Podcasts about Databricks

Today we sit down with Doug May, SVP of Productivity at Harness, to discuss one of the most critical yet overlooked aspects of a healthy organization: Sales Productivity. Doug has had an illustrious career at elite organizations including Datadog and Databricks, and he brings that expertise to Harness, where he has cut ramp time in half and increased per-rep contribution by 43%. We explore the "F1 engineering team" analogy of GTM support, why productivity metrics are the ultimate indicator of a company's health, and the specific questions every candidate should ask to de-risk their next career move.

Today I'm joined by Deductive AI. Sameer is an absolute powerhouse, one of the Founding Engineers at Databricks, spent 5 years at Meta building large scale services and now building Deductive AI.Deductive are building self-healing, self-learning services. They are building reinforcement learning into their product offering to heal legacy (..even new code) being generated by AI.Sameer's background and being a Founding Engineer at DatabricksRecognising an opportunity in reinforcement learning and self-healing / self-learning code and systemsBuilding an AI-SREGenerating a course of action in ambiguous cases -> an open-ended engineering challenge that has never been seen before and planning for cases like thisBets that Deductive are placing in the next 6-12 monthsIf you're keen to share your story, please reach out to us!Guest:Powered by Artifeks!https://www.linkedin.com/company/artifeksrecruitmenthttps://www.artifeks.co.ukhttps://www.linkedin.com/in/agilerecruiterLinkedIn: https://www.linkedin.com/company/enginearsioTwitter: https://x.com/EnginearsioAll Podcast Platforms: https://smartlink.ausha.co/enginearsHosted on Ausha. See ausha.co/privacy-policy for more information.

Dean Quiambao anticipates a “very, very strong year” for IPOs, stretching into 2027. He expects a lot of exciting names in the back half of the year, especially from AI-native companies. He thinks they'll make a big splash in the markets, comparing it to the Olympics. Anticipated IPOs include Anthropic, OpenAI, SpaceX, and Databricks, and other names with massive market caps. Dean also speaks to why companies are staying private longer, and what valuation risks could be hanging over the IPO space.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

"They are changing venture capital from a 30% tax to 0% tax. If Robinhood succeeds, it makes Sequoia and Andreessen's business model untenable." — Keith TeareThe Silicon Gods must have their blood. And they've finally come for the funders of disruption, the venture capitalists, who are now being disrupted by something called Public Venture Capital (PVC). That, at least, is the view of That Was The Week publisher Keith Teare, who leads his newsletter this week with Robinhood's new venture fund. This new stock-trading app for millennials is going after Sequoia and Andreessen Horowitz—not by competing on deal flow, but by charging 0% carry instead of 20-30%. Robinhood promises it blows the doors off traditional venture capital.But Keith urges caution over PVCs. Robinhood is packaging late-stage private assets—companies like Databricks that would have IPO'd years ago but are staying private longer. By the time retail investors get access, employees are already cashing out through tender offers because they think the peak is near. The poster child: Figma, which did secondaries at $12 billion after Adobe's $20 billion acquisition failed. A lot of (dumb) people bought at the top and are now slightly less stupid.Fortunately, this week's tech roundup isn't just about get-rich-quick investment schemes. We also discuss Yasha Mounk's sobering experiment: he asked AI to write a political philosophy paper and found it "depressingly good"—publishable in an academic journal. Keith reframes this supposed "death of the humanities" as automation, not democratization. The humans aren't being leveled up; they're masquerading as producers while AI does the work. But craft still matters. When technology relieves humans of the mundane, he hopes, it elevates the special.Lastly but not least, we get to the abundance debate. Peter Diamandis and Singularity University have promised something called "exponential abundance" by 2035. Keith is sympathetic. I am not. The only thing I'm willing to guarantee is that we'll still be talking abundantly about abundance in 2035. And that the Silicon Valley Gods will have their blood. Five Takeaways●      Robinhood Is Charging 0% Carry: Sequoia and Andreessen take 20-30% of profits. Robinhood takes nothing. If they scale, the traditional VC model becomes untenable.●      But You're Buying at the Top: These are late-stage assets. Employees are selling through tender offers because they think peak valuation is near. Ask the people who bought Figma at $12 billion.●      AI Is Automating the Humanities: Yasha Mounk found AI could write "depressingly good" political philosophy. This isn't democratization—it's humans masquerading as producers.●      Craft Still Retains Its Power: Technology relieves humans of the mundane—and elevates the special. Creativity that breaks through will always command attention.●      The Abundance Debate Continues: Diamandis says abundance by 2035. Keith agrees land is already abundant. Andrew calls this "such a stupid thing to say." About the GuestKeith Teare is the publisher of That Was The Week and Executive Chairman of SignalRank. He is a serial entrepreneur and longtime observer of Silicon Valley. Keith joins Keen On America every Saturday for The Week That Was.ReferencesCompanies mentioned:●      Robinhood is launching a publicly listed venture fund, raising up to $1 billion at $25/share with 0% carry. They already have $340 million in assets including Databricks.●      Figma is cited as a cautionary tale: after Adobe's failed $20 billion acquisition, it did secondaries at $12 billion—many bought at the top.●      Polymarket is a prediction market platform that Robinhood has responded to by adding prediction markets to its offerings.People mentioned:●      Yasha Mounk wrote about AI writing "depressingly good" political philosophy papers that could be published in academic journals.●      Peter Diamandis and Dr. Alexander Wisner-Gross of Singularity University argue that exponential abundance is coming by 2035.●      Packy McCormick wrote about power in the age of intelligence.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States—hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction: If it's Saturday, it must be revolution (02:11) - Robinhood's venture fund announcement (03:17) - What is Robinhood's day job? (07:43) - Secondary markets and tender offers (10:33) - Democratization or late-stage risk? (14:09) - Is Robinhood just gambling? (16:08) - Private vs. public market returns (19:02) - Is finance merging with betting? (24:23) - Blowing the doors off Sequoia and Andreessen (26:27) - Yasha Mounk: AI automating the humanities (28:47) - Where does power go in the age of AI? (30:42) - Craft retains its power (31:33) - The abundance debate (34:00) - Is land abundant? Andrew loses patience (00:00) - Chapter 15 (00:00) - Chapter 16 (00:00) - Introduction: If it's Saturday, it must be revolution (02:11) - Robinhood's venture fund announcement (03:17) - What is Robinhood's day job? (07:43) - Secondary markets and tender offers (10:33) - Democratization or late-stage risk? (14:09) - Is Robinhood just gambling? (16:08) - Private vs. public market returns (19:02) - Is finance merging with betting? (24:23) - Blowing the doors off Sequoia and Andreessen (26:27) - Yasha Mounk: AI automating the humanities

Recorded 10/29/25Vincent's Slava Rubin and Sacra's Jan-Erik Asplund discussed Databricks, Groq, Anduril, Anthropic, and Canva, five of the hottest pre-IPO companies in the asset class - and how investors can get access to them.Presented by the Fundrise Innovation Fund.https://fundrise.com/Vincent

We're back! In this episode of Alter Everything, Josh Burkhow sits down with Ari Kaplan, Head of Evangelism at Databricks and a pioneer of AI in sports. From building operating systems as a kid and studying at Caltech to transforming baseball analytics and now shaping enterprise AI strategy, Ari shares how physics-inspired thinking, relentless curiosity, and better data have driven his career. They explore the evolution from databases to generative AI, common mistakes organizations make with GenAI, why data engineering matters more than prompt engineering, and how true evangelism is about planting seeds and not pushing hype.PanelistsAri Kaplan, Head of Evangelism @ Databricks – LinkedInJoshua Burkhow, Chief Evangelist @ Alteryx – @JoshuaB, LinkedInTopicsDatabricksMajor League Baseball analytics & the Moneyball eraAI in sports, healthcare, and enterpriseGenerative AI & data engineering foundationsData + AI governanceRaoul Wallenberg humanitarian investigationAlter Everything podcast

Jaeden & Jamie discuss the evolving landscape of Software as a Service (SaaS) in the context of artificial intelligence (AI). They explore how AI is not only enhancing SaaS but also creating opportunities for businesses to build custom solutions that meet their specific needs. The discussion highlights the impressive growth of Databricks and the potential for AI to disrupt legacy software systems, emphasizing the importance of adapting to new technologies for efficiency and cost savings.Our Skool Community: https://www.skool.com/aihustleGet the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiWatch on YouTube: https://youtu.be/7nKioteck-cChapters00:00 The Future of SaaS in the Age of AI09:43 The Role of AI in Business Efficiency13:00 Disruption of Legacy Software Systems

Jaeden & Jamie discuss the evolving landscape of Software as a Service (SaaS) in the context of artificial intelligence (AI). They explore how AI is not only enhancing SaaS but also creating opportunities for businesses to build custom solutions that meet their specific needs. The discussion highlights the impressive growth of Databricks and the potential for AI to disrupt legacy software systems, emphasizing the importance of adapting to new technologies for efficiency and cost savings.Our Skool Community: https://www.skool.com/aihustleGet the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiWatch on YouTube: https://youtu.be/7nKioteck-cChapters00:00 The Future of SaaS in the Age of AI09:43 The Role of AI in Business Efficiency13:00 Disruption of Legacy Software Systems See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Collate is building a semantic intelligence platform that unifies fragmented metadata tooling across the modern data stack. With 12,000+ community members, 3,000+ open source deployments, and 400+ code contributors, the company has proven that open source can be a systematic GTM engine, not just a distribution tactic. In this episode of BUILDERS, I sat down with Suresh Srinivas, Co-Founder & CEO of Collate, to explore his journey from the Hadoop core team at Yahoo, through founding Hortonworks, to architecting data systems processing 4 trillion events daily at Uber—and why that experience led him to rebuild metadata infrastructure from scratch. Topics Discussed: Why platform builders at Yahoo and Hortonworks struggled to drive business value despite powerful technology The metadata fragmentation problem: how siloed tools lack unified vocabularies and end-to-end context Collate's contrarian decision to build Open Metadata from zero rather than spinning out Uber's internal tooling Engineering an open core GTM model that generates nearly 100% inbound sales from technical practitioners Scaling community contribution: moving from feedback loops to 400+ code contributors Hiring a CMO to translate technical value into business-leader messaging without losing practitioner trust The convergence thesis: structured data, knowledge graphs, and semantic layers as the foundation for reliable AI GTM Lessons For B2B Founders: Architect your open source for GTM leverage, not just distribution: Suresh built Open Metadata as a unified platform consolidating data discovery, observability, and governance—previously fragmented across multiple tools. This architectural decision created natural upgrade paths to Collate's managed offering. The lesson: open source architecture should solve a complete job-to-be-done that reveals commercial value through usage, not just demonstrate technical capability. 100+ daily practitioner conversations beats any user research: Collate maintains ongoing dialogue with their community across Snowflake, Databricks, and other integrations. Suresh called this "a product manager's dream"—immediate feedback on what breaks, what's missing, and what workflow improvements matter. For infrastructure startups, this beat rate of validated learning is nearly impossible to replicate through traditional customer development. High-velocity releases build credibility faster than pedigree: Starting from scratch without Yahoo or Uber's brand meant proving commitment through shipping cadence. Collate's strategy: demonstrate you'll be around and responsive before asking for production deployments. This matters more in open source than closed-source where sales cycles force commitment conversations earlier. Separate technical-buyer and business-buyer GTM motions explicitly: Collate's founding team spoke fluently to data engineers and architects who lived the metadata problem daily. Their CMO hire (after establishing product-market fit) brought expertise in articulating business impact—ROI on data initiatives, compliance risk reduction, AI readiness—without the founders faking business-speak. The timing matters: hire for the motion you're entering, not the one you're in. Play the long game with builder-culture companies: At Uber, internal tools were 2-3 years ahead of vendor solutions but became technical debt as teams moved to new problems. Suresh's advice: "Keep in touch with these larger companies. Your technology will improve and you will have better conversation with larger technical companies." The wedge is timing—catch them when maintenance burden outweighs building pride, typically 24-36 months post-launch. Design for all company scales from day one: Unlike Uber's internal metadata platform built for massive scale with corresponding complexity, Open Metadata works for small teams through enterprises. This wasn't just good design—it was GTM expansion strategy. Building only for scale locks you into enterprise-only sales. Building only for simplicity caps your ACV. The middle path requires architectural discipline upfront. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

i'm wall-e, welcoming you to today's tech briefing for tuesday, february 10th. delve into the latest developments in tech: waymo's robotaxi expansion: waymo intends to launch a driverless robotaxi service in nashville, partnering with lyft and expanding upon its operations in cities like atlanta, austin, los angeles, and miami. databricks' saas insights: ceo ali ghodsi discusses databricks' standout $5.4 billion revenue run rate, attributed to ai advancements, and highlights its llm tool, genie, as a potential growth catalyst following a $5 billion funding round. ouster's acquisition move: ouster acquires stereolabs for $35 million to enhance its vision-based perception systems, aiming to lead in the "physical ai" applications space through integrated platforms. mrbeast enters fintech: youtube star mrbeast acquires step, a fintech app for gen z, aiming to broaden financial literacy by leveraging his expansive audience. openai's ad introduction: openai trials ads on chatgpt in the u.s., ensuring they're clearly labeled and non-intrusive, as part of efforts to support broader access while maintaining user trust. that's all for today. we'll see you back here tomorrow with more tech updates!

