person at your home for two hours at dinner? And wanting to keep them for another hour? Get the whole life story. Scott wants the arc from the beginning to the present in an interview. If someone can't tell their own story coherently, they probably don't know themselves yet. The 11-year-old with the piggy bank. He told his parents he was going to fund a whole village. They told him to set a realistic goal. He went knocking on doors. He came back with $10,000. Scott's experience lab in Nashville. A 60-minute immersive tour. A 100-degree room with a treadmill where you carry a 40-pound water vessel. Microscopes that show you parasites. A VR film that ends in celebration. The "give shop," not the gift shop. 53% of visitors donate. 10,000 visitors. $3.9 million raised in year one. Scott's champagne moment: a single billionaire who picks water. The water sector doesn't have one. Republicans and Democrats agree on it. Atheists and people of faith agree on it. Everyone has to drink. Reflection Questions What is the 10X version of your current goal? Where are you asking for too little because the smaller ask felt safer? Who in your work or life is the Michelin three-star guest, the customer, donor, or partner who deserves your most thoughtful experience design?  When was the last time you went 10% talking, 90% genuinely curious about someone else's story?  More Learning:  #290: Scott Harrison – Redemption, Compassion, & The Transformative Power Within Us #680: Scott Galloway - Don't Follow Your Passion, Follow Your Talent #682: Will Guidara - Adversity is a Terrible Thing to WasteAudio Chapters 00:00 The Price of Becoming - Pre-Order Now! 01:18 Welcome Back, Scott Harrison 02:56 From a $20 Bill to Over $1 Billion Raised 04:59 Why the Goal Should Always Be 10X (or 100X) 07:54 Storytelling: How to Get People to Care About a Problem They Don't Feel 10:30 Being Early to Instagram, Twitter, and VR 16:10 Radical Transparency: The Bank Account That Built Trust 19:51 The Beauty of a Healthy Obsession 21:22 Drilling Deep for the Artesian Wells of Generosity 25:04 What It Feels Like in the Room When Generosity Breaks Through 27:01 "Nobody Wants to Be Mid." 30:56 Design Everything: We're Always Dating 32:13 Treat Your Donor Like a Michelin Three-Star Guest 35:39 Selling With Integrity: Talk 10%, Listen 90% 39:15 16,000 Applicants for 36 Jobs: What Scott Looks For 43:12 The Power of Vulnerability in Hiring 45:39 Inside the Nashville Experience Lab 50:34 The Champagne Question: A Billion-Dollar Vision 52:10 The 11-Year-Old Who Raised $10,000 Door-to-Door 54:25 EOPC  

