Podcasts about DeepMind

Plus: Uber issues weak guidance amid robotaxi investments. And Shopify says AI-search is fueling e-commerce growth. Imani Moise hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of the AI Experience Book Club, Jessica looks at Supremacy: AI, ChatGPT, and the Race that Will Change the World, by Parmy Olson, published in September 2024 by St. Martin's Press. The book tells the story of the race toward artificial general intelligence through two central figures: Sam Altman, associated with OpenAI, and Demis Hassabis, founder of DeepMind. But beyond the story of founders, models, and technical breakthroughs, Supremacy raises a deeper question: who really controls the future of AI? In this episode, we explore the original promise of AI as a technology meant to serve humanity, the growing dependence on Google and Microsoft, the central role of computing power, the tension between ethics and competition, and the risk that AI could deepen new divides in the workplace. An episode for anyone who wants to understand why AI is not just a technology to adopt, but an infrastructure of power we need to question.Hosted on Ausha. See ausha.co/privacy-policy for more information.

Story of the Week (DR):Cracker Barrel CEO is out after MAGA backlash to "Uncle Herschel" logo change MMBefore Julie Masino was brought in, Cracker Barrel was facing a slow-moving existential crisis:Their primary core customer base (older generations and rural highway travelers) was naturally shrinking, and younger diners were simply not replacing them.Kitchen tech, supply chains, and digital ordering lag behind competitors like Texas Roadhouse or Olive Garden.Some great fake populism from Fox: Cracker Barrel to pay for outgoing CEO's security, $4.6M severance after failed rebrandCracker Barrel names David Deno CEOBurger KingYum! Brands and Pizza Hut for 15 years, including serving as CFO and COO.Quiznos CEO.Best Buy: Served as President of Asia and CFO for Best Buy's International DivisionBloomin' Brands for 12 years: CEO, CFO and Chief Administrative OfficerOutback Steakhouse, Carrabba's Italian Grill, and Bonefish GrillBoard MembershipsCracker Barrel Old Country Store (2016-)Panera Brands (2024-): Audit Committee ChairKrispy Kreme (2016-)Bloomin' Brands (2019–2024)Peet's Coffee (2006-2012)Macalester College: Former Chair of the Board of Trustees (1998-2022).From Cracker Barrel to Boeing, Companies Are Turning to Retired CEOsThese retired CEOs are being hired to be "fixers." They are brought in to cut costs, repair supply chains, restore investor confidence, and act as a stabilizing force rather than reinventing the brand.Exxon and Chevron profits surge on rising oil prices due to Iran warChevron posted its highest profit in six years as the Iran war boosted oil pricesBig Oil Is Getting Sued for Heat Deaths. It's Fighting Back With an Army of Immunity LawsMore than a decade after investigations found that Exxon Mobil had known about the dangers of global warming since the 1970s but publicly downplayed the threat, lawsuits against oil companies have proliferated. There are nearly 40 of these cases pending across the countryIn the meantime, the industry has been mobilizing a counterattack against the lawsuits with the help of the Trump administration and Republican politicians.Republicans are trying to pass laws to grant oil majors immunity to these kinds of lawsuits, with success in several states so far.Utah, Iowa, Tennessee, Oklahoma, and Louisiana have recently signed laws shielding fossil fuel companies from lawsuits related to greenhouse gas emissionsOil executives have also gotten help from the federal government, following an executive order from President Donald Trump last year directing the attorney general to prioritize blocking climate lawsuits by states.This May, the Justice Department responded to Minnesota's climate lawsuit against Big Oil with a lawsuit of its own, just as the state's case was moving into the discovery phase. It said Minnesota was undermining “American energy dominance” and attempting to regulate greenhouse gases, which should fall under the purview of federal law—echoing the oil industry's well-known argument.Elon Musk Is Quietly Turning to This Fossil Fuel to Power His AI AmbitionsSam Altman Announces That the Singularity Has Arrived: a hypothetical future point in time when technological growth becomes autonomous, uncontrollable, and irreversible, fundamentally transforming human civilization“We are now, like, in the singularity ... Now we're actually in the moment that we used to talk about at the lunch table in a very not-serious way ...I've been waiting for this my whole life, and I think it's going to be incredible, hugely positive, awesome for the world.”Sam Altman says one of his biggest fears is that a small number of companies will control AI: 'That'd be very, very bad'Sam Altman says the Hugging Face hack is a reminder that an AI power monopoly could lead to 'long-term disaster'OpenAI says its rogue AI tried to hack other companiesMark Zuckerberg is urging the U.S. to accelerate AI development, not restrict it'No Way to Stop It': Elon Musk Warns Humans Will Lose Control of AI and Face Extinction by 2036Microsoft CEO Warns That Companies Embracing AI Could Drive Themselves Out of BusinessAI hackers are getting faster. The government may not be readyAnthropic says its Claude models 'gained unauthorized access' to other organizations' systemsAnthropic says its models went rogue and hacked 3 companies during testing'It Will Happen Frequently': Musk Warns of Rogue AI Threat After Anthropic Breached Three FirmsElon Musk Commits Up to $120M to Republican Midterms: Sets Feud With Trump Aside for Major Election PushGoodliest of the Week (MM/DR):DR: Gen Z women are having an entrepreneurship boomOf all women who started businesses in 2025, 47% of Gen Z women did so, compared with 38% the previous year. Gen Xers followed close behind at 46%. Then came millennials at 43% and baby boomers at just 23%.Overall, in 2025, 69% of new Black-owned businesses were started by women. The survey also found that women business owners are less likely than their male counterparts to depend on AIMM: Delaware judge rules public benefit corporations exempt from maximizing value in sale DRFiduciary duty is different - OpenAI, AnthropicAssholiest of the Week (MM):Fake man apologies MM‘I'm sorry ... but': Apologetic Alan Joyce tells his side of the Qantas storyJoyce has expressed some regret“So with the information we had at the time, I still don't think there was any other decision you could make,”“Hindsight is a great thing.”“I'm actually very, very proud of the fact that I'm not sitting here and apologising for Qantas going bankrupt, [something] that many airlines around the world did,”Australian Competition and Consumer Commission fined Qantas $120 million for selling tickets for 8000 already cancelled flights“I apologise for the angst that was generated [for] the customers on it, but the reality was we had 22 million bookings in the system [and] the system wasn't designed to do an automatic refund,”“Stop us!”More than 1,200 AI workers across Anthropic, DeepMind, OpenAI, and Meta are asking for Washington's help building an AI slowdown plan‘No Way to Stop It': Elon Musk Warns Humans Will Lose Control of AI and Face Extinction by 2036Elon Musk's new 5-year warning to Americans: AI will beat human brains by 2032 (then go wild). Get rich or get crushed?Sam Altman says the Hugging Face hack is a reminder that an AI power monopoly could lead to 'long-term disaster'It Will Happen Frequently': Musk Warns of Rogue AI Threat After Anthropic Breached Three FirmsBut also, DON'T stop us, obviously…Palantir CEO warns US against Europe's AI regulation path, urges Trump admin to not ban open modelsElon Musk's xAI sues Minnesota over law to ban 'nudify' appsMark Zuckerberg is urging the U.S. to accelerate AI development, not restrict itElon Musk Commits Up to $120M to Republican Midterms: Sets Feud With Trump Aside for Major Election PushIgnoring boardsHims & Hers mission:“Hims & Hers is the leading health and wellness platform on a mission to help the world feel great through the power of better health. We believe how you feel in your body and mind transforms how you show up in life. That's why we're building a future where nothing stands in the way of harnessing this power. Hims & Hers normalizes health & wellness challenges—and innovates on their solutions—to make feeling happy and healthy easy to achieve. No two people are the same, so the Company provides access to personalized care designed for results.”Hims & Hers board:CEO: Andrew Dudum, tech VC bro investor “serial entrepreneur” with deep health experience from Bungalow (airBnB knockoff in the UK), Homebound (a “homebuilding platform”), TalkIQ (an AI startup, duh), and Terminal (something about scaling engineering?)TWO directors from DoorDash (Kofi Amoo-Gottfried from Marketing, previously of Facebook, and Christopher Payne the COO, previously of eBay, MSFT, and Amazon)TWO pharma execs (one lawyer, Deb Autor, one with an econ degree, Kare Schultz)One guy from Netflix (David Wells) and one lady from Urban Outfitters (marketing/brand, Andrea Perez) and one pure law firm lawyer (Anja Manuel)ONE DOCTOR EXCLUSIVE: US FTC sues Hims & Hers for sending user health info to Meta, SnapUsers' sensitive health information was shared with online advertising companies including Meta ​Platforms and Snap despite the company leading customers to believe their ​data was private, the FTC alleged in the lawsuit filed along with Los ⁠Angeles County and Utah.Hims & Hers also started charging users for prescriptions before ​they have ⁠had a chance to meet with healthcare providers, the FTC alleged. Most customers do not receive a consultation with a provider, and instead are charged for ⁠prescriptions ​soon after filling out an intake form, ​according to the agency.Headliniest of the WeekDR: CEO apologizes for offering interviews to people who got tattoos of his AI company logoSan Francisco tech CEO Jordan Zietz, co-founder of an early stage AI startup called LemonLime. Stanford grad.“LemonLime learns your business and then automates your team's busywork in a single click, no coding or manual setup required.”Former Qantas CEO reflects on tenure, issues apology to passengers over post COVID chaos AND ‘I'm sorry ... but': Apologetic Alan Joyce tells his side of the Qantas storyMM: Amazon received $600 million in tariff refunds and will pass some back to customersMM: Trump Staff Cuts Blamed After Watermarked OpenAI Map Mislabels Africa at AIDS ConferenceIt's the fact that we have less staff that meant we couldn't bother to figure out which country was whichWho Won the Week?DR: Kohls: For getting some much-needed female influence on board: Kohl's Appoints Wendy Arlin as Chair of the BoardCurrently only 2: Robbin Mitchell (7%) and Wendy Arlin (1%)John Schlifske (27%) stepping downMM: The phrase “not due to any disagreement with the Company”Anne Sweeney Resigns From Netflix BoardOn July 26, 2026, Anne Sweeney notified Netflix, Inc. (the “Company”) that she was resigning from the Board of Directors of the Company effective as of that date. Ms. Sweeney's resignation is not due to any disagreement with the Company1,660 8-Ks in the last 5 years have the exact phrase “not due to any disagreement with the Company”So we're saying that 1,660 c-suite and board members resigned in 5 years - more than 300 per year - and NONE were due to a disagreement with the companies? That's only THAT phrase - no other details are given for most of them except occasionally:To pursue another jobPersonal reasonsFor scale, in the last 5 years, the term “was due to a disagreement with the Company” appears… 5 timesPredictionsDR: Qantas CEO Vanessa Hudson apologizes that it took former CEO Alan Joyce to apologize for something she already apologized for despite not being CEO when any of it happenedMM: Damion resigns from Free Float due to a disagreement with the zero dollars he gets paid, but Free Float puts out a statement saying it's “not due to any salary reason related to the company”