Databricks announcing a new $5B funding round at a $134B valuation. Making the company the fourth largest private company in the U.S. We speak to CEO Ali Ghodsi about the company's future and how AI is disrupting the software ecosystem. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ghousia Sultana is a data analyst with a strong foundation in data analytics, engineering, and business intelligence. She began her career as an HR Process Analyst, later transitioned into IT, and now works as a Data Analyst, leveraging tools like Python, SQL, Power BI, Azure, and Databricks to build scalable data pipelines and drive insights. She holds a Master's in Business Analytics and brings a deep interest in the intersection of AI and data. Currently, she is conducting research and writing on how data infrastructure, analytics, and machine learning come together to enable real-world AI solutions. Her work reflects a blend of hands-on technical expertise and a forward-looking perspective on the future of intelligent systems. 

Welcome to another episode of Christopher Lochhead: Follow Your Different, featuring the legendary Ray Wang. In this memorable conversation, Christopher and Ray dive deep into the latest developments shaping the world of technology, business, and careers. From dissecting recent tech earnings from giants like Apple, Meta, Tesla and Microsoft to sharing insights from Davos and contemplating the implications of AI for the future of work and entrepreneurship. This episode delivers high-caliber analysis and practical takeaways for anyone navigating today’s rapidly evolving landscape. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go. Lessons from Davos and the New Economic Realities Returning from a bustling Davos, Ray Wang shares his observations on how global leaders and executives are tackling an era defined by uncertainty, rapid technology adoption and a relentless pursuit of efficiency. One of Ray's core takeaways is the prevailing theme of “margin compression,” where even the world's largest corporations are working harder than ever just to achieve modest growth. Companies are now measured by their ability to scale exponentially, as illustrated by India's ISRO launching rockets at a fraction of NASA's cost, fundamentally altering competitive dynamics across industries. Ray explains that the rise of AI turbocharges this transformation by opening up “infinite possibilities.” Companies no longer just compete on physical or financial assets, but on their ability to harness vast data resources, quickly innovate and make sharp strategic choices about what problems to solve—and, crucially, what not to do. Privacy challenges, especially for companies like Apple, arise in this new era, making it difficult to deliver world-class AI solutions while maintaining rigorous data protection standards. Both Christopher and Ray emphasize that managing growth, inflation and investment are more complex than ever, with the U.S. outpacing much of the world in GDP growth, yet operating in a global environment rife with policy and market uncertainties. AI, Tech Earnings, and the Rise of the New IPO Era The conversation pivots to the massive investment and exuberance surrounding generative AI and tech infrastructure. Ray points out that while there are fears about overbuilding capacity or creating a circular funding loop among AI companies, there is still significant real opportunity. The current phase has seen enormous capital pour into building data centers and scalable AI platforms. Landmark IPOs from OpenAI, Databricks and others are expected to reshape the tech landscape. Despite market fluctuations and some outsized reactions to earnings, the fundamentals for big tech remain robust. Companies like Apple have solidified their status as luxury brands, even as others like Tesla and Meta retool and pivot to sustain long-term relevance and unlock new revenue streams such as robotics and energy. At the structural level, venture capital itself is in flux. Many VC firms have become indistinguishable from private equity, constrained both by too much and too little available capital relative to the demands of today's tech startups. The gap between small angel, family office, or solo GP funds and the mega funds has widened so much that the “middle” has all but disappeared. It is now entirely possible for one-person companies, through the leverage of AI and autonomous agents, to achieve scale and revenues previously thought impossible. Ray predicts it is likely we will see a single founder build a billion-dollar annual revenue company within the next five years, echoing the democratization and disruption that generative AI promises. Building Legendary Companies and Careers in the Age of AI Christopher and Ray close their discussion by exploring what all these rapid changes mean for leaders and individuals. For CEOs and entrepreneurs, the formula for thriving is clear but audacious. Leaders must design their companies to be fully autonomous and authentic, constantly reinventing their business as if they were attempting to disrupt themselves. Boards need to be stacked with people who grasp the new fundamentals: margin compression, exponential scale, and infinite possibilities brought by AI. Combining domain expertise with technical agility is more critical than ever, as the fusion of seasoned judgment and lightning-fast, innovative execution is where breakthroughs occur. On a personal level, Ray stresses that knowledge and execution are becoming commodities, rapidly automated by advances in AI. To stay relevant, individuals must become “macro analysts,” adept at synthesizing big ideas and patterns, deeply immersed in experimenting with new technologies and surrounded by others who are passionate about their own crafts. The traditional playbooks for career building, education, and even family strategies are being rewritten in real-time. The U.S. faces global competition for talent and innovation, and entrepreneurial energy is no longer confined to Silicon Valley or New York. The nature of immigration, investment and even educational choices must be reconsidered for new generations. In a world where the location and structure of opportunity are shifting, only those who embrace change, foster diverse collaborations and pursue purpose will continue to define the next era of legendary achievement. As both Christopher and Ray reflect, living and leading like Rob Burgess—embracing boldness, curiosity and authenticity—remains the path to being truly legendary in this rapidly changing world. To hear more from Ray Wang and his updates on the world of Tech and AI, download and listen to this episode. Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and authors a business strategy and technology blog that has received millions of page views per month.  Wang also serves as a non-resident Senior Fellow at The Atlantic Council's GeoTech Center. Since 2003, Ray has delivered thousands of live and virtual keynotes around the world that are inspiring and legendary. Wang has spoken at almost every major tech conference. His ground-breaking bestselling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015.  Ray's new book about Digital Giants and the future of business titled, Everybody Wants to Rule the World will be released July 2021 by Harper Collins Leadership. Wang is well quoted and frequently interviewed in media outlets such as the Wall Street Journal, Fox Business News, CNBC, Yahoo Finance, Cheddar, CGTN America, Bloomberg, Tech Crunch, ZDNet, Forbes, and Fortune.  He is one of the top technology analysts in the world. Links Follow Ray Wang! Website | Twitter | LinkedIn | Constellation Research | DisrupTV We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), Instagram, and subscribe on Apple Podcast / Spotify!

Fresh out of the studio, Patrick Kelly, Vice President for Asia Pacific at Arize AI, joins us to explore the critical world of AI observability, evaluation, and infrastructure and how Arize AI will start their go to market across the region. Beginning with his transition from Databricks to Arize AI, Patrick explained how the company's mission centers on making AI work for people by helping teams observe, evaluate, and continuously improve their AI agents in production. Emphasizing that evaluations are the most important requirement for AI systems in 2025-2026, he revealed a striking insight: approximately 50% of AI agents fail silently in production because organizations don't know what's happening. Through compelling case studies from Booking.com, Flipkart, and AT&T, Patrick explained how Arize AI enables real-time observability and online evaluations, achieving results like 40% accuracy improvements and 84% cost reductions. Patrick concluded by sharing his vision for success across Asia Pacific's diverse markets - from regulatory frameworks in Korea and Singapore to language localization challenges in Vietnam - emphasizing the three pillars that remain constant: helping customers make money, control costs, and manage risk in an era where AI governance has become paramount. Last but not least, he shares what great would look like for Arize AI in the Asia Pacific"The mission is to make AI work for the people. It's about getting AI working for everybody—consumers, customers, and businesses at large. Evals are the most important things that we've seen through 2025 and will see more of into 2026; they are the most important thing for systems to work. When I'm working with a customer, I ask: How are we going to help them make money? How are we going to help them control costs? And how are we going to help them manage risk? A lot of AI now is about managing risk."Episode Highlights: [00:00] Quote of the Day by Patrick Kelly[01:10] Bernard introduces AI evaluation and infrastructure topic[02:24] Patrick's journey from Databricks to Arize AI[03:20] Arize AI's mission: making AI work for people[04:00] Understanding agentic systems and their complexity[05:18] Observability, evaluation, and development framework explained[06:27] Creating continuous feedback loops for AI improvement[07:00] On-premises and air-gapped deployment capabilities[08:00] Open Telemetry and Open Inference standards[09:08] Evaluations are critical for 2025-2026 success[10:36] Booking.com case: real-time production AB testing[14:36] Phoenix open source and Open Inference: entry to Arize ecosystem[16:00] Travel industry use cases: Skyscanner and Flipkart[17:53] AT&T case: 40% accuracy improvement, 84% cost reduction[19:36] 50% of production agents fail silently[20:26] Korea and Singapore MAS launches AI risk management framework[22:08] Arize AI CEO's 10 predictions for AI 2026[22:41] Cursor for X: AI engineering everywhere[24:06] Context and session state matter critically[26:27] Harness: new buzzword for agent orchestration[34:13] Three pillars: make money, control costs, manage risk[36:00] Asia Pacific diversity: India to Japan[37:12] Language and cultural nuances in evaluations[38:00] ClosingProfile: Patrick Kelly, Vice President, Asia Pacific, Arize AILinkedIn Profile: https://www.linkedin.com/in/patrick-kelly-aab6168/?ref=analyse.asiaPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