With the Co-Authors of The Greater Game and Dan Sullivan of Strategic Coach and John Bowen of CEG Insights Louis Diamond speaks with Dan Sullivan of Strategic Coach® and John Bowen of CEG Insights about founder dependency, enterprise value, and the architecture behind scalable businesses. In Summary Many advisory firms grow successfully while remaining highly dependent on their founders. Dan Sullivan and John Bowen argue that the difference between a successful practice and a valuable enterprise comes down to architecture. Louis sits down with the co-authors of The Greater Game to discuss founder dependency, enterprise value, intellectual property, and why some businesses scale beyond their owners while others do not. The conversation offers advisors a framework for thinking differently about growth, succession, and long-term optionality. The Storyline Many advisors spend their careers helping clients build valuable businesses. Far fewer stop to ask whether their own firms are being built the same way. That tension sits at the center of Louis Diamond's conversation with Dan Sullivan, co-founder of Strategic Coach®, and John Bowen, founder of CEG Elevate Group and CEG Insights. Their new book, The Greater Game, challenges a common assumption about growth: that bigger businesses are simply the result of working harder, adding more clients, or improving existing systems. Instead, they argue that enterprise value is created through architecture—the deliberate design of a business that can scale, transfer, and thrive without its founder at the center. The discussion introduces a framework for understanding why some entrepreneurs remain trapped in optimization while others build enterprises that compound in value over time. Along the way, Dan and John explore founder dependency, intellectual property, succession planning, strategic partnerships, and the role advisors can play in helping entrepreneurial clients navigate each stage of growth. For advisors, the framework creates an important mirror. The same forces that limit enterprise value for entrepreneurial clients often exist inside advisory firms themselves. The result is a conversation that extends well beyond business growth and into questions of optionality, transferability, and what ultimately makes a firm valuable. Topics Covered Enterprise Value Creation Founder Dependency Risk Business Architecture vs. Optimization Intellectual Property & Scalability Strategic Partnerships & Leverage Succession Planning & Optionality Legacy, Impact & the “Greater Game” Mindset > Download a transcript of this episode… Listen and Learn Highlights for Advisors What is The Greater Game—and why does it matter to advisors? (17:57) Dan and John introduce the framework behind their new book and explain why advisors should think about it both for entrepreneurial clients and for their own businesses. Why do only a small percentage of entrepreneurs create exponential enterprise value? (22:24) The discussion explores the difference between “architects” and “optimizers” and why most business owners remain focused on improving what exists rather than designing what comes next. Why is founder dependency such a significant valuation risk? (35:00) John explains how businesses that depend on a single individual often struggle to scale, transfer, or command premium valuations. How does expertise become intellectual property—and why does that matter? (35:00) The transition from expertise to transferable systems may be the most important bridge in the entire framework, creating leverage that extends beyond the founder. What prevents many advisors from fully serving entrepreneurial clients? (18:00) The conversation examines why most advisors are well-equipped for traditional planning needs but less prepared for the governance, succession, and enterprise-value challenges entrepreneurs eventually face. What does the next game look like after you've already “won”? (50:00) Dan and John discuss why many successful entrepreneurs and advisors eventually shift their focus from accumulation to significance, impact, and legacy. What's the single most important move an entrepreneur can make? (52:30) Dan shares the concept of Unique Ability® and explains why simplifying around your highest-value strengths often creates the greatest multiplier effect. Key Takeaways Enterprise value is created through architecture, not effort. Many successful businesses continue to grow while remaining highly dependent on their founders. The firms that command premium valuations are often built differently from the start. Founder dependency acts as a hidden valuation discount. The more a business depends on one person, the more difficult it becomes to scale, transfer, or sell at a premium. Intellectual property is often the bridge between a practice and an enterprise. When expertise becomes codified, transferable, and repeatable, value begins to exist independently of the founder. Advisors and entrepreneurs often face the same challenge. The same founder-dependency issues advisors help clients solve frequently exist within their own firms. Strategic partnerships create leverage that expertise alone cannot. Many of the most successful entrepreneurs grow through collaboration, ecosystems, and coordinated expertise rather than attempting to solve every challenge themselves. Most advisors are trained to solve early-stage problems. Entrepreneurial clients eventually require guidance around succession, governance, scalability, and enterprise value—areas that extend beyond traditional planning. The next stage of growth is often not about growth at all. For many successful entrepreneurs, the question eventually shifts from accumulation to significance, impact, and the legacy they want their business to create. https://www.youtube.com/watch?v=JY5xOB8GTQY Quotable Moments “The exit multiple is downstream of the architecture.” “The difference between a three-times and a fifteen-times multiple is often whether the business depends on the founder.” “You have to simplify in order to multiply.” “We're not talking about a 10x game anymore. We're talking about a 100x game.”     FAQs Why do some advisory firms command higher valuation multiples than others? Dan Sullivan and John Bowen argue that valuation is often determined long before a transaction occurs. Firms that reduce founder dependency, codify intellectual property, and build transferable systems typically command higher multiples than those built around a single rainmaker. What is founder dependency and how does it impact enterprise value? Founder dependency occurs when clients, revenue, and decision-making remain concentrated around one individual. While those businesses can be highly successful, advisors find they are often more difficult to scale, transfer, or sell. What is the difference between an architect and an optimizer? An optimizer focuses on improving an existing business model. An architect builds systems, intellectual property, and structures designed to create leverage, scalability, and long-term enterprise value. What does Dan Sullivan mean when he says “100x is easier than 2x”? The concept challenges entrepreneurs to stop thinking incrementally. Rather than working harder within the current model, transformational growth often comes from redesigning the model itself through better leverage, collaboration, and systems. How can advisors better serve entrepreneurial clients? Many entrepreneurial clients eventually need guidance beyond investment management, including succession planning, governance, intellectual property strategy, and enterprise value creation. Understanding where a client sits in their business journey can help advisors provide more relevant advice and coordination. What is the expertise trap and why does it matter for advisory firms? The expertise trap occurs when critical knowledge, relationships, and processes remain inside the founder's head. Until that expertise becomes transferable and repeatable, enterprise value often remains limited regardless of growth. Dan Sullivan and John Bowen argue that valuation is often determined long before a transaction occurs. Firms that reduce founder dependency, codify intellectual property, and build transferable systems typically command higher multiples than those built around a single rainmaker. Founder dependency occurs when clients, revenue, and decision-making remain concentrated around one individual. While those businesses can be highly successful, advisors find they are often more difficult to scale, transfer, or sell. An optimizer focuses on improving an existing business model. An architect builds systems, intellectual property, and structures designed to create leverage, scalability, and long-term enterprise value. The concept challenges entrepreneurs to stop thinking incrementally. Rather than working harder within the current model, transformational growth often comes from redesigning the model itself through better leverage, collaboration, and systems. Many entrepreneurial clients eventually need guidance beyond investment management, including succession planning, governance, intellectual property strategy, and enterprise value creation. Understanding where a client sits in their business journey can help advisors provide more relevant advice and coordination. The expertise trap occurs when critical knowledge, relationships, and processes remain inside the founder's head. Until that expertise becomes transferable and repeatable, enterprise value often remains limited regardless of growth. Related Resources The Greater Game by Dan Sullivan and John Bowen Strategic Coach® CEG Elevate Group The Greater Game Dashboard Diamond Consultants Advisor Transition Report Dan Sullivan The world's foremost expert on entrepreneurship in action, Dan Sullivan has spent the past five decades empowering business owners to reach their full potential in both their professional and personal lives. His strong belief in and commitment to the power of the entrepreneur is evident in all areas of his company, Strategic Coach®, and its successful membership community. Dan is married to Babs Smith, his partner in business and in life. They jointly own and operate The Strategic Coach Inc., with offices in Toronto, Chicago, and the UK Dan and Babs reside in Toronto. John Bowen John J. Bowen Jr. is the founder and CEO of CEG Elevate Group, the holding company that includes CEG Worldwide and CEG Insights. Through these companies, he helps elite financial advisors serve fewer, wealthier clients exceptionally well while building more valuable and scalable businesses. Before founding CEG, John spent 26 years as a financial advisor and built a $2 billion wealth management business. That firsthand experience grounds CEG’s work today across advisor coaching, enterprise programs, empirical research through CEG Insights, and practical frameworks for advisors who want to move beyond practice growth to enduring enterprise value. John is the author of 21 books on wealth management, entrepreneurship, and success. His newest book, The Greater Game: Your 100x Blueprint for Exponential Growth, Freedom, and Legacy, co-authored with Dan Sullivan of Strategic Coach, will be published by Hay House Business in May 2026. Today, John and the CEG team work with leading advisors and enterprise firms — including some of the largest advisor organizations in the United States — to help advisors deepen relationships with affluent clients, build scalable practices, and design lives of greater significance. NOTE: The views and opinions expressed by the guests on this podcast are their own and do not necessarily reflect the views and opinions of Diamond Consultants. Neither Diamond Consultants nor the guests on this podcast are compensated in any way for their participation. View the transcript of this episode… Architecting 100x Growth: A “How-To” From Legends Dan Sullivan and John Bowen A conversation with Louis Diamond and Co-Authors of The Greater Game, Dan Sullivan of Strategic Coach and John Bowen of CEG Insights.      Louis Diamond: Welcome to the latest episode of our podcast series for financial advisors. Today’s episode is Architecting 100x Growth: A “How-To” From Legends Dan Sullivan and John Bowen, a conversation with the industry’s top coaches and co-authors of The Greater Game. I’m Louis Diamond, and this is the Diamond Podcast for Financial Advisors. Mindy Diamond: At Diamond Consultants, we help elite advisors identify the right environment for their businesses to thrive, whether that’s at a wirehouse, boutique, or independent firm. With nearly three decades of experience, we’ve guided thousands of advisors and represented more than a quarter of a trillion dollars in assets transitioned. And each year, one in four advisors managing a billion dollars or more who change firms are our clients. Our process is education-driven and based on building relationships, starting as your strategic partner well before you’re even thinking of a move. To schedule a confidential conversation, call us at 908-879-1002. Wondering why advisors change firms and where they’re headed? Are transition deals going up or down? Those very questions and more inspired us to create our annual Advisor Transition Report. It’s the award-winning data-driven resource designed for advisors that connects the dots between the motivations around movement and the firm’s appetite for top talent. Arm yourself with the knowledge you need to make smart decisions. Download your copy at diamond-consultants.com/transitionreport. Louis Diamond: Most entrepreneurs and many advisors spend years optimizing for growth without realizing they’re building a business that still depends entirely on them. Revenue and complexity grow; enterprise value, transferability, and freedom often lag far behind. Dan Sullivan and John Bowen argue that the issue isn’t effort or intelligence; it’s architecture. No doubt these are familiar names in the wealth management industry, but just to set the stage, Dan is the co-founder of Strategic Coach, and John is the founder of CEG Elevate Group and CEG Insights. Together, they spent decades coaching and studying high-performing entrepreneurs and advisory firms. Their latest book, one they joined forces on, The Greater Game, lays out a very different framework for thinking about growth, one built around scalability, transferrable value, and long-term leverage rather than incremental optimization. What makes this conversation especially relevant for advisors is that the framework cuts both ways. It applies to the entrepreneurial clients that advisors serve, as well as to the advisory firms themselves. And in many cases, the same founder dependency and expertise trap that limits a client’s enterprise value is quietly limiting the advisor’s business too. We talk about the difference between operators and architects, why 100 times growth can actually be easier than two times growth, where businesses tend to stall as they scale and how advisors can start thinking differently about their own firms, particularly when it comes to enterprise value, succession, and long-term optionality. It’s rare access to a conversation with two of our industry’s legends whose advice and counsel has not only helped to transform the business lives of many of our listeners, but also my own. So let’s get to it. Dan and John, thank you both for joining us today. Dan Sullivan: Thank you, Lou. It’s a real pleasure. John Bowen: I’ve had the privilege of joining you before, but never with my co-author, Dan Sullivan, and I’m excited to share what we’re doing because I think it can make a big impact in our advisor industry. Louis Diamond: No doubt about it. Yeah, this has been an interview I’ve been very excited to host. So let’s jump right in. Dan Sullivan, I think you are a man that needs little introduction. So many advisors in the industry are fans or clients of your firm, Strategic Coach, but for those who aren’t as familiar or need a refresh, can you just give some quick context into why you started Strategic Coach and what the company does today? Dan Sullivan: Yeah. Well, it goes back to 1974. I was a copywriter at BBDO, the Canadian branch of BBDO, big global advertising agency. It still is. But I’ve been sort of a lifetime coach. I remember once when my mother finally caught up with what I was doing in life and I was describing what I was doing, she says, “Well, you were doing that when you were a child. You were talking to adults and you were asking adults about their experiences.” And I said, “Yeah, I could do this when I was eight or nine years old, but it took me a long time to get a business model wrapped around it.” But I jumped out in 1974 and started coaching anybody, but it actually turned out that entrepreneurs were the best people to coach because they would write a check on the spot and they would make a decision on the spot and I needed cashflow and I did it. So I’ve been personally, as a Strategic Coach, which was named by someone else. You’re just out there trying to get cashflow to pay for the rent. So I started in ’74, and I was lucky and it really relates to your target audience, Lou. Right off the bat, I got what are called top-of-the-table life insurance agents. And that was really, really great because life insurance agents are purely a conceptual business. So someone can get a new idea at breakfast and they can have a new business by dinnertime just because they can change their mindset. And that moved on. And I did that for 15 years, just one-on-one, 1970s, 1980s. And then, I’d had enough experience that we turned it into a workshop program in 1989. We’ve been at it ever since. So I was at a talk. Joe Polish is a great friend of ours, Joe Polish with Genius Network. And he had a speaker there, and he says, “You’re one of the original gangsters, aren’t you? You’re one of the first people.” And I said, “I don’t know if I’m the original, but I think I’m the only surviving one.” So it’s 52 years that I’ve been doing what I’m doing. And I had the good fortune to meet John in around 2009. John, was that the year? 2009? John Bowen: Yeah, in the little economic downturn that everybody knows about here. Dan Sullivan: Yeah. And John had a great coaching program and we had a great coaching program. And over the years, we’ve talked a lot about what makes a entrepreneur exponential in their thinking. And finally, about two years ago, we decided, let’s write a book about this. And that’s the new book, which is called The Greater Game. That’s where this all started. It’s just been a great pleasure because we sync very well. Louis Diamond: Amazing. And Dan, I think a lot of people likely know you either from Strategic Coach. I know I’m personally a big fan of two of your books and I know of others, The Gap and The Gain and Who Not How. We’re going to talk about your new book, but I think it’d just be helpful. Can you talk about the key premise of some of your prior books, The Gap and The Gain and Who Not How? Dan Sullivan: As a result of my membership, I’m a member in other groups. And so Joe Polish of Genius Network fame, he’s been in my program for 28 years, and I’ve been in his program for 15 years. And there was a writer who was in one of the first Genius Network workshops, and he approached me. And I created a lot of books, but I create small books and they’re self-published. I do a book a quarter. I’m 82 in about three weeks. So when I was 70, I said, “I’m going to give myself a 25-year project. I’ll write 100 books in 100 quarters.” And this is quarter number 47, and I’m writing my 47th book. But they’re little books. They’re 60, 70 pages. They’re one-idea books. And Ben Hardy, who was, at that time, the number one writer on Medium, which is a blogging type medium, he approached me, and he said, “I know you don’t write big books and you don’t have publisher books. But,” he said, “if you ever did,” he said, “I’d like to collaborate.” And that was a great good fortune on my part. So we produced three books in five years. The first book was Who Not How. Who Not How basically says when you have a goal, the biggest problem with the goal, you’re excited about the goal, but you’re not excited about doing it. So you find “Whos” who help you and you build teamwork around it. And that was a big seller. And then, we had another concept which was called The Gap and The Gain that entrepreneurs, depending on how they measure their progress, can be perpetually unhappy or they can be perpetually motivated. And it all depends on how they measure their progress, how they measure their goal setting and their goal achievement. And then the third book, which has really turned out to be the big one, up until this book, this book will be bigger. It’s called 10x Is Easier Than 2x. So hence, Coach, everybody has a 10x game plan. Whatever number they want to choose, revenues, personal net worth, whatever, you have a framework of 10x, which is sometime in the future, but you use that future framework for deciding what you’re going to do today that will end up as a 10x result. I thought that was going to be our formula for the rest of my life until I met John. And then John is a great AI practitioner. And I began to realize that that 10x is now becoming 100x for really top-notch entrepreneurs, but the 10x is easier than 2x. And we just crossed the million mark with the three books, which is really good. And it’s great for lead… we’re having people show up and they’ve really bought into what Strategic Coach is. We have a good size company. We’re not a small company. We have 120 team members. We’re in five centers: Los Angeles, Vancouver, Chicago, Toronto and London, England. But it’s been really great because we’ve really grown with technological change and it’s basically, we teach people how to think about their thinking. And Lou, you were in for three years, both in-person and virtual. So you know what the starting structure of it is, but I’m in love with entrepreneurs. Entrepreneurs are crucial characters on the planet, but mostly they operate alone and what we’ve done is create a community for them. Louis Diamond: Fantastic. Thank you, Dan. And John, I think perfect segue to you, because I know you’ve spent your career serving and helping entrepreneurs as well, mostly within financial services or within wealth management. And you’ve been very kind to share some of your amazing research on advisors serving entrepreneurial clients in the past. But for anyone who’s missed those episodes, similar question for you, can you share what your companies do? CEG Elevate, CEG Insights, your new research, and then we’ll dive into your exciting new book. John Bowen: Thank you, Louis. And Dan and I are very excited about just entrepreneurs in general. Dan is, because he’s working with them directly. The best clients for financial advisors are entrepreneurs, largely, if you’re going to go high net worth, ultra-high net worth. So we have a company, CEG Elevate, which is our parent company. Two of the companies that are really interesting for this podcast is CEG Insights and this is our research arm. And we’ll study about 20,000 high net worth, ultra-high net worth clients this year in depth and 6,000 up to 7,000 we’ll do just of entrepreneurs. And this is in the partnership. Lou, I invited you up to… We were skiing two years ago in Park City and you couldn’t join us. But Dan and I made a deal to do a 25-year partnership studying entrepreneurship, one for Strategic Coach and his coaching clients, but really the opportunity for financial advisors. And it’s probably just as well because I came down, and I think, Dan, you were 80 at the time and I was 69. I’m 70 now. And I was skiing with a whole bunch of 40-year-olds, and they’re all going, “You guys are way too optimistic.” And Dan and I are just getting started on this. And the other company that’s applicable is CEG Worldwide, where we have the privilege of coaching and training some of the top financial advisors, those aspiring, and also working with the enterprises to really help move up market and do this great experience. Louis Diamond: Fantastic. Dan, question for you. What was the core problem you and John were trying to solve in your new book, The Greater Game? What is it that existing frameworks weren’t touching? And then John, I’ll have a follow-up question for you after that. Dan Sullivan: Yeah. Well, by the very nature of what we do, we’re not going for wannabes. We’re not going for entrepreneurs who hope to be really successful someday. We’re engaging with and we’re registering into both of our communities, people who, they’re already great. They’re already doing so many things right, but they’re kind of doing it unconsciously. They just have a unique ability for growth. They have a unique ability for networking and expansion, but the very, very core is they’ve done it on their own. And they’ve done it out of intuition and they’ve done it out of ambition and motivation. But their biggest problem is that they’re really lonely. I’m in my sixth decade now of coaching entrepreneurs, and people say, “Well, what’s the number one problem that entrepreneurs face?” And I said, “Loneliness.” They can’t explain themselves to the family they grew up with. They can’t explain themselves with their lifetime friends. They have thoughts about how they’re operating. And they take enormous pride in their ability to transform difficulties into breakthroughs, but they don’t have anybody to talk to. So what we’ve created is a community where when you walk in the room, everybody in that room immediately understands you. Everybody immediately applauds what you’ve done. Everybody is inspired by you. So my framework is I call, “What you’ve done on your own, you’re great. You’re a winner already, but who do you talk to?” You have to hide a lot of your success because they just won’t understand what it is that actually motivates you. And the beauty of the partnership with John is the vast majority of our clients are in 70 or 80 different industries, so they’re not peculiar. We start off with financial services, especially life insurance. But what I notice is that all the difficulty they get into life is they’re trying to communicate with people who don’t understand them. And what we’re saying is, “Stage one, you did it on your own, you’re great by any standard whatsoever. You check all the boxes for being a successful person, but you don’t really have any way to actually check out how other people are doing this.” And so we’ve created a community, and John has created a community where people, immediately, there’s understanding. And not only that, but there’s opportunity because they’re unique in their own ways. Every one of our entrepreneurs has created a very, very unique pattern of success that if they were with 10 other people, they could learn from this. If they were with 30 other people, they would learn even more. So that’s what we’ve done. So stage two is now joining a community where everybody gets you. Louis Diamond: Interesting. And that’s the premise of the book. We don’t want to have people not buy it, but what is the greater game? What’s the game that folks are playing and pursuing and how do you make it greater? Dan Sullivan: I tell you, what I’ve always been lacking, I’m sort of intuitive like most entrepreneurs are. We’ve done about 300 times growth since we started the program. But it’s intuitive. I don’t have any research to back this up. I’m low on fact finder. I find, generally speaking, the best facts are just the facts that I make up, but at a certain point, you’d like to have some actual research to back me up. So I’ve gone as far as I can go with our company without real research. Then John comes into the picture, and now we got some real research. And I will say this, this is generally true. It’s not just a problem with me that I don’t have research. I find that entrepreneurism is one of the least researched subjects on the planet. And John comes along and he’s done all the backfill for how entrepreneurs actually perform and I’ve got research to prove it. Louis Diamond: Perfect. Yeah, John, question for you. So what is The Greater Game? And then, how do you think it relates to what financial advisors have been missing? John Bowen: One of the things that we as financial advisors all want to work with people who have already won. And there’s no better group than entrepreneurs, successful entrepreneurs. If we look at people with 25 million or more of investible assets across all households in the US, 90% are entrepreneurs. And at the 5 to 25 million of investible assets, it’s three out of four. So at CEG Worldwide, we’ve always wanted to really understand advisors. And we said we’ll partner with Dan and his passion with entrepreneurs, we’ll go ahead and study them so that we can bring insights on how we can better serve them. And the very first thing we want to do is understand, yeah, there’s very different stages that we see of entrepreneurs and we talk about the whole concept of The Greater Game. And the idea here is we wanted to identify… And I’ll share some PowerPoint slides. I know a lot of us are listening and I just want to walk through this, but Louis will have it in show notes, his team will. We really saw four areas. The first one was level one, stage one was foundation for freedom. They had ambition, the vision, but they really needed security. And Dan calls this, and I love this term, “cash confidence.” But it’s really using a financial advisor to have security. And one of the things, the last time I was on with you, Louis, we talked about there’s 59.2% of entrepreneurs who want to switch advisors because they don’t believe they have that security. And that’s kind of the foundation. And this is why you’re never going to read a more friendly financial advisor book for entrepreneurs than this because in our coaching program, we’re developing workshops and so on to bring this message out. And then the second level is where now we saw… and there were four levels. Dan and I identified 5.4% of these entrepreneurs that were just killing it and they were going through all four levels. The second level was energy for expansion. They were very motivated, they were excited about getting up and really the intellectual property, and Dan’s been one of the big leaders in this, is so much of what we know… And as I go through this too, I want every one of the advisors to think about it’s not only your entrepreneurial clients, this is for you too, is having this intellectual property, getting it out of your head so that your business is not founder-dependent or personality-dependent. You’ve got this enterprise. And then, the third level where it really took off was collaboration and multiplication. And Dan talked about the power of community and this is so big. And