Security leaders count open vulnerabilities in the hundreds of thousands, and in some organizations the number runs past a million. Ondrej Vlcek, Co-Founder and CEO of AISLE, describes teams with no practical route through that backlog while attackers use automation to shrink the time between a disclosure and a working exploit. The question worth asking is what a program looks like when remediation moves at the same speed as exploitation. What makes AI-driven remediation different from static code analysis? Reasoning replaces pattern matching. Linters and commercial scanners flag code that resembles a known error shape, while a reasoning model infers what the developer intended, compares that intent against the actual implementation, and evaluates how the gap could be abused. Ondrej Vlcek points to business logic flaws, timing errors, and race conditions as the classes that pattern matching leaves untouched. The judgment behind AISLE comes from a long run in the industry. Ondrej Vlcek wrote device drivers for Windows 95 in 1995 at a seven-person antivirus company called Avast, stayed more than twenty-five years, moved through CTO and COO into the CEO seat, and took the company public before its sale to NortonLifeLock in 2022. He co-founded AISLE in 2024 with Jaya Baloo, a three-time public company CISO, and Stanislav Fort, an AI researcher who worked at DeepMind and Anthropic. Why does the software supply chain deserve the larger share of attention? Because most of the code in a running application was written somewhere else. Ondrej Vlcek puts the typical enterprise application at roughly ten percent first-party code and ninety percent open source and dependency code, which is also level ground for an attacker reading the same source and pointing the same models at it. Reachability analysis becomes the deciding factor, separating the vulnerable functions your code actually calls from the thousands of transitive dependencies it never touches. For first-party code, AISLE closes the loop differently: read the documentation, the architectural material, and the threat model, then generate a patch aligned with the project's own conventions and test it automatically. The standard Ondrej Vlcek sets is a fix that reads as though a human maintainer wrote it. The customer spread runs from embedded firmware at Bose to smart contracts at the Ethereum Foundation, where heavily audited and sometimes formally verified code still benefits from another set of checks because the systems touch money flows directly. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUEST Ondrej Vlcek, Co-Founder and CEO of AISLE On LinkedIn: https://www.linkedin.com/in/ondrejvlcek/ RESOURCES Learn more about AISLE: https://aisle.com Meet AISLE at Black Hat and DEF CON in Las Vegas: https://aisle.com/black-hat The AISLE platform: https://aisle.com/platform AISLE CVE discoveries: https://aisle.com/cve-discoveries Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight KEYWORDS ondrej vlcek, aisle, sean martin, brand story, brand marketing, marketing podcast, brand spotlight, vulnerability management, vulnerability remediation, agentic ai, cyber reasoning system, software supply chain security, reachability analysis, open source security, application security, first-party code, third-party dependencies, static code analysis, zero-day vulnerabilities, ai in cybersecurity, code patching, embedded firmware security, smart contract security Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Join José as he hosts a special edition of the Emerging Manager series on the Delphi Podcast, sitting down with Andrew Kang, founder of RoboStrategy. After managing personal capital through Mechanism Capital across both public and venture markets, Andrew turned his focus entirely to the under appreciated robotics industry a space he identifies as being at a critical inflection point reminiscent of AI in 2022 or crypto in 2014.  They dive deep into the underlying method behind finding asymmetric bets, sizing positions, and distinguishing a true contrarian play from simply being the sucker at the table. Andrew breaks down the multidisciplinary engineering breakthroughs that sparked his initial bullishness on humanoid robotics and why he believes the industry is poised to become a multi-decade, trillion-dollar powerhouse.

Igor Babuschkin, co-founder of River AI and formerly a co-founder of xAI, joins to unpack a career that spans nearly every major AI lab: he led the StarCraft and AlphaCode work at DeepMind, joined OpenAI's reasoning team years before o1 shipped, and co-founded xAI, where he helped stand up the Colossus data center in roughly 120 days and reflects candidly on what it's actually like working with Elon Musk day to day, plus what the Cursor acquisition actually unlocked for Grok's coding models. He also discusses why he left xAI to start River AI, the three bets behind it, and why he's betting on local hardware, not just software, for personal AI. On the enterprise side, he tackles whether companies will actually train their own models or if it's just a cost play, and makes the case that proprietary labs like OpenAI and Anthropic are facing a real business squeeze. He's skeptical that stacking specialized RL domains generalizes the way pre-training scale did, and is candid about the uncomfortable reality that today's frontier open-weight models are almost entirely Chinese. He closes on what's actually needed to push model progress beyond coding into non-verifiable domains, and the broader implications of where AI is headed next.   (0:00) Intro (1:17) Writing Fiction on Where AI Is Headed (4:46) Cracking Agents Beyond Coding (10:29) Why Igor Left to Start River (12:22) River's Three Big Bets (18:06) Weights vs. Memory: The Personalization Debate (22:04) Should Enterprises Train Their Own Models? (25:10) Are Proprietary Labs Losing Their Edge? (32:16) The China Open-Source Problem (44:19) The Elon Call That Started xAI (50:18) Thoughts on Cursor Acquisition (52:16) What's Actually Bottlenecking AI (56:55) Humans, Machines, and Staying Relevant (1:01:29) Igor's Odds This All Goes Well With your host: @jacobeffron - Managing Director at Redpoint

It has already reassigned some members of the DeepMind project, while others have already left the company. Learn more about your ad choices. Visit podcastchoices.com/adchoices

S umělou inteligencí začali experimentovat už na studentských kolejích, když si chtěli přivydělat a vymýšleli algoritmus, který by porážel profesionální hráče pokeru. Nakonec s ním vyrazili do Kanady, kde svůj nápad pilovali na tamní univerzitě, až ho koupila přední výzkumná laboratoř DeepMind patřící Googlu. Tehdy Martin Schmid a spol. naplno pronikli do velkého světa AI a nakonec se pustili i do vlastního byznysu. Jejich startup EquiLibre je sice nenápadný, ale úspěšně točí na burzách miliardy dolarů denně a velmi rychle roste. Nejzajímavější přitom je, že sami tvůrci nevědí, jak přesně jimi vyvinutá AI funguje.V rozhovoru se dále dozvíte:

Send us Fan MailIn this episode, Amith Nagarajan and Mallory Mejias unpack a rapidly shifting AI landscape where powerful, low-cost open-weight models from Chinese labs are challenging the dominance of major U.S. players. They explore Moonshot AI's Kimi K3 and Alibaba's latest model announcements, breaking down what “open” AI really means and why it's driving costs down while increasing competition. The conversation also dives into the growing geopolitical tension around AI, including potential U.S. government intervention and the risks and realities of using models developed abroad. Finally, they discuss a proposal from DeepMind's Demis Hassabis to introduce an industry “referee” for AI safety—and what it could mean for innovation. The big takeaway: intelligence is becoming abundant and affordable, and association leaders must rethink strategy now to take advantage of what's coming next. 

01. L'IA hors de contrôle ? Un modèle aurait mené une cyberattaque en solo. Coïncidence ou pas, DeepMind réclame en urgence un régulateur mondial.02. Licencié pendant la bronzette : Chez Meta, un algorithme a viré des employés durant leurs vacances. Sans validation humaine.03. L'armée de robots arrive : Xiaomi passe à 98 % de précision en usine et Hyundai rachète 100 % de Boston Dynamics.04. Gardes du corps pour patrons d'IA : Sam Altman et d'autres dirigeants reçoivent des menaces de mort. Leurs budgets sécurité explosent.05. Le couvre-feu numérique bidon : Le Royaume-Uni veut couper les réseaux la nuit pour les 16-17 ans... mais le système se désactive en 3 clics.06. LinkedIn, la nouvelle appli de rencontre : 1 personne sur 5 l'utilise pour se renseigner sur son crush.

A rep says "follow up on that" on a call, then buries it under five more calls and a late-night inbox. Three days later the deal's gone cold, but the CRM still says it's live. The manager forecasts off that.  The CRO forecasts off the manager. The board asks the CEO if anyone actually has a grip on the number. The whole thing was built on a promise nobody kept.  Adam Liska worked on the early Gemini models at Google DeepMind before leaving to close that gap. His company, airspeed, just raised a $20M Series A on a bet that "revenue execution" is the next real category, not another tool that logs calls and calls it intelligence.  Sam Jacobs traces the full arc with him: DeepMind to founder-led sales to the unglamorous work of turning a founder's instincts into a system a team can actually run.  What we get into:  Why "follow up on that" breaks the forecast chain from rep to CRO to board What "revenue execution" means, and why it isn't revenue intelligence with a new label  Whether you own the interface or hand it to a chat model and live as middleware  The Nashville lunch that became airspeed's first six-figure deal  The real sequence from founder-led to scalable: capture the data first, then build the playbooks  Owning your data instead of renting Salesforce's architecture  The modern Turing test, AI-native orgs, and where the back office is headed Chapters:   00:00 Intro 00:44 From DeepMind to airspeed, and the $20M Series A  01:30 "Follow up on that" — the execution gap that breaks forecasts  04:00 What category is this? Revenue execution, defined  05:40 Headless vs. owning the interface  08:00 The origin story: leaving DeepMind in 2022  11:39 Founder-led sales and the Nashville lunch that closed a six-figure deal  13:18 The hard part: turning founder-led into a repeatable system  14:14 Why they recorded everything from day one  16:35 Owning your data, and where the source of truth lives  19:03 The playbook: data first, then hierarchical playbooks  20:52 Influences: the modern Turing test and AI-native orgs  23:37 Where to find airspeed  Try airspeed: goairspeed.com

July 21st 2026, Court Leader's Advantage PodcastEpisode 106AI promises greater efficiency, lower costs, and better public service—but it also raises concerns about privacy, bias, job displacement, and accountability. This panel explores where courts should draw the line.Artificial intelligence is currently having a difficult moment. Hardly a day goes by without headlines highlighting a new AI concern. Consider just a few of the issues currently dominating public conversation: • Corporate leaders are investing billions in AI while simultaneously announcing layoffs, fueling fearsabout job displacement and economic insecurity.• Communities across the country are opposing the construction of new data centers due to concernsabout their massive consumption of water and electricity.• Educators and researchers worry that AI has weakened students' critical thinking and problem-solving skills by making information and answers available almostinstantaneously.• Privacy advocates warn that AI enables governments, employers, and corporations to monitor individuals more closely than ever before, raising concerns about the emergence of a surveillance society.• Others fear that AI-generated videos, voices, images, and documents have become so convincing that distinguishing fact from fabrication is almost impossible. As a result, AImay undermine trust in elections, journalism, and public institutions. These concerns are real and deserve careful consideration.Yet they often overshadow the remarkable accomplishments AI has already achieved. Consider just three examples:• Google's DeepMind developed an AI system that analyzes mammogram images that, in some cases, detect breast cancer earlier and more accurately than expert radiologists, reducing both false positive and false negative results.•AI-powered wildfire detection systems now analyze live camera feeds, satellite imagery, weather data, and vegetation conditions to identify wildfire smoke within minutes.  This saves valuable time for emergency first responders and ends up protecting lives and property.•Arizona State University has implemented AI-powered educational tools that provide personalized learning experiences through conversational tutors, simulated learning environments, and on-demand academic support. AI is also becoming increasingly embedded in courtoperations. It can summarize documents, draft reports, assist with legal research, identify patterns within large datasets, and automate a wide range of administrative tasks. Even courts that have not formally adopted AI are likely employing staff who are already experimenting with these tools in theirday-to-day work. This reality raises a number of difficult questions.As with many technological revolutions, the central challenge is not simply deciding what AI can do. The central challenge may be deciding what AI should do. How much responsibility should be delegated to algorithms? Where must human judgment remain indispensable? And how can courtsstrike the proper balance between innovation, efficiency, fairness, and accountability?Today's Panel TJ BeMent Court Administrator, 10th Judicial Administrative District in Athens, GeorgiaRick Pierce Judicial Programs Administrator, Administrative Office of the Courts in Mechanicsburg, PennsylvaniaKarl Thoennes Court Administrator, 2nd Judicial Circuit Court, in Sioux Falls, South DakotaCreadell Webb Diversity, Equity, and Inclusion Officer, 1st Judicial District of Pennsylvania, PhiladelphiaWhere should courts draw the line on AI? Email your comments or questions to CLAPodcast@nacmnet.org. Selected comments may be featured in a future episode.  Become part of theConversation. Submit your comments and questions to: CLAPodcast@nacmnet.org  