Viele KI-Initiativen scheitern nicht an Algorithmen, Modellen oder mangelndem Willen – sondern an der Basis. In dieser Folge von AI or DIE sprechen Andreas Wiener und Christian Bühler darüber, warum KI ohne eine sauber aufgebaute Daten- und Cloud-Plattform kaum eine Chance hat. Es geht um überzogene Erwartungen, Quick-and-Dirty-Leuchtturmprojekte und den fatalen Irrglauben, man könne KI einfach „on top“ auf bestehende Strukturen setzen. Christian erklärt, warum Begriffe wie Security, Resilienz, Performance, Operational Excellence und Kosten keine Buzzwords sind, sondern harte Voraussetzungen für produktive KI-Use-Cases. Die Folge zeigt klar: Ohne belastbares Fundament wird aus jeder KI-Idee eine teure Krücke. Wer jetzt nicht aufräumt, skaliert später nur Probleme. Eine ehrliche Episode für Entscheider:innen, die KI nicht nur ausprobieren, sondern nachhaltig nutzen wollen.  ⸻ Timestamps 00:00 – Einstieg: Warum KI-Initiativen scheitern 00:33 – Überzogene Erwartungen und falsche Annahmen 01:30 – Warum alte Cloud-Plattformen KI ausbremsen 01:50 – Was „well-architected“ wirklich bedeutet 02:34 – Die fünf Säulen einer belastbaren Plattform 02:59 – Für wen diese Leitplanken relevant sind 03:28 – Cloud-Zoo, Komplexität und Realität im Konzern 04:26 – Sicherheit & Cloud-Souveränität 05:06 – Datenplattform als Fundament für KI 05:15 – Resilienz, Verfügbarkeit & Governance 06:14 – Performance als Akzeptanzfaktor 07:24 – Always-on-Architekturen und Orchestrierung 08:19 – Wer ist verantwortlich: IT oder Fachbereich? 09:14 – Warum Fachbereichs-KI oft scheitert 10:15 – Budgets, ROI und Wirtschaftlichkeit von KI 11:20 – KI als Wette auf die Zukunft 12:29 – Was Resilienz technisch wirklich heißt 13:41 – Klassische Fehler bei KI-Plattformen 14:31 – Leuchtturmprojekte und „Quick & Dirty“ 15:11 – Die berühmte Krücke aus BI-Projekten 16:18 – Proof of Concept vs. produktiver Betrieb 17:20 – Welcher Tool-Stack ist „vernünftig“? 18:21 – SAP, Databricks & offene Architekturen 19:52 – Best-of-Breed statt Tool-Dogma 21:13 – Plattform vs. Mensch: Was ist wichtiger? 22:02 – Data Culture & echte Nutzung 23:04 – Online-Assessment: Standortbestimmung 24:35 – Klare Worte: Was Unternehmen jetzt tun müssen 26:07 – Ausblick: Sicherheit & nächste Folge 29:15 – Abschluss & Call to Action 29:40 – Ende der Folge

Anna Anisin is a seasoned entrepreneur, ecosystem builder, and business owner with deep roots in the tech world and a passion for creativity.Starting her entrepreneurial journey at 16, Anna has since achieved multiple successful exits and built a career around scaling brands, building communities, and pioneering new paths in marketing innovation.Today, Anna leads DataScience.Salon, one of the most trusted communities in AI and machine learning, and runs FormulatedBy, a boutique B2B marketing firm specializing in demand generation, experiential strategy, and AI-driven marketing. Under her leadership, FormulatedBy has served over 100 brands including AWS, IBM, Databricks, Oracle, and many of the most influential startups in AI/ML and deep tech.Most recently, Anna launched the

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

In this episode of Tank Talks, Matt Cohen and John Ruffolo unpack Prime Minister Mark Carney's China agreement and his Davos speech, calling out the collapse of the rules-based international order and pushing “middle powers” to coordinate against coercion. John and Matt agree the speech was sharp, but they hammer the real issue: Canada has to build leverage at home (resources, infrastructure, internal trade, and actual execution) or “diversifying” becomes a vibes-only strategy.The conversation then pivots to Trump's Greenland framework, rare earth realities, and why the real choke point is processing, not just “owning minerals.” Finally, they switch lanes into markets, covering the biggest anticipated IPOs of 2026 (SpaceX, OpenAI, Databricks, Stripe, Revolut, Canva), why liquidity could snap back for LPs, and why SPACs are creeping back as a funding path for deep tech, including General Fusion's SPAC and the emergence of the Canadian Rocket Company as Canada tries to repatriate space talent.Canada–China trade reset and what it actually means (02:13)Matt tees up the January 16 China agreement and the idea of trade diversification under U.S. tariff uncertainty. John frames it as a fix for specific trade pain (not a full political pivot) and warns against treating China as a “safe alternative.”Davos speech: “truth bombs” vs real-world action (04:11)They break down Carney's Davos message on coercion, great power tactics, and middle-power coalitions. John calls it “spectacular,” but both stress the gap between rhetoric and measurable outcomes.Canada's leverage problem: “build Canada first” (06:39)John argues Canada can't diversify trade if it has nothing competitive and scalable to trade. The conversation turns into a blunt call for domestic execution: resources, pipelines, and the hard stuff that moves GDP.Matt's frustration: Why no national address to Canadians? (08:06)Matt goes off on the lack of direct, plainspoken communication to Canadians about what has to change, what's coming, and what tradeoffs might be required.Trump and Greenland: Bond markets, politics, and power (12:32)John calls Trump's posture performative and points to constraints that actually matter, including internal GOP pressure and market reactions (he highlights the bond market as the real “adult in the room”).Top anticipated IPOs of 2026: the mega list (19:12)They run through what's being floated as the monster class of potential offerings: SpaceX, OpenAI, Databricks, Stripe, Revolut, Canva (and more speculation). The bigger point: it's not number of IPOs, it's dollar value and liquidity unlock.Canada's space bets: Canadian Rocket Company emerges (21:15)Matt shares CRC's emergence from stealth with $6.2M funding (all Canadian investors including BDC and Garage). Focus: repatriating SpaceX/Blue Origin talent and pushing Canada deeper into the space industrial base.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

In this episode of Tank Talks, Matt Cohen sits down with Timothy Chen, the sole General Partner at Essence VC. Tim shares his remarkable journey from being a “nerdy, geeky kid” who hacked open-source projects to becoming one of the most respected early-stage infrastructure investors, backing breakout companies like Tabular (acquired by Databricks for $2.2 billion). A former engineer at Microsoft and VMware, co-founder of Hyperpilot (acquired by Cloudera), and now a solo GP who quietly raised over $41 million for his latest fund, Tim offers a unique, no-BS perspective on spotting technical founders, navigating the idea maze, and rethinking sales and traction in the world of AI and infrastructure.We dive deep into his unconventional path into VC, rejected by traditional Sand Hill Road firms, only to build a powerhouse reputation through sheer technical credibility and founder empathy. Tim reveals the patterns behind disruptive infra companies, why most VCs can't help with product-market fit, and how he leverages his engineering background to win competitive deals.Whether you're a founder building the next foundational layer or an investor trying to understand the infra and AI boom, this conversation is packed with hard-won insights.The Open Source Resume (00:03:44)* How contributing to Apache projects (Drill, Cloud Foundry) built his career when a CS degree couldn't.* The moment he realized open source was a path to industry influence, not just a hobby.* Why the open source model is more “vertical than horizontal”, allowing deep contribution without corporate red tape.From Engineer to Founder: The Hyperpilot Journey (00:13:24)* Leaving Docker to start Hyperpilot and raising seed funding from NEA and Bessemer.* The harsh reality of founder responsibility: “It's not about the effort hard, it's about all the other things that has to go right.”* Learning from being “way too early to market” and the acquisition by Cloudera.The Unlikely Path into Venture Capital (00:26:07)* Rejected by top-tier VC firms for a job, then prompted to start his own fund via AngelList.* Starting with a $1M “Tim Chen Angel Fund” focused solely on infrastructure.* How Bain Capital's small anchor investment gave him the initial credibility.Building a Brand Through Focus & Reputation (00:30:42)* Why focusing exclusively on infrastructure was his “best blessing” creating a standout identity in a sparse field.* The reputation flywheel: Founders praising his help led to introductions from top-tier GPs and LPs.* StepStone reaching out for a commitment before he even had fund documents ready.The Essence VC Investment Philosophy (00:44:34)* Pattern Recognition: What he learned from witnessing the early days of Confluent, Databricks, and Docker.* Seeking Disruptors, Not Incrementalists: Backing founders who have a “non-common belief” that leads to a 10x better product (e.g., Modal Labs, Cursor, Warp).* Rethinking Sales & Traction: Why revenue-first playbooks don't apply in early-stage infra; comfort comes from technical co-building and roadmap planning.* The “Superpower”: Using his engineering background to pressure-test technical assumptions and timelines with founders.The Future of Infra & AI (00:52:09)* Infrastructure as an “enabler” for new application paradigms (real-time video, multimodal apps).* The coming democratization of building complex systems (the “next Netflix” built by smaller teams).* The shift from generalist backend engineers to specialists, enabled by new stacks and AI.Solo GP Life & Staying Relevant (00:54:55)* Why being a solo GP doesn't mean being a lone wolf; 20-30% of his time is spent syncing with other investors to learn.* The importance of continuous learning and adaptation in a fast-moving tech landscape.* His toolkit: Using portfolio company Clerky (a CRM) to manage workflow.About Timothy ChenFounder and Sole General Partner, Essence VCTimothy Chen is the Sole General Partner at Essence VC, a fund focused on early-stage infrastructure, AI, and open-source innovation. A three-time founder with an exit, his journey from Microsoft engineer to sought-after investor is a masterclass in building credibility through technical depth and founder-centric support. He has backed companies like Tabular, Iteratively, and Warp, and his insights are shaped by hundreds of conversations at the bleeding edge of infrastructure.Connect with Timothy Chen on LinkedIn: linkedin.com/in/timchenVisit the Essence VC Website: https://www.essencevc.fund/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Google about to snatch the crown… and a lot of y'all still stuck worshipping Nvidia like it's the only AI play that matter. I'm telling you right now: the market switches leaders — and when that leadership flips, it leaves people behind who don't see the shift coming.In this episode I break down why I believe Alphabet (Google) can become the #1 most valuable company, how AI chips + Gemini + YouTube + Cloud partnerships are stacking the deck, and why Nvidia still can run… but the competition is finally heavy. We also get into Apple picking Gemini, big tech power moves, Meta spending like a maniac on nuclear energy, and the 2026 IPO watchlist (SpaceX, OpenAI, Anthropic, Databricks, Stripe, Revolut, Canva — and my sleeper pick will surprise you).High-intent SEO keywords we touch naturally: Google stock, Alphabet stock, Gemini AI, Nvidia competition, AI chips, Big Tech leadership rotation, Apple Gemini deal, Google Cloud, YouTube revenue, AI investing, market leadership switching, Meta nuclear energy deal, 2026 IPOs, SpaceX IPO, OpenAI IPO, Anthropic IPO, Databricks IPO, Stripe IPO, Canva IPO, AI infrastructure stocks.Apple Picked Google Gemini. Bad News for Nvidia?Join our Exclusive Patreon!!! Creating Financial Empowerment for those who've never had it.