for advisors, the community is often working with other professionals, the accountants, the attorneys, the investment bankers. Matter of fact, when we survey, we found that 40% of the people with 25 million or more that they invest with an advisor came through an investment banker. So creating that community, teamwork, having the right team and then autonomy. Can you step away from your practice? The entrepreneurs step away 30 days, 60 days, 90 days, making that independence, moving from the founder-dependent to the enterprise. And the last level was exponential. And this is all along the way, the AI opportunities to accelerate this and augment this is really real, but the agency where the blue ocean, creating new markets, then getting the commitment and courage. And at each of these levels, we saw different entrepreneurs just really taking off. And one of the things that’s so important, Louis, for what we’re talking about today is advisors all are ready to treat stage one, the foundation for freedom, but they don’t really understand the other stages, and that’s really what entrepreneurs want. So if you want to work in this market, it’s very important for you to understand what you can do to help. The difference is often for an entrepreneur, a three to five multiplier versus 15, the level one or stage one to stage four. And this is where it gets really exciting. Louis Diamond: This would be a question for John. You found, and he’s mentioned it, that only 5.4% of entrepreneurs operate as architects versus optimizers. Can you explain the difference between those two personas? John Bowen: Well, I’m going to set up the research and let Dan really bring it home. But Dan and I came up with this framework, The Greater Game and the 10 Multipliers, and we’ve got that and we’re putting it in order and we wanted to really confirm. And everything we do is empirical research. So we reached out to 1,000 very successful entrepreneurs, 1,016. And it became very clear that the 5.4% of them were actually executing on all these levels and they were just distancing everyone else. And what we came up with, and Dan mentioned it earlier, that his book, 10x Is Easier Than 2x, but we said, what we’re seeing… and we’ve got a whole bunch, I think it’s 26 stories in the book of entrepreneurs, we’re seeing so many people blow this out that 100x is easier than 2x, and it forces a whole different mindset where if you’re optimizing, you’re kind of looking incrementally. But when you step back as an architect, big picture, wow, huge opportunity, both for entrepreneurs and advisors that are entrepreneurs to make a real big difference. This is something you’ve really coached to and had the privilege of working with thousands of entrepreneurs helping them on that journey. Dan Sullivan: Yeah. One of the things that was confusing for me, Lou, when I first started coaching, because everybody who came in to coach, you remember when you came into your first Chicago workshop, that everybody in the room was motivated. I’m not a motivational speaker. I don’t have to motivate the entrepreneurs who are in Coach. They’re already motivated. The problem is the focus of their ambition and focus. And what we discovered was that there were two types that showed up. I didn’t really understand it, but they’re what I call status-oriented entrepreneurs. And what they are when they were a kid, they didn’t have anything. Their family wasn’t at the top of the pole. When they were born, they grew up in a certain community, but there were certain people who lived in the right part of town and they had really big houses and everything about their lifestyle was way above everybody else in the lifestyle. And they saw the lack of what they had, because of the way they were born, that they were going to match it. But the matching was based in not only what the big home looks like. They’ve got other homes, they’ve got vacation homes. They belong to clubs. There’s clubs for the winners, and the losers aren’t part of those clubs, golf courses and boating clubs and everything else. And what I noticed was their motivation was simply to get to that point where they had the same sort of status. And they’re interesting for a while, but once they’ve gotten to that level of status, they’re not interesting anymore. They go on cruise control at that point and they just want to stay within that framework. But the really interesting entrepreneurs, and we really highlight them in the book, it’s just about growth. So when they get to one level, they say, “That’s great. Okay, now I’ve got a new baseline and now I want to grow even further.” And we have one story, very, very interesting. When he came into my Chicago workshop, I met him and he said, “I’ve got a big engineering company.” This is Paul VanDuyne. He’s out of the Quad City area of Iowa. And he says, “My ambition for your program is for three years, I’m just going to plan my retirement.” And I said, “Well, we’ve got some thoughts about that.” So I said, “Just do your first workshop and we’ll talk about it 90 days from now.” And he came back and he had an entirely different game plan, and he’s grown basically 250 times in his last 13 years. He’s completely transformed the industry that he’s in and he had this growth. So what we’re looking for in The Greater Game, we’re looking for those entrepreneurs who are already successful, but they don’t see any stopping point. They’ll grow to one level and then they say, “Okay, that’s the new baseline. Now I grow to another level.” Meanwhile, three years ago, what happened is the world got a new capability called AI. AI, you’re not talking 10x. If you use it properly… a lot of people are in the very early stages here, but we can see the ones who are applying it for growth. John has set up an entire research structure just to measure the people, and what are the people who are just motivated by growth? They don’t see any stopping point. They don’t see any retirement age. They’re just growing. They’re in better health now than they were when they started their ambition. One of the great breakthroughs we’re having now is the impact of AI on physical fitness and health right now. And so you have 70-year-olds now who are way more ambitious at 70 than they were at 50. So we think a whole new world is being created in front of us, but there isn’t the research to measure what the real winners of this new game are actually doing. And The Greater Game is a lot of Strategic Coach thinking tools, but it’s also the phenomenal research that John is doing, and we’re measuring exactly what are these people who just constantly grow, what are they actually doing? John Bowen: Louis, if I can jump in, I want to go back to Paul just for a second because he was going to do something classical, and Dan is also my coach and I was going to do something similar. Paul told Dan that he was going to retire at 65, and his wife. And he were going to open up a little mom-and-pop coffee shop. And the reason so many of the entrepreneurs are caught in the 2x optimization is they’re grinding it out. They’re working harder to be more successful and the desire to do that isn’t very high. That’s why you retire. On the other hand, what we found, the ones working on 100x are building platforms and ecosystems. They’re architected. And as we were writing the book, CEG grew by 58%. I’m going to give a lot of credit to the book, because as Dan and I were working on the processes, I wanted to walk all the talks. This is where the world is changing. I want everybody to think as a financial advisor, you’re being served twice, one with The Greater Game, they don’t care about a few basis points on returns. That’s table stakes. So much of the level one is taking care of the investment side, mitigating taxes, taking care of the areas, protecting the assets, some charitable planning, maybe shoot in some succession planning. I can tell you only 6% of the entrepreneurs actually feel they’re getting that from you, but that’s only level one. If you can help them from each of the stages, stage one through four, and help them create that vision, they’re going to love you to death. Because many of them want to continue in this path and create tremendous value, bigger impact, not creating legacies in the sense of enduring legacies, but active legacies. Last year, my wife and I set up a private foundation. I called it The Greater Game Foundation. I just love this so much, the difference that you can make, and I want to do it while I’m living, not while I’m gone type of thing. I think that’s one Dan and I very much share. Louis Diamond: Awesome. You wrote the book 10x Is Easier Than 2x, but now you’re claiming 100x is easier than 2x. How can that be the case? Dan Sullivan: The interesting thing, one of my points of proof on the original idea, the 10x Mind Expander, I use a lot of what the entrepreneurs have already done to prove the future. In other words, I said… You’ll remember the exercise, Lou. And I said, “I want you to pick your best number.” Everybody’s got a best number. It’s revenue, it’s net worth, whatever. And I said, “I just want you to multiply by 10.” And immediately there’s this reaction. He says, “You know how hard it was to get to just where I am 10 times?” And I said, “Well, you’ve already done 10 times. You’ve probably done 10 times twice. So let’s go back to the beginning. When were you 1/10 of where you are right now?” And they can nail it. They can tell you the year, they can tell you the month when they were 1/10 of where they were. And I said, “Let’s write the actual structure that got you from 1/10 to where you are right now.” And there’s five stages, and usually it’s an event, it’s a new relationship and all of a sudden they get a big check. And we measure, as entrepreneurs, size of check is a good scorecard. When you’re first starting, you got a $10,000 check, that was the biggest check. But about five years later, you get a $100,000 check, and all of a sudden it seems strange at breakfast, but by dinner you’ve normalized the idea, “Well, I know what it’s like to get a much bigger check, a 10 times check.” And so I have them create five growth stages that took them from where they were 1/10 to where they are right now, and I said, “Now let’s go back and talk about doing 10 times more.” And what they recognize, 80% who’ve got them 10 times the first time is going to be the same. It’s relationship, it’s having a great team, it’s having a simple approach that always works and it’s about the kind end customer. It’s not about them. It’s about who is it that you’re being a hero to in the marketplace. Because the truth is people don’t want to have a lot of relationships as they grow. They’d like to have one relationship to grow. They’d like to have an advisor who’s growing with them. But then John introduced me to the whole world of AI and I said, “We’re not talking 10 times anymore. We’re talking 100 times.” I said, “If you apply this new form of thinking, because it is an entirely new form of thinking, to what you’re doing right now, you can see that 10 times is going to happen just by doing three or four things where you’re eliminating waste, you’re eliminating things that just don’t work anymore, changing relationships, changing teamwork, changing collaborations in the marketplace.” But meanwhile, this new world of thinking is making you healthier. It’s making you more fit. So where before you thought you wouldn’t have the energy at 70, you now have more energy at 70 than you had at 50. So you’re the only one who says when it’s going to stop. I’m 82 in three weeks. We’re having this… I’m 82 and I’m way more ambitious at 82 than I was at 52. And the world is, because the world outside in terms of technological capability and access is way, way bigger in my 82nd year than it was in my 52nd year, and I love the growth. I have to tell you that the greatest point where AI is going to have the impact is going to be making money. The big titans, the Metas, the Googles, the Nvidias, what do they have in common? It’s about the money and where AI is being applied most is how you do new things with money. So that’s where the 100 times now comes from. I’ve normalized it. I said, “We’re not talking a 10x game anymore. We’re talking 100x game.” But the number on the scoreboard isn’t the issue. The scoreboard is, are you actually having fun? Louis Diamond: Yeah, we call it living your best business life. That’s our major barometer in charge. John, I don’t know if you could pull up your slides again, but I want to talk about the bridge between stage two in your pyramid to stage three. So that’s from expertise into scalable property. Can you explain how this relates to a financial advisor or an independent business owner and why this concept is so important for the valuation of a business? John Bowen: The book, it’s written for entrepreneurs, but I wanted to create some bridges while we’re together with Louis on really what’s going on for financial advisors and how you can help them. So if they’re at our stage one, Dan and my stage one of The Greater Game, and they want to go to two, they’re kind of dreaming oftentimes, and we want to help them begin creating the architectural structure. And as an advisor, this is really going to encourage everybody to read chapter two, The Greater Security. It talks about really the VFO, Virtual Family Office structure that they want, and you got to help them get financially solid, building personal wealth outside of the business, tax, estate, insurance, business structure. That’s what we all do today. Then though, if they want to move from level two to three, what we find over and over again, advisors are not equipped to do this, because what we’re taking is that founder where everything’s in its head, we’re now helping them move from just having that expertise to having scalable property. This is that codifying the process of building IP that’s transferable. And this is where the real valuation changes. Now, I’m not asking financial advisors to be the IP experts, but what the entrepreneurs want is they want somebody to help them curate and then coordinate between each of these levels. We go from three to four that the founder is indispensable, oftentimes at three. Now we want the team there to be invincible. And it’s not just the individual team as Dan was talking about. It’s the community. The collaboration is where this really takes off. The noise of AI is making it harder to market, but by partnering, particularly as financial advisors, we can very quickly have groups. One of the reasons why I’m collaborating with Dan, I want to help our financial advisors to work with entrepreneurs. Dan wants that research. So this is the natural collaboration. But they’re interested here in governance, self-managing teams. One of the things that Strategic Coach is brilliant at, the pre-transaction they want. And what we find so often is the indispensable discount. So many businesses sell, if they sell at all, they’re selling for three to five times multiplier, not advisory, but traditional businesses. Well, if you can make it to four, all of a sudden you’re now talking to 10 to 15 times multipliers. And think of it as if I’m a buyer and I’ve been involved in 50-some transactions, what happens is if the business is the guy, the gal, they’re the business, then you’re buying a very expensive job type thing. So let’s just keep a simple one. They’re having a couple million dollars of EBITDA. And let’s say the high range of that, five times EBITDA is $10 million. Well, the difference at 15 times two million is 30. Now, a few basis points I don’t really care about. I really care about capturing that difference. And because there’s a machine working without, I can buy that machine and generate that cash flow and it’s also taking advantage of the vision. And then when we get to level four, this is where most advisors make the biggest mistake is, “I’ve won. I’m at level four. I’ve got tremendous wealth.” Okay, but I’m now looking at significance. And I do want to go, “It’s not enduring legacy I’m looking for. I’m looking for active legacy. I’m looking for family governance.” Do I want to continue to build it like Dan and I’m doing at 70? I’m building the business so I can continue doing it as long as I want to do it. At the same time, and I love the impact we have and I know you do too, Louis, for the impact you have. Why not build the platform that’s going to allow you to do that as long as you want to do that? And if you don’t want to do it, let’s create the most value to transfer. When you start having conversations like that with families, entrepreneur families, it just changes, and very few advisors can do that. And that’s what we’re finding. We have a coaching company, training company, we train those things. They’re winning, quite honestly, almost 100% of the time because entrepreneurs didn’t know that was available to them. Louis Diamond: Interesting. It seems like the difference between stage two in your pyramid, to leap to stage three or four, that seems like a pretty massive pivot point for valuation for building a scalable business, having a self-managing company, et cetera. Do you find or have you seen that advisors or entrepreneurs that are in stage two themselves, they kind of pattern-match when they’re working with their own clients and kind of manage their own clients into stage two, or is it not really connected? John Bowen: I think that once you get the bigger picture and see the greater game, you can help your clients. That is a very small percentage. Remember, it was only 5.4 of when we surveyed successful entrepreneurs were actually playing the greater game, all four levels, the 10 greater multipliers. So I think what we tend to do is we get stuck on what we can do. And all the training is for level one for financial advisors. We don’t know how to guide them through the other levels. And really, the big difference from two to three, Dan and I’ve talked about this a lot, and I think Dan’s one of the biggest champions of this, is collaboration, putting together strategic partnerships. It could be with your competitors. This is for entrepreneurs, competitors, it could be various vendor partnerships. But the ability to open up markets that way when you have now put together in level two your IP, value creation’s huge. For advisors, it’s putting together partnerships with centers of influence. When we survey top financial advisors, 70% of their best clients came through COI, Centers of Influence with accountants, attorneys, investment bankers, and so on. Well, let’s do it on purpose, be successful on purpose. Louis Diamond: Dan, question for you. In all your experience working with successful financial advisors, insurance producers, probably any entrepreneur, what do you feel are the most common things that folks do unintentionally to really hurt their enterprise value even long before, or if ever, they decide to sell their business? Dan Sullivan: Yeah, I think the biggest thing is they stay entirely within their industry. One of the first questions that we ask our entrepreneurs when they come into the program and where you see it most is in the professions: lawyers, accountants, engineers, architects. I’ll say, “Well, what is it that you are?” And they’ll say, “Well, I’m a lawyer. I’m a tax lawyer.” And I said, “Are you a tax lawyer or are you an entrepreneur who has a specialty in tax law?” Okay. It makes a big difference, because if you see yourself as a tax lawyer, then you’re saying that you’re a better paid factory worker. You’re a manual laborer. But if you’re an entrepreneur, it’s a fairly recent idea in human history. There’s always been entrepreneurs, but it wasn’t until about the beginning of the 1800s that you start seeing this really different class of people in the marketplace, who, it didn’t matter how they were born, they were taking advantage of some new multiplier technology. Steam power being a great example. Around 1800, steam power came on. And anybody who had a bright vision for themselves and had the wherewithal to figure out what needs could be satisfied with a new technology, all of a sudden they became rich. They became rich. And it was very disruptive, because up until then it was based on aristocracy and you were born into wealth or you were born into poverty. There was no crossover. So what we’re saying is anybody who comes into Strategic Coach, I said, “I’m not going to tell you anything about your particular industry.” I said, “You know all the best practice people in your industry and they have workshops and they have conferences and you go to them, but they don’t know how to be entrepreneurs. You know how to create a really well-paying job, but you haven’t created a company.” A company is a totally different realm and I would say the vast majority of entrepreneurs, 95% of entrepreneurs haven’t really created a company. They’ve just created a really well-paying job which requires their presence and their attendance. I said, “You don’t get any payout for your company. If you’re the company, you need to have a structure.” I’ll give you an example. We started the company in 1989, and we’re about 270 times what our first year revenues were, and that was a great year. I was very happy for the first year, but we’re about 270 times. Along the way, what I did is I created other coaches so it wasn’t just Dan, the coach. So we have 16 other coaches. And I’ll give you a little example. In 1994, that year our company did 144 workshop days, 36 per quarter. One coach: me. Last year we did 600 workshop days and I did 12. 588 were done by other coaches. And our coaches are great. They’re clients who have coaching instincts and they do it. So about four years ago, I met one of our clients who’s an M&A specialist, and I laid out all the facts just in conversation, “This is our revenues. We have no debt. It’s repeatable income, around 70% is repeatable for one year.” I put the whole structure together. And I said, “So right off the top, I don’t have any relatives on staff.” The first thing they look for, “Any relatives working for you?” And he gave me a number. It was a big number. It was probably four times revenue for that year. He said, “We got a lot of structures.” Then something happened in the marketplace, and this is a great breakthrough that the US Patent Office sometime in the last 10 years recognized that up until about 10 years ago, to get a patent, you had to have a technological component for what you were doing. Sometime in the last 10 years, the patent bureaus decided that the internet is the technological component. So they’ve introduced education and entertainment as patentable processes. So in the last three years, we’ve gotten 82 patents. 82 patents. And these are our thinking tools, Lifetime Extender, Free Focus and Buffer Days. You know the routine that you learn in the first three days, and we’ve got 82 of them. We’re averaging about 25. I get a new patent about every two weeks. So I saw this M&A specialist, and I said, “This has happened in the last three years.” And he said, “Immediately it doubles the valuation of your company.” So what John’s saying here, as you go through the four stages, more and more you get paid for your creativity, retail, you get paid for your retail. But if you structure it, you record it, you package it, it is even greater than what you got paid for your creativity. Louis Diamond: Super interesting personal anecdote, and I appreciate you sharing that because that definitely did drive the point home for me. I see the applicability to probably any industry, but especially to any financial advisor. Dan Sullivan: Oh, yeah. Louis Diamond: The best RIA firms, the best advisors, they pretty much all start off with a cult of personality founder who’s the rainmaker. And then the practices that really grow and scale and are valuable are more platforms. That’s what private equity wants to invest in. And those are the firms that get the higher multiples. Dan Sullivan: Yeah. So the big thing is there’s a really, really great IP lawyer. He’s in our program and he’s made the breakthrough, and he’s the first IP lawyer that doesn’t charge by the hour. He charges by the patent. If the IP lawyer charges by the hour, it’s a very slow patent. If he charges by the patent, it’s a very fast patent. But the big thing, he showed a slide that in just big corporations, 1980, you took big corp, Fortune 500, the S&P 500, more than 80% of their valuation was tangible. It was property, it was real estate, it was fleets, it was equipment. Last year, more than 80% were intangibles. It was your ideas, intellectual. If you look at Elon Musk, it’s all intellectual capital. If you look at Meta, you look at anything, it’s intellectual. It’s not tangibles. So we’ve entered into that new world and AI has introduced us to that new world. It’s new processes, new structures, new approaches and it’s really interesting. It’s hard for entrepreneurs to get their idea that your creativity is actually property. Louis Diamond: It sounds like the ultimate challenge for anyone listening is translate your process, your ideas, the stuff that you’re doing by instinct as you both had said, and turn it into something patentable or something repeatable that another advisor, another executive, another owner can pick up and deploy and scale. John Bowen: We share the process in chapter four. It’s the fourth greater multiplier. And we actually share Caldwell, the attorney that Dan’s talking about, his story and the value creation. He’s now the major player in that space. And this is where we as advisors, we’re given a twofer, Dan and Louis, is that you can help your clients, but you can do this yourself too. You’ve been involved in a number of large transactions. The difference, I had a $2 billion advisory practice I sold in ’98, and we sold for 16 times earnings. And a big part of it, we were in that blue ocean. We had agents that we created and strategic process that would run without me, and it did type thing. And it continued to grow and went for about 10 fold what I sold for a number of years later. This is something that’s very real. Louis Diamond: Absolutely. I got two more questions for you guys because I know you’re both busy. For an advisor who feels like they’ve won the growth game, they grow 10, 15, 20% per year, they’re charged up, they’re on the Barron’s list, the Forbes list, they’re hitting their AUM milestones, they built an amazing team, they have a family member in the business. They have everything that anyone could want. What does the next game look like for them? What’s the next frontier once you’ve achieved all those things that from the outside looking in, seems like you have it all? What’s the next game to play? John Bowen: Well, we’re going to both say The Greater Game, but the- Dan Sullivan: Well, tell them about the dashboard, John, because the book is just part of the deal here. It gives you the landscape. There’s a great tool that comes with the book. So tell them about the dashboard. John Bowen: Really what we wanted to do is to create kind of a community just around the book. Dan and I and team built a dashboard. We were very creative on naming, thegreatergamedashboard.com. You can go in and we’re now studying every month over 500 successful entrepreneurs. We have that data in here. You’ll be able to see how you compare at each of these stages, the four stages, the 10 multipliers. And you’re going to get specific recommendations. This is for entrepreneurs. But again, you should do it. If you’re a financial advisor, you have an equity ownership, you should definitely be doing it as well. And one of the things that we see over and over again, and Louis, you probably see this a lot in the conversations. They have advisors who have already won. They don’t know what the next game is. And it’s easy to check out at that point. It’s easy to frustrate the next generation of leaders and so on. If you take the time to really see what the opportunities are and architect to realize that vision, you can create, whether it’s selling the practice, creating tremendous value there or designing a role for yourself, maybe it’s executive chairman type for that business that you can guide it with the vision and what you’ve brought and strategy. But bring that team up. That’s going to create so much value, so much impact and you can design it for the life that you want. And that’s where I get very excited. Louis Diamond: I can hear the passion in your voice. Dan, let’s finish with you. Given all of your experience working with entrepreneurs, advisors, business owners, et cetera, what’s the one move that you’ve seen the most successful entrepreneurs in your orbit make that’s changed the trajectory of their firms and their life more than anything else? Dan Sullivan: I’ll answer it in a little roundabout way. Periodically, I have a thinking tool. I said, “If everything was taken away from you as an entrepreneur and they moved you 1,000 miles away, what’s the one thing that you would take with you? It has to be portable. So what is the most portable thing that you have that you would start over again with the greatest value that you had created previously? What would it be? And then you would rebuild what you’ve already created, but you would do it much faster. What would be the one thing?” It’s an interesting thought. But in our concept, it’s called unique ability, that there’s something about you, as an individual, that first of all gave you enough confidence to become an entrepreneur because it’s risky. It’s a risky proposition. It’s guessing and betting and it’s risky business and it’s unique ability. So the starting point for all growth in Strategic Coach is that there’s something about you that’s absolutely unique. You don’t have any competitors on this and it has two qualities. One is that you’re so good at it, you don’t take it seriously. You’ve done this since you were a child and it just comes to you naturally and you don’t see the significance of it. When you’re in Coach, you start seeing the significance of it. And the second thing is you just absolutely love doing it. It’s what you love doing most of all. It comes to you naturally. You don’t even have to think about it. And then you begin to realize that anything else you’re doing as the founder and the owner of your company, probably somebody else can do. So you’re doing 20 things, but really you should be doing three things. The other 17 things still need to be done but not by you. And that’s the breakthrough. You have to simplify in order to multiply. Louis Diamond: I absolutely love that. I know when I was in Coach, that was my biggest takeaway or realization was figuring out what my unique ability was because I think the two components,