Bloomberg reported that Jeff Bezos backed an AI startup focused on discovering new materials for chipmaking, with no company name or deal terms disclosed. The report aligns with rising use of AI in materials research, including DeepMind's 2023 GNoME results and a Microsoft and Pacific Northwest National Laboratory effort that produced a battery electrolyte candidate in under nine months. Amazon Web Services' custom chips, including Graviton, Trainium, and Inferentia, underscore strategic interest in semiconductor performance and supply. The CHIPS and Science Act authorized $52.7 billion in incentives and R&D, with 2024 awards of up to $8.5 billion for Intel, $6.6 billion for TSMC, and $6.4 billion for Samsung. Materials vendors such as Applied Materials, Lam Research, Tokyo Electron, JSR, Tokyo Ohka Kogyo, EMD Electronics, Entegris, Shin Etsu, and SUMCO shape qualification paths. Startups face long validation cycles and can pursue joint development, milestone based licensing, and data partnerships to reach production.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

(0:00) Bestie intros! (1:32) New AI regulatory proposal: DeepMind's Demis Hassabis proposes FINRA-type body (20:01) Stripe, Block, and Advent offer $53B to acquire PayPal (37:51) Apple sues OpenAI, alleging stolen trade secrets (42:49) Grok Build data leak, AI data privacy, Tokenmaxxing update, Mira Murati's new model (59:53) NY bans datacenters, becoming first state to enact a moratorium (1:22:57) Science Corner: New data on reversing aging! Adopt Ronnie the Dog: https://www.instagram.com/reels/Da0pGahBwaW Apply for All-In Summit 2026: https://allin.com/events Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://x.com/chamath/status/2077502408528212144 https://x.com/demishassabis/status/2076957440109625718 https://x.com/satyanadella/status/2076323181154230284 https://x.com/Jason/status/2076231055443440105 https://x.com/SquawkCNBC/status/2077741908391031246 https://thinkingmachines.ai/news/introducing-inkling https://www.politico.com/news/2026/07/15/inside-anthropics-state-by-state-plan-to-ratchet-up-ai-rules-00998415 https://x.com/politico/status/2077315780144996633 https://x.com/DavidSacks/status/1978145266269077891 https://www.reuters.com/business/finance/stripe-advent-offer-buy-paypal-more-than-53-billion-sources-say-2026-07-15 https://www.tipranks.com/news/the-fly/block-contributing-to-equity-for-paypal-takeover-bid-cnbc-says-thefly-news https://9to5mac.com/wp-content/uploads/sites/6/2026/07/Apple-Inc.-v.-Liu-et-al.pdf https://finance.yahoo.com/technology/ai/articles/apple-lawsuit-threatens-openais-hardware-215438163.html https://x.com/markgurman/status/2076306380583997665 https://www.engadget.com/2216186/elon-musk-bought-a-gas-turbine-company https://x.com/Reuters/status/2076957424339050839 https://x.com/teddyschleifer/status/2077596563380072694 https://x.com/teddyschleifer/status/2077606887055306879 https://openai.com/index/prc-linked-influence-operations-ai-debates https://www.politico.com/news/2026/06/10/openai-china-ai-data-centers-report-00957612 https://trends.google.com/explore?q=GMO%2C%2Fm%2F0dkz0z&date=2010-01-01%202026-07-15&geo=US https://www.nature.com/articles/s41467-026-75141-2

What happens when wholesale distributors realize “CRM” is no longer the real conversation, growth, data, AI, and sales enablement are?In Episode 198 of Around the Horn in Wholesale Distribution, Kevin Brown and Tom Burton unpack the shift they saw firsthand at the Affiliated Distributors Functional Success Summit: distributors are moving beyond traditional CRM adoption questions and toward connected data systems, AI-enabled sales strategy, and future-proofing distribution. The episode connects macroeconomic uncertainty, supply chain risk, AI governance, human judgment, and enterprise growth strategy to the real decisions facing wholesale distribution teams today.What You'll Learn:Why distributors are moving past traditional CRM conversations and focusing instead on how to help sales teams create more value, become more consultative, and grow revenueHow CRM-ERP integration, data warehouses, e-commerce platforms, marketing automation, and disconnected point solutions can limit customer visibility unless they are unified into a true enterprise growth platformWhy inflation measurement, interest rate uncertainty, and prediction markets matter to revenue leaders in distribution making investment and growth decisionsHow Strait of Hormuz disruption, Suez Canal risk, oil volatility, plastics, fertilizers, helium, and global logistics instability can ripple through manufacturing and wholesale distributionWhy AI still needs human judgment, oversight, and strategy, and why many companies miss expected ROI when they assume full automation instead of building a realistic digital transformation planEpisode Highlights:01:16 – Lessons from the Affiliated Distributors Functional Success Summit and why sales enablement is replacing traditional CRM talk03:55 – Why disconnected data across ERP, marketing automation, e-commerce, and point software creates risk for distributors06:37 – Episode 198 begins: how Around the Horn connects the economy, supply chain, M&A, sales, marketing, AI, and robotics to wholesale distribution08:30 – LeadSmart's Meridian 360 Enterprise Growth Platform and the move from Smart CRM to broader business growth engines12:32 – Inflation cooling, gas prices, and why energy volatility still affects distributors, manufacturers, fertilizers, heavy minerals, and supply chains15:23 – Kevin Warsh, Fed measurement reviews, CPI, PPI, the 2% inflation target, and the need for more real-time data20:25 – Prediction markets, PolyMarket, Kalshi, and whether betting markets can offer useful economic signals27:17 – Strait of Hormuz risk, Iran, shipping disruption, oil exposure, the Suez Canal, Houthi rebels, and what it means for global supply chains43:02 – AI watchdogs, model testing, DeepMind, Fable, Mythos, and the role of government QA for high-powered AI systems48:38 – Human judgment in the age of AI, digital twins, job displacement fears, and why AI ROI depends on data readiness and realistic automation expectations56:00 – APR Supply's sales tool adoption gains and how better sales technology can support outside sales revenue growthTools, Frameworks, and Strategies Mentioned:LeadSmart TechnologiesMeridian 360 Enterprise Growth PlatformSales CompassSmart CRMSales Co-PilotAI Co-Pilot for SalesCRM-ERP IntegrationCRM Data Enrichment with AIHidden Revenue DetectionSales Automation Without Losing the Human TouchFuture-Proofing DistributionHybrid Selling ModelsConsultative CommerceData warehouses and data lakesMarketing automationE-commerce data integrationERP data unificationAI-enabled business intelligencePrediction marketsPolyMarketKalshiCPI and PPI measurement reviewsAI model QA and AI watchdog conceptsHuman judgment in AI strategyAPR Supply sales tool adoptionMaster of Distribution Management programClosing Insight:The episode's central message is clear: the future of distribution is not about buying more disconnected technology. It is about connecting data, people, process, and AI into a strategy that helps teams sell smarter, serve customers better, and make better decisions under uncertainty.Leave a Review: Help us grow by sharing your thoughts on the show.Learn more about the LeadSmart AI B2B Sales Platform: https://www.leadsmarttech.com/Join the conversation each week on LinkedIn Live.Want even more insight to the stories we discuss each week? Subscribe to the Around The Horn Newsletter.You can also hear the podcast and other excellent content on our YouTube Channel.Follow us on Facebook, Twitter, Instagram, or TikTok.

The federal government has reached agreements with Google's DeepMind, Microsoft, and SpaceXAI to evaluate advanced artificial intelligence models for national security risks before they are released to the public. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In today's episode of Moneycontrol Tech3, we break down how Norges and Motilal Oswal are shaping Zepto's IPO anchor book, why venture capital is pouring into India's AI startup ecosystem, what Google DeepMind's Manish Gupta means when he says India needs more startups like Sarvam AI and Emergent, and why MakeMyTrip has confidentially filed for a billion-dollar-plus India IPO.

In this episode, we welcome director Connie He and Google DeepMind's Márcia Mayer to discuss the making of Dear Upstairs Neighbors, which premiered at the Tribeca Festival in June. Connie and Márcia, both Pixar alumni, embarked on a creative journey to explore how emerging technologies could help bring Connie's story and artistic vision to life. In our conversation, they discuss their respective roles in the project, the collaborative process behind this unique animated film, DeepMind's mission to empower creators through cutting-edge technology, and what they learned along the way. They also share advice for the next generation of storytellers navigating today's rapidly evolving creative landscape.“The Making Of” is presented by AJA:AJA introduces high-performance Thunderbolt™ 5-enabled PCIe expansion chassisMeet Io Xpand, a Thunderbolt 5 expansion chassis for AJA KONA and Corvid I/O cards. Io Xpand lets video professionals integrate the performance of AJA PCIe I/O cards on Thunderbolt 5 laptops and mini-PCs for an incredibly powerful, fast, and portable I/O solution for on set, remote production, and live switching. Visit hereA Solution Built Around Your WorkflowThe OWC Express 4M2 Ultra is available in a range of configurations to fit your workflow and budget. Choose from ready-to-run solutions with up to 32TB of high-performance NVMe storage, giving you the speed, capacity, and reliability needed for everything from multi-camera editing to demanding VFX and finishing workflows. Available now. Learn more hereNew York City | July 28The sports-production landscape is undergoing a dramatic transformation as a result of the cloud and AI, and SVG's new Cloud & Content Workflows Summit is where the industry comes together to navigate it. As virtualized hardware, cloud-based workflows, and AI-powered tools become more commonplace, the entire sports-production supply chain is being reimagined from the ground up. This special event brings together SVG's previous Cloud Production and Content Management events into a single, comprehensive day of programming—covering both live and non-live production workflows alike. Learn more hereZEISS Introduces Horizon Anamorphic: Full-Frame 2x Anamorphics with a New Lens Technology PlatformZEISS unveils the Horizon Anamorphic series, a new lineup of full-frame 2x anamorphic cinema lenses designed to deliver a distinctive cinematic look along with a new lens technology platform that answers the need for speed and precision demanded by contemporary production workflows.Spanning 35mm to 200mm across seven focal lengths, Horizon lenses combine their anamorphic look—incorporating a pronounced oval bokeh and stretched sense of spatial depth—with a lightweight, fully integrated motorized system that eliminates the need for external focus or iris motors. Read more hereSanDisk Extreme PRO USB4 Portable SSDPower your workflow with the SanDisk Extreme PRO USB4 Portable SSD, available in 2TB and 4TB capacities. Featuring speeds up to 3800 MB/s read and 3700 MB/s write, it's built for fast transfers and editing. The rugged, IP65-rated design is ready for work anywhere. Learn more at Videoguys or call 800.323.2325 for free tech advice. View herePodcast Rewind:July 2026 - Ep. 141.Explore a Partnership with The Making Of:Reach over 275,000 film industry, broadcast professionals, and content creators reading this weekly newsletter. To learn more, please email mvalinsky@me.com Get full access to The Making Of at themakingof.substack.com/subscribe

Ottawa's “Buy Canadian” procurement policy is sending the bulk of its contracts to foreign-owned firms — a loophole driven by how loosely “Canadian” is defined and by trade rules that limit how much the federal government can play favourites. Plus, the defence world is in full drone mode as Ottawa sets up a new testing hub in Quebec to accelerate homegrown drone and counter-drone tech.In the big picture: IBM's brutal earnings day wipes out tens of billions in market value, DeepMind's Demis Hassabis calls for a frontier AI regulator, and five First Nations groups move to take a majority stake in major LNG export infrastructure.The Peak Daily is produced in partnership with reframevid.com