Corey Zumar is a Product Manager at Databricks, working on MLflow and LLM evaluation, tracing, and lifecycle tooling for generative AI.Jules Damji is a Lead Developer Advocate at Databricks, working on Spark, lakehouse technologies, and developer education across the data and AI community.Danny Chiao is an Engineering Leader at Databricks, working on data and AI observability, quality, and production-grade governance for ML and agent systems.MLflow Leading Open Source // MLOps Podcast #356 with Databricks' Corey Zumar, Jules Damji, and Danny ChiaoJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterShoutout to Databricks for powering this MLOps Podcast episode.// AbstractMLflow isn't just for data scientists anymore—and pretending it is is holding teams back. Corey Zumar, Jules Damji, and Danny Chiao break down how MLflow is being rebuilt for GenAI, agents, and real production systems where evals are messy, memory is risky, and governance actually matters. The takeaway: if your AI stack treats agents like fancy chatbots or splits ML and software tooling, you're already behind.// BioCorey ZumarCorey has been working as a Software Engineer at Databricks for the last 4 years and has been an active contributor to and maintainer of MLflow since its first release. Jules Damji Jules is a developer advocate at Databricks Inc., an MLflow and Apache Spark™ contributor, and Learning Spark, 2nd Edition coauthor. He is a hands-on developer with over 25 years of experience. He has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud, VeriSign, ProQuest, Hortonworks, Anyscale, and Databricks, building large-scale distributed systems. He holds a B.Sc. and M.Sc. in computer science (from Oregon State University and Cal State, Chico, respectively) and an MA in political advocacy and communication (from Johns Hopkins University)Danny ChiaoDanny is an engineering lead at Databricks, leading efforts around data observability (quality, data classification). Previously, Danny led efforts at Tecton (+ Feast, an open source feature store) and Google to build ML infrastructure and large-scale ML-powered features. Danny holds a Bachelor's Degree in Computer Science from MIT.// Related LinksWebsite: https://mlflow.org/https://www.databricks.com/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Corey on LinkedIn: /corey-zumar/Connect with Jules on LinkedIn: /dmatrix/Connect with Danny on LinkedIn: /danny-chiao/Timestamps:[00:00] MLflow Open Source Focus[00:49] MLflow Agents in Production[00:00] AI UX Design Patterns[12:19] Context Management in Chat[19:24] Human Feedback in MLflow[24:37] Prompt Entropy and Optimization[30:55] Evolving MLFlow Personas[36:27] Persona Expansion vs Separation[47:27] Product Ecosystem Design[54:03] PII vs Business Sensitivity[57:51] Wrap up

a16z cofounders Marc Andreessen and Ben Horowitz join a16z general partner Erik Torenberg and Not Boring founder Packy McCormick for a conversation on how the media and information ecosystem has changed over the past decade. The discussion breaks down the shift toward a more open and decentralized speech environment, the rise of writer- and creator-led platforms like Substack, and the erosion of centralized media gatekeepers. Marc and Ben also tie these dynamics to their investing worldview, outlining how supply-driven markets, major technological step changes, and reputation-driven venture platforms shape outcomes in the AI era.Timecodes: 00:00  Introduction00:46  How the media ecosystem is changing4:20  Why a16z invested in Substack6:28  Supply-driven markets and new content creation8:07  Why writers felt trapped by media companies10:09  Databricks and the 10x cloud multiplier13:58  Long-form podcasting proves demand15:40  What the new fund signals about the future16:24  AI as a universal problem solver18:49  Why market sizing is broken20:45  Go-to-market, policy, and platform power22:37  Turning inventors into confident CEOs25:58  Borrowing power to scale faster27:29  Building dreamers, not killing dreams30:46  Reputation as a core competitive advantage35:57  Taking arrows in public38:56  Avoiding big company failure modes40:39  Autonomous teams inside a16z41:54  Venture capital as the last job46:01  Why intangibles matter more than ever48:17  Original thinkers with charisma50:06  Why Zoomers are differentResources: https://www.notboring.co/p/a16z-the-power-brokershttps://www.a16z.news/p/firm-fundFollow Marc Andreessen on X: https://twitter.com/pmarcaFollow Ben Horowitz on X: https://twitter.com/bhorowitzFollow Erik Torenberg on X: https://twitter.com/eriktorenbergFollow Packy McCormick on X: https://twitter.com/packyM Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://twitter.com/eriktorenberg](https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.  Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What does real AI transformation look like when leaders stop chasing prototypes and start demanding outcomes they can actually measure? That question sat at the center of my conversation with Alex Cross, Chief Technology Officer for EMEA at CI&T, alongside Melissa Smith, as we unpacked why so many organizations feel stuck between AI ambition and business reality. There is no shortage of excitement around AI, but there is growing skepticism too, especially from leadership teams who have seen pilots come and go without clear return. This episode focuses on how CI&T is addressing that gap head on. Alex shared how CI&T frames its work as AI-enabled transformation rather than simply layering AI tools onto existing processes. The distinction matters.  Instead of using AI to speed up broken workflows, CI&T reshapes how work gets done so AI becomes part of value creation itself. We explored a standout example from ITAU, the largest bank in Latin America, where deep modernization work helped deliver gains that most executives only ever see in strategy decks.  Productivity rose sharply, digital launch cycles collapsed from years to months, customer satisfaction jumped, and the commercial impact reached hundreds of millions in uplift. These are the kinds of results that change boardroom conversations. A big part of how CI&T gets there is its proprietary Flow platform. Alex explained how Flow gives clients a day-one AI environment, removing the heavy upfront cost and complexity that often slows momentum. Instead of spending months building platforms before any value appears, teams can move from proof of concept to production in as little as six to eight weeks. Flow also plays a second role that many AI programs miss, acting as a measurement layer so performance, efficiency, and ROI are visible rather than assumed. We also talked about why partnerships matter when execution is the goal. CI&T works closely with hyperscalers like AWS and Databricks, combining native tools with its own codified expertise. That combination has helped the company achieve an unusually high success rate in bringing AI initiatives to production, a challenge many organizations still struggle with. For Alex, the difference comes down to a relentless focus on production readiness and collaboration between business and technology teams from day one. Looking ahead, the conversation turned to CI&T's expansion across EMEA and what the company's 30th year represents. Rather than chasing every new trend, the focus is on productizing services around real client problems, whether that is legacy modernization, efficiency, or growth. The goal is to bridge strategy and execution in a way that feels practical, fast, and accountable. If you are leading AI initiatives and wondering why progress feels slower than the hype suggests, this episode offers a grounded perspective from the front lines. So, as organizations head into another year of bold AI plans, the real question becomes this. Are you building faster caterpillars, or are you ready to do the harder work required to turn ambition into something that can truly scale? Useful Links Connect with Alex Cross Connect With Melissa Smith Learn more about CI&T Follow CI&T on LinkedIn and YouTube Thanks to our sponsors, Alcor, for supporting the show.

2026 could be a historic year for the IPO market with over $3.6 trillion in expected valuations hitting the public stage. In this episode, we break down the hottest and most talked-about names in the pipeline: SpaceX, OpenAI, Anthropic, Stripe, Revolut, Canva, Databricks, and TikTok US.We discuss what makes SpaceX such a unique and defensible business, why OpenAI's losses and leadership noise might be red flags, and how Anthropic is quietly building a much more sustainable AI model. Plus, the strategy behind Stripe's long-awaited listing, Revolut's super-app ambitions, Canva's AI monetisation push, and the jaw-dropping TikTok U.S. deal that could become one of the biggest financial giveaways of all time.We also dive into who's really making money here with banks like Morgan Stanley, J.P. Morgan, and Goldman Sachs all fighting for billion-dollar fees.Bullish or bearish, overhyped or undervalued, this is our full take on the biggest IPOs to watch in 2026.(00:00) 2026 IPO Landscape Overview(02:07) SpaceX: Trillion Dollar Debut(11:59) OpenAI: Timing and Challenges(16:02) Anthropic: A Different Approach(21:12) ByteDance and TikTok: The Crony Bonanza(25:31) Databricks: Data Intelligence Platform(28:45) Stripe: Payments Powerhouse(31:26) Revolut: Challenger Bank(33:37) Canva: Design Tool with Potential

Aishwarya Naresh Reganti and Kiriti Badam have helped build and launch more than 50 enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they've developed a small set of best practices for building and scaling successful AI products. The goal of this conversation is to save you and your team a lot of pain and suffering.We discuss:1. Two key ways AI products differ from traditional software, and why that fundamentally changes how they should be built2. Common patterns and anti-patterns in companies that build strong AI products versus those that struggle3. A framework they developed from real-world experience to iteratively build AI products that create a flywheel of improvement4. Why obsessing about customer trust and reliability is an underrated driver of successful AI products5. Why evals aren't a cure-all, and the most common misconceptions people have about them6. The skills that matter most for builders in the AI era—Brought to you by:Merge—The fastest way to ship 220+ integrations: https://merge.dev/lennyStrella—The AI-powered customer research platform: https://strella.io/lennyBrex—The banking solution for startups: https://www.brex.com/product/business-account?ref_code=bmk_dp_brand1H25_ln_new_fs—Transcript: https://www.lennysnewsletter.com/p/what-openai-and-google-engineers-learned—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/183007822/referenced—Get 15% off Aishwarya and Kiriti's Maven course, Building Agentic AI Applications with a Problem-First Approach, using this link: https://bit.ly/3V5XJFp—Where to find Aishwarya Naresh Reganti:• LinkedIn: https://www.linkedin.com/in/areganti• GitHub: https://github.com/aishwaryanr/awesome-generative-ai-guide• X: https://x.com/aish_reganti—Where to find Kiriti Badam:• LinkedIn: https://www.linkedin.com/in/sai-kiriti-badam• X: https://x.com/kiritibadam—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Aishwarya and Kiriti(05:03) Challenges in AI product development(07:36) Key differences between AI and traditional software(13:19) Building AI products: start small and scale(15:23) The importance of human control in AI systems(22:38) Avoiding prompt injection and jailbreaking(25:18) Patterns for successful AI product development(33:20) The debate on evals and production monitoring(41:27) Codex team's approach to evals and customer feedback(45:41) Continuous calibration, continuous development (CC/CD) framework(58:07) Emerging patterns and calibration(01:01:24) Overhyped and under-hyped AI concepts(01:05:17) The future of AI(01:08:41) Skills and best practices for building AI products(01:14:04) Lightning round and final thoughts—Referenced:• LevelUp Labs: https://levelup-labs.ai/• Why your AI product needs a different development lifecycle: https://www.lennysnewsletter.com/p/why-your-ai-product-needs-a-different• Booking.com: https://www.booking.com• Research paper on agents in production (by Matei Zaharia's lab): https://arxiv.org/pdf/2512.04123• Matei Zaharia's research on Google Scholar: https://scholar.google.com/citations?user=I1EvjZsAAAAJ&hl=en• The coming AI security crisis (and what to do about it) | Sander Schulhoff: https://www.lennysnewsletter.com/p/the-coming-ai-security-crisis• Gajen Kandiah on LinkedIn: https://www.linkedin.com/in/gajenkandiah• Rackspace: https://www.rackspace.com• The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper• Semantic Diffusion: https://martinfowler.com/bliki/SemanticDiffusion.html• LMArena: https://lmarena.ai• Artificial Analysis: https://artificialanalysis.ai/leaderboards/providers• Why humans are AI's biggest bottleneck (and what's coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead): https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck• Airline held liable for its chatbot giving passenger bad advice—what this means for travellers: https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know• Demis Hassabis on LinkedIn: https://www.linkedin.com/in/demishassabis• We replaced our sales team with 20 AI agents—here's what happened | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents• Socrates's quote: https://en.wikipedia.org/wiki/The_unexamined_life_is_not_worth_living• Noah Smith's newsletter: https://www.noahpinion.blog• Silicon Valley on HBO Max: https://www.hbomax.com/shows/silicon-valley/b4583939-e39f-4b5c-822d-5b6cc186172d• Clair Obscur: Expedition 33: https://store.steampowered.com/app/1903340/Clair_Obscur_Expedition_33/• Wisprflow: https://wisprflow.ai• Raycast: https://www.raycast.com• Steve Jobs's quote: https://www.goodreads.com/quotes/463176-you-can-t-connect-the-dots-looking-forward-you-can-only—Recommended books:•  When Breath Becomes Air: https://www.amazon.com/When-Breath-Becomes-Paul-Kalanithi/dp/081298840X• The Three-Body Problem: https://www.amazon.com/Three-Body-Problem-Cixin-Liu/dp/0765382032• A Fire Upon the Deep: https://www.amazon.com/Fire-Upon-Deep-Zones-Thought/dp/0812515285—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