The reason your team isn't performing has nothing to do with their skills.It's because you stopped telling them they're doing a good job.As an entrepreneur, the market praises you; every sale, every client, every dollar in your account tells you you're winning.Your employees don't have that.Watch this before your next team meeting: https://youtu.be/8mkrfHLXve8

Apple went public in 1980 at 15X revenue.   SpaceX is scheduled to go public on 6.12.26 at 100X revenue. The market cap is estimated to be $1.5 -2.0 trillion valuation. This would be higher than Microsoft. SpaceX would be the third largest company by market cap in the US, behind only Apple and Nvidia.   SpaceX lost over $4 billion in Q1 2026. Total losses since founding have been over $40 billion.   95% of all SpaceX shares are held by insiders.   Typically, insiders are frozen from selling their shares for 180 days after listing to prevent the stock price from getting crushed.   SpaceX insiders will be able to sell 20% of their shares in 60 days. Several more chunks of shares will be unlocked for insiders.   By late November 2026, over 90% of insider shares would be unlocked for potential sale.   The median stock price on most large IPO's, including Facebook, Coinbase, and Uber, have lost over 50% of their stock price after one year. Many were leading companies in their space and "must-own stocks".    There may be a short-term speculative price increase due to the hype and interest around SpaceX. The valuation is extraordinarily expensive. Proceed with extreme caution.