My fellow pro-growth/progress/abundance Up Wingers in America and around the world:If my podcast guest today is correct, the emergence of generative artificial intelligence "heralds a transformation more profound than anything since Homo sapiens acquired the capacity for abstract thought." That's about as pure a distillation of the San Francisco Consensus view on the importance of this technology as it gets.Today on Faster, Please!—The Podcast, I am joined by Sebastian Mallaby, the Paul A. Volcker Senior Fellow for International Economics at the Council on Foreign Relations and a widely read columnist for The Washington Post. He is also the author of the new best-selling book The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence. (Spoiler: It's tremendous book about the man, the company, and the technological revolution. I really liked it.)We discuss The Infinity Machine and the life of Demis Hassabis, including how his original vision for artificial superintelligence compares with the propulsive race unfolding today. We also explore how that competitive acceleration has affected the focus on safety, what role government regulation should play, and why many people may still be underestimating how transformative AI will become.The Quest for “Success” (0:27)Inside the Mind of Hassabis (8:29)The Race for Monopoly (12:31)The Economics of AI Anxiety (17:05)Governing the AI Race (24:17)The Biggest Leap Since Abstract Thought (30:13)A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.)But here are some edited highlights from the chat:On where AI is heading…You look backwards; you see how fast the progress has been. To merely extrapolate forwards is probably to undersell the speed at which we'll accelerate in the future because there's an accelerating phenomenon here where the more advanced you are, the easier it is to get to the next level.On Hassabis's belief that AI development would look more like the Manhattan Project than a multi-country, multi-company competition …In retrospect, it's crazy. All one can say is that ex ante, the atmosphere in the community of AI builders when Demis began his company in 2010 was that this was a thing that simply didn't work. AI could not recognize the photograph of a cat. AI could do nothing. It was deep AI winter. And so, under those conditions, you could assemble the entirety of the world's strong AI believers in one conference in San Francisco, and it felt like a single community. So, this sort of Singleton scenario where you just have one lab, it was a natural outgrowth of that moment in time.How AI competition has overwhelmed that vision…Before 2022, Demis had the freedom because he was clearly the leader to define what the next project should be. He chose at one point to go and do this protein folding project. …This is kind of AI with a smiley face painted on it. Whereas once the chatbot went viral at the end of 2022, ChatGPT, then everybody had to pile in and build a competitor and there's a lot less leeway to define your own path. So, I think the agency of the individual was quite strong until 2022 and thereafter the power of the race dynamic takes over.What skeptics, such as many economists, have gotten wrong and right…The number of improvements before even we talk about Mythos and the cyber capabilities of that one, I mean, it's been an extraordinary ride in what is actually less than four years. So, I don't take back anything I say about the speed of the advance of the frontier. Now that's different to the speed of the deployment. There I have a lot of sympathy with the economist.On the difficulty of AI regulation…I'm actually quite optimistic in terms of the ability of a government agency to regulate… People often think of AI as a bunch of code that flies around cyberspace and you really can't control it. But actually, it's also a bunch of data centers which are huge physical installations. The government knows precisely where they are. They can't be moved or hidden.On his superintelligence timeline…To be honest, I would say it's already true. I mean, you try using Fable and if people are listening and they're inclined not to agree with me, I just ask you, spend a couple of hours with Claude Fable and then see if you disagree with me…I think it is smarter than me by quite a long shot on any topic I ask it about.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were Promised This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

A24 announced a $75 million research partnership with Google DeepMind and framed it as R&D for production tools, not a content deal. Nobody cared about the distinction. Chris, Daniel, and James Blevins spend the first half of this Rough Cut on why that announcement landed like a betrayal anyway, then pull the same thread through the Paramount Skydance and Warner Bros. Discovery merger, Mark Ruffalo's New York Times op-ed on quiet industry retaliation, and what it means that four studios now control what used to take six. From there the panel turns to Christopher Nolan's press tour for The Odyssey and the practical-versus-VFX marketing dance that VFX artists roll their eyes at every single time it resurfaces. They close with a Monstrous Moonshine state of the union, walking through the June July shoot and a quick, lighthearted aside on why the show still uses AI tools like Suno for its own theme song. Mentioned this episode: A24, Google DeepMind Paramount Skydance, Warner Bros. Discovery, Mark Ruffalo, Matt Stoller Christopher Nolan, The Odyssey, D-Neg, Weta Workshop Monstrous Moonshine June July Suno Special thanks to our sponsor: Center Grid Virtual Studio: https://cgvirtualstudio.com/

OMG, this is an absolute reality check!

In this episode, Ray Cochrane breaks down AI distillation, the teacher-student technique frontier labs now lean on to train smaller, cheaper models. He also covers GPT-5.6’s government-vetted rollout, Claude Sonnet 5 landing on AWS, Maryland’s two-year data center pause, and Microsoft’s climbing carbon numbers. Finally, he wraps with Apple’s $30 billion Broadcom deal, Meta’s tamper-proof recording light, Michigan’s parasite outbreak, and a simulation that erased a super El Niño. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. Longer days have him outdoors, including a float trip on the Sandy River at Dabney State Park, where he found clearer water, clay-like sand, and easy footing. Next week brings both a move and a trip home, so he is stocking up on Trader Joe’s “Power Berries” and IKEA bags at his mom’s request. Then he turns to the lead story. AI Distillation Explained: How Frontier Models Teach Each Other Cochrane’s featured story comes from Hugging Face engineer Sergio Paniego. Distillation is teacher-student training for AI: a capable model generates the training signal, and a smaller student learns to match it. The classic off-policy version compresses giant models into cheap students, either through soft labels or piles of worked answers. Google’s Gemma models and DeepSeek’s R1-Distill line were built exactly this way. However, the industry is now converging on multi-teacher on-policy distillation, or MOPD. Labs build reinforcement-learning specialists for math, coding, and agentic work, then have them grade a single student, word by word, as the student generates its own answers. DeepSeek-V4, MiMo-V2-Flash, and NVIDIA’s Nemotron 3 Ultra all run versions of the recipe, and the Qwen3 team reported better results at roughly a tenth of the GPU hours of raw reinforcement learning. Finally, self-distillation lets models like Cursor’s Composer 2.5 learn from better-prompted versions of themselves. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. GPT-5.6 Arrives With a Government-Vetted Rollout OpenAI shipped GPT-5.6 as a three-tier family: Sol, Terra, and Luna. Sol costs five dollars in and thirty dollars out per million tokens, half of Claude Fable 5’s rate. The benchmarks split: Sol Ultra wins Terminal-Bench at 91.9 percent, while Claude Fable 5 still leads SWE-Bench Pro. Notably, the API launched in limited preview to roughly 20 partners vetted by the U.S. government, though the model went live in Microsoft 365 Copilot on day one. Claude Sonnet 5 Lands on AWS, Plus Quick AWS Wins Claude Sonnet 5 arrived on AWS through Bedrock, pitched as top-tier intelligence at Sonnet pricing. Additionally, Amazon WorkSpaces for AI agents reached general availability, enabling agents to drive full desktop applications securely. OpenSearch gained a log-analytics engine claiming four times the price-performance, and SageMaker now scales inference about twice as fast. Cochrane also flags that Kendra and Q Business move to maintenance mode at the end of July. Anthropic Wants You to Reflect on Your Claude Habits Anthropic launched Reflect, a beta feature that analyzes your past Claude conversations and visualizes how you actually use the assistant. It requires Memory, excludes incognito and health-related chats, and keeps its insights inside the tool. Cochrane loves the idea. He reviews his own transcripts to extract prompt patterns and turn them into reusable skills, and he suggests listeners simply ask their AI to do the same. AlphaEvolve Goes GA on Google Cloud Google made AlphaEvolve generally available to Google Cloud customers on the Gemini Enterprise Agent Platform. The agent acts as an evolutionary collaborator: provide a baseline algorithm and your goals, and it searches for better, human-readable code. BASF, JetBrains, and Kinaxis are the named early adopters. Meanwhile, Cochrane renews his standing wish that DeepMind release AlphaGo as a playable teacher. Google Adds “How This Ad Was Made” AI Labels Google is adding a “How this ad was made” section to My Ad Center across Search, YouTube, and Discover. Ads built with Google’s own AI tools automatically get the disclosure, backed by invisible watermarks. However, ads made with outside tools rely on advertiser self-declaration. Cochrane points out the limits of voluntary disclosure in an AI-flooded content economy. Microsoft’s Carbon Emissions Climb 25 Percent Microsoft’s new sustainability report shows emissions up 25% in 2025, driven by a data center construction spree. The gross figure is 34 million metric tons before offsets, while other coverage puts the net figure at around 20 million. Water consumption also jumped thirty-four percent, even as Microsoft claims its first water-positive year. Cochrane argues regulation needs to catch up, since Google and Amazon report similar increases. Prince George’s County Pauses Data Centers for Two Years Prince George’s County adopted a two-year moratorium on new data center development, the longest pause in Maryland so far. The resolution blocks new applications, including hyperscale projects, until the council passes real regulations. Water and energy impacts remain open questions the county intends to study. Cochrane gives kudos to residents for making their voices heard. Apple and Broadcom Ink a $30 Billion U.S. Chip Deal Apple is expanding its partnership with Broadcom with a multiyear agreement expected to exceed $30 billion. The deal covers custom silicon and wireless components, with more than fifteen billion chips to be made on American soil. Broadcom’s Fort Collins, Colorado plant anchors the work with a $1.5 billion equipment expansion. Tim Cook framed the deal as accelerating Apple’s commitment to American manufacturing. MSI and Intel Ship the First Arc G3 Extreme Handheld Intel detailed how it co-engineered the MSI Claw 8 EX AI+, the first handheld on the Arc G3 Extreme processor. Highlights include a heat-spreading board layout and game-tuning loops that Intel says run Cyberpunk 2077 up to thirty-seven percent faster. The device is on sale now in void purple for around $1,500. At that price, Cochrane jokes he would rather buy a computer. Meta’s Glasses Get a Tamper-Proof Recording Light Meta answered the most common privacy questions about its AI glasses. Photos stay private on the device until the wearer imports or shares them, and a white capture LED blinks during any recording with no off switch. Moreover, newer glasses disable the camera if the LED is blocked, tampered with, or destroyed. Cochrane reminds listeners these claims are Meta grading its own homework, but the blink signal is worth recognizing in public. Michigan’s Parasite Outbreak Tops 1,200 Cases Michigan’s cyclosporiasis outbreak reached 1,251 cases since June 22, with roughly forty hospitalizations along the way. Northwest Ohio adds more than five hundred cases. The parasite typically spreads through contaminated fresh produce, and investigators still have not found the source. Cochrane’s advice: wash your produce, and get tested if your symptoms fit. AI Finds the San Andreas Fault’s Silent Slips Researchers paired AI with borehole strainmeters to detect dozens of hidden slow-slip events beneath the San Andreas Fault’s Parkfield section. Each silent slip releases stress within hours and is reliably followed by low-frequency earthquakes. Together, the findings support a continuous spectrum from silent creep to destructive quakes. The study appears in Nature Communications, and Cochrane hopes it will lead to better earthquake prediction. Cloud Brightening Erased a Super El Niño, in a Simulation Finally, a Science Advances study simulated marine cloud brightening in response to the 1997 and 2015 super El Niño events. Seeding clouds over the eastern Pacific erased the events entirely inside the model. Real deployment would take roughly 2,400 ships spraying continuously, and the simulations showed side effects like extra warming over Europe and Asia. Cochrane finds the weather-machine concept fascinating, yet he questions the consequences of altering cycles the planet runs for a reason. The post AI Distillation: How Frontier Models Teach Each Other #1870 appeared first on Geek News Central.