This week, we discuss AI's impact on Stack Overflow, Docker's Hardened Images, and Nvidia buying Groq. Plus, thoughts on playing your own game and having fun. Watch the YouTube Live Recording of Episode (https://www.youtube.com/live/LQSxLbjvz3c?si=ao8f3hwxlCrmH1vX) 554 (https://www.youtube.com/live/LQSxLbjvz3c?si=ao8f3hwxlCrmH1vX) Please complete the Software Defined Talk Listener Survey! (https://docs.google.com/forms/d/e/1FAIpQLSfl7eHWQJwu2tBLa-FjZqHG2nr6p_Z3zQI3Pp1EyNWQ8Fu-SA/viewform?usp=header) Runner-up Titles It's all brisket after that. Exploring Fun Should I go build a snow man? Pets Innersourcing Two books Michael Lewis should write. Article IV is foundational. Freedom is options. Rundown Stack Overflow is dead. (https://x.com/rohanpaul_ai/status/2008007012920209674?s=20) Hardened Images for Everyone (https://www.docker.com/blog/docker-hardened-images-for-every-developer/) Tanzu's Bitnami stuff does this too (https://blogs.vmware.com/tanzu/what-good-software-supply-chain-security-looks-like-for-highly-regulated-industries/). OpenAI OpenAI's New Fundraising Round Could Value Startup at as Much as $830 Billion (https://www.wsj.com/tech/ai/openais-new-fundraising-round-could-value-startup-at-a[…]4238&segment_id=212500&user_id=c5a514ba8b7d9a954711959a6031a3fa) OpenAI Reportedly Planning to Make ChatGPT "Prioritize" Advertisers in Conversation (https://futurism.com/artificial-intelligence/openai-chatgpt-sponsored-ads) OpenAI bets big on audio as Silicon Valley declares war on screens (https://techcrunch.com/2026/01/01/openai-bets-big-on-audio-as-silicon-valley-declares-war-on-screens/) Sam Altman says: He has zero percent interest in remaining OpenAI CEO, once (https://timesofindia.indiatimes.com/technology/tech-news/sam-altman-says-he-has-zero-percent-interest-remaining-openai-ceo-once-/articleshow/126350602.cms) Nvidia buying AI chip startup Groq's assets for about $20 billion in its largest deal on record (https://www.cnbc.com/2025/12/24/nvidia-buying-ai-chip-startup-groq-for-about-20-billion-biggest-deal.html) Relevant to your Interests Broadcom IT uses Tanzu Platform to host MCP Servers (https://news.broadcom.com/app-dev/broadcom-tanzu-platform-agentic-business-transformation). A Brief History Of The Spreadsheet (https://hackaday.com/2025/12/15/a-brief-history-of-the-spreadsheet/) Databricks is raising over $4 billion in Series L funding at a $134 billion (https://x.com/exec_sum/status/2000971604449485132?s=20) Amazon's big AGI reorg decoded by Corey Quinn (https://www.theregister.com/2025/12/17/jassy_taps_peter_desantis_to_run_agi/) “They burned millions but got nothing.” (https://automaton-media.com/en/news/japanese-game-font-services-aggressive-price-hike-could-be-result-of-parent-companys-alleged-ai-failu/) X sues to protect Twitter brand Musk has been trying to kill (https://www.theregister.com/2025/12/17/x_twitter_brand_lawsuit/) Mozilla's new CEO says AI is coming to Firefox, but will remain a choice | TechCrunch (https://techcrunch.com/2025/12/17/mozillas-new-ceo-says-ai-is-coming-to-firefox-but-will-remain-a-choice/) Why Oracle keeps sparking AI-bubble fears (https://www.axios.com/2025/12/18/ai-oracle-stock-blue-owl) What's next for Threads (https://sources.news/p/whats-next-for-threads) Salesforce Executives Say Trust in Large Language Models Has Declined (https://www.theinformation.com/articles/salesforce-executives-say-trust-generative-ai-declined?rc=giqjaz) Akamai Technologies Announces Acquisition of Function-as-a-Service Company Fermyon (https://www.akamai.com/newsroom/press-release/akamai-announces-acquisition-of-function-as-a-service-company-fermyon) Google Rolling Out Gmail Address Change Feature: Here Is How It Works (https://finance.yahoo.com/news/google-rolling-gmail-address-change-033112607.html) The Enshittifinancial Crisis (https://www.wheresyoured.at/the-enshittifinancial-crisis/) MongoBleed: Critical MongoDB Vulnerability CVE-2025-14847 | Wiz Blog (https://www.wiz.io/blog/mongobleed-cve-2025-14847-exploited-in-the-wild-mongodb) Softbank to buy data center firm DigitalBridge for $4 billion in AI push (https://www.cnbc.com/amp/2025/12/29/digitalbridge-shares-jump-on-report-softbank-in-talks-to-acquire-firm.html) The best tech announced at CES 2026 so far (https://www.theverge.com/tech/854159/ces-2026-best-tech-gadgets-smartphones-appliances-robots-tvs-ai-smart-home) Who's who at X, the deepfake porn site formerly known as Twitter (https://www.ft.com/content/ad94db4c-95a0-4c65-bd8d-3b43e1251091?accessToken=zwAGR7kzep9gkdOtlNtMlaBMZdO9jTtD4SUQkQ.MEYCIQCdZajuC9uga-d9b5Z1t0HI2BIcnkVoq98loextLRpCTgIhAPL3rW72aTHBNL_lS7s1ONpM2vBgNlBNHDBeGbHkPkZj&sharetype=gift&token=a7473827-0799-4064-9008-bf22b3c99711) Manus Joins Meta for Next Era of Innovation (https://manus.im/blog/manus-joins-meta-for-next-era-of-innovation) The WELL: State of the World 2026 with Bruce Sterling and Jon Lebkowsky (https://people.well.com/conf/inkwell.vue/topics/561/State-of-the-World-2026-with-Bru-page01.html) Virtual machines still run the world (https://cote.io/2026/01/07/virtual-machines-still-run-the.html) Databases in 2025: A Year in Review (https://www.cs.cmu.edu/~pavlo/blog/2026/01/2025-databases-retrospective.html) Chat Platform Discord Files Confidentially for IPO (https://www.bloomberg.com/news/articles/2026-01-06/chat-platform-discord-is-said-to-file-confidentially-for-ipo?embedded-checkout=true) The DRAM shortage explained: AI, rising prices, and what's next (https://www.techradar.com/pro/why-is-ram-so-expensive-right-now-its-more-complicated-than-you-think) Nonsense Palantir CEO buys monastery in Old Snowmass for $120 million (https://www.denverpost.com/2025/12/17/palantir-alex-karp-snowmass-monastery/amp/) H-E-B gives free groceries to all customers after registers glitch today in Burleson, Texas. (https://www.reddit.com/r/interestingasfuck/s/ZEcblg7atP) Conferences cfgmgmtcamp 2026 (https://cfgmgmtcamp.org/ghent2026/), February 2nd to 4th, Ghent, BE. Coté speaking - anyone interested in being a SDI guest? DevOpsDayLA at SCALE23x (https://www.socallinuxexpo.org/scale/23x), March 6th, Pasadena, CA Use code: DEVOP for 50% off. Devnexus 2026 (https://devnexus.com), March 4th to 6th, Atlanta, GA. Coté has a discount code, but he's not sure if he can give it out. He's asking! Send him a DM in the meantime. KubeCon EU, March 23rd to 26th, 2026 - Coté will be there on a media pass. Whole bunch of VMUGs, mostly in the US. The CFPs are open (https://app.sessionboard.com/submit/vmug-call-for-content-2026/ae1c7013-8b85-427c-9c21-7d35f8701bbe?utm_campaign=5766542-VMUG%20Voice&utm_medium=email&_hsenc=p2ANqtz-_YREN7dr6p3KSQPYkFSN5K85A-pIVYZ03ZhKZOV0O3t3h0XHdDHethhx5O8gBFguyT5mZ3n3q-ZnPKvjllFXYfWV3thg&_hsmi=393690000&utm_content=393685389&utm_source=hs_email), go speak at them! Coté speaking in Amsterdam. Amsterdam (March 17-19, 2026), Minneapolis (April 7-9, 2026), Toronto (May 12-14, 2026), Dallas (June 9-11, 2026), Orlando (October 20-22, 2026) SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: Why Data Doesn't Always Win, with a Philosopher of Art (https://podcasts.apple.com/us/podcast/the-points-you-shouldnt-score-a-new-years-resolution/id1685093486?i=1000743950053) (Apple Podcasts) Why Data Doesn't Always Win, with a Philosopher of Art (https://www.youtube.com/watch?v=7AdbePyGS2M&list=RD7AdbePyGS2M&start_radio=1) (YouTube) Coté: “Databases in 2025: A Year in Review.” (https://www.cs.cmu.edu/~pavlo/blog/2026/01/2025-databases-retrospective.html) Photo Credits Header (https://unsplash.com/photos/red-and-black-love-neon-light-signage-igJrA98cf4A)

Send us a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:00 - Intro00:08 - xAI Lands a Massive $20B Round, $230B Valuation01:22 - xAI Financials Show Hypergrowth Economics With Losses Still Expanding02:51 - Grok for Business Targets Enterprise Wallet Share With Security and Admin Controls04:05 - Anthropic Signs Up a $10B Round, $350B Valuation05:07 - OpenAI Targets a $100B Raise, $830B Valuation05:51 - OpenAI Pushes Deeper Into Healthcare With ChatGPT Health06:45 - OpenAI's New $50B Stock Compensation Program07:45 - Lambda Lines Up a Pre-IPO Raise, IPO in Next 12 Months08:43 - Databricks Raises Over $4B, $134B Valuation09:30 - Figure's Adcock Launches New AI Lab, Hark, With $100M Personal Capital 10:05 - Lovable Raises $330M, $6.6B Valuation, 267% Step-Up in Five Months10:58 - OpenEvidence Targets a $12B Valuation on 150M Annualized Ad Revenue and 90 Percent Gross Margins12:04 - Waymo Explores a $15B Raise With Valuation Talks at $100B12:48 - Rain Raises $250M at a $1.95B Valuation to Expand Stablecoin Cards Across 150 Countries13:26 - ServiceNow Buys Armis for $7.75B Cash After a Fresh $6.1B Pre-IPO Mark14:05 - Cursor Acquires Graphite and Reinforces a $38.5B Secondary Mark14:56 - Plaud Expands Hardware Line With a $179 NotePin S Ahead of CES 202615:51 - Mobileye Buys Mentee Robotics for $900M and Expands the Physical AI Playbook16:42 - LMArena Reprices to $1.7B and Hits $30M Annualized Consumption in Under Four Months17:22 - Swap Commerce Raises $100M Six Months After Its $40M Series B17:51 - Discord Files Confidentially for IPO With Secondaries Pricing at $7B to $8B18:25 - Commonwealth Fusion Systems Builds a Digital Twin and Targets 19-Magnet Completion This Summer

Today we're breaking down Databricks, a $130B private company that helps companies collect, store, and process very large amounts of data, and then use that data to run analytics and train machine learning models. Databricks sits in the middle of modern data systems, connecting raw data pipelines to the tools teams use to analyze information and build AI. If you've worked on large-scale data or AI projects, there's a good chance Databricks was part of the stack, often operating behind the scenes. My guest is Alan Tu, portfolio manager and analyst at WCM Investment Management, which invested in Databricks in late 2024. Alan explains what Databricks actually does for customers, why it remains one of the least understood large private software companies, and how its academic origins and founding team shaped its evolution from an early data-engineering product into a broad commercial platform. We also discuss common misconceptions about the business, how Databricks fits into the modern AI stack, what has changed since the last time we covered the company, and how its scale, product strategy, and capital position differentiate it from competitors. Note: This conversation was recorded on December 10, 2025, so all numbers are reflective of what was publicly available on that date. Please enjoy this breakdown of Databricks. For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠⁠⁠ —- This episode is brought to you by⁠⁠⁠⁠ ⁠Portrait Analytics⁠⁠⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at⁠⁠⁠⁠ ⁠portraitresearch.com⁠⁠⁠⁠⁠ — Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit⁠⁠⁠⁠⁠⁠⁠ joincolossus.com/episodes⁠⁠⁠⁠⁠⁠⁠. Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠). Timestamps  (00:00:00) Welcome to Business Breakdowns (00:02:34) Introducing Databricks and Guest Alan Tu (00:03:22) Understanding Databricks' Core Functionality (00:09:15) The Founding Story of Databricks (00:23:54) Databricks' Evolution and Product Expansion (00:30:06) Databricks vs. Snowflake: Market Competition (00:35:36) Databricks' Strategic Vision and Market Impact (00:38:14) The Rise of Big Data and Databricks' Core Value (00:39:27) Understanding Databricks Through a Credit Card Fraud Use Case (00:44:35) Databricks' Role in AI and Machine Learning (00:51:12) The Competitive Landscape and Cloud Partnerships (00:54:54) Financial Dynamics and Pricing Strategies (01:09:37) The Future of Databricks: Risks and Long-Term Vision (01:12:54) Conclusion and Final Thoughts