Joe Polish sits down with Strategic Coach Founder Dan Sullivan and The CEO of CEG Worldwide John Bowen to explore the research-backed framework behind their new book, The Greater Game — a 100x blueprint that reveals why only 5.4% of Entrepreneurs are playing a completely different game than everyone else. Together they unpack the shift from Founder-dependent businesses to scalable ecosystems, the finite-vs-infinite game divide, and why AI is less a technological revolution and more a cognitive one. Here's a glance at what you'll discover in this episode: The number that reveals whether you're winning or losing the only game that matters... and why 94.6% of Entrepreneurs are optimizing a game that's already coming to an end (you've probably already done 10x without calling it that — what you do next is the whole point) Dan Sullivan's quiet observation after 52 years and 7,000+ Entrepreneurs... the exact moment a successful person stops growing isn't failure — it's something far more seductive, and almost no one catches it in themselves (the first exercise he runs at Strategic Coach is designed to show you you've already crossed the line once) Joe typed a question into AI and got back the most brutal case study in modern business history... Blockbuster, Kodak, Borders, Toys "R" Us — and the one invisible shift every company on that list missed before it was too late (this isn't a technology story — it's a thinking story) Why John Bowen started three new companies on his 70th birthday... and the dashboard he and Dan built for roughly $2,000 that a top vendor quoted them $50,000 a year to provide (his tech team called after the first meeting and said "we'll just build it and give it to you tomorrow") The four-hour version of something that used to take Dan Sullivan four weeks... and what it reveals about the only AI upgrade that actually changes your trajectory (this isn't about using AI more — it's about using it in the right direction entirely) What Joe Polish teaches Genius Youth Members that no business school has ever covered... and why writing handwritten postcards in an age of AI might be the single highest-leverage thing you do this week (the killer app of 2026 is not what anyone is selling you) If you'd like to join world-renowned Entrepreneurs at the next Genius Network Event or want to learn more about Genius Network, go to www.GeniusNetwork.com. Show Notes: The Book: The Greater Game and the 5.4% Dan and John's new book — published by Hay House and instantly a #1 Amazon bestseller — grew out of a 25-year research partnership to study what separates the highest-performing Entrepreneurs from everyone else. Their research across 7,000+ Entrepreneurs found that 94.6% are still optimizing the game they're in — while only 5.4% are architecting a completely different one. The book maps out exactly what those 5.4% are doing. The book's central premise: "Every system that got you here is optimized for a game that's coming to an end." From 10x to 100x: Dan's Framework Dan has been coaching Entrepreneurs to 10x since the 1990s — starting with an exercise where he had Clients identify when they were one-tenth of where they are today. Everyone in his program had already done 10x without labeling it that way. When he challenged a Client who said they couldn't go 10x in three years, the Client responded they could do it in 15 — and then voluntarily suggested doing it again. That's when the 100x idea crystallized. Dan's thesis: give yourself a long enough time horizon, use AI as a genuine collaborator, and constant growth becomes the natural state — not the exception. The Four Levels of The Greater Game Level 1 — Foundation for Freedom: Vision, security, and financial confidence. Getting off the couch. Level 2 — Energy for Expansion: Motivation and IP development. Dan has built an extraordinary amount of intellectual property; John and Joe have too. Level 3 — Platform / Ecosystem: Moving from Founder-dependent to a scalable system. John's own company grew 58% while writing the book — by walking the talk of this level. Level 4 — Agency: Creating markets. Courage, commitment, and building an ecosystem where you're generating the category itself. Finite vs. Infinite: What the Game Shift Really Means Finite game: competing for market share, managing dependencies, staying indispensable personally, reacting to market pressure. Business value: 3–5x EBITDA. Infinite game: designing an ecosystem, multiplying unique genius through others, engineering your own absence, redefining the market. Business value: multiples that reflect systems, not the Founder. Joe's examples (finite → infinite): Blockbuster → Netflix, Kodak → Apple, Borders → Amazon, taxi companies → Uber, Toys "R" Us → Lego. The pattern: finite players optimize the current game; infinite players keep changing what the game is. Dan's real-world example: Paul Van Dyne came to Strategic Coach planning to retire at 65. He went on to take his engineering firm from #40 to #1 nationally in nine years through M&A — and now plans to build his gourmet coffee shop inside one of his medical centers. AI as a Cognitive Revolution Dan's framing: AI isn't a technological revolution — it's a cognitive revolution. He compares its impact to the introduction of zero in mathematics, which made economics, double-entry bookkeeping, and science possible. Practical example: Dan used to need four weeks to structure a new book. With AI, the same work takes four hours. He now writes a new book every quarter. John's vibe-coding story: his Team built the entire Greater Game Dashboard for roughly $2,000–3,000 using Lovable — after being quoted $50,000/year from a top vendor. They own the code and iterate freely. Joe's counterpoint: the killer app today is being fully human — knowing how to bond, connect, and think for yourself. "Write with your hands, think with your brain."  The Greater Game Dashboard John built this free interactive tool at TheGreaterGameDashboard.com to put the book's framework into action. The 15-minute assessment shows you exactly where you stand relative to peers and the 10 Greater Multipliers. The dashboard automatically calculates what your company is worth to a buyer today — and shows how each improvement raises that number. Dan calls it the greatest tool he's seen in 52 years of coaching Entrepreneurs. Monthly updates include an Entrepreneur Pulse confidence index. Useful whether you ever intend to sell or not — knowing your number changes how you invest in your business. Building Great Teams: Cast, Don't Hire Dan's principle: Strategic Coach treats itself as a theater company — with backstage and front-stage roles. They don't hire for jobs, they cast for roles. Every new hire is there to free up someone already in the company. Babs Smith built the Strategic Coach Team around Dan from the start — several Team members have now been with the company 20–30+ years. Beware the Founder-as-salesperson trap: if you're great at selling, you'll hire the wrong people — you'll confuse their excitement for the role with fit for the role. John, Joe, and Dan all find talent primarily through communities — mastermind groups, Genius Network, Strategic Coach — rather than ads. Great people seek out great people. Dan's upcoming book (Hay House): Casting Not Hiring. IP as a Strategic Asset Dan has had 82 thinking tools patented by the US Patent Bureau (none rejected), with 75 more pending. Each patent is a borrowable asset — you can borrow up to half the appraised value, creating a private intellectual property bank. Joe Polish's company operates as an ESOP — all Team members become equity owners after a vesting period, creating a true ownership culture without requiring employees to buy in upfront. Genius Youth and the Human Connection Advantage Joe's Genius Youth program focuses on skills AI can't replicate: human connection, handwritten notes, cold plunges, cooking and hospitality, ethical influence. Joe's 2026 Genius Network Annual Event —  features Peter Diamandis and Steven Kotler (Authors of We Are as Gods), live robots, and a mystery musician on 300M+ albums. Resources: The Greater Game (Book) — Dan Sullivan & John Bowen The Greater Game (Audiobook) — narrated by Gord Vickman, Hay House Business TheGreaterGameDashboard.com — free 15-minute assessment & company valuation tool 10xTalk Podcast — Subscribe — 10xTalk.com 10xTalk on Apple Podcasts Strategic Coach — Dan Sullivan's coaching program Genius Network — Joe Polish's community for elite Entrepreneurs Joe Polish's Genius Network Annual Event CEG Worldwide (John Bowen) — research and coaching for financial advisors Cleator Ghost Town, Arizona — Joe's 40-acre ghost town & the Cleator Bar and Yacht Club Inside Strategic Coach Podcast — Episode on Hiring — Dan Sullivan & Shannon Waller AI Killed the Modern Company (Video) — Peter Diamandis & Salim Ismail Why Microsoft AI Chief Predicts AI Automation of White-Collar Work in 18 Months — Fortune / Mustafa Suleyman

We've informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire!We covered the MAI model technical takeaways on yesterday's AINews, so I will focus our recap of Satya's main messages around three elements:* Satya's adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft's position on this as the ur-SaaS titan, and Satya had great answers* Making the Impossible Possible: Kevin Scott's inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.Enjoy!Full VideoTranscriptVoiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya NadellaSarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it's great to be with both of you. I listen to both of you or b- both the podcasts all the time. It's great to be on it.Thank you so much. [00:01:00] So you're just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what's the most important reflection or takeaway you have?AI as an Ecosystem PlatformSarah Guo: I, I'd say there are, uh, perhaps the, the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you, you view what's happening right now, I think this morning's keynote was how can any company, whether it's an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.MAI Models & Training StrategySarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it's becoming even harder to build a clean lineage model just because there's so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.In fact, that's one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?So it's not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they're not really that critical- They're work, yeahSwyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there's a new frontier.Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this AI strategy that you're rolling out.Lessons from Two Years of AI DevelopmentSwyx: I'm wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we're gonna really throw a lot of computer transformers.”Satya Nadella: Uh, and they've helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they're climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it's very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.Sarah Guo: Because right now I think when people say, “Wow, I don't want a token max,” it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.Real-World Value & Use CasesSarah Guo: What are some of the use cases that you've seen that have created the most value for your customers?Because I know that people talk a lot about code, and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it's interesting, by the way, Elijah, to even talk about the coding, right?Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that's why we need a canvas.So it's kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I'm positive that six months from now we'll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?I can... Sort of given even my identity, did a bunch of work, then of course I'll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that's where compressing of workflows, uh, completing of tasks, uh, that's where I think a lot of the value gets created. I think you raised a really interesting point, which is there's the actual agent that's doing the code, and then there's a harness around it, and that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent.The Harness Concept for Enterprise AISarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That's right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn't matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient.Uh, and then you're feeding it with very rich context because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products.It's available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we're gonna have these models, and we'll have an API business, and we'll support enterprises and startups.Sarah Guo: ButPlatform Strategy & Developer EcosystemSarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing, I think, with the Microsoft ecosystem. Uh, if, if that's the case, tell me if it's the case, uh, ‘cause obviously you have first-party products and you have enablement products.Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there's always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.It's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company, having private evals may be the biggest IP, right? Think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.Like, so in other words, another te- acid test is you have an eval that's private. You're using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you're in control. If you can't, you're not in control, and that's where even the harness decision becomes super important, right?swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.Maybe like the third act is that you're a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?Satya Nadella: That's it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That's it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what's a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that's not a developer conference. Uh,IP, Evals & Company Valueswyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it's this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.Satya Nadella: It's a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That's so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn't know how to capture the tacit knowledge.swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that's what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you're talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.Future of SaaS & Business ModelsSarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against.Elad Gil: Um, I'm sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what's happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that's kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.I don't need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That's right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That's business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?I mean, if you look at it, d- what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there's a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that's all gonna move to the cloud.”But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...Sarah Guo: For example, there's going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that's sort of what we are doing.Pricing Models: Per-User, Consumption & OutcomesSarah Guo: I don't believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we've had... Like, let's even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it's the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.Satya Nadella: And, and per user is just a set of entitlements to usage, right? That's kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.So people will say, “I want consumption.” And it's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption's outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, [00:21:00] right?Mm. I mean, like I, I've talked to customers who love, you know, outcome-based pricing, and I say, “I'm all in,” until they, “Oh my God,” like, “what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.Durability of SaaS & Build vs BuySarah Guo: How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We're not gonna work with you anymore,” or, “We're considering an internal project.”And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can't rebuild everything.” How do you think about what's durable in this world and what isn't? Yeah, it's a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right?Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it's a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through.And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?Sarah Guo: Because I think there'll be very little tolerance for anybody who's inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We're selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...Swyx: There was a section of you building your [00:24:00] own software. I'm curious if you're building anything now. Yeah. So I, I think the... You know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right?Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler's, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn't mean every one of us should be doing the same thing.The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Uh, right? So there's autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?We're gonna have that obviously in, uh, Scout. That's what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,Future Engineering RolesSarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there's, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents, it'll be four deployed engineers or FDEs, it'll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.Satya Nadella: Yeah, I- Do you think that's a correct view of the world? Yeah, I mean, I think, I think we'll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?So they went and said, “Hey, let's bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It's not like the design person still doesn't have the design edge, or the front end [00:27:00] person doesn't have the front end edge, but you can give yourself bigger scope in roles so that you're not confined to one role.Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we've realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it's a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we'll see how these evolve, right? Where's the s- real... I mean, always the world will have a bunch of specialists.Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I'm now a gen- Like, what... I've basically translated [00:28:00] knowledge work Right?Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It's in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we're gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea peopleSarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, butAmbition & Making the Impossible PossibleSatya Nadella: how do you think about how Microsoft can be more ambitious now? It's a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?What was impossible and what can we build? And I'll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.I mean, it's crazy. Wild. Yeah. Right? It's pretty wild. And it's the same team. So they saw that and they said, “Bob, this just ain't gonna work if we don't reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don't need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would've been, we need 4 billion typists?But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right, which is whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.Data Center Build-Out & Community ImpactSarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I'm just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you're seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it's great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it's clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways, uh, at the community level, right?Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it's bringing down prices because long term there's going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.What's the tax base that's there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.Um, but at the end of the day, if there's-- if we can really be the produ-- Wait. I've always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don't [00:34:00] do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we'll be in a great place.Sarah Guo: Uh, and that's at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you're doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?Satya Nadella: That's I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It's sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can't be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.Elad Gil: Yeah. I guess I wanna talk aboutSocietal Impact & Optimism About AIElad Gil: what you're most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you're saying what's the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.Satya Nadella: Right? That's kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.It's just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can't wait. See, the one thing, Eli, that I've now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we've got it. The g- future is gonna be glorious.”Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it's too important this time around. It's too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yepit's [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,Education & Future of LearningSarah Guo: education seems like another one that's an- Yep ... obvious good where we haven't seen as much impact as I'd expect.Swyx: Do you have a hypothesis on why that might be, or if it'll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it's fascinating to listen, uh, to how to even rethink- MmSatya Nadella: uh, what does education really look like. Because I think it's actually very important. Mm. Uh, and I'm not saying anything traditionally being done is less important, right? I was even looking at the, uh... It's fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It's going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?So I think that there's a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that's highly valuable.Well, that has felt, uh, perhaps impossible for a long time, but it's a great note to end on and something that might be possible. It's still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

What changes when you stop investing in startups and start building one yourself?James Johnson is joined by Anders Hammerbäck, Co-Founder and CEO of RedpineAI, to discuss his journey from venture capitalist to founder.After spending six years backing early-stage startups at Antler, Anders left the world of investing to tackle one of AI's biggest challenges: unlocking access to high-quality data outside the public internet.Together they explore:• Why Anders left venture capital• The realities of founder resilience• What investors often misunderstand about entrepreneurship• Building AI companies in a rapidly changing landscape• How founders can stay ahead of technological change• Why thinking 100x bigger creates different decisions• The role of people, teams and AI agents in future businessesThis is a conversation about ambition, resilience, technology and what it really takes to build at the frontier of AI.New episodes every week.More from James:Connect with James on LinkedIn or at peer-effect.com 

Build AI engineering skills at Parsity. Spots filling fast.It's been a rough week. Meta just laid off 8,000 people, and then the CEO of ClickUp — a company most people have never heard of — went online to brag about cutting 20-something percent of his staff even though they're profitable. No financial pressure. Just vibes. Just "AI made our engineers 100X more capable" so we don't need these people anymore. Then he had the nerve to talk about million-dollar salary bands for the survivors while publicly dunking on the people he just fired.I'm not here to cover the Meta layoffs. There are a hundred channels doing that. I'm here to talk about the people nobody covers: the developer at the 50-person company who gets two weeks and a Slack message. The person who doesn't have a FAANG brand on their resume to fall back on. That's who I was when I got laid off in 2023.In this episode I get into what actually happened when I got canned - the Zoom call with the person you've never seen before, the access revocation, the immediate panic. What I did right after (not much). What I did wrong (a lot). The psychological damage that nobody talks about, and the things I wish somebody had told me before I spent weeks spiraling.This isn't a "5 tips to be layoff-proof" episode. I don't think layoff-proof exists. This is what it actually feels like, what actually helps, and what I'd do differently if it happened again tomorrow. Because it might. Your company doesn't have any loyalty to you, no matter how good things seem right now.If you're going through it, you're not alone.