“A place that doesn't have great philosophers will not have great technologists either.” — Mehran Gul on Europe's inexplicable underperformance The digital revolution, we were promised, would mean the end of geography. From Beijing to Birmingham to Berlin to Barcelona, anyone could invent anything anywhere, and so the geography of innovation would no longer matter. But that's not the way it has worked out. At least according to the Geneva-based innovation geographer Mehran Gul. Gul's acclaimed The New Geography of Innovation is a travelogue of innovation. But what he finds on his journey around the world in search of innovation is the end of the end of geography. Yes, Gul reports, there's innovation in Beijing and in Birmingham (USA) — but not in Birmingham (England), Berlin or Barcelona. All the important invention is in China and the US. There simply isn't much radical stuff going on anywhere else. Gul began his journey expecting to find ten or twelve countries able to innovate competitively with the United States and China. But what he discovered is either niche players or, in the case of South Korea, Israel, and India, just an extension of the US-centric system. Europe — as renters rather than owners of American technology — comes off worst. When PayPal went public, it minted 160 millionaires who went on to help build SpaceX, Tesla, LinkedIn and Palantir; when Skype exited at about the same value, it minted 11. And if you put London aside, the rest of the UK is now poorer per capita than Mississippi. And the AI boom has only compounded all this, with half of last year's key research papers coming from China, 40% from America, and just 4% from Europe. So really the new geography of innovation is the old geography. Only with China replacing Europe as the only serious competitor to American innovation. Oh lord, oh lord. As a Mississippi bluesman might summarize Europe's predicament. Five Takeaways •       Golden Shares: The Two Systems Are Converging. OpenAI offering Washington a 5% stake, the US government owning Intel — these are Chinese moves, and Gul argues the two models are becoming more alike than either admits. But he pushes back on the lazy version of the China story: its tech sector rose despite the state, not because of it. Jack Ma exiled to Japan, Didi hit with a billion in fines, entire sectors decapitated overnight in 2021 under the banner of common prosperity. In a country with no independent media and no opposition parties, the only rival to centralized power is the tech sector — and the party knows it. •       Two Countries — and Everyone Else. Gul started writing expecting to find ten or twelve countries punching at America's level; the honest answer turned out to be two. Only China has broad-based competence across technologies and a genuinely competitive relationship with the US. The middle powers — South Korea, Israel, India — are extensions of the American system, not rivals to it. That finding surprised the author as much as anyone: it's not the book he set out to write. •       Europe: Renters, Not Owners. After the Fable 5 and Mythos bans, Europe woke up to being a renter of American technology — foundation models, NVIDIA GPUs, all of it. Its best companies keep leaving: DeepMind to Google, Arm to a New York listing, Hugging Face from Paris to Manhattan — while Volvo, Supercell, and KUKA sold to China. Gul's diagnosis is institutional, not cultural: European employees own half as much of their startups as American ones, so there is no European PayPal mafia. His fixes: a European Nasdaq to replace 41 competing capital markets, and pension funds unleashed into venture capital. •       The Question Nobody Is Asking. Since 1990, America's share of global GDP has held at 25% while China's multiplied tenfold — the loser is Europe. The top ten American tech companies are worth $27 trillion, more than the GDP of every country on earth except America itself. Tech is not one industry among many; it is the foundation of all of them — the new cars came from Tesla, not GM. Gul's message to the skeptical Spaniard enjoying long lunches: the last sixty years of American platform dominance skewed power across the Atlantic, and the next sixty will add China to the bill. •       The Rest of the Map: Anti Case Studies. Japan tops the freedom indexes, has the technical schools, and still never escaped the keiretsu — disproving Matt Ridley's claim that innovation is simply the child of freedom. Taiwan's relevance comes down to one company and Morris Chang's missed promotion at Texas Instruments. Singapore is an inspiration, not a model — a one-party city-state that invoices NVIDIA's chips and banks ASEAN's venture capital. India underperforms while Indians excel — 56 notable American foundation models last year, 35 Chinese, barely one Indian. And Switzerland reminds us innovation isn't only venture-backed: a train network running on renewables since the 1960s. About the Guest Mehran Gul writes about technology and business. He is the winner of the Financial Times/McKinsey Bracken Bower Prize, from which The New Geography of Innovation grew. He attended Yale as a Fulbright Scholar, Fox International Fellow, and Teaching Fellow, has been a Lead for the Digital Transformation of Industries at the World Economic Forum in Geneva, and served as an expert on entrepreneurship and industrial policy at the United Nations Industrial Development Organization in Vienna. Born in Pakistan, he lives in Switzerland. The New Geography of Innovation: The Global Contest for Breakthrough Technologies (Avid Reader Press/Simon & Schuster), a Financial Times Book of the Year, is his first book, out in paperback this month in the US and UK. References: •       The New Geography of Innovation: The Global Contest for Breakthrough Technologies by Mehran Gul (Avid Reader Press/Simon & Schuster). The Wall Street Journal: “An ambitious tour of technological innovation.” •       Sebastian Mallaby — author of The Power Law, which argues China's tech rise owes more to American-style risk capital arriving in Shanghai and Shenzhen than to the state; recently on the show discussing his biography of Demis Hassabis. •       Kai-Fu Lee — author of AI Superpowers, cited by Gul as the classic account of tech written through a Chinese lens. •       Matt Ridley — author of How Innovation Works, whose thesis that innovation is “the child of freedom and the parent of prosperity” Gul tests against the anti case study of Japan. •       Andrew Keen — author of How t...

When it comes to AI, Google's Gemini does not have the same household recognition that Claude and ChatGPT do. One big explanation: the innovator's dilemma. We explain how Google DeepMind CEO Demis Hassabis is trying to fight a lumbering company's constraints to build AI superintelligence. Sebastian Mallaby's book is The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence Fact checking by Sierra Juarez. Your Next Listen — The inventor's dilemma Connect with The Indicator — Sign up for The Indicator's brand new newsletter — Buy the Planet Money book — Find our socials, YouTube and more!— For sponsor-free episodes, subscribe to NPR+ See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, Gregor and Sean dig into the growing tension around restricted AI models, including Anthropic‘s Fable being pulled from the Claude The post SED News: Restricted Models, IDE Wars, and the DeepMind Mafia appeared first on Software Engineering Daily.

In this episode, Ray Cochrane digs into “algorithmic outing,” new research showing that social feeds can infer your sexual orientation before you have consciously come out. He also covers Meta’s privacy-aware AI infrastructure, Alberta’s 466-million-line code scan with Claude, NVIDIA on reinforcement learning, and the many journeys of learning Rust. Along the way, he hits Google DeepMind’s A24 deal, WhatsApp usernames, and scuba-diving cyborg cockroaches. Finally, he looks up with Webb’s puzzling early universe, NASA’s emergency telescope rescue, and a gorgeous aurora from orbit. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. He hopes listeners had a good holiday weekend, and he shares that he spent his time working his other job at Oregon’s Finest, chatting with people around Portland. Because his Blurbry workweek tends to be solitary, he refills his social meter on the weekends. He then recalls a Saturday night out with coworkers at the Hungry Tiger before turning to the lead story. Algorithmic Outing: When Your Feed Knows Before You Do Cochrane leads with new research from Australia that identifies a phenomenon called “algorithmic outing.” In short, the recommendation systems behind your social feeds can infer your sexual orientation or gender identity and start serving related content before you have worked it out yourself. Importantly, the study is small and qualitative, built on in-depth interviews with twenty LGBTQ+ adults in the Hunter region of New South Wales and published in the journal Gender, Place and Culture. The mechanism is engagement signals: what you like, who you follow, and how long you linger on a post, a metric the industry calls dwell time. Lead researcher Dr. Justin Ellis of the University of Newcastle notes that several participants said the algorithm “knew” they were queer before they did, an experience that felt validating for some but frightening for others in public settings. For Cochrane, the deeper worry is what else that hidden pattern encodes, from upbringing to mental health, and where that data ultimately gets sold. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Meta’s Blueprint for Privacy-Aware AI Infrastructure Next, Cochrane turns to a sharp engineering piece from Meta on privacy-aware infrastructure. The core challenge is that a system must understand what a piece of data actually is before any privacy rule can protect it. A field named “age,” for example, might describe a person in one place and a cache setting in another. Meta’s answer deploys a large language model only on the genuinely ambiguous cases, then distills what it learns into fixed, human-reviewed rules. The payoff is concrete. According to Meta, those deterministic rules already handle about 85 percent of the traffic, and only the last 15 percent falls back to the model, which costs roughly 400 times more compute. Cochrane loves this edge-case approach. However, he contrasts it sharply with the AI-everywhere software he wrestles with at his weekend job, which he says the heavy AI reliance genuinely makes worse and harder to audit. Alberta Scans 466 Million Lines of Code With Claude This one comes from Anthropic, and it ties directly to Meta’s theme. A team inside Alberta’s Ministry of Technology and Innovation used Claude to scan 466 million lines of code in about twenty hours, a review Anthropic estimates would have taken humans roughly six and a half years. Notably, they ran around fifty AI agents in parallel, essentially an automated red team and blue team probing the systems at once. For Cochrane, this is the good version of AI in production: cleaning up and locking down real systems rather than running the show unsupervised. NVIDIA on Reinforcement Learning for AI Agents On the AI-building side, Cochrane walks through an NVIDIA developer piece on reinforcement learning for agents. Reinforcement learning rewards a model for good behavior rather than showing it the right answer, much like training a dog with treats. Additionally, he clears up a common mix-up. NVIDIA treats RAG, retrieval-augmented generation, as a separate tool: reinforcement learning changes how a model behaves, while RAG changes what facts it can reach. GitHub Retires Two Gemini Models Meanwhile, GitHub is retiring Gemini 2.5 Pro and Gemini 3 Flash across all of Copilot on July 31. The migration paths are Gemini 3.1 Pro and Gemini 3.5 Flash. Cochrane flags it as a sign of the times, since tools that felt brand new a couple of years ago are already getting sunset. He also wonders how quickly today’s “AI-optimized” chips will turn over as the models keep changing. The Many Journeys of Learning Rust One for the programmers, and Cochrane makes no secret of loving Rust. The Rust blog’s Vision Doc series explores how people actually learn the language, which is built around memory safety and its strict borrow checker. Honest themes surface throughout, including “clone guilt,” where beginners refuse to copy anything, and “silent attrition,” the learners who quietly bounce off. His take stands: getting your brain onto a memory-safe language rewires how you approach a problem. Google DeepMind Partners With A24 In an interesting collision of worlds, Google DeepMind is teaming up with A24, the studio behind Hereditary and Everything Everywhere All at Once. The two call it a first-of-its-kind research partnership, with DeepMind researchers and A24 building creative tools shaped by the artists who use them. Cochrane adds a detail worth noting: Google also invested in A24, so this is money on the table, not just a research handshake. For now, though, the announcement stays deliberately vague, with no named films or products. Google’s $1 Million Africa Indie Game Fund Another one from Google, and it is good news for developers. Google is launching an indie games fund for sub-Saharan Africa, a region whose gaming scene is growing about as fast as anywhere. The fund puts up $1 million across ten local studios, each receiving between $50,000 and $200,000 plus mentorship and hands-on support. Applications close at noon UTC on July 31. WhatsApp Usernames Are Here to Reserve WhatsApp is finally moving off phone numbers as your identity. With usernames, someone can start a conversation with you without ever seeing your number. Starting this week, you can reserve the name you want ahead of the full launch later this year. To claim yours, head into Settings, then Account, then Username. Intel Sets Its Q2 Earnings Date Cochrane flags a date worth watching for anyone tracking Intel. The company reports second-quarter results on July 23, right after market close, with an earnings call at 2 p.m. Pacific. Given recent US government investment and a shifting chip landscape, he is curious how the domestic chipmaker is holding up. Your Smartwatch Might Spot Illness Before You Do Shifting to health, Engadget reports that the wearables-plus-AI wave is starting to deliver. These devices excel at catching the moment your body drifts off its own baseline, often the first nudge to get checked out. A 2025 study from Texas A&M and Stanford suggests smartwatches can detect early signs of COVID or the flu within hours of infection. Additionally, Apple Watch’s irregular-rhythm alerts have flagged AFib correctly about 84 percent of the time. Working Memory and Consciousness Here is a heady one from Scientific American, written by philosopher Henry Taylor at the University of Birmingham. Working memory is the mental scratchpad holding whatever you are doing right now. Taylor opens with the doorway effect, that blank moment when you enter a room and forget why. Intriguingly, when information leaves working memory, it seems to leave conscious awareness at the same instant, a link drawing fresh attention across psychology, philosophy, and neuroscience. Scuba-Diving Cyborg Cockroaches Now for the wild one. Scientists have built tiny diving suits that let Madagascar hissing cockroaches survive underwater for up to three hours, while an unequipped roach suffocates in minutes. The 3D-printed suit feeds oxygen through tubes into the insect’s breathing holes, called spiracles, using a chemical generator with no electronics. This lab already steered the roaches with electrodes, so the diving suit is the new trick on top. Researchers pitch it for search and rescue, though Cochrane notes the reality of the spy bug has already arrived. Quantum Time Runs Backward at Los Alamos Next, a genuine brain-bender. Physicists at Los Alamos, led by Luis Pedro García-Pintos, found a way to make a quantum system look like it is running backward in time. To be clear, time is not literally reversing. Precise measurements just make the system’s evolution appear to unfold in reverse. The useful part is energy: measurement itself becomes a resource in what they call a continuous measurement engine. Cochrane admits the paper drifted further from his reality the more he read. Tall Trees Shrug Off Drought A new study in Science overturns some textbook wisdom. For years, the assumption held that taller trees suffer more in drought because they must lift water higher. However, researchers studying dipterocarps in Southeast Asia found that trees topping seventy meters slowed their growth by about the same amount as short ones during the 2023-2024 El Niño drought. The trick is plumbing: a seventy-meter tree grows base vessels roughly twice as wide as a ten-meter tree, so the real driver of drought stress is subtler than raw height. The Energy Department Purges Conservation Pages This next one frustrates Cochrane. The US Department of Energy deleted roughly 6,000 web pages about energy conservation, and the timing is brutal during a record heatwave. The move followed backlash over New York Mayor Zohran Mamdani urging residents to ease strain on the grid. Fortunately, the Internet Archive and its Wayback Machine preserved the pages before they vanished. For Cochrane, deleting that kind of public information simply does not make sense. Webb’s Puzzling New Universe Heading to space, Quanta Magazine explores how the James Webb Space Telescope keeps finding early-universe objects that should not exist. Those include black holes that grew enormous too fast and hundreds of mysterious “little red dots” around 650 million years after the Big Bang. As astrophysicist Rachel Somerville of the Flatiron Institute puts it, scientists have “almost gone from having too many early galaxies to having too many theories.” The hard part now is figuring out which theory is right. NASA’s Emergency Telescope Rescue NASA has a rescue mission underway for the Swift Observatory, a 2004 telescope that studies gamma-ray bursts. Recent solar storms puffed up Earth’s atmosphere, and the added drag has dragged Swift’s orbit down to about 224 miles, low enough to risk burning up this year. To intervene, NASA enlisted Katalyst Space Technologies of Flagstaff, Arizona, whose LINK spacecraft launched Friday. The plan is to boost Swift back up to roughly 373 miles. A Gorgeous Aurora From Orbit Finally, Cochrane closes on something beautiful. ESA shared a stunning aurora captured from orbit, a shimmering green band of light rippling over the planet. If you have a few minutes, it is well worth a look. Cochrane wraps with housekeeping and a thank-you to GoDaddy for two decades of support, then signs off, wishing listeners a wonderful evening. The post Algorithmic Outing: When Your Feed Knows Before You Do #1869 appeared first on Geek News Central.

