Podcasts about compute

Varant Zanoyan was born in Washington DC, and grew up there and in Switzerland as well. He now lives in the Bay Area, specifically San Mateo. He's spent time at Palantir Technologies, as well as a stint building ML tech at AirBnB. But outside of tech, he loves the outdoors, tending to his garden of plants and vegetables. After taking a good hike, he's been digging a good uni pizza for dinner. Prior to his current venture, Varant was working at AirBnB, developing Chronon - an open source data management engine, used to power AI/ML infrastructure. It was then that he and his team realized that building and managing data pipelines was a bottleneck for AI dev, and decided to spin into a standalone platform. This is the creation story of Zipline AI. Linkshttps://zipline.ai/https://www.linkedin.com/in/vzanoyan Current Sponsors: Tiger Data Protected Harbor Render Fitnexa Perplexity Entelligence Checkout our Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor. Our Sponsors:* Check out Perplexity and use my code CODESTORY for a great deal: https://www.perplexity.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

My guest today is Sarah Guo, founder and managing partner of Conviction, the venture firm she built to back AI-native companies from their earliest days.  Sarah has become one of the most sought-after early-stage investors in AI, often the first check into the companies defining the frontier.  In this conversation, we go inside that frontier: what the small group of people actually building AI believe right now, why some of the field's best researchers are wrestling with their own sense of purpose, and how close we are to robots in the home and a genuine acceleration in scientific discovery.  At the center is Sarah's conviction that no single company will own the future of AI, and what that means for founders, investors, and anyone allocating their time and resources in a world moving this fast. Our managing editor Dom Cooke wrote a profile of Sarah for Colossus, "Sarah's Wager," on how she built the firm closest to the AI frontier and why she's now betting against its biggest companies.  Please enjoy this conversation with Sarah Guo. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:16) Investing Without a Backtest (00:03:31) The AI Wager (00:06:16) Building the Best Investment Firm (00:08:37) Finding Non-Obvious AI Opportunities (00:11:00) The Frontier AI Talent Race (00:13:50) Compute as the Constraint (00:19:10) The Future of Robotics (00:22:15) Making Investment Decisions (00:26:13) How Sarah Spends Her Time (00:28:15) Raising a Venture Fund (00:30:49) Lessons From Her Parents (00:34:38) The Case for Open Source AI (00:39:01) Abundant Intelligence Isn't Inevitable (00:40:55) Compute Independence (00:43:14) Debates Inside Conviction (00:45:27) AI's Opportunity in Biology (00:48:58) Why Conviction (00:50:31) Finding Truth and Taking Risk (00:54:16) What Changes in the Next Year (00:56:46) The Kindest Thing

In this episode of the Joe Reis Show, I sit down with David Aronchick, co-founder of Expanso and one of the early pioneers behind Kubernetes, Kubeflow, and the CNCF. We dive deep into the evolving data landscape and discuss why the architectural pendulum is swinging back from pure cloud centralization toward distributed edge compute. David breaks down the real operational nightmares of edge data collection, why treating your bronze tier