The 2026 IPO market is heating up: John Jannarone and Evan Schlossman break down what to expect. John anticipates debuts from Kraken and OpenAI, while Evan is also watching Canva. Other expected names are Anthropic, Databricks, Huntress, SpaceX, and more tech companies. “We're seeing a huge amount of enthusiasm from investors,” Evan says. John notes capital raises from names like Anthropic, saying private markets are “red hot,” so retail investors need caution if these companies are continuing to look for funding. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Welcome to episode 335 of The Cloud Pod, where the forecast is always cloudy! Welcome to the first show of 2026, and it's a full house, too! Justin, Jonathan, Ryan,  and Matt are all here to reflect on 2025, plus bring you their predictions for 2026. Let's get started!  Titles we almost went with this week SQL Me Maybe: AlloyDB Gets Chatty With Your Database **OpenAI SELECT * FROM natural_language WHERE accuracy LIKE ‘100%’ **Anthropic etcd You Were Worried About Database Limits: CloudWatch Has Your Back CSV You Later: Looker Adds Drag-and-Drop Data Uploads AWS Spots an Opportunity to Manage Your Container Costs EKS Network Policies: No More IP Address Whack-a-Mole AWS Security Hub Splits: It’s Not You, It’s CSPM Spot On: ECS Finally Manages Your Cheapest Compute TOON Squad: DigitalOcean’s New Format Makes JSON Look Bloated The Price is Wrong: AWS Breaks Two Decades of Downward Pricing Tradition Show Your Work: Why AI-Generated Code Without Tests is Just Expensive Spam No More Agent Orange: Google Simplifies VM Extension Deployment AWS Discovers Prices Can Go Both Ways, Raises GPU Costs 15 Percent Sovereignty Washing: When Your European Cloud Still Answers to Uncle Sam Agent Builder Gets a Memory Upgrade: Google’s AI Finally Remembers Where It Put Its Keys Ctrl+F for the Future: A year-end Scorecard & Next-Gen Bets AI Agents, GPU Prices, and The best of the Cloud Pod 2025 Beyond the Hype: The Cloud Pods Definitive 2025 Year in Review Apocalypse Now… What? Our 2026 Forecast Follow Up  01:27 RYAN’S PREDICTIONS Prediction Status Notes Quick LLM models for individuals ACCURATE Meta-Llama-3.1-8B-Instruct, GLM-4-9B-0414, and Qwen2.5-VL-7B-Instruct—each chosen for an outstanding balance of performance and computational efficiency, making them ideal for edge AI deployment. A new AI inference application called Inferencer allows even modest Apple Mac computers to run the largest open-source LLMs. AI at the edge natively (Lambda-esque) ACCURATE Akamai launched a new Inference Cloud product for edge AI using Nvidia’s Blackwell 6000 GPUs in 17 cities. AWS IoT Greengrass with Lambda functions for edge logic. “Edge AI allows for instant decision-making where it matters most—close to the data source.” Cloud native security mesh multi-cloud UNCLEAR Service mesh technologies continue to evolve (Istio, Linkerd), but I didn’t find a breakthrough “app-to-app at the edge” security mesh product announcement in 2025. This one needs more specific evidence. Ryan Score: 2/3 02:25 MATTHEW’S PREDICTIONS Prediction Status Notes FOCUS adopted by Snowflake or Databricks ACCURATE FOCUS version 1.2 was ratified on May 29, 2025. Three new providers announced support: Alibaba Cloud, Databricks, and Grafana. Databricks officially adopted FOCUS! AI security/ethical standard (SOC or ISO) ACCURATE ISO 42001 is the first international standard outlining requirements for AI governance. Major companies achieving certification in 2025: Automation Anywhere is among the first 100 companies worldwide to earn ISO/IEC 42001:2023 certification. Anthropic also achieved ISO 42001 certification. Amazon deprecates 5+ services (WorkMail bonus) ACCURATE (no bonus) 19 services are mothballed, four are being sunset, and one is end of its supported life. Deprecated services include CodeCommit, Cloud9, S3 Select, CloudSearch, SimpleDB, Forecast, Data Pipeline, QLDB, Snowball Edge, and more. WorkMail NOT deprecated – WorkDocs was (April 2025), but WorkMail remains active. Matthew Score: 3/3 03:22 JONATHAN’S PREDICTIONS Prediction Status Notes Company claims AGI achieved ACC

Nik and Michael discuss the events and trends they thought were most important in the Postgres ecosystem in 2025. Here are some links to things they mentioned: Postgres 18 release notes https://www.postgresql.org/docs/18/release-18.htmlOur episode on Postgres 18 https://postgres.fm/episodes/postgres-18LWLock:LockManager benchmarks for Postgres 18 (blog post by Nik) https://postgres.ai/blog/20251009-postgres-marathon-2-005PostgreSQL bug tied to zero-day attack on US Treasury https://www.theregister.com/2025/02/14/postgresql_bug_treasuryPgDog episode https://postgres.fm/episodes/pgdogMultigres episode https://postgres.fm/episodes/multigresNeki announcement https://planetscale.com/blog/announcing-nekiOur 100TB episode from 2024 https://postgres.fm/episodes/to-100tb-and-beyondPlanetScale for Postgres https://planetscale.com/blog/planetscale-for-postgresOracle's MySQL job cuts https://www.theregister.com/2025/09/11/oracle_slammed_for_mysql_jobAmazon Aurora DSQL is now generally available https://aws.amazon.com/about-aws/whats-new/2025/05/amazon-aurora-dsql-generally-availableAnnouncing Azure HorizonDB https://techcommunity.microsoft.com/blog/adforpostgresql/announcing-azure-horizondb/4469710Lessons from Replit and Tiger Data on Storage for Agentic Experimentation https://www.tigerdata.com/blog/lessons-replit-tiger-data-storage-agentic-experimentationInstant database clones with PostgreSQL 18 https://boringsql.com/posts/instant-database-clonesturbopuffer episode https://postgres.fm/episodes/turbopufferCrunchy joins Snowflake https://www.crunchydata.com/blog/crunchy-data-joins-snowflakeNeon joins Databricks https://neon.com/blog/neon-and-databricks~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Jason Lemkin is the founder of SaaStr, the world's largest community for software founders, and a veteran SaaS investor who has deployed over $200 million into B2B startups. After his last salesperson quit, Jason made a radical decision: replace his entire go-to-market team with AI agents. What started as an experiment has transformed into a new operating model, where 20 AI agents managed by just 1.2 humans now do the work previously handled by a team of 10 SDRs and AEs. In this conversation, Jason shares his hands-on experience implementing AI to run his sales org, including what works, what doesn't, and how the GTM landscape is quickly being transformed.We discuss:1. How AI is fundamentally changing the sales function2. Why most SDRs and BDRs will be “extinct” within a year3. What Jason is observing across his portfolio about AI adoption in GTM4. How to become “hyper-employable” in the age of AI5. The specific AI tools and tactics he's using that have been working best6. Practical frameworks for integrating AI into your sales motion without losing what works7. Jason's 2026 predictions on where SaaS and GTM are heading next—Brought to you by:DX—The developer intelligence platform designed by leading researchersVercel—Your collaborative AI assistant to design, iterate, and scale full-stack applications for the webDatadog—Now home to Eppo, the leading experimentation and feature flagging platform—Transcript: https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/182902716/my-biggest-takeaways-from-this-conversation—Where to find Jason Lemkin:• X: https://x.com/jasonlk• LinkedIn: https://www.linkedin.com/in/jasonmlemkin• Website: https://www.saastr.com• Substack: https://substack.com/@cloud—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jason Lemkin(04:36) What SaaStr does(07:13) AI's impact on sales teams(10:11) How SaaStr's AI agents work and their performance(14:18) How go-to-market is changing in the AI era(19:19) The future of SDRs, BDRs, and AEs in sales(22:03) Why leadership roles are safe(23:43) How to be in the 20% who thrive in the AI sales future(28:40) Why you shouldn't build your own AI tools(30:10) Specific AI agents and their applications(36:40) Challenges and learnings in AI deployment(42:11) Making AI-generated emails good (not just acceptable)(47:31) When humans still beat AI in sales(52:39) An overview of SaaStr's org(53:50) The role of human oversight in AI operations(58:37) Advice for salespeople and founders in the AI era(01:05:40) Forward-deployed engineers(01:08:08) What's changing and what's staying the same in sales(01:16:21) Why AI is creating more work, not less(01:19:32) Why Jason says these are magical times(01:25:25) The "incognito mode test" for finding AI opportunities(01:27:19) The impact of AI on jobs(01:30:18) Lightning round and final thoughts—Referenced:• Building a world-class sales org | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/building-a-world-class-sales-org• SaaStr Annual: https://www.saastrannual.com• Delphi: https://www.delphi.ai/saastr/talk• Amelia Lerutte on LinkedIn: https://www.linkedin.com/in/amelialerutte/• Vercel: https://vercel.com• What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google): https://www.lennysnewsletter.com/p/what-the-best-gtm-teams-do-differently• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• ElevenLabs: https://elevenlabs.io• The exact AI playbook (using MCPs, custom GPTs, Granola) that saved ElevenLabs $100k+ and helps them ship daily | Luke Harries (Head of Growth): https://www.lennysnewsletter.com/p/the-ai-marketing-stack• Bolt: https://bolt.new• Lovable: https://lovable.dev• Harvey: https://www.harvey.ai• Samsara: https://www.samsara.com/products/platform/ai-samsara-intelligence• UiPath: https://www.uipath.com• Denise Dresser on LinkedIn: https://www.linkedin.com/in/denisedresser• Agentforce: https://www.salesforce.com/form/agentforce• SaaStr's AI Agent Playbook: https://saastr.ai/agents• Brian Halligan on LinkedIn: https://www.linkedin.com/in/brianhalligan• Brian Halligan's AI: https://www.delphi.ai/minds/bhalligan• Sierra: https://sierra.ai• Fin: https://fin.ai• Deccan: https://www.deccan.ai• Artisan: https://www.artisan.co• Qualified: https://www.qualified.com• Claude: https://claude.ai• HubSpot: https://www.hubspot.com• Gamma: https://gamma.app• Sam Blond on LinkedIn: https://www.linkedin.com/in/sam-blond-791026b• Brex: https://www.brex.com• Outreach: https://www.outreach.io• Gong: https://www.gong.io• Salesloft: https://www.salesloft.com• Mixmax: https://www.mixmax.com• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr• Clay: https://www.clay.com• Owner: https://www.owner.com• Momentum: https://www.momentum.io• Attention: https://www.attention.com• Granola: https://www.granola.ai• Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff• Palantir: https://www.palantir.com• Databricks: https://www.databricks.com• Garry Tan on LinkedIn: https://www.linkedin.com/in/garrytan• Rippling: https://www.rippling.com• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth): https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna• Pluribus on AppleTV+: https://tv.apple.com/us/show/pluribus/umc.cmc.37axgovs2yozlyh3c2cmwzlza• Sora: https://openai.com/sora• Reve: https://app.reve.com• Everything That Breaks on the Way to $1B ARR, with Mailchimp Co-Founder Ben Chestnut: https://www.saastr.com/everything-that-breaks-on-the-way-to-1b-arr-with-mailchimp-co-founder-ben-chestnut/• The Revenue Playbook: Rippling's Top 3 Growth Tactics at Scale, with Rippling CRO Matt Plank: https://www.youtube.com/watch?v=h3eYtzBpjRw• 10 contrarian leadership truths every leader needs to hear | Matt MacInnis (Rippling): https://www.lennysnewsletter.com/p/10-contrarian-leadership-truths—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Manvir Sandhu is the Founder and Chief Innovation Officer at Zennify, a Salesforce consulting firm that helps financial services organizations drive digital transformation across platforms like Salesforce, Databricks, and AI. Under his leadership, Zennify has become a trusted advisor to CIOs and C-suite executives, earned Platinum-level Salesforce partner status and scaled to over 150 employees, serving clients ranging from regional banks to large enterprises.  Known for leading innovative initiatives, Manvir helped spearhead a vaccine supply chain solution in Haiti with UNICEF and the Clinton Foundation, which was presented at Salesforce Dreamforce. He brings a strong focus on AI, agile transformation, and change management to regulated industries. In this episode… Digital transformation is reshaping entire industries, yet organizations in highly regulated sectors often struggle to choose the right tools and execute change effectively. As AI, data platforms, and compliance requirements evolve at breakneck speed, innovation can stall under the weight of risk and resistance. How are today's leaders pushing past these barriers to create secure, lasting transformation? For Manvir Sandhu, a digital transformation and AI innovation leader, lasting impact comes from pairing deep industry understanding with a practical, iterative mindset. He traces this philosophy back to his early work in healthcare, where his team reimagined post-disaster vaccine management in Haiti by combining Salesforce and IoT to enable real-time tracking and alerts — an approach that later became a model for broader adoption. Building on those lessons, Manvir pivoted to financial services, using focused proof-of-concept projects to earn trust, modernize legacy systems, and deliver a true 360-degree customer view. His experience demonstrates how thoughtfully applied AI can move far beyond basic automation to drive meaningful operational and customer impact. In this episode of the Inspired Insider Podcast, Dr. Jeremy Weisz sits down with Manvir Sandhu, Founder and Chief Innovation Officer of Zennify, to explore data-driven transformation in highly regulated industries. They discuss proof-of-concept strategies, agile change management, and practical AI use cases across healthcare and financial services. Manvir also shares insights on empowering early adopters, navigating growth, and maintaining culture through leadership transitions.