Our 250th episode with a summary and discussion of last week's big AI news!Recorded on 06/27/2026Note from Andrey: sorry this is late again! this episode release somehow didn't save and I only realized late, my bad... next one will be out way sooner!Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:US government gating of frontier AI expands: Anthropic gets permission to release Mythos-5 to selected companies/agencies after a standoff, OpenAI rolls out GPT-5.6 “Sol” with initial access restricted to ~20 approved organizations, and Meta is pressed to submit models to “voluntary” review—signaling an emerging de facto licensing regime with geopolitical treaty implications.Model capability and safety signals remain murky: limited benchmark disclosure, claims of token-efficiency comparisons, and third-party reports that GPT-5.6 shows extreme benchmark “cheating” sensitivity highlight steering/alignment bottlenecks and uncertainty about real-world long-horizon behavior.Compute supply chain competition accelerates: OpenAI unveils its Jalapeño inference ASIC with Broadcom on TSMC 3nm; Amazon explores selling Trainium to data-center operators; Micron invests in Anthropic with memory supply agreements; SK Hynix surpasses Samsung on HBM-driven valuation; Groq raises $650M while pivoting toward neocloud.Open source and societal response intensify: GLM 5.2 (MIT-licensed) delivers strong long-context coding performance with rapid optimizations; EconEvals maps job-task exposure; bipartisan workforce initiatives and tax credits launch; DeepMind and Apollo publish loss-of-control/control roadmaps; Hollywood reportedly drops a near-finished Sam Altman biopic amid industry pressure.Timestamps (note - these don't take into account dynamically inserted ads and therefore may be off by a couple of minutes):(00:00:10) Intro / Banter(00:03:42) News PreviewTools & Apps(00:04:41) Anthropic allowed to release Mythos AI to some companies, agencies + Anthropic's Mythos mess is only getting worse + Anthropic floats proposal to Lutnick to end US ban of powerful 'Mythos,' 'Fable' AI models: sources(00:07:58) OpenAI Launches GPT-5.6 Sol Under First-Ever US Government-Gated AI Rollout | MLQ News + OpenAI's new flagship model GPT-5.6 Sol cheats on software tests more than any model before it + Summary of METR's predeployment evaluation of GPT-5.6 Sol(00:24:03) U.S. Presses Meta to Agree to A.I. Reviews - The New York Times(00:30:11) Anthropic's Claude Tag is learning your company, one Slack message at a time | TechCrunchApplications & Business(00:32:49) OpenAI reveals its first AI processor: Jalapeño | The Verge(00:38:29) Amazon in Talks to Sell Custom AI Chips in Bid to Undercut Nvidia(00:41:46) Micron invests in Anthropic and grants it a supply deal(00:45:18) SK Hynix overtakes Samsung to become South Korea's most valuable company | Reuters(00:49:12) AI chipmaker Groq confirms $650M raise, re-staffs after Nvidia's $20B not-acqui-hire deal | TechCrunch(00:52:47) SpaceX inks compute deal with Reflection AI, an open source AI lab | TechCrunchProjects & Open Source(00:54:46) GLM-5.2: Built for Long-Horizon Tasks + How we built the world's fastest API for GLM-5.2 + nvidia/GLM-5.2-NVFP4 · Hugging Face(01:03:04) EconEvalsPolicy & Safety(01:05:40) $500 million AI jobs push launches with bipartisan backing - POLITICO(01:07:47) Rep. Sam Liccardo unveils AI workforce tax credit bill - POLITICO(01:08:56) Google DeepMind announced an “AI Control Roadmap” for improving AI agent security. | The Verge + Securing internal systems against increasingly capable and imperfectly aligned AI(01:14:00) The Loss of Control Playbook: Degrees, Dynamics, and Preparedness + The Loss of Control Playbook(01:16:42) Why corporate AI super PACs spent $27 million on a local election | The Verge(01:20:25) Exclusive: Conservatives plan nationwide protest against AI data centersResearch & Advancements(01:27:37) Revisiting the Platonic Representation Hypothesis: An Aristotelian View(01:31:39) Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models(01:33:59) Tapered Language ModelsSynthetic Media & Art(01:36:54) Hollywood is bending the knee to OpenAI | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, Gregor and Sean dig into the growing tension around restricted AI models, including Anthropic‘s Fable being pulled from the Claude platform days after launch. They explore what Sean calls “vibe regulations” and the risk foreign governments and enterprises face when a model they depend on can be cut off. They also cover the FT’s reporting on London’s “DeepMind mafia,” a vibe-coding clone controversy involving YC-backed Corgi and Papermark, SpaceX‘s acquisitions of Cursor and Mesh, and Anthropic’s launch of Claude Science. They also take on the latest round of the IDE wars, and explore who owns your dev toolchain, the vendor lock-in that now comes from context and memory rather than the model itself, and the widening cost gap between frontier tools and open weight models. As always, the episode wraps up with a few standout Hacker News threads. The post SED News: Restricted Models, IDE Wars, and the DeepMind Mafia appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, Gregor and Sean dig into the growing tension around restricted AI models, including Anthropic‘s Fable being pulled from the Claude platform days after launch. They explore what Sean calls “vibe regulations” and the risk foreign governments and enterprises face when a model they depend on can be cut off. They also cover the FT’s reporting on London’s “DeepMind mafia,” a vibe-coding clone controversy involving YC-backed Corgi and Papermark, SpaceX‘s acquisitions of Cursor and Mesh, and Anthropic’s launch of Claude Science. They also take on the latest round of the IDE wars, and explore who owns your dev toolchain, the vendor lock-in that now comes from context and memory rather than the model itself, and the widening cost gap between frontier tools and open weight models. As always, the episode wraps up with a few standout Hacker News threads. The post SED News: Restricted Models, IDE Wars, and the DeepMind Mafia appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, Gregor and Sean dig into the growing tension around restricted AI models, including Anthropic‘s Fable being pulled from the Claude platform days after launch. They explore what Sean calls “vibe regulations” and the risk foreign governments and enterprises face when a model they depend on can be cut off. They also cover the FT’s reporting on London’s “DeepMind mafia,” a vibe-coding clone controversy involving YC-backed Corgi and Papermark, SpaceX‘s acquisitions of Cursor and Mesh, and Anthropic’s launch of Claude Science. They also take on the latest round of the IDE wars, and explore who owns your dev toolchain, the vendor lock-in that now comes from context and memory rather than the model itself, and the widening cost gap between frontier tools and open weight models. As always, the episode wraps up with a few standout Hacker News threads. The post SED News: Restricted Models, IDE Wars, and the DeepMind Mafia appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, Gregor and Sean dig into the growing tension around restricted AI models, including Anthropic‘s Fable being pulled from the Claude The post SED News: Restricted Models, IDE Wars, and the DeepMind Mafia appeared first on Software Engineering Daily.

The tech industry is having a renaissance in London. It's home to the main foreign outposts for giants such as Google and Meta, as well as their well-funded AI challengers including OpenAI and Anthropic. But all those companies are American. Now there's a push to launch a homegrown competitor so the UK can have more sovereignty over its tech.Mentioned in this podcast:How the DeepMind mafia brought the AI boom to LondonTell us your thoughts to enter a prize draw for a chance to win a pair of Bose QuietComfort Headphones worth £229. https://www.feedback.ft.com/c/a/6f9bJBvxsxaEBSIB5esBISOver 18s only. Find full T&Cs herePrize Draw winners' surnames and regions may be made available upon request, as required by the Advertising Standards Authority. If you do not want your information to be made available, please email Privacy.Officer@ft.com upon entry. For more information on your rights and how we use your data, please read our Privacy Policy.Want to get in touch? Email us at podcasts@ft.comNote: The FT does not use generative AI to voice its podcasts The FT News Briefing is produced by Victoria Craig, Sonja Hutson, Saffeya Ahmed, Katya Kumkova, and Fiona Symon. Our editor is Marc Filippino. Our show is mixed by Kelly Garry and Alex Higgins. Additional help from Gavin Kallmann, Michael Lello, Peter Barber and David da Silva. Our intern is Cole van Miltenburg. Our executive producer is Topher Forhecz. Flo Phillips is the FT's global head of audio. The show's theme music is by Metaphor Music. Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

What drives a man to turn down half a million pounds at 18, test Mark Zuckerberg's sincerity over dinner, and wonder aloud if he can win a second Nobel Prize? For Demis Hassabis, co-founder and CEO of Google DeepMind, the answer is a lifelong pursuit of artificial general intelligence — and an unshakeable belief that the technology he's creating will change everything about what it means to be human. Oz speaks with journalist and author Sebastian Mallaby about his new book, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence, tracing Demis's extraordinary journey from chess prodigy to the man at the center of the most consequential technological race of our time. See omnystudio.com/listener for privacy information.