Most companies are using AI completely wrong. They use ChatGPT in isolation, run random prompts, and wonder why nothing compounds. In this video, I break down the exact Single Brain system we use to connect agents like OpenClaw, Hermes, and NemoClaw into one unified intelligence layer that helps teams move dramatically faster. We cover how these AI fleets plug into Slack, HubSpot, Salesforce, Google Search Console, analytics tools, ad accounts, and internal data systems to create a compounding workflow engine that actually generates revenue. I also walk through real examples including AI generated ad creatives, automated reporting, scaling top performing campaigns into hundreds of variants, reducing operational costs by $500,000, and how one person with agents can outperform entire traditional teams. If you want to understand where AI agents are actually heading and how businesses are using them to create leverage right now, this is the framework. Chapters: (00:00) Why most AI adoption fails (02:06) Connecting all your business tools into one brain (03:22) The AI org chart of the future (05:20) Why most teams are still using AI wrong (06:31) Human timelines no longer work (07:10) Building ad creatives with AI agents (08:06) Scaling campaigns into 200 variants automatically (08:47) How $7,500 in tokens saved $500,000 (10:02) Why AI agents will replace traditional workflows

The Great Talent Redistribution: Where is Talent Actually Going in 2026 and beyond?  Is the start-up compensation model broken? How about big Big Tech? How about non-tech small & medium businesses? What is happening to talent, going forward? This and many other topics in this episode of Tech Deciphered. Navigation: Intro The Broken Contract? The Great Unbundling The Three (?) Destinations Alternative Cap Tables, Alternative Compensation Models Investor Landscape Fragmentation Operator Playbook and Predictions 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 Nuno Goncalves Pedro Introduction Welcome to episode 77 of Tech Deciphered. This episode will focus on the great talent redistribution. Where’s talent actually going in 2026 and beyond? The Silicon Valley deal of the last 30 years, very low salary, stock options, you will either sell for a ton of money or IPO, and everyone gets rich, is seemingly broken. Or is it really? The dominant narrative says the tech middle class is dying. We disagree. There is obviously a lot of stuff going on whereby big tech is partially barbelling. There’s a superstar concentration on the top. There’s a bit of a seemingly allowing of the belly. We’ll come back to that. We don’t quite believe that is totally true. There’s a collapse at entry level. The belly is migrating into three, potentially even more, very different destinations: AI native startups, human-verified premium businesses, and the read the industrialized middle of the S&P 500 and SMB world. Each has its own cap table, each will have its own compensation model, and each will have its own investor profile. In some ways, this is the third episode in our Reset trilogy. We started with episode 75 on the SaaS-apocalypse. We talked about the great private capital reset in episode 76, and now we talk about talent redistributions. Bertrand, exciting times, not always positive times.   Bertrand Schmitt Yeah, it’s exciting times because it’s a time of change. Of course, we have the doomsayers. If you listen to Dario Amodei of Anthropic, every white-collar job on Earth is going to disappear. I think I strongly disagree, and I suppose you too as well, we strongly disagree. It’s going to be more of a redistribution. If you look at the history of technology, this is what always happened. We forget how many jobs have disappeared over the past 150 years. We move from a time of 150 years ago. People were mostly in agriculture. Then you had a lot of weird jobs that disappeared from people transporting water to people bringing ice from the pools to people doing the job of computers. People forget that computer was a title given to human beings. We’re doing calculations. Then, of course, secretory jobs in the ’80s, ’90s, where suddenly anyone can type using a word processor, the rise of Excel, that sort of stuff. Many things have changed. Some jobs have indeed disappeared. Some jobs have totally transformed. Where you do these jobs have changed. I think we are at a similar stage where, thanks to AI, and I would say for now, or at least the rise of AI coding, there is a dramatic change happening. I don’t think it means that people will be without a job. It just means, from my perspective, that jobs are changing. You are not just doing a lowly coding level task that actually indeed could be replaced, but you are going to have more of builder type of mindset, a product manager type of mindset going forward. We also expect that the distribution of jobs, depending on the type of business, will be quite different.   Nuno Goncalves Pedro The Broken Contract? Maybe let’s reset a little bit to the broken contract, or if it’s really a broken contract. There’s been this image in technology and tech that basically you get paid very little to work in tech. You get a bunch of stock options. The earlier you are in the company, the higher the level of stock option grants you get. Then you make a ton of money at some point because the company will either sell or IPO, and that’s heard of it. Obviously, there’s a lot of movements happening right now that are changing how these dynamics work. The first part is obviously AI, and in some ways, AI is shrinking companies. It’s not unheard of that companies with as little as four or five people reach 50 million in ARR. There’s companies with one person that have gotten bought for hundreds of millions of dollars or billion of dollars. Obviously, things are moving very, very fast, and therefore, there isn’t a large employee cap table. How would you share the upside? Would you actually give a couple of percentage points to an early employee rather than your 0.2-0.5% kind of thing for early employees? The second part is a little bit the other side of the table, which is the IPO market is seemingly in a drought. There’s not much happening in IPOs. Maybe 2026, at some point, there will be an unlock, but right now, it’s seemingly difficult to get your upside. Even if you’re an employee, you have to wait a long time. The median time of IPO has climbed over 10, 11 years, the longest in over a decade. Basically, not only you have to wait a long time as if there is an IPO drought, like we might be going through right now, when do I actually get my cash back? Unless the company gets bought, maybe there are secondary transactions along the way, maybe there’s something else. But obviously there’s a little bit of a reduction and lowering of the upside seemingly for this contract and for this place. The easy conclusion that I think many are taking is, because of all of this and all the layoffs that are happening, even in big tech, that serve the tech middle class is dying, that basically AI screwing the workers, et cetera, there’s also a lot of discussion that even it might be affecting the entry-level jobs as well. Everyone coming out of undergrad right now can’t get a job, et cetera. There’s this doomsday scenario that you’re alluding to that everything is changing. We have a slightly different perspective. We think there’s a realignment of market. In layoffs, there was a lot of layoffs that were warranted. Big tech, in particular, had actually hoarded a lot of engineering capacity over the last decade or so. There’s a little bit of a realignment that needed to happen in any case. When everyone’s saying, “Well, AI is compressing everything,” well, it’s compressing right now, but we don’t think actually it’s going to compress over time. You’ll still need engineering and science talent to come on board for you to be able to scale up. It’s not like AI is going to take care of everything and teams are going to be five people for companies that are worth a trillion dollars. That’s not happening. Today’s thesis, I think a little bit of this doomsday scenario needs to be seen with a more nuanced lens. I think that’s how we’re framing today’s episode, that there’s a bit of a nuance, there are some extremes happening. We’re going to talk about those extremes, but ultimately, it’s not quite as simple as saying that the tech middle class is disappearing in early jobs are going to be a thing of the past.   Bertrand Schmitt At the same time, what you started with is true. I mean, that 50 million ARR company, just five people. At a bigger scale, that’s exactly the matrix for Anthropic. They have reached a stage where they are at a range of 12 million ARR per staff per employee. It’s metrics that are definitely never seen before. I don’t think any company raised to this level. Best in class, best run companies, one, two million per employees. I mean, that was your target if you can make it. We are definitely in a different game. But I think what matters at the end of the day, and that’s what we’re arguing, is that you have to see the big pictures. Yes, some positions might disappear inside some companies, but some other positions will be created in other companies. Usually, what people do is keep talking about the jobs who disappear and not looking at the bigger picture of jobs that are being created as well. What is true, and I think you alluded to that, is that the big tech the past 10, 15 years had some strategy of hoarding talent in a war where having the best talented people will make the difference in numbers, will make the difference between winning or losing. The Google of the world, the Microsoft of the world, the Amazon of the world, they were hoarding talent. They would try to make sure that they might not have such needs in talented number of people. But if they have the talent, it means their competitors didn’t have the talent. It means that the startup trying to reach scale couldn’t pay the giant salaries that the Google of the world were paying. There was definitely some hoarding. But it went so far in the 2020, 2021, that I think since then there has been a coming back to normal. There is also now in 2026, the recognition that it’s not true anymore. Yes, talent can be very valuable, but there is now a bigger and bigger gap between the extremely talented versus the rest that are merely talented because of AI. AI is able to replace at scale your software engineers, your software managers. I would say it’s quite new. I don’t think it was true a year ago. We’re really talking about a recent dramatic change in what can be achieved thanks to AI. We can see most of the big AI companies are moving to coding. It was started by Anthropic as a trend, OpenAI has followed through. Obviously, the Cursor of the world existed before, but they were not as successful. All the Chinese open-source models are moving very fast to coding optimization the past few weeks. It’s quite an incredible change. I think there is that dramatic change, recognition that coding can be done differently. As a result, we are going to see change in the distribution of jobs. I think it will start from the top because we see the news of the big Google, Microsoft, Amazon, and others who used to hold talented software developers to a change in realization that no, we actually need to invest in AI. We need to invest in compute because compute is going to do the job of most of these people. Therefore, we can’t pay for both at the same time, even us with all our money, we cannot. Wall Street is not going to let us do that. They start by removing a lot of position. I think we see that accelerating, quite frankly. We have only seen the beginning, but in the next 2 years, we see a dramatic shift. But I think my position, I guess yours, and you know as well, is that there will be a lot more opportunities created as well, probably by also entities.   Nuno Goncalves Pedro The Great Unbundling Yeah, there will be more opportunities created. The hoarding is just taken also a little bit of a different view. To your point, there’s hoarding of resources, compute, et cetera. But there’s also hoarding of top talent. We are seeing people getting paid, packages all in that could run up to 100 million, in some cases even over 100 million over several years. This is unheard of. I mean, an officer of Meta would make, I don’t know, maybe 20, 25 million a year. It’s like now there are people that are on the top end of AI researchers that are getting paid around that amount just to join some of these companies. There’s a little bit of a different hoarding. It’s very selective hoarding of certain talent. We’ve seen some acqui-hires. We’ve talked about it in previous episodes that are just literally about getting one or two people specifically to come on board. Alexander Wang, again, going to Meta to lead their intelligence labs there. I feel, I don’t know what you feel, but I feel this is a transition moment where there is overpaying for certain talent on the top of the market. At some point, this will stabilize. You can’t keep paying people 100 million over 4 years or something like that across the board. To your point, a lot of this is actually going to scale up quickly also on the AI side. There’s a little bit of a different hoarding happening on the top end, not just the resources, but also of people, which seems to give further this notion of barbell, that there’s two extremes, the haves and have-nots, the super-duper talented people that get paid a ton of money, tens of millions of dollars a year at the very least. Then the emptying of the middle where there’s a ton of tech layoffs going on in some ways, the belly, as they would call it, is being expelled. The middle market, the managers are being fired because there’s nothing to manage. There’s a lot of positions going away. In some cases, you might keep some of the more junior talent, but with a little bit of experience. But even the talent coming out of colleges is not getting hired either. It’s a little bit of a weird thing where there’s hoarding at the top, there’s an emptying of the belly, the middle, and then the early, early, early is also not getting recruited. It’s like what gives? How is this going to look in the future? I agree fully with you, Bertrand, that there’s a migration of this talent, not only to other companies, but also to other jobs. There will be new jobs that will emerge out of this. The DevOps, dev tools market didn’t exist until maybe 20 years ago at scale, and it got created. In some ways, we’re seeing there will be new markets, there will be new roles and new jobs that will be created around engineering teams going forward. We can’t anticipate all of them. But basically, the emptying of the belly is true as it’s happening right now. The low hiring on the early and the top end, getting tons of money. We think this is a transition to something else. There’s the hoarding of engineering in general is coming to an end at momentum. Now it’s time to rightsize teams, to get the right at the table, et cetera, and start figuring out what works and what doesn’t work. We’ve already had some horror stories coming out even from Amazon where they were breaking systems with their use of AI tools, and I’m sure it’s happening across the board. I’m on a board of a company and been tremendously affected by Meta and its algorithms, where basically because of advertising, there have been people served with ads for this specific company where the ad doesn’t match the company, so basic stuff like that. It’s been actually very, very difficult because in some ways, the company goes back to Meta. It’s like, “Hey, dudes, you guys are serving ads that are not even our ads with our copyright and stuff. How does this work?” They’re like, “Oh, it’s AI.” It’s like, “Well, it’s AI but can you give me my money back?” They’re like, “No, we won’t give you money back.” This creates huge issues for companies, for example, that are very dependent on advertising, which obviously there’s a lot of industries that are. They’re actually in production systems at scale. Meta is, I think now, the largest digital advertising in the world. I think they outgrew Google in one of the last quarters. Basically, this has a tremendous effect that systems that are in production at scale are getting inputs and changes driven by AI tooling, and somehow nobody can say what the hell is happening. Again, there will be a reckoning, there will be a redistribution, there will be a rightsizing of teams and an adequacy of teams going forward. I personally think this is a transition period.   Bertrand Schmitt I think we are moving from hoarding or software engineering to hoarding the top of the top scientists in AI and hoarding of GPUs, GPUs/data center. For me, it was quite interesting to see the deal of Cursor with xAI, where basically they couldn’t get access to computing resources to run their model. But xAI had, I forgot the exact numbers, but close to half a million GPUs that no one, I mean, “no one was using” because their services are not so successful yet in terms of AI chatbot and the like. Basically, suddenly they are like, “You know what? We control access to resource.” But the new resource is, again, a mix of extremely talented AI engineering or AI scientists versus GPUs/data center. There is this race of controlling boss and everything else is going to be collateral damage. Some examples, I think, are quite interesting. You talk about some example of Amazon, even some production issues. I remember reading a quick post-mortem of one of the issues, and the conclusion was it was AI, definitely part of the issue. But the other part of the issue was AI used by junior engineers. For me, it’s interesting. It shows that actually junior plus AI is actually a danger zone. That’s why many companies are going to be way more careful. “Why do we need the junior people if they are just playing with fire?” I think we go back to that situation of barbell, as you call it. The top talents are extremely valuable because they know how a production system works. They are here to develop better AI systems. But the junior guys playing with fires, yeah, maybe it’s cute in startups, but in a big time production environment, a different story.   Nuno Goncalves Pedro There will be a barbell with top-end talent super-mega paid and then mid-level talent that is individual contributors still doing a lot of great work, et cetera. Along the way, a lot of emptying of entry, a lot of emptying of the middle. Where does the talent go? The Three (?) Destinations I think we could say there’s three destinations for this talent. Maybe there’s four, maybe there’s more. Three that we can immediately identify. One is the AI native startup piece, where we have smaller teams that potentially get to a lot of revenue or top line over time, and where the Series Seed is the primary round, where we’re seeing Series Seed being raised of tens of millions of dollars, actually even hundreds of millions of dollars in Series Seed. In some ways, the stars there can get incredible compensations in terms of stock. They will stay for private and selling in secondaries later down the road because there’s so much capital at the table. Actually, in some ways, salaries are very high as well in some of these companies. It’s not like you’re trading off anything. You can get paid a lot of money. If your company at Series Seed for 10 or 15 employees has raised 50-$100 million, you can pay great salaries. In some ways, this is the extreme destination. The AI native startups that can make it is the extreme destination. Now, there aren’t a ton of AI native startups that can raise 50-100 million to 400 million in Series Seed, just to be clear. There’s a handful of hot deals in that space, but that’s one clear destination for top-end talent going through that. In that market, I think that’s one of the destinations. The second one is more what we would call the human-verified premium. It’s more of a play of companies that has still the need of human in the loop, either in terms of development, also in terms of activity, either because go-to markets are very intensive, and so therefore you need to have sales forces, partnership teams, et cetera. Or on the engineering side, it needs to have a lot of customization, integration. Companies are not just going to the, “Oh, you can come in and just apply your AI tooling and somehow magically the systems all work.” there needs to be quite a lot of and work and high touch work in getting stuff done. A significant part of that market, I’m not sure, is super VC investible. Maybe it’s a hybrid of private equity in VC, more PE style in many cases. It’s a PE-hold, sell to someone else market. As we’ve discussed in a previous episode on the SaaS-apocalypse, that hasn’t quite worked out for PEs. Question marks on how that human-verified premium market is going to evolve. But obviously, there’s a lot of work still to be done there, even on the engineering and science side. That’s the second potential destination. Then the third more aggressive destination is the reindustrialized middle companies that have a lot of specificity in going after small and medium businesses, local or regional affectations like ERPs or CRMs for specific markets, et cetera. Those are the three natural destinations. I would add the fourth, which is big tech. I mean, big tech doesn’t magically disappear, and I don’t think it fits neatly into any of these three markets. In some ways, big tech is now looking at the extreme for top talent a little bit like the AI native startup because they can pay. They can pay the 100 million every four years, et cetera. I do think it will typify taxonomically into a fourth type emerging, where, as we discussed, you’ll have top-end individual contributor talent. You’ll have the absolute top-end of the market because they can get paid. Then you’ll start having the emergence of earlier talent that is highly capable, et cetera. That will go back to a bit of a normal distribution in terms of talent on big tech. For me, those are the four destinations that I would put at the table.   Bertrand Schmitt For me, big tech moving to big tech, I’m not sure if it’s really a destination. I mean, yes, in some ways it’s a reshuffle between the big tech companies. They are definitely all fighting in some ways for some of the same people. I can see that dramatic shift where big tech has to remove a lot of positions in order to replace by AI. Again, I think at this stage, it’s mostly driven by AI coding. We are still at the beginning because this is brand-new phenomenon that AI coding is so successful at its task. I don’t think it was true even 6 months ago. Some companies, take Anthropic, take OpenAI, are definitely there or close to be there in terms of no more writing of a single line of code by a human, zero. This is, again, 6, 12 months ago. Not true. But now it’s true in a few top companies. Take OpenClaw as well, most successful GitHub project of all time, not a single line written by its author. It would have been impossible. We’re talking about hundreds of thousands of line of code in a few months. It’s impossible to achieve that manually. If you look at the other big tech companies, the Google of the world, the Meta of the world, the Microsoft of the world, they are absolutely not there yet. They are going to be there because they have no choice. It’s you either go fast there or you die. You are not going to be able to survive competitors that are shipping 10, 50, 100 times faster than you are shipping. It’s a life and death situation. All the big tech companies are going to move, and mark my word, in the next 2 years from 10, 20% of AI-written code to 100%. During that transition, the next 2 years max, if you don’t do it in 2 years, you are going to die. Your stock price is going to crash. Then, of course, you will have to make changes. You will have to invest more in GPUs. You will have to invest less in your standard typical software engineer employees. Like you, I’m very optimistic that there are new buckets. AI-native startups definitely will be there. It will be transformational. Human-verified premium, very interesting category. In a way, it will be businesses that are inevitably less scalable through AI, and there is definitely a spot from there. I think the biggest would be the reindustrialized middle SMBs. Most of S&P 500 type of business are going to dramatically offer new software opportunities, new opportunity story to talented software employees because they will need to implement AI in everything they do. They will do it. They will need people who have software engineering knowledge in order to implement these systems. For them, what’s changing dramatically really is that thanks to much cheaper cost as thanks to AI coding, a lot of software projects that they couldn’t afford to do, that they couldn’t imagine doing by themselves, they are able to do it. They will invest in a lot more software capabilities than ever before. That will be a big game changer. And software, very tuned to their business model. There might be less buying of your traditional off-the-shelf SAF software and a lot more investment in a highly custom software by their own team, assisted with AI. I think that would be the part that is most transformed by all of this in a positive way.   Nuno Goncalves Pedro Alternative Cap Tables, Alternative Compensation Models This will lead to a very fundamental shift, right back to the broken contract. What does the new contract look like? It looks like alternative cap tables depending on which bucket are you transitioning into. If you’re going into your AI-native bucket, and you’re a top-end talent, you’re like, “Dude, I’m worth 100 million over 4 years, so just compensate me accordingly with a mix of options in the company plus my salary.” If you’re top 1%, you can probably get away with salaries that you’d get anyway at mid-level from 300K, 400K and above, and you can get actually a lot of options already in the company. A lot of this is happening right now. There’s a premium for AI, we know that. There’s a premium for AI at the top end of AI researching, in particular on companies that are doing hardcore research on staff AI engineers, so companies that require actual AI engineering. There is a premium that is significant. It could be as high as 18% over non-AI peers, and it widens actually with seniority, shockingly enough. This is more of an average than anything else. Now, for me, and it’s for debate, but the perspective is this extreme comp will need to compress at some point. There will still be the haves and have-nots paid much better than the have-nots, so to speak, but there will be a compression. The variance can’t be the variance we’re seeing today for absolute top-end talent. That said, there will be variants. We know that big tech for over a decade, decade and a half, for example, in the Bay Area, has been paying a lot of money for director and above levels that used to be the VPs, so a million, a million and a half a year, all in compensations. It’s not unheard of that this will actually increase after this stage. That said, I do think that the compensation extreme that we’re in will get diluted down the middle. It will actually come down at some point. It’s part of where we are today. As we know, it is still a bubble.   Bertrand Schmitt Yeah, it’s an interesting point. I think it’s possible. At the same time, that compression coming 2, 3, 5 years. At the same time, we have examples where there is no such compression. Take the top sports players in the world, golfing, basketball, NBA players. There has not really been any compression at all. For me, it’s interesting. If you look at the big tech companies, each being one of this top NBA team, why would such compression happen? As long as they are competing against each other and generating plenty of cash, I think there will be some fair question. We will see. I don’t have a strong opinion, but for me, it’s not a total given.   Nuno Goncalves Pedro For me, the shocking thing is the faster AI becomes better, the more that compression will happen, because at some point, it’s like, why do you need the top talent as well? I don’t know. It feels like you’re trying to evolve a system that’s there to replace you. It’s like, “Okay, I’m getting paid 100 million over the next 4 years”, and then you develop something that’s so good that replaces you. Thank you. That’s cool.   Bertrand Schmitt That’s a total possibility, yes, because we are in that very unusual market where the game is to only replace yourself and people like yourself. At some point, it is a possibility, I guess this one. Right now, we’re talking about replacing your “average software talent”. In 2 years, could we absolutely replace the absolute best top experts in the world? Probably. I think it’s just that at some point we’ll be reaching the stage where we strictly have no control anymore on our AI systems because no human is able to challenge and understand what’s produced. It’s not just a question of scale anymore. We’re talking about a gap in IQ, basically.   Nuno Goncalves Pedro Exactly. It will happen at some point in history. We don’t know exactly when. For the second bucket, the human-verified premium bucket, it’s difficult to see how an HVAC company or an HVAC roll-up of scale or a regional health care platform or high touch go-to-market, B2B, SaaS play, et cetera, for a vertical will compete. At the same end, they have to compete and they will compete. There will be more and more jobs, we believe, for engineering talent in these companies. They’ll have to be more and more AI-enabled themselves. The cash salaries will have to be competitive within the local markets, not necessarily with Silicon Valley. There will be potentially profit sharing and revenue sharing and actual dividends played at the table. The model there on the cap table needs to change a little bit, needs to be probably propped up more on salary and on some way of doing profit sharing or actually having dividends paid to employees and figuring out employee to equity in a more aggressive manner. This is the market that probably was already very attacked, so to speak, or let’s say, occupied by private equity firms. There are still obviously part of that model that would work well. There needs to be a fundamental shift, certainly on the quantum of salary compensation, dividend compensation, profit sharing, and all of that. Then last but not the least, obviously, we had the bucket around basically the reindustrialization of the middle, so everything else, which will take most of the belly that we were talking about. This is probably a poor analogy, the belly fat. It’s not belly fat, it’s people that were doing their jobs that now are getting disrupted. In some ways, that bucket will absorb a lot of that belly, will absorb a lot of talent. The small and medium businesses that Bertrand was saying will need to crucially become more AI, software-enabled by themselves, even with some core stuff and underpinnings that actually might not even require AI in terms of infrastructure platforms. There, you need to get properly paid. Again, how many people do you need in your engineering team if you’re a small business? Probably not a lot. It’s maybe you need one or two people and that’s it. They’ll need to be very nicely paid because they’re running the stuff in the rails. This is probably a market that over time, as AI gets more and more competent, will also be disrupted, but let’s not talk about the disruption to the disruption because otherwise, we’ll stay here the whole day, but certainly a market that has a lot of potential to shift and to absorb a lot of the moments that we’re seeing in terms of layoffs happening in the US in particular.   Bertrand Schmitt This category was a category that historically could not compete with Silicon Valley salaries, could not attract the most talented engineers. It’s not a category that didn’t want to bring these people on board. It’s a category that just couldn’t afford to bring this talent on board, typically. I think it would be a dramatic shift for them when suddenly there are opportunities to hire these people. There is an opportunity to hire them at maybe more reasonable prices from this company’s perspective. You talk about small companies, the great thing is that there are millions of small companies at some point. I think things could be truly transformational. Of course, some of these engineers, software engineers, might decide to become entrepreneurs on their own. Solo entrepreneurs, small businesses, build their own, easier to build their own product to market so to serve other companies. I think there will be quite dramatic changes because not all companies will be disrupted by AI as much, but not every company will benefit from improving processes, improving software through AI. At least early on, you will need this human touch to make it work inside a business. Interestingly enough, I was hearing that some companies like IBM were hiring more younger people to do the work of going to the client, understand their needs, propose implementation plans. That forward deployed engineer, those positions, I think there will be more and more available.   Nuno Goncalves Pedro Investor Landscape Fragmentation What happens to investor into the landscape? We already had an episode, the previous one, Episode 76, where we talked quite a lot about the big capital reset on the private equity and private reset, including venture capital. Just maybe to summarize, how does it align with the buckets that we’ve just been discussing? I think the AI-native bucket clearly is going to be the key bucket. There, we’re going to see two movements. One movement, which is the mega funds, as we discussed in the last episode, are no longer just VC funds. They’re really mostly multi-asset private equity funds, maybe even private equity hedge funds in some cases. Those funds will be all over the high-growth AI-native companies and will be pouring money into companies that are scaling really, really quickly. The early stage, so to speak, VCs, the actual VCs that will stay in the market will be the guys probably identifying the next big wave of AI-native companies. We’ve discussed that as well in the last episode, some research that we did at Chamaeleon that I shared in episode 76. We’ll see that as emerging. What happens to the second bucket, the bucket around human premium, human in the loop? Likely we’ll have more and more private equity capital going into it and the large-scale VC guys, the Thrives of the world, they’ve just announced Thrive Holdings, and others going after those markets as well. It’s trying to converge into the private equity market, which aligns with the point we made in the previous episode that the VC mega funds are no longer VC, that they are private equity, multi-asset class. They’re going after a bunch of things. There’s a conversion happening from VC into private equity. It was going to happen anyway because the private equity guys were coming into VC as well and the hedge funds were coming to VC as well. There’s a convergence in the middle of very, very large funds and large assets under management happening to go after some of these opportunities, certainly in Bucket B. Then this Bucket C, so to speak, the bucket of reindustrialization, as Bertrand was saying, very well, likely will be self-funded for a significant period of time. Will self-fund with their own cash flow. Doesn’t need to have a ton of capital intensity. Maybe you need one or two engineers to do stuff, but that’s it. You don’t need tons of capital. You didn’t need in the past, you won’t need it today. Not sure there’s going to be a fundamental shift to that market.   Bertrand Schmitt Yes, I certainly, overall, agree with you. That last pocket, probably little change to the capital and capital structure. Again, I see that as the biggest opportunity for a lot of people who might be less needed by big tech and also top tech companies. What is sure for the first category, the high native startups? I would say more overall in the VC ecosystem, there is no space left for SaaS anymore. I think SaaS, as we used to know it, is dead in some ways in the sense that new pure SaaS software startup are definitely out. Existing ones that are critical to run your infrastructure, the Salesforce of the world, I think they’re in a decent spot. Actually, interestingly, they changed their pricing model to now sell to AI agents, not just per seat. There is a change in pricing there. But this day and age of funding a pure SaaS software startup through VC money, no way. VC money going to AI-native startups, AI-focused startups, to biotech, to deep tech, to defense tech, yes. SaaS as a fundable category early on, I think it’s over.   Nuno Goncalves Pedro I’m a bit more nuanced as we shared in The SaaS Apocalypse episode. We can call it whatever we call. It’s applied AI is the new SaaS thing. Horizontal applied AI is the new horizontal SaaS or vertical applied AI is the new vertical SaaS. I agree in common with your point that very specific point solutions around SaaS will be disrupted by nature with all the easy stuff you can do today with AI. It will take a while. This is not something that’s going to happen this year. It’s going to happen over the next years. Maybe interesting to also talk about the exit markets. I think the IPO market, as we’ve also discussed in the past, there is, in my view, going to be a reopening of the IPO market, I think this year, probably later in the year, third or fourth quarter. The median time to IPO actually is going to be really weird because there’s going to be potentially some companies in the current landscape, bubble or no bubble, that are going to IPO, the OpenAIs of the world, Anthropics of the world, et cetera. There will be more and more aggression, I think, on M&A. Big tech has already shown it, that they want to buy into markets. Large non-tech companies have also started doing acquisitions in space. To prop up their IT teams, their engineering teams with this world that we’ve also discussed in previous episodes that I’m going to own my own engineering stack for now. As we see, that normally doesn’t withstand the test of time. At some point it will get unbundled and served by someone else. Then finally, the secondary market is very hot right now. Obviously, there’s heavy discounting on some areas, high premiums on others. The exit market, strangely enough, is going to be propped up, in my opinion, over the next year to 2 years, dramatically. Then we’ll see if there’s a big reckoning around the bubble that we are clearly in or not, if it’s a soft landing or hard landing. Definitely, there’s going to be a lot of exit paths over the next year to 2 years.   Bertrand Schmitt Concerning the “bubble”, I have two perspectives on this. One is it’s a bubble in the sense that money is going to a lot of players and some players are going to blow it up. There will be a concentration of players at the end, like it usually happens. If you look at, for instance, long time ago, the railway revolution, there was that intense influx of capital. At the end of the day, there was a dramatic change in transportation in the US and a complete railway system put in place. Yes, some investors lost money, some companies went bankrupt, but the transformation was fully real. There were a lot of top leaders at the end of this revolution. The change after that only happened, we guess, post-World War II, with the construction of the highway system and the rise of airlines and plane transportation overall. Here I feel it’s similar in the sense that, yes, there is a lot of money going in. Some players are going to blow it. They will misuse the money in different ways, but that’s part of dynamic allocation of capital. Of course, you make mistakes. That’s what happens. At the same time, I feel it’s a similar level in the sense of this is a dramatic change in the US infrastructure. This buildup of AI data centers filled with GPUs, integrated at scale with some of the best software in the world and running it, supported by a dramatic shift in energy infrastructure. This is for me similar to the Railroad Revolution. Some players might not own the data center they build because they didn’t manage well their debt, they didn’t manage to run proper software. You know what? They will get acquired by somebody else. I think we are at this level of fundamental transformation. The fact that in a matter of maybe 2 years, the move from 0% of code written by AI to 100 % written by AI is an insane dramatic shift. Just to be clear, when you move from manually coded to AI coded, we’re talking about a 100X difference in terms of speed at similar, if not better level of quality. The shift is dramatic, and on top of it, you don’t pay salaries anymore to achieve that. You pay CapEx, and with GPUs and OpEx with electricity. It’s a very big shift, positive shift in business model. New unions, no management over it, AI working 24/7. Personally, I think for me, bubble has a bad connotation in the sense of it was all for a waste. I don’t think it’s all for a waste. I think we are witnessing a dramatic revolution of our lifetimes, quite frankly, bigger than SaaS, bigger than mobile. From my perspective, it’s exciting times.   Nuno Goncalves Pedro Operator Playbook and Predictions Let’s move to if you are this person, what would you do in the future? Let’s start with two extremes and go from there. One is you’re non-tech, so you’re not an engineer, et cetera. You’re trying to figure out, how do I scale my activity? Maybe physical labor is where I want to go. It’s not, “Go west” anymore. Definitely not necessarily go west. You should go to, I guess, the states that have no sales tax with very cheap energy because that’s where the data centers are being built if you want to be in that market. Obviously, there’s a lot of stuff that needs to be done: HVAC, electricity work, et cetera. Don’t go west. Go low sales taxes, low cost of energy. That’s likely where the data centers are being built. You probably can just follow. There’s, I’m sure, some way for you to follow where the data centers are being built, but that’s next, I think on that extreme of the table. The other extreme of the table, let’s say you are super ambitious, maybe you’re no longer an engineer, but you’re a product manager in your prompt engineering. You could do prompt engineering all day long. You’re 28, 29-year-old superstar. What do you go and do? Likely either you start your own thing, start your own company because you’re so good at prompt engineering, you probably can do a lot of the code yourself, particularly if you have an engineering background, or you go and join very early an AI-native startup that you think has the chance of going through the roof, and you take a pretty good salary early on, a ton of upside on the company because guess what? Companies like that need product managers. They need people to figure out UX, UI. It’s not going to be, at least for now, yet AI figuring that out for you. Those are two extremes, just to give two of the extremes, like engineering, product management persona, and physical labor at the other extreme, non-tech, et cetera.   Bertrand Schmitt In some ways, every software engineering job is going to become the equivalent of a software engineering manager or a product manager, because suddenly you don’t have to do the coding anymore. You’re managing AI that is coding for you. Either you start to have some manager hat, but we saw the humans, so it’s a very different type of manager, obviously, or you are going to be really an empowered product manager. You’re skipping the middleman. You’re skipping the traditional engineering organization because your engineering organization is AI running and doing the work for you. I still believe that it requires some serious skills. I don’t believe in the vibe coder type of value proposition. I don’t believe in the prompt engineer becoming suddenly super incredible, able to manage that. I still think it requires some serious chops to do the best from all of this and to do it in a safe and sane way. It’s very easy to have poor taste, make mistakes. I don’t know you, but keep reading these stories on the heads of companies who lost everything because of the AI agents. That deleted stuff in production, and they had no backups or the backups weren’t deleted as well. Crazy situation. You cannot run companies like this if you let your agents running wild. You could argue it’s the early days. I would argue it that that issues would be there for a while. You need to have some engineering discipline at core in the company running the business to make sure things don’t go sideways because it would be easy for things to go sideways.   Nuno Goncalves Pedro I totally agree. If you’re thinking, Oh, should my kid go into science and engineering and computer science, et cetera? Absolutely, still, because of everything that Bertrand just said. You need to understand actually what code does and what technology does and what all of that does. That’s still a skill of the future. It’s not a skill of the past. In some ways, it’s still a skill of the future very much. Maybe let’s try two more extremes. Around the same level, the person that decided to do an AI native company bootstrapped initially, having difficulty raising a mega round, but could probably get away with raising a 2-3 million seed round, et cetera. Is that still viable? The answer is yes. There’s tremendous capital efficiency right now happening in the market still, 10 plus higher than if you were doing a SaaS company, and you were a founder in 2019 or something like that. That capital efficiency is going to reverberate. You can run a tighter team, smaller team. Actually, you don’t need that many salaries. If you’re a decent engineer as a founder or if you understand enough as a product manager to just generate that code, you can do a lot of stuff yourself, can bring in maybe one or two technical elements to the team early on as you would have done if you were bootstrapped anyway. There’s obviously a path for that. The other extreme is you’re in big tech, you’re level five, individual contributor, making a ton of money, or you were a manager, and you’re now out of a job, where do you go? You can go to a big company that is non-tech, S&P 500 company that’s non-tech, something like that. You join the company, you’ll probably get paid pretty well, maybe not as high as you were paid in big tech. There’s some stock at the table, but guess what? You’ll have probably more work-life balance than you ever did. That’s the trade-off. You’ll have a better job. On the upside, you can transform the company. You can help and be part of transforming a company from non-AI to AI-first or AI-enabled in the future, whatever BS that will look like in terms of the argumentation to the board. You can actually create tremendous productivity enhancements in a big non-tech company if you come with that background. Again, you’ll have certainly a better work-life balance, so not a bad deal, to be honest.   Bertrand Schmitt Also, to be clear, I talk a lot about AI coding because it’s truly transformational. You could argue that it’s going to be self-improving. We are in the situation of a self-improving AI that keeps improving itself thanks to automated coding. It’s a dramatic, virtuous loop. Obviously, AI is also going to improve everything else. It’s going to improve your marketing, it’s going to improve your search process, it’s going to improve your DNA. Improvements will be everywhere. It’s just that right now we are at a point in the quote-unquote revolution where there is one clear piece of the puzzle that is moving faster than the rest.   Nuno Goncalves Pedro Bertrand, the senior executives at non-tech don’t know anything about that. It could be just a great prompt engineer. That’s the only job you do. “I’m the chief marketing officer. I have someone below me that’s doing the whole work.” Nobody knows. Nobody’s the wiser, I guess. I’m being facetious, but not fully.   Bertrand Schmitt Yeah. There would be a transition period where what you described happen. I want to say, going back to AI coding, I think that the part of AI that as of today has reached a stage of limited AGI. We have reached, from my perspective, a limited type of AGI for coding. If you take coding as a discipline today, I think we reach AGI. If you go beyond coding, that’s true. If we are talking about coding, leveraging the latest LLMs: OPUS 4.7, ChatGPT 5.5, combined with Claude Code, Codex, and OpenCode for harness, I think we’ve reached AGI in the context of coding. I’m not sure everyone fully realize that and the consequence of that. I think the rest is going to come as well. We are going to see that category by category, usually categories that are more scientific in nature, where you can replicate, where you can test easily, where you can create clear success. Metrics will be the “easiest” to follow in that direction of self-improvement. I just want to highlight that this part is truly transformational, the root cause of everything we’re talking about today. At the same time, it’s coming beyond coding.   Nuno Goncalves Pedro I think it is true. There are a couple of markets where that might not hold true, which is maybe the final path. If you’re thinking of starting your own business in plumbing and in HVAC maintenance and installation, this is a pretty good time for the reasons we already said before. There’s a lot of buildup of data centers and all that stuff, but also for other reasons, because it’s an activity that won’t be disrupted by AI yet. You need them embodied AI. You need physicality to AI to do stuff like actually fixing pipes.   Bertrand Schmitt Until Optimus replace you.   Nuno Goncalves Pedro Yeah, but if we’re 3, 4 years out in terms of a lot of these optimizations that we’re talking about at the software layer, we’re 10 years plus out on embodied AI, right?   Bertrand Schmitt Oh, yeah, it’s 10 years.   Nuno Goncalves Pedro We’ll probably be optimistic as we speak. That’s a nice business. I’m thinking of starting to go into that market. If you guys are interested in listening to this, just reach out to me. What’s the angle? I think there’s a lot of stuff you can do in the buildup of some of these businesses, plumbing, HVAC, all sorts of maintenance. There are markets that are just totally messed up. Handyman market in the US is totally messed up. There’s a bunch of companies out there that try to go after it with marketplaces and stuff. I honestly just start something from scratch, a small business, and go from there.   Bertrand Schmitt Yes. They’re an interesting middle. Think about accounting firms, consulting firms. I think they are not as easy to replace, but at the same time, there is no way on what they do is not going to be dramatically changed with AI. I don’t know if it’s 50, 80, 90% of the job, but this is changing quite dramatically, would be my expectation in the coming few years. Conclusion Thanks for listening episode 77 of Tech Deciphered about that great talent redistribution. As you heard it from us, we believe there is a dramatic change in play, enabled by AI coding, and that ultimately a lot of the big tech companies are changing their employee distribution, way more focused on the top talents and bringing more GPUs. As a result, we will see a change in their staffing. Some of this change will benefit AI-focused startups, but probably more likely will benefit the bigger SMBs, the S&P 500 companies of the world that will finally be able to bring inside and afford some of the talent that were in some ways trapped by the top 5, 10, 20 software companies of the world. Thank you, Nuno.   Nuno Goncalves Pedro Thank you, Bertrand