Citadel fortified by $4B at $134B Databricks citadel withstands data complexity sieges. Unity Compute allocates resources elastically enterprise-wide. Citadel becomes industry fortress.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this episode of The Six Five Pod, hosts Patrick Moorhead and Daniel Newman discuss the latest tech news stories that made headlines. This week's handpicked topics include: THE DECODE Big Funding Headline: OpenAI's reported mega-round and valuation https://www.reuters.com/technology/openai-discussed-raising-tens-billions-valuation-about-750-billion-information-2025-12-18/ https://x.com/danielnewmanUV/status/2001366643436315110  https://x.com/danielnewmanUV/status/2001362761247527174  https://x.com/PatrickMoorhead/status/2001267663490646200 https://techcrunch.com/2025/12/17/amazon-reportedly-in-talks-to-invest-10b-in-openai-as-circular-deals-stay-popular/  AWS "Circular deal" / Corporate venture logic AI build-out constraints https://www.theverge.com/news/846696/electricity-cost-ai-data-center-democrat-investigation https://www.axios.com/2025/12/17/democrats-data-centers-ai-fight  https://www.politico.com/news/2025/12/12/arizona-city-rejects-data-center-after-ai-lobbying-push-00688543 Marvell Industry Analyst Day highlights https://x.com/MoorInsStrat/status/2000359388264161710  Government "Tech Force" for AI Talent https://www.cnn.com/2025/12/15/tech/government-tech-force-ai  Google works to erode Nvidia's software moat (TPU + PyTorch + Meta) https://www.reuters.com/business/google-works-erode-nvidias-software-advantage-with-metas-help-2025-12-17/ Judge rules Tesla engaged in deceptive marketing for Autopilot and full self-driving features https://techcrunch.com/2025/12/16/tesla-engaged-in-deceptive-marketing-for-autopilot-and-full-self-driving-judge-rules/ Tesla tests autonomous vehicles without safety drivers in Austin, Tx https://techcrunch.com/2025/12/15/tesla-starts-testing-robotaxis-in-austin-with-no-safety-driver/  Adobe Firefly now supports prompt-based video editing, adds more third-party models https://techcrunch.com/2025/12/16/adobe-firefly-now-supports-prompt-based-video-editing-adds-more-third-party-models/ https://youtu.be/SjtULo8qs88?si=quE7pEptW8xph1OI  Google's Opal for vibe coding comes to Gemini https://techcrunch.com/2025/12/17/googles-vibe-coding-tool-opal-comes-to-gemini/ THE FLIP OpenAI - Tulip Bubble and Canary in the Coal mine or The Real AI Deal? https://x.com/danielnewmanuv/status/2001487733823541634?s=46&t=8QBZggR299yC4bcbbox-Xg https://x.com/danielnewmanuv/status/2001366643436315110?s=46&t=8QBZggR299yC4bcbbox-Xg BULLS & BEARS AI infrastructure stocks tumble on debt fears: Oracle, Broadcom, CoreWeave selloff https://www.cnbc.com/2025/12/16/cnbc-daily-open-ai-infrastructure-stocks-are-taking-a-beating.html  Recent Fed rate cut & speculation of another coming soon: https://x.com/danielnewmanUV/status/2001041850669404473 Oracle earnings (Q2) — CapEx reality check https://www.forbes.com/sites/greatspeculations/2025/12/18/whats-happening-with-oracle-stock/ https://finance.yahoo.com/news/oracle-plunges-12-despite-earnings-145626357.html Micron crushes earnings as AI data center demand tightens memory supply https://finance.yahoo.com/news/why-wall-street-expects-micron-183836008.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAKf8aUugkk7hJbCnmiZWS2q5x1WWjD07AUywz6vzxnw6btX2iK0-aNmQBgg3sU67GWZXIKHz74cGnjnzZYeuBDv1A8_Rwp67iIKAtMI1A94LhJTRlcqdnN2_QYPWB_5ZTkO96ZSpFMjMsAwDUBf1yz-RIQnA-78Yk-zhD6VFqr- https://x.com/danielnewmanuv/status/2001404328997712349?s=46&t=8QBZggR299yC4bcbbox-Xg Broadcom earnings (Q4) — custom silicon tension https://finance.yahoo.com/news/broadcom-q4-earnings-beat-estimates-154300300.html Databricks raises $4B at $134B valuation as its AI business heats up https://techcrunch.com/2025/12/16/databricks-raises-4b-at-134b-valuation-as-its-ai-business-heats-up/ Smartphone Prices Set to Jump 6.9% as AI Data Centers Devour Memory Chips: The shortage of DRAM chips used in both AI servers and smartphones could threaten to cut smartphone shipments by 2.1%. To cope, some manufacturers may downgrade cameras, displays, and audio or reuse older components. https://www.cnbc.com/2025/12/16/smartphone-prices-to-rise-in-2026-due-to-ai-fueled-chip-shortage.html  Adobe Earnings https://finance.yahoo.com/news/adobe-q4-earnings-beat-estimates-145000488.html Synopsys Earnings https://finance.yahoo.com/news/synopsys-q4-earnings-surpass-estimates-153300031.html  

Skyrocketing $4B at $134B for Databricks' Photon ML acceleration. Ad tech personalizes at billions RPM. Valuation mirrors AI infrastructure boom.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we break down Databricks' $4B funding round and how it pushed the company to a $134B valuation. We explore what this massive raise says about the surging demand for AI data platforms and Databricks' growing role in the AI boom.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle----See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Plus: Invictus Growth Partners to acquire Informed.IQ, an AI-based fraud detection company. And PayPal applies to establish its own bank. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Elliott of Unlimited joins the show to break down the market backdrop as investors weigh growth, risk, and positioning, before Leslie Picker reports on what could be the biggest IPO of 2025 with Medline set to price. Databricks CEO Ali Ghodsi discusses his company's latest valuation and what it signals for private AI companies. Collapsing oil prices and unusual Venezuelan shipping activity with Bill Perkins of Skylar Capital. Julia Boorstin explains Instagram's push onto the TV screen. Eric Mandl of Guggenheim on the outlook for tech M&A and what deals could define the next phase for the sector. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Information's Kevin McLaughlin breaks down Databricks' massive $4 billion funding round and the growing liability of corporate chatbots. We also talk with Crypto Reporter Yueqi Yang about Lead Bank tightening its grip on the stablecoin industry and Sapphire Ventures' Rajeev Dham about his enterprise AI predictions for 2026. Finally, we look at whether NVIDIA's Jensen Huang will fund AI-powered alien hunting with AI Reporter Rocket Drew and discuss the future of humanoid robots and their role in the AI boom with Centific SVP Prithivi Pradeep.Articles discussed on this episode: https://www.theinformation.com/articles/small-bank-critical-stablecoin-payments-tightens-risk-controlshttps://www.theinformation.com/articles/corporate-chatbots-gone-wildhttps://www.theinformation.com/articles/alien-hunters-want-jensen-huang-fund-ai-telescopehttps://www.theinformation.com/articles/servicenow-sell-highlights-jittery-marketTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

David George is a General Partner at Andreessen Horowitz, where he leads the firm's Growth investing team. His team has backed many of the defining companies of this era, including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI, and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. AGENDA: 03:05 – Why Everyone is Wrong: Mega Funds Does Not Reduce Returns 10:40 – Is Public Market Capital Actually Cheaper Than Private Capital? 18:55 – The Biggest Advantage of Staying Private for Longer 23:30 – The #1 Investing Rule for a16z: Always Invest in the Founder's Strength of Strengths 31:20 – Why Fear of Theoretical Competition Makes Investors Miss Great Companies 35:10 – Does Revenue Matter as Much in a World of AI? 44:10 – Does Kingmaking Still Exist in Venture Capital Today? 49:20 – Do Margins Matter Less Than Ever in an AI-First World? 53:50 – My Biggest Miss: Anthropic and What I Learn From it?  56:30 – Has OpenAI Won Consumer AI? Will Anthropic Win Enterprise? 59:45 – The Most Controversial Decision in Andreessen Horowitz History 1:01:30 – Why Did You Invest $300M into Adam Neumann and Flow?    