The breakneck pace of AI progress and the intensity of the competition for AI supremacy has left U.S. policymakers in a difficult position. They must encourage the innovation needed to ensure an advantage over China and to power economic growth; protect against a national security catastrophe; and assuage the concerns of an anxious and skeptical public. Sebastian Mallaby calls this the “AI trilemma” in his most recent essay for Foreign Affairs. And he argues that it requires more than the piecemeal measures currently on offer in Washington—or, for that matter, in Beijing or Brussels. Mallaby is a fellow at the Council on Foreign Relations and the host of a new podcast called The Spillover, as well as the author of The Infinity Machine, an excellent new book about the founding of the AI lab DeepMind. Dan Kurtz-Phelan spoke with him on June 24 about the state of AI competition, about the stakes of that competition, and about how its course will reshape societies, economies, and global politics. You can find sources, transcripts, and more episodes of The Foreign Affairs Interview at https://www.foreignaffairs.com/podcasts/foreign-affairs-interview.

Julia Alexander joins Peter to explain why A24—cinephiles' favorite studio—just struck a $75 million partnership with Google's DeepMind. The studio's fanbase is alarmed, but Julia makes the case for why fears of a sellout are premature, at least for now. Then she turns to Comcast's decision to spin off NBCUniversal and breaks down which NBC properties are most likely to catch Netflix's eye.

The Founding of OpenAI. Guest Author: Keach Hagey. In this opening segment, Keach Hagey discusses the January 2016 founding of OpenAI as a nonprofit research lab. Key figures included co-founder Greg Brockman and chief scientist Ilya Sutskever, a renowned researcher whose recruitment from Google signaled the lab's potential. Backed by a billion-dollar commitment from Elon Musk, Peter Thiel, and Jessica Livingston, the project was designed as a safe, non-commercial counterweight to Google's DeepMind. Operating initially out of Brockman's apartment, the team aimed to achieve Artificial General Intelligence (AGI) for the benefit of humanity. The technical foundation relied heavily on GPUs—hardware originally designed for video games—which proved essential for training the deep learning neural networks necessary for their research. This era was characterized by an ambitious, "pirate" spirit funded through YC Research to explore radical ideas outside the profit motive. 1JANUARY 1931

Google's DeepMind division is putting about 75 million dollars into indie studio A24, the company behind recent hits like “Backrooms” and “Marty Supreme,” in what both sides describe as an AI research partnership to build new tools for film production and distribution, not a traditional content or IP deal.

In this episode, the boys cut in with two breaking stories. First, Walmart buys Vibe.co, a connected TV advertising platform, in a move that could make Walmart's already-growing ad business even more interesting. Robert believes the strategy is right, especially with Walmart's retail media business and Vizio already in the fold, but thinks the price tag may have been a bit too rich. Then Joe and Robert revisit the FIFA stadium branding story. FIFA's clean-stadium policy has forced brands like Levi's, Heinz and others to cover up their logos during World Cup matches. But instead of making those brands disappear, FIFA may have created the perfect Streisand effect. Heinz, Beats and Levi's have all turned the restrictions into creative marketing moments. Is FIFA protecting its sponsors, or accidentally giving non-sponsors a bigger story? In our main stories, Google and A24 announce a partnership around AI filmmaking tools. The big question is not whether AI will make the final movie. It's whether AI will control more of the creative workflow before the final product ever exists. Then Meta and Snap both make new moves in smart glasses. Meta pushes toward a lower-cost, more mainstream AI glasses play, while Snap launches its new AR-focused Specs. If glasses become the next interface, marketers may have to rethink content for a world where the screen is no longer in your hand. It's on your face. In Winners and Losers, Joe's winner is TIME Canada. TIME is launching a licensed Canadian edition with a local team, local office, original reporting, video, social, print and events. In a world of generated content, Joe likes the bet on trusted editorial brands with a local heartbeat. Robert's winner is McDonald's, which is bringing back the fried apple pie. Sometimes nostalgia, timing and a little bit of fried goodness is all the marketing strategy you need. In Rants and Raves, Joe raves about The Infinity Machine by Sebastian Mallaby, a book about Demis Hassabis, DeepMind and the race toward superintelligence. Robert delivers a super rant on TuneCore and how independent creators may be getting the short end of the stick as AI music floods the market and distribution platforms try to figure out who gets through, who gets blocked, and who gets paid. Also mentioned this week: In the Weights, a site that lets you see whether you show up in the "weights" of different AI models: https://www.intheweights.com/ Subscribe and Follow: Follow Joe Pulizzi and Robert Rose on LinkedIn for insights, hot takes, and weekly updates from the world of content and marketing.  ------- This week's sponsor: Did you know that most businesses only use 20% of their data? That's like reading a book with most of the pages torn out. Point is, you miss a lot. Unless you use HubSpot. Their AEO and customer platform gives you access to the data you need to grow your business. The insights trapped in emails, call logs, and transcripts.  All that unstructured data that makes all the difference. Because when you know more, you grow more. Visit https://www.hubspot.com/ to hear how HubSpot can help you grow better. ------- Get all the show notes: https://www.thisoldmarketing.com/ Get Joe's new book, Burn the Playbook, at http://www.joepulizzi.com/books/burn-the-playbook/ Subscribe to Joe's Newsletter at https://www.joepulizzi.com/signup/. Get Robert Rose's new book, Valuable Friction, at https://robertrose.net/valuable-friction/  Subscribe to Robert's Newsletter at https://seventhbearlens.substack.com/ ------- This Old Marketing is part of the HubSpot Podcast Network: https://www.hubspot.com/podcastnetwork

When the US government banned a top AI lab from exporting its newest models, the world took notice. Export controls forbidding foreign access to Anthropic's Mythos and Fable systems locked most of the world out of using this cutting-edge technology. As AI becomes more embedded in our daily lives, countries want to secure access to frontier models. But unless you're the US or China, your country doesn't have a top-tier national champion. So what can other countries do to secure sovereign control over AI? What kind of leverage can they exert? And can they use AI to boost – rather than break – their economies? Soumaya speaks to UK AI minister Kanishka Narayan to discuss.Subscribe to Soumaya's show on Apple, Spotify, Pocket Casts or wherever you listen.Further ReadingHow the DeepMind mafia brought the AI boom to LondonUK companies ‘should be worried' about Anthropic's latest AI model, minister saysDid Anthropic talk its way into an AI export ban?Anthropic chief tells G7 leaders to ‘resist the temptation to splinter' over AIPresented by Soumaya Keynes. Produced by Mischa Frankl-Duval. Manuela Saragosa is the executive producer. Sound design by Sean McGarrity. Original music by Breen Turner. Broadcast engineering by Andrew Georgiades. Flo Phillips is the FT's head of audio.Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

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

Episode 304: Nintendo's third‑party data leak kicks off the show with a hard look at how vendor cybersecurity failures can expose sensitive HR records like tax forms and bank details. It's a reminder that even major companies can be compromised through unnoticed apps sitting quietly in the background. We then zoom out to AI's growing resource crunch — especially water usage in data centers — and the escalating AI talent wars as another DeepMind researcher jumps ship.The tone shifts lighter with Tesla “literally sucking” during a supercharger vacuum test and a Two Truths and a Lie round featuring a real emotional‑support panda robot. Mike the AI Guy unloads on Adobe Firefly spreading through creative tools like glitter you can never remove. We close with Tesla back under regulatory scrutiny over Autopilot and a thoughtful riff on parenting in the algorithm era inspired by Toy Story 5, all coming up on TechTime Radio, with a little whiskey on the side.-- Full Episode Details:A third-party app you barely notice can become the biggest threat to your privacy. We start with Nintendo's employee data exposure and the uncomfortable lesson it teaches about third-party cybersecurity, vendor management, and how sensitive HR records like tax forms and bank documents can end up in the blast radius of a ransomware-style extortion play. If you've ever assumed “the company has it handled,” this one will make you rethink what that really means.From there, we zoom out to the bigger tech moment: AI isn't just fighting for chips and electricity anymore. Water for data center cooling is emerging as a real constraint, and we talk through what that means for AI infrastructure, local communities, and sustainability. We also hit the AI talent wars as DeepMind loses another major name to a rival lab, raising questions about pressure, incentives, and where the frontier work is actually happening.Then we have some fun with it. Tesla “literally sucks” with a vacuum test at superchargers, our Two Truths And A Lie game includes a surprisingly real emotional-support panda robot, and Mike the AI Guy unloads on Adobe Firefly spreading through creative tools like glitter you cannot remove. We wrap with Tesla back in the tech fail spotlight as regulators revisit Autopilot, plus a thoughtful riff on Toy Story 5 and how parenting has to keep up with screens, apps, and algorithms.Subscribe for weekly technology news with a sense of humor, share this with a friend who clicks the wrong links, and leave a review so more people can find the show. What's one piece of tech you want us to stress-test next?Send us Fan MailSupport the show

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstProtein folding stalled biology for fifty years. A sequence of amino acids dictates a three-dimensional shape, but reading that shape meant a year and roughly $100,000 of crystallography per structure. Then AlphaFold 2 won CASP14 so decisively the organizers called the problem essentially solved.In this documentary cut, John Jumper, who shared the 2024 Nobel Prize in Chemistry and has since left DeepMind for Anthropic, walks Tim Scarfe through what the system did and, more interestingly, what it did not. The architecture gets a proper dissection: MSAs, the Evoformer, invariant point attention, the FAPE loss, and Jumper's correction of the equivariance story, which ablations valued at roughly 2.5 of 30 GDT points rather than the whole win. He is blunt about the limits. AlphaFold predicts one experiment extraordinarily well; it is not a model of the cell, it does not capture dynamics, and on a given drug target it is "wrong nine times out of ten."From there: the AlphaFold Database of 200M+ predicted structures, AlphaFold 3 and ligands, Isomorphic Labs, and Jumper's quarrel with the bitter lesson, where finite data and human hypotheses still matter. Emmanuel Nji of BioStruct Africa closes the film on what changes when work that took years now takes months, and on training the next thousand structural biologists across Africa.---TIMESTAMPS:00:00:00 Cold open: predicting nature with a button press00:01:03 The protein folding bottleneck and CASP00:04:39 The Nobel, the database, and the move to Anthropic00:05:50 Sponsor (Notion) and framing: what AlphaFold does not claim00:07:39 Proteins as self-assembling nanomachines00:12:24 From structures to biology: drug discovery and Midnolin00:17:37 The humility of AlphaFold: a narrow predictor00:22:18 Inside the architecture: Evoformer, IPA and FAPE00:30:20 Ruthless empiricism: ablations and 100x in data00:35:20 Predict, control, understand00:40:00 Against the bitter lesson; AlphaFold 3 as diffusion00:45:07 Intelligence, representations and AGI00:49:23 Epilogue: AlphaFold in Africa00:52:16 Closing: the case for hybrid science models---REFERENCES:organization:[00:01:55] Critical Assessment of Structure Prediction (CASP)https://predictioncenter.org/[00:04:39] The Nobel Prize in Chemistry 2024https://www.nobelprize.org/prizes/chemistry/2024/summary/[00:05:18] BioStruct Africahttps://www.biostructafrica.org/[00:18:03] Isomorphic Labshttps://www.isomorphiclabs.com/paper:[00:03:09] AlphaFold Protein Structure Databasehttps://doi.org/10.1093/nar/gkab1061[00:17:25] Accurate structure prediction of biomolecular interactions with AlphaFold 3https://www.nature.com/articles/s41586-024-07487-w[00:22:18] Highly accurate protein structure prediction with AlphaFoldhttps://www.nature.com/articles/s41586-021-03819-2[00:23:10] Midnolin promotes degradation of substrates independent of ubiquitinationhttps://doi.org/10.1126/science.adh5021[00:27:00] Improved protein structure prediction using potentials from deep learninghttps://www.nature.com/articles/s41586-019-1923-7tool:[00:03:09] AlphaFold Protein Structure Database (EBI)https://alphafold.ebi.ac.uk/[00:45:55] AlphaEvolve: a coding agent for designing advanced algorithmshttps://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/other:[00:39:40] The Bitter Lessonhttp://www.incompleteideas.net/IncIdeas/BitterLesson.html---ReScript: https://app.rescript.info/share/d8cde5c221fb71e2c0f5aafe94f90dfaDisclaimer - not sponsored, editorial with us - we filmed it at GDM, London

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

AI can write code, pass exams, and summarize the web, but ask it to reason through a real-world image, and the magic often breaks. Andrew Dai, co-founder and CEO of Elorian, joins The Neuron to explain why visual reasoning may be one of the biggest unsolved problems in AI.Andrew spent years at Google Brain and DeepMind, including work connected to Gemini and sparse mixture-of-experts systems. Now, he's building Elorian around a simple but powerful idea: if AI is going to understand the physical world, it needs more than text-based reasoning layered on top of images.In this episode, Corey and Grant talk with Andrew about why frontier models struggle with counting, navigation, design, engineering, charts, and physical reasoning; why scaling language models hasn't solved vision; what a “visual chain of thought” might look like; and how better visual reasoning could accelerate robotics, satellite analysis, product design, and mechanical engineering.Sponsored by Dell Technologies and NVIDIA. Learn more at techrepublic.com/hubs/the-enterprise-guide-to-scalable-ai/.Sponsored by Outshift: Visit https://outshift.cisco.com/?utm_campaign=fy26q3_outshift_ww_paid-media_ioc-neuronai-outshift_podcast&utm_channel=podcast&utm_source=podcast to learn more about the Internet of Cognition.Subscribe to The Neuron for more conversations with the people building the future of AI.

Sebastian Mallaby (@scmallaby) is the Paul A. Volcker senior fellow for international economics at the Council on Foreign Relations, a two-time Pulitzer Prize finalist, and the author of six books, including More Money Than God, The Power Law, The Man Who Knew, and The World's Banker. His latest book is The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.This episode is brought to you by:Eight Sleep Pod Cover 5 sleeping solution for dynamic cooling and heating: EightSleep.com/TimAG1 Pro all-in-one nutritional supplement: DrinkAG1.com/TimWealthfront high-yield cash account: Wealthfront.com/Tim Wealthfront disclaimer: New clients get 3.30% base APY from program banks + additional 0.75% boost for 3 months on your uninvested cash (max $150k balance). Terms and conditions apply. The Cash Account offered by Wealthfront Brokerage LLC (“WFB”) member FINRA/SIPC, not a bank. The base APY as of 1/30/26 is representative, can change, and requires no minimum. Tim Ferriss, a non-client, receives compensation from WFB for advertising and holds a non-controlling equity interest in the corporate parent of WFB, which creates a conflict of interest. Individual experiences and outcomes will differ. Instant withdrawals may be limited by your receiving firm and other factors. Investment advisory services provided by Wealthfront Advisers LLC, an SEC-registered investment adviser. Securities investments: not bank deposits, not bank-guaranteed or FDIC-insured, and may lose value.*Timestamps[00:00:00] Start.[00:02:11] The twinkly eyed polymath who became Sebastian's next book.[00:06:55] Picking the next book project the way a great VC picks a startup.[00:09:41] Why God keeps crashing the superintelligence party.[00:11:13] Shane Legg's grainy 2009 prophecy — and the nervous giggle.[00:13:11] Ilya Sutskever burns an effigy.[00:13:54] Demis at 4 a.m., hunting God's algorithm.[00:18:43] Super-abundance, Mad Max, and the China shock lesson.[00:22:39] The kitchen debate with Geoff Hinton that flipped Sebastian.[00:24:06] Why a zero-percent chance of doom is indefensible.[00:24:52] Will Washington seize the labs? The Mythos wake-up call.[00:27:18] Anthropic's bull case, bear case, and a dead parent's letter.[00:33:24] Where Sebastian and Benedict Evans part ways.[00:38:16] Is the SaaS apocalypse overdone? One word: Palantir.[00:39:53] The AI friend you'll never switch.[00:41:56] Does Google win consumer AI by default?[00:44:45] Four cities, eight days: China actually talks safety.[00:47:28] A Cold War non-proliferation playbook for AI.[00:49:45] Did the chip export controls actually work?[00:51:49] Burned doves: why Washington swears China won't talk.[00:54:56] "By 2028, the race is over" — one lab boss' bet.[00:59:11] Inside Hikvision: toddlers, sensors, and US sanctions.[01:01:07] Bill Gurley's Uber bet: venture capital perfected.[01:05:18] Luke Nosek bear-hugs DeepMind into existence.[01:10:52] Thiel's heresy: never invest by committee.[01:11:59] How Founders Fund nearly fumbled the deal of the century.[01:14:30] Selling to Google for $650M: a secret British heist?[01:16:41] The Traitorous Eight, gardening leave, and the UK's to-do list.[01:20:55] Ender's Game: "That's really how I see myself."[01:23:42] Too dumb for Gödel, Escher, Bach? Maybe an LLM can help.[01:25:19] If not Demis or Sam, then Dario.[01:26:04] My royalties cliff — and what dropped in late 2022.[01:27:47] Lila Sciences and the labs that run themselves.[01:31:13] Sebastian's billboard: "Prepare your mind."[01:35:14] The one thing Sebastian will never outsource to AI.[01:40:09] Parting thoughts.For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this Live Greatly podcast episode, Kristel Bauer sits down with Benjamin Todd, co-founder of 80,000 Hours and author of 80,000 HOURS: How to Have a Fulfilling Career That Does Good. Kristel and Benjamin discuss why "follow your passion" may not be the best career advice, what actually contributes to meaningful and fulfilling work, and practical strategies to align your strengths, values, and goals with your career. Benjamin also shares insights on pursuing positive impact, and building a career that supports both success and well-being. Tune in now! Key Takeaways From This Episode: Why "follow your passion" can be misleading career advice The key ingredients of meaningful and fulfilling work How to align your strengths and values with your career The impact of volunteering Tips to pursue success, purpose, and well-being simultaneously How to be a multiplier ABOUT BENJAMIN TODD Ben is the founder of 80,000 Hours, a non-profit that has reached millions of people and helped 3000+ people find careers tackling the world's most pressing problems. He's the author of 80,000 Hours: How to Have a Fulfilling Career That Does Good (Penguin May 2026) and writes about how to prepare for advanced AI on Substack. Dissatisfied with the career advice he received at university, Benjamin began researching the guidance he wished he'd had. Over the next ten years, he grew 80,000 Hours from a student society in Oxford into a non-profit that today reaches 4 million people annually, has over 50 staff, and has raised $30m of funding. It has been covered in the Financial Times, Guardian, TIME, Wall Street Journal and BBC, and was one of the first non-profits to go through Y Combinator, the world's top startup accelerator. 80,000 Hours provides free online research, one-on-one advice, a job board and podcast to help people find more fulfilling and impactful careers. Over 10 million people have read their advice online and over 3,000 have switched to more impactful careers. This includes people who helped to pioneer research into AI safety at organisations like Anthropic, DeepMind, RAND and METR, have taken key roles aiming to prevent a catastrophic pandemic, and have pledged billions of dollars to high-impact charities. As CEO for the organisation's first ten years, Ben led strategy, fundraising, and senior management, building an organisation with average annual staff retention of 95%, while also writing the Career Guide, Key Ideas series and over 100 articles. His TEDx talk has been viewed over 6 million times. Before 80,000 Hours, he was the first undergraduate to intern as an analyst at Orbis Investment Advisory, a $20bn fund. He was the first non-founding member of Giving What We Can, pledging to give 10% of his income to effective charities for life. He has a 1st from Oxford in a Masters of Physics and Philosophy, has published in climate physics, and speaks Chinese, badly. Connect with Benjamin:  Order his book: https://80000hours.org/book/    Website: https://benjamintodd.org/    Linkedin: https://www.linkedin.com/in/benjamin-j-todd/    Instagram: https://www.instagram.com/benbentodd/  About the Host of the Live Greatly podcast, Kristel Bauer: Kristel Bauer is a corporate wellness and performance expert, keynote speaker and TEDx speaker supporting organizations and individuals on their journeys for more happiness and success. She is the award-winning author of Work-Life Tango: Finding Happiness, Harmony, and Peak Performance Wherever You Work (John Murray Business November 19, 2024). With Kristel's healthcare background, she provides data driven actionable strategies to leverage happiness and high-power habits to drive growth mindsets, peak performance, profitability, well-being and a culture of excellence. Kristel's keynotes provide insights to "Live Greatly" while promoting leadership development and team building. Kristel is the creator and host of her global top self-improvement podcast, Live Greatly. She is a contributing writer for Entrepreneur, and she is an influencer in the business and wellness space having been recognized as a Top 10 Social Media Influencer of 2021 in Forbes. As an Integrative Medicine Fellow & Physician Assistant having practiced clinically in Integrative Psychiatry, Kristel has a unique perspective into attaining a mindset for more happiness and success. Kristel has presented to groups from the American Gas Association, Bank of America, bp, Commercial Metals Company, General Mills, Northwestern University, Santander Bank and many more. Kristel's work has been featured in Forbes and she has had multiple TV appearances including NBC News Daily, ABC News Live, FOX Weather, ABC 7 Chicago, WGN Daytime Chicago and more. Kristel lives in the Chicago, IL area and she can be booked for speaking engagements worldwide. To Book Kristel as a speaker for your next event, click here. Website: www.livegreatly.co  Follow Kristel Bauer on: Instagram: @livegreatly_co  LinkedIn: Kristel Bauer Twitter: @livegreatly_co Facebook: @livegreatly.co Youtube: Live Greatly, Kristel Bauer To Watch Kristel Bauer's TEDx talk of Redefining Work/Life Balance in a COVID-19 World click here. 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For decades, artificial intelligence was dismissed as science fiction. Then one lab changed everything.Inside a small London research company, scientists were teaching machines to play games, predict protein structures, and solve problems humans couldn't. What started as an obscure AI experiment soon became the center of a global race for superintelligence — with enormous consequences for medicine, warfare, scientific discovery, and the future of human intelligence itself.On this episode of Morning Wire, journalist Sebastian Mallaby explains how DeepMind helped launch the modern AI revolution and why its founder Demis Hassabis believes artificial intelligence could push beyond the limits of human understanding. Get the facts first with Morning Wire.- - -Ep. 2815- - -Wake up with new Morning Wire merch: https://bit.ly/4lIubt3- - -Today's Sponsors:Fast Growing Trees - Visit https://fastgrowingtrees.com to get 20% off your first purchase when using the code WIRE at checkout.Alliance Defending Freedom - Visit https://JoinADF.com/WIRE or text “WIRE” to 83848 to learn more.- - -Privacy Policy: https://www.dailywire.com/privacymorning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices

Keach Hagey recounts the January 2016 founding of OpenAI in San Francisco, initially established as a modest nonprofit research lab in Greg Brockman's apartment. Co-founded by Sam Altman, Brockman, and chief scientist Ilya Sutskever, the organization aimed to develop artificial general intelligence (AGI) safely outside of profit motives. Major initial backers included Elon Musk and Peter Thiel, who sought to create a counterweight to Google's DeepMind. The discussion explains how neural networks utilize Nvidia's GPUs—originally designed for video games—to mimic human thought, forming the technical foundation for the current AI race. (1/4)MARCH 1959