(Note: we had a technical issue that affected the first 12 minutes of Eric's camera feed, so you'll see a bit more wide cam than usual in the beginning. Our apologies!) Why is it that people with half your talent and twice your luck seem to be winning all the time? Because they understand leverage. And you're still trading hours for dollars. Eric Jorgenson spent a decade studying Naval Ravikant, one of the most influential entrepreneurs and thinkers in Silicon Valley, and distilled his wisdom into his classic book “The Almanack of Naval Ravikant”. In this episode, we break down the exact frameworks Naval used to build wealth, happiness, and freedom without getting crushed by the game everyone else is playing. In this episode, you'll learn: Why leverage is the only game that matters now and how code, media, and capital separate the rich from everyone else "Productize yourself” — the two word formula that unlocks specific knowledge and makes competition irrelevant Why hard work is overrated and judgment is the real multiplier that creates 100X outcomes The four types of luck, from blind to built, and how to engineer serendipity into your destiny Why rising above status is the ultimate form of freedom If this conversation about leverage and ownership resonates with you, come to Mainstreet Millionaire Live, where we're breaking down how to source, negotiate, finance, and buy a business. If you're ready to own your upside, grab your tickets here: http://info.contrarianthinking.co/msmlbig-deal ___________ [FLIGHTCAST_CHAPTERS] ___________ MORE FROM BIGDEAL

In a world where tech leaders jump ship every two years, Jake Blough (CTO of Service Express) has spent a quarter-century building a legacy from the ground up. Starting in a family auto shop and eventually entering Service Express in shipping and receiving, Jake's journey is a masterclass in stewardship, grit, and technical innovation. Today, Jake oversees Supply Chain, Data Science, and R&D for a global leader in datacenter infrastructure maintenance. In this conversation, we dive deep into how a "mechanic's heart" and a "No Rearview Mirror" leadership style propelled a company from $4 million to over $325 million. Key Takeaways from this Episode:

Your body already makes the most powerful wound-healing molecule on the planet, and now you can use it at home. This episode breaks down hypochlorous acid (HOCl), the compound your white blood cells produce naturally, and why it outperforms Neosporin, hydrogen peroxide, and synthetic antibiotics for everything from cuts and burns to eczema, rosacea, and chronic wounds. -Watch this episode on YouTube for the full video experience: https://www.youtube.com/@DaveAspreyBPR -Grab a generous discount on your next purchase of Active Skin Repair by using code ‘Dave' at: https://www.activeskinrepair.com Host Dave Asprey sits down with Justin Gardner, a 20-year leader in health, wellness, and regenerative medicine who has founded and sold multiple companies and introduced over 50 innovative products into hospitals and doctors' offices. When Justin discovered hypochlorous acid, he shifted his entire focus to making this medical-grade, non-toxic molecule available to the public, founding Active Skin Repair to give people a cleaner, more effective alternative to the chemical-laden products that dominate the skincare aisle. Dave and Justin cover the full biohacking case for HOCl: how it kills 99.9% of bacteria, viruses, and fungi in 15 seconds, why it does not trigger antibiotic resistance, how it avoids destroying the healthy growth factors that hydrogen peroxide nukes, and how it supports the skin microbiome instead of wrecking it. They also get into AI as a tool for functional medicine research, why the standard of care is the floor not the ceiling, and how smarter not harder approaches to wound care and chronic skin conditions are finally going mainstream. Dave shares the story of treating a mystery acid beetle wound from his Amazon trip using this exact compound, and why it belongs in every medicine cabinet alongside your supplements stack. You'll Learn: Why hypochlorous acid is 100 times more powerful than bleach but has the same safety profile as saline How HOCl mimics your immune system's own wound response instead of overriding it Why hydrogen peroxide and Neosporin can actually stall healing and damage your skin microbiome How HOCl disrupts biofilms in chronic wounds, sinuses, and on the skin during eczema flare-ups Why most HOCl products on the market contain little to no active ingredient How to use AI tools like Claude and Perplexity for functional medicine research that most doctors haven't done Why the US standard of care is the floor, not the ceiling, for anyone serious about longevity and anti-aging How to stack HOCl with red light therapy and other biohacking tools for faster recovery Thank you to our sponsors! - Viome | Check it out at https://www.viome.com/ and use code 10DAVE for 10% off. It's time to stop guessing and start knowing your body. - The One Device | Use code DAVE for $10 off at https://www.theonedevice.com/dave - STEMREGEN | Go to https://www.stemregen.co/dave30 Use code DAVE30 for 30% OFF your next order. - Puori | Go to https://www.Puori.com/DAVE or use code DAVE at checkout to get 32% off your Puori Fish Oil subscription. You save more than $18. Dave Asprey is a four-time New York Times bestselling author, founder of Bulletproof Coffee, and the father of biohacking. With over 1,000 interviews and 1 million monthly listeners, The Human Upgrade brings you the knowledge to take control of your biology, extend your longevity, and optimize every system in your body and mind. Each episode delivers cutting-edge insights inhealth, performance, neuroscience, supplements, nutrition, biohacking, emotional intelligence, and conscious living. New episodes are released every Tuesday, Thursday, Friday, and Sunday (BONUS). Dave asks the questions no one else will and gives you real tools to become stronger, smarter, and more resilient. Keywords: Justin Gardner, Active Skin Repair, hypochlorous acid, HOCl, wound healing, skin repair, antimicrobial, biofilm, eczema, rosacea, acne, skin microbiome, regenerative medicine, functional medicine, biohacking, anti-aging, human performance, longevity, supplements, AI healthcare, Dave Asprey, Neosporin alternative, natural healing, chronic wounds, skin inflammation, topical antibiotics, wound care, clean skincare, medical grade skincare, smarter not harder Resources: • Get A Discount On Active Skin Repair By Using Code ‘Dave' At: https://www.activeskinrepair.com • Get My 2026 Clean Nicotine Roadmap | Enroll for free at https://daveasprey.com/2026-clean-nicotine-roadmap/ • Dave Asprey's Latest News | Go to https://daveasprey.com/ to join Inside Track today. • Danger Coffee: https://dangercoffee.com/discount/dave15 • My Daily Supplements: SuppGrade Labs (15% Off) • Favorite Blue Light Blocking Glasses: TrueDark (15% Off) • Dave Asprey's BEYOND Conference: https://beyondconference.com • Dave Asprey's New Book – Heavily Meditated: https://daveasprey.com/heavily-meditated • Join My Substack (Live Access To Podcast Recordings): https://substack.daveasprey.com/ • Upgrade Labs: https://upgradelabs.com Timestamps: 00:00 – Trailer 00:47 – Dave's Injury Story 01:55 – What Is Hypochlorous Acid? 02:41 – HOCl vs. Neosporin 03:23 – History of HOCl 04:49 – HOCl vs. Hydrogen Peroxide 06:19 – Off-Label Use 08:41 – Skin Microbiome 15:29 – Chronic & Slow-Healing Wounds 16:30 – HOCl for Eczema & Rosacea 18:31 – Skincare Industry Problems 21:38 – Integrity in Product Design 28:24 – Using AI for Health Research 34:17 – GHB, Pharma & Suppressed Compounds 35:58 – Wrap-Up & Discount Code See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Have you ever felt like you were hanging on by a thread? Are you in need of healing from overwhelming stress, anxiety, or significant and traumatizing life circumstances?   In our authentic conversation, special guest Teresa Blaes shares her "A Ragged Thread" God Story about what inspired her to produce an album with her husband and best friend, with songs of different genres and powerful lyrics that meet others struggling with mental health issues in their darkest places. She shares with raw emotion how God has aligned the music on her new album with her soon-to-be-published book, "Protected", too. Teresa is a former guest and writes the Altered Stories Ministry faith-building blogs. She is an entrepreneur and ministry leader of 20 years and co-owner of Kadosh Media. She has a passion for podcasting and helping others bring their God-given message to the mic. She is a certified 100X and Life Coach, and also hosts podcasts: Equipped and Blaes and Gottsch. She is also raising her daughter and three cats.  Enjoy listening to Teresa's powerful God story!  Special thanks to Kadosh Media for sponsoring this Episode.

Have you ever felt like you were hanging on by a thread? Are you in need of healing from overwhelming stress, anxiety or significant and traumatizing life circumstances?   In our authentic conversation, special guest Teresa Blaes shares her "A Ragged Thread" God Story about what inspired her to produce an album with her husband and best friend with songs of different genre's and powerful lyrics that meet others struggling with mental health issues in there darkest places. She shares with raw emotion how God has aligned the music on her new album with her soon to be published book, "Protected", too. Teresa is a former guest and writes the Altered Stories Ministry faith-building blogs. She is an entrepreneur and ministry leader of 20 years and co-owner of Kadosh Media. She has a passion for podcasting and helping others bring their God-given message to the mic. She is a certified 100X and Life Coach and also hosts podcasts; Equipped and Blaes and Gottsch. She is also raising her daughter and three cats.  Enjoy listening to Teresa's powerful God story!  Special thanks to Kadosh Media for sponsoring this Episode.  Be sure to catch the full episode on April 15!  

The tech landscape is shifting under our feet. In this episode of Life on Mars, our CEO Àlex Rodríguez Bacardit sits down with Linus Ekenstam to discuss the 100x era, a world where technology becomes 100x faster and cheaper every 6 to 12 months.Linus breaks down why the ability to write code has moved from a competitive advantage to a basic commodity. He warns that if you are 100% dependent on a platform's reach, you are no longer in control because your boss is an algorithm.The discussion covers the 100x rule and why you must assume tech will accelerate by 100x every year to plan effectively. We explore the death of the coding moat and why execution is now a commodity while taste is the new differentiator. Linus also discusses algorithm slavery and how to humanize your brand to build your own platform for true defensibility.We also touch upon the future of teams and why MarsBased is moving toward multi-faceted engineers over specialized roles. Finally, Linus provides candid feedback on his experience consulting for giants like Adobe and shares the hilarious story of the 1,000 burger party mistake.Support the show

Are you working HARD but not getting the results you deserve? The problem might not be your deals — it might be the ROOMS you're in.   In this episode of The Vinney & Beau Show, Vinney Chopra and Beau dive deep into the #1 growth strategy most real estate investors overlook: intentional relationships. Plus, how AI is completely transforming hotel underwriting, deal analysis, and syndication — and how YOU can use it TODAY.  

Today on The Survival Podcast the expert council answers your questions on the Iran war, hunting dogs, back up power, technology, gun optics, tax strategy, prep security and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X … Continue reading →

Luke Gromer hosts Seth Kindig and Kevin DeShazo from Better, discussing their journey from coaching and education to leadership development in sports. Together, they highlight the importance of self-awareness and intentional leadership in coaching, culminating in their recent book, "Lead Yourself First," which aims to provide actionable insights for coaches to enhance their leadership skills and team performance.—RYG x NIKE SPORTS CAMPS: Become a Camp DirectorThe Better Coaching Podcast is powered by RYG Athletics, a proud provider of NIKE Sports Camps.If you're interested in becoming one of our NIKE Sports Camp directors, fill out the form below.- Director interest form:⁠⁠ ⁠⁠https://docs.google.com/forms/d/e/1FAIpQLScFXxRUOb9-pdYbDkRktNiCTD1PDwm4zisPexHCLH0341YlRg/viewform?usp=dialog⁠⁠- RYG Website:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://rygathletics.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠—FREE PODCAST NOTES, NEWSLETTER, & COACHES COMMUNITY

Today on The Survival Podcast the expert council answers your questions on foreign policy, guns, escalation of force, herbal medicine, diet and fitness, technology, knives, AI and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more … Continue reading →

As we move deeper into March 2026, Bittensor (TAO) is staging a massive "takeover" that has caught the attention of both retail "moon-hunters" and institutional giants. 

Two simple mindset shifts that can completely change how you approach your habits, health, and life in 2026.In this episode, we talk about:• Why many ambitious women live in high-functioning autopilot • The difference between reacting to life vs intentionally designing it • How small daily decisions quietly shape your body, energy, & future • Powerful questions that can shift your standards and direction this yearIf you've been feeling stuck, reactive, or like you're capable of more — this episode will make you think differently.Screenshot the episode and tag me @michelleestallings on Instagram if something hits home.

Want to Start or Grow a Successful Business? Schedule a FREE 13-Point Assessment with Clay Clark Today At: www.ThrivetimeShow.com   Join Clay Clark's Thrivetime Show Business Workshop!!! Learn Branding, Marketing, SEO, Sales, Workflow Design, Accounting & More. **Request Tickets & See Testimonials At: www.ThrivetimeShow.com  **Request Tickets Via Text At (918) 851-0102   See the Thousands of Success Stories and Millionaires That Clay Clark Has Helped to Produce HERE: https://www.thrivetimeshow.com/testimonials/ Download A Millionaire's Guide to Become Sustainably Rich: A Step-by-Step Guide to Become a Successful Money-Generating and Time-Freedom Creating Business HERE: www.ThrivetimeShow.com/Millionaire   See Thousands of Case Studies Today HERE: www.thrivetimeshow.com/does-it-work/  

Want to Start or Grow a Successful Business? Schedule a FREE 13-Point Assessment with Clay Clark Today At: www.ThrivetimeShow.com   Join Clay Clark's Thrivetime Show Business Workshop!!! Learn Branding, Marketing, SEO, Sales, Workflow Design, Accounting & More. **Request Tickets & See Testimonials At: www.ThrivetimeShow.com  **Request Tickets Via Text At (918) 851-0102   See the Thousands of Success Stories and Millionaires That Clay Clark Has Helped to Produce HERE: https://www.thrivetimeshow.com/testimonials/ Download A Millionaire's Guide to Become Sustainably Rich: A Step-by-Step Guide to Become a Successful Money-Generating and Time-Freedom Creating Business HERE: www.ThrivetimeShow.com/Millionaire   See Thousands of Case Studies Today HERE: www.thrivetimeshow.com/does-it-work/  

Send a textLearn how to make AI 100x better at marketing using Claude Code skills — downloadable prompt frameworks that turn AI into an expert-level marketer for email marketing, SEO audits, copywriting, and automation. In this episode, Corey Haines, creator of the open-source Marketing Skills repo and founder of Conversion Factory, breaks down how agentic AI tools like Claude Code and OpenClaw let you go from manual copy-paste workflows to fully autonomous marketing systems. Discover how to use AI skills to run email campaigns through Mailchimp or Resend, automate sponsor and backlink outreach with web scraping tools like Firecrawl, optimize on-page SEO at scale, and build self-learning AI agents that improve over time. Whether you're a business owner, content creator, or marketer — this episode gives you the exact playbook to stop being the middleman and start using AI the way Don Draper would use a team of typists. Drop a comment with the first skill you're going to try!#AIMarketing #ClaudeCode #MarketingAutomationTIMESTAMPS00:00 – How AI Skills Make Marketing 100x Better 04:06 – Where to Find and Download AI Marketing Skills 07:06 – How to Use Skills in Claude Cowork and Claude Code10:19 – Why the Terminal Is a Marketer's Secret Weapon 14:21 – The Don Draper Paradigm: AI as Your Typist 16:06 – How to Get Started with Claude Code as a Beginner 19:35 – Why Context Is King for AI Marketing Agents 21:11 – Automating Email Campaigns with Claude Code and Mailchimp 25:07 – Agent-Friendly Email Tools: Resend, Customer.io, and Kit 26:35 – SEO and Shopify Optimization Using AI Agents 32:02 – On-Page SEO Audit Automation at Scale 33:55 – AI-Powered Sponsor Outreach and Backlink Building 38:06 – Hermes Agent vs OpenClaw: Autonomous AI Compared 43:36 – The Future of Autonomous AI Marketing Agents 50:13 – Self-Learning AI Loops and Automated Task Triggers 52:55 – Why Marketers Should Think Like AI EngineersConnect with Us!https://www.instagram.com/alchemists.library/https://twitter.com/RyanJAyala

Send me a Text Message!The first problem we have in discovering our identity is that we simply don't know his voice. As my friend Terry says, "We need to develop voice recognition, because we don't recognize his voice." The problem is that we've let so many other voices into our souls.  We let the voices that shout lies into our hearts and miss the voice that whispers truth. Ultimately our identity is shaped by the direction of our ears. So in this episode we'll start in John 10, but we'll spend a little bit of time in Mark 4, where Jesus tells a story about listening and the power of listening to give us a life of great impact. If you want a life of 100x impact, you need to get good at listening for His voice!

Today on The Survival Podcast the expert council answers your questions on war in Iran, joint pain, bull pups, foraging, business, cooking, dogs, homesteading and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely your … Continue reading →

February 28, 2026 Ex. 9:1-35; Ps. 25:1-7; Prov. 10:19; Matt. 19:25-30

This week, Bryan Pellegrino and Raz Zarick from LayerZero join the show to discuss Zero, the 100x breakthrough in blockchain architecture. We deep dive into the Zero origin story, the roadmap for 2026, LayerZero's ultimate vision, how to build a moat in crypto, hiring top talent and more. Enjoy! -- Follow Bryan: https://x.com/PrimordialAA Follow Raz: https://x.com/ryanzarick Follow Santi: https://x.com/santiagoroel Follow Empire: https://x.com/theempirepod -- Coinbase crypto-backed loans, powered by Morpho, enable you to take out loans at competitive rates using crypto as collateral. Rates are typically 4% to 8%. Borrow up to $5M using BTC as collateral and up to $1M using ETH as collateral. Manage crypto-backed loans directly in the Coinbase app with ease. Learn more here: https://www.coinbase.com/onchain/borrow/get-started?utm_campaign=0126_defi-borrow_blockworks_empire&marketId=0x9103c3b4e834476c9a62ea009ba2c884ee42e94e6e314a26f04d312434191836&utm_source=empire -- Join us at DAS (Digital Asset Summit) in New York City this March! Follow the link below to grab your ticket, and use code EMPIRE200 to get $200 off your ticket! https://blockworks.co/event/digital-asset-summit-nyc-2026 -- Timestamps: (00:00) Introduction (00:53) The Zero Origin Story (13:55) Zero's 100x Breakthrough (22:10) Achieving 2 Million TPS (32:29) Coinbase Ad (33:14) DAS Plug (33:40) How To Build A Moat In Crypto? (48:05) LayerZero's Ultimate Vision (57:37) The Impact of AI (01:06:25) Zero's Roadmap In 2026 -- Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, Rob and our guests may hold positions in the companies, funds, or projects discussed.

Today on The Survival Podcast the expert council answers your questions on politics, cooking, food preservation, fodder systems, water systems, passkeys, dog training, biochar and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely your … Continue reading →

Today on The Survival Podcast the expert council answers your questions on Bitcoin, politics, urban foraging, health, meat rabbits, shagbark hickory syrup, investing, biochar and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely your … Continue reading →

Bio:  Kurt Avery is the founder and president of Sawyer Products, a company at the forefront of innovation in outdoor protection and humanitarian aid. With a background in marketing for Fortune 500 companies and an MBA from Northwestern University's Kellogg School of Management, Kurt launched Sawyer with a mission to develop practical, life-saving solutions—most notably in water filtration and insect repellents. Under his leadership, the company has grown into a global force, serving both outdoor enthusiasts and vulnerable communities around the world. Through partnerships with over 140 nonprofits in more than 80 countries, Sawyer has helped provide clean water to millions, significantly reducing waterborne diseases and improving health outcomes in underserved areas. Kurt's approach to business is deeply rooted in purpose, faith, and impact—a philosophy he shares in his book, Sawyer Think: How a Small Company Disrupts Markets and Changes the World. Kurt and his company have been featured in major outlets, including New York Times, Yahoo Finance, and Newsbreak. This episode is sponsored by the coaching company of the host, Paul Zelizer. Consider a Strategy Session if you can use support growing your impact business. Resources mentioned in this episode include: Sawyer Products site Sawyer Think book Sawyer Impact Stories Paul's services Pitch an Awarepreneurs episode

Today on The Survival Podcast the expert council answers your questions on politics, rental property, cooking, silver pricing, dogs, high end handguns, Bitcoin and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely your question … Continue reading →

Today on The Survival Podcast the expert council answers your questions on checks and balances, dog training, athletic footwear, solar power, foraging, mortgages and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely your question … Continue reading →

“For faster service, try our customer service portal.” Big Tech's AI rush is the next step in stripping humanity from customer service – promising speed and efficiency. But the data shows something darker: collapsing trust, rising customer rage, and dehumanized systems people can't escape. In this episode of The Deep, Erika breaks down why we all hate AI customer service, who benefits, and why monopolies are betting you have nowhere else to go.Timestamps:0:00 - Intro: Customer service is broken2:33 - Why is customer service worse than ever?5:19 - Corporations sacrifice human touch for scalability 8:43 - A race to the bottom10:03 - Surveys show customers despise AI support12:06 - The psychology behind why customers hate it14:48 - Conclusion: Resisting fatalismSubscribe to the LOOPcast on YouTube: https://www.youtube.com/@theLOOPcastSources:Banks, Alex (@alexbanks). “Note on AI and Attention.” Alex Banks (Substack), June 2, 2025. Accessed January 5, 2026. https://substack.com/@alexbanks/note/c-192787692?utm_source=notes-share-action&r=1htswx.Customer Experience Dive. “Klarna Reinvests in Human Talent for Customer Service as AI Chatbot Use Grows.” Customer Experience Dive, April 15, 2025. Accessed January 5, 2026. https://www.customerexperiencedive.com/news/klarna-reinvests-human-talent-customer-service-AI-chatbot/747586/.HBR Editors. “Fixing Chatbots Requires Psychology, Not Technology.” Harvard Business Review, May 2025. Accessed January 5, 2026. https://hbr.org/2025/05/fixing-chatbots-requires-psychology-not-technology.StoryBoard18 Staff. “Human Touch Trumps AI: 88% of Consumers Prefer Human Agents for Customer Service.” StoryBoard18, October 22, 2025. Accessed January 5, 2026. https://www.storyboard18.com/digital/human-touch-trumps-ai-88-consumers-prefer-human-agents-for-customer-service-78916.htm.The Agent Architect (@theagentarchitect). “AI Customer Service Con: Customer Abandonment.” The Agent Architect (Substack), November 11, 2025. Accessed January 5, 2026. https://theagentarchitect.substack.com/p/ai-customer-service-con-customer-abandonment.Unknown Author. “Title Not Provided.” Substack, (p-171273784), 2025. Accessed January 5, 2026. https://substack.com/home/post/p-171273784.

Today on The Survival Podcast the expert council answers your questions on Greenland, dogs, guns, eye infections, homestead automation, investing, protests and more. Make sure if you submit content for an expert council show you do the following…. Email it to me at jack @ thesurvivalpodcast.com Put TSPC Expert in the subject line Ask you question and state the expert you have the question for in one coherent sentence Hit the return key a few times and then give all the details you think are necessary ` Following that procedure makes it about 100X more likely your question will get … Continue reading →