What does it actually take to move machine learning from experiments into production reliably, responsibly, and at scale?In this episode of Alexa's Input (AI), Alexa talks with Maria Vechtomova, co-founder of Marvelous MLOps and an O'Reilly author-in-progress on MLOps with Databricks. Maria shares how her background in data science led her into MLOps, and why most teams struggle not because of tools, but because of missing processes, traceability, and shared understanding across teams.Alexa and Maria dive into what separates good MLOps from fragile deployments, why shipping notebooks as “production” creates long-term pain, and how traceability across code, data, and environment forms the foundation for reliable ML systems. They also explore how LLM applications are reshaping MLOps tooling, and where the biggest skill gaps still exist between platform, data, and AI engineers.A must-listen for anyone building, operating, or scaling machine learning systems and for teams trying to make MLOps less magical and more marvelous.Learn more about Marvelous MLOps and Maria's work below.LinksWatch: ⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠Read: ⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠Listen: https://creators.spotify.com/pod/profile/alexagriffith/More: ⁠⁠https://linktr.ee/alexagriffith⁠⁠Website: ⁠⁠https://alexagriffith.com/⁠⁠LinkedIn: ⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/maria-vechtomova/TakeawaysMaria started as a data analyst and transitioned into MLOps.She emphasizes the importance of tracking data, code, and environment in MLOps.MLOps is a practice to bring machine learning models to production reliably.Good deployment processes require modular code and proper tracking.MLOps differs from DevOps due to the complexities of data and model drift.Education is crucial for bridging gaps between teams in AI.Small steps can lead to better MLOps practices.Scaling MLOps requires understanding the unique data of different brands.The rise of LLMs is changing the MLOps landscape.Effective teaching methods involve step-by-step guidance.Chapters00:00 Introduction to MLOps and Maria's Journey02:11 Maria's Path to MLOps and Knowledge Sharing04:41 The Importance of MLOps in AI Deployments10:12 Defining MLOps and Its Challenges11:38 MLOps vs. DevOps: Key Differences13:00 Overcoming Stagnation in MLOps16:04 Small Steps Towards Better MLOps Practices19:29 Scaling MLOps in Large Organizations21:58 The Impact of LLMs on MLOps23:58 The Shift from Traditional ML to AI Applications26:51 Evolving Roles in AI Engineering28:33 Databricks: A Comprehensive AI Platform31:45 Future of AI Platforms and Regulations34:26 Bridging Skill Gaps in AI Teams38:42 The Importance of Context in AI Development40:40 Foundational Skills for MLOps Professionals45:43 Integrating Personal Passions with Professional Growth47:30 Building Impactful AI Communities

Is AI finally ready to do your job — better, faster, and cheaper?In this week's Leveraging AI news recap, host Isar Meitis unpacks a flurry of groundbreaking developments in the world of artificial intelligence — from the release of GPT-5.2 to jaw-dropping advances in recursive self-improving AI (yes, it's as intense as it sounds).Whether you lead a business, a team, or just need to stay ahead of the AI curve — this episode is your executive summary for everything that matters (and nothing that doesn't).We'll also dig into the billion-dollar OpenAI–Disney partnership, how real users are actually leveraging AI in the wild, and why the Fed is finally admitting AI is changing the job market.In this session, you'll discover:The GPT-5.2 release: performance benchmarks and real-world capabilitiesIs GPT-5.2 better than humans at actual work? (71% of the time, yes)Why OpenAI's new “not-an-ad” ad rollout caused a user revoltOpenAI x Disney: Why $1B is being bet on AI-generated Mickey Mouse contentGPT-5.2's weak spots and where Claude Opus still dominatesWhat Recursive Self-Improving AI means (and why Eric Schmidt is nervous)AI designing its own hardware: A startup that could rewrite Moore's LawNew usage data from OpenRouter, Microsoft, SAP & Perplexity – how people actually use AI Why prompt length is exploding (and what that means for your business)AI agents in browsers: the productivity revolution or a security nightmare?Databricks proves AI sucks at raw documents (and how to fix it)The psychological bias against AI-created work — it's realClaude's new Slack integration: is this the dev team you didn't hire?Apple's AI brain drain & why it mattersGartner says: Block AI browsers (for now)AI and unemployment: The Fed finally connects the dotsWant to future-proof your team's AI skills? Isar's AI Business Transformation Course launches again in January — a proven, real-world guide to using AI across content, research, operations, and strategy.

AGENDA: 03:46 SpaceX's $800 Billion Valuation: A Deep Dive 09:18 IPO Market Predictions for 2026 18:18 Netflix's Bold Move: Acquiring Warner Brothers 27:43 Tiger's New Fund Strategy 33:02 Databricks' Head of AI $500 Million Seed Round 36:38 Harvey Raises $160M at an $8BN Valuation 48:22 Will LLMs Kill the App Layer 01:02:02 Google's AI Capabilities 01:06:58 Chinese Open Source Models in US Startups 01:08:57 Airwallex Raises $330M at an $8BN Valuation 01:23:50 Prediction Markets and Insider Trading  

Naveen Rao is cofounder and CEO of Unconventional AI, an AI chip startup building analog computing systems designed specifically for intelligence. Previously, Naveen led AI at Databricks and founded two successful companies: Mosaic (cloud computing) and Nervana (AI accelerators, acquired by Intel). In this episode, a16z's Matt Bornstein sits down with Naveen at NeurIPS to discuss why 80 years of digital computing may be the wrong substrate for AI, how the brain runs on 20 watts while data centers consume 4% of the US energy grid, the physics of causality and what it might mean for AGI, and why now is the moment to take this unconventional bet. Stay Updated:If you enjoyed this episode, please be sure to like, subscribe, and share with your friends.Follow Naveen on X: https://x.com/NaveenGRaoFollow Matt on X: https://x.com/BornsteinMattFollow a16z on X: https://twitter.com/a16zFollow a16z on LinkedIn:https://www.linkedin.com/company/a16zFollow the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXFollow the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AGENDA: 04:20 Thrive and OpenAI Partnership  07:14 Databricks Raising $5BN at $134BN Valuation: Cheap or Not? 17:39 Eventbrite Acquired by Bending Spoons for $500M 21:39 Pagerduty's $1BN Market Cap, Just 2x Revenue 26:59 The TAM Trap: Why SaaS Is Like Japan 37:42 Lessons from Companies Hitting $100M ARR 44:57 The Future of Labour Markets is F****** 52:10 The Importance of Compounding in Investments 56:45 The Relevance Game in Venture Capital 01:05:01 Supabase at $5BN or Lovable at $6BN: Which One?  

Varun Puri, CEO and cofounder of Yoodli, joins the show to talk about using AI role play to transform how people practice for high stakes conversations, from sales calls to job interviews to tough manager chats. He breaks down how Yoodli went from a consumer public speaking tool to a serious enterprise platform used by teams at Google, Snowflake, Databricks, and more, all while staying anchored in one mission, helping humans communicate with confidence. We dig into product led growth, honest feedback loops, and why real human communication will matter even more as AI makes information instant.Key takeaways• Why Yoodli started with public speaking anxiety and grew into an AI role play simulator for any important conversation, not just conference talks or pitch decks• How watching real user behavior inside companies like Google pulled the team into enterprise without abandoning their consumer product• A simple approach to product feedback, talk to end users constantly, then prioritize changes by business impact, renewal risk, and how many people benefit• What it really takes to move from consumer to enterprise, new roles, new processes, and a very different mindset around reliability, security, and expectations• Why Varun draws clear ethical lines, using AI to coach and prepare people, not to replace human judgment in hiring, promotion, or high trust decisionsTimestamped highlights[00:35] What Yoodli actually does today, from solo practice to training sales and go to market teams inside large enterprises[01:43] The original vision, helping people who are scared of public speaking, and the insight that interviews, sales calls, and manager talks are all just role plays[03:37] How the team listens to end users, the channels they rely on, and why the consumer product is still their testing ground for new ideas and experiments[05:20] Following users into the enterprise, why it was an addition and not a full pivot, and how product led growth inside companies like Google works in practice[07:42] The early shock of selling to enterprises, learning about new roles, SLAs, InfoSec, and bringing in leaders from Tableau and Salesforce to build a real B2B engine[11:10] Two paths for AI in sales, tools that try to replace humans versus tools that make humans better, and why Varun has drawn a hard line on what Yoodli will not do[15:26] A future where information is commoditized and instant, and why communication and presence become the real edge for top performers in that world[20:48] Designing for trust and adoption, how Yoodli keeps practice private by default, when data is shared, and why control has to sit with the end userA line worth saving“In a world where AI makes everyone smarter and faster, the thing that will be at the biggest premium is how you communicate as a human with other humans.”Practical ideas you can use• Keep a consumer like surface in your product so you can experiment faster than your enterprise roadmap would ever allow• Treat feedback from large customers like a queue you rank by renewal risk, strategic value, and number of users helped, not as a list you must clear• Look for product led growth signals inside your user base, if thousands of people in one company are using you, someone there probably wants a team level solution• Draw explicit boundaries for your AI product, write down what you will not automate, so you can build trust with users and buyers over the long termCall to actionIf you care about the future of sales, interviewing, and communication in an AI rich world, this conversation is worth a listen. Follow the show, leave a quick rating, and share this episode with a founder, product leader, or sales leader who is thinking about AI in their workflow. And if you want feedback on your own speaking, check out what Varun and his team are building at Yoodli.

My guest today is David George. David is a General Partner at Andreessen Horowitz, where he leads the firm's growth investing business. His team has backed many of the defining companies of this era – including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI – and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. This conversation is a detailed look at how David built and runs the a16z growth practice. He shares how he recruits and builds his team a “Yankees-level” culture, how his team makes investment decisions without traditional committees, and how they work with founders years before investing to win the most competitive deals. Much of our conversation centers on AI and how his team is investing across the stack, from foundational models to applications. David draws parallels to past platform shifts – from SaaS to mobile – and explains why he believes this period will produce some of the largest companies ever built. David also outlines the models that guide his approach – why markets often misprice consistent growth, what makes “pull” businesses so powerful, and why most great tech markets end up winner-take-all. David reflects on what he's learned from studying exceptional founders and why he's drawn to a particular type, the “technical terminator.” Please enjoy my conversation with David George. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about the platform. ----- This episode is brought to you by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AlphaSense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Alpha-Sense.com/Invest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Show Notes: (00:00:00) Welcome to Invest Like The Best (00:04:00) Meet David George (00:03:04) Understanding the Impact of AI on Consumers and Enterprises (00:05:56) Monetizing AI: What is AI's Business Model (00:11:04) Investing in Robotics and American Dynamism (00:13:31) Lessons from Investing in Waymo (00:15:55) Investment Philosophy and Strategy (00:17:15) Investing in Technical Terminators (00:20:18) Market Leaders Capture All of the Value Creation (00:24:56) The Maturation of VC and Competitive Landscape (00:28:18) What a16z Does to Win Deals (00:33:06) David's Daily Routine: Meetings Structure and Blocking Time to Think (00:36:34) Why David Invests: Curiosity and Competition (00:40:12) The Unique Culture at Andreessen Horowitz (00:42:46) The Perfect Conditions for Growth Investing (00:47:04) Push v. Pull Businesses (00:49:19) The Three Metrics a16z Uses to Evaluate AI Companies (00:52:15) Unique Products and Unique Distribution (00:54:55) Tradeoffs of the a16z Firm Structure (00:59:04) a16z's Semi-Algorithmic Approach to Selling (01:00:54) Three Ways Startups can Beat Incumbents in AI (01:03:44) The Kindest Thing

The 5 things you need to know before the stock market opens today: President Trump says he's made his choice for next chair of the Federal Reserve, Disney had brought in more than $500 million globally on the “Zootopia 2” box office, South Korean police are investigating a data breach at e-commerce site Coupang, data analytics firm Databricks is in talks to raise $5 billion at a valuation topping $134 billion, and UnitedHealth Group will reportedly sell off its last South American business. Squawk Box is hosted by Joe Kernen, Becky Quick and Andrew Ross Sorkin.  Follow Squawk Pod for the best moments, interviews and analysis from our TV show in an audio-first format. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happened with Cloudflare this morning. Grok's new model wants to be creative. Catching you up on the bloodbath in crypto if you were unaware. Databricks is 12 years old but it seems to be one of the big AI winners. And debt continues to pile in to the AI buildout. A massive Cloudflare outage brought down X, ChatGPT, and even Downdetector (The Verge) Grok 4.1 has arrived — and it's bringing the fight to ChatGPT with these new features (Tom's Guide) Crypto market sheds $1.2tn as traders shun speculative assets (Financial Times) Google boss says trillion-dollar AI investment boom has 'elements of irrationality' (BBC) Amazon Raises $15 Billion in First US Bond Sale in Three Years (Bloomberg) Databricks in Talks to Raise Capital at a Valuation Above $130 Billion (The Information) Roblox will require age estimation to chat starting next year (The Verge) Learn more about your ad choices. Visit megaphone.fm/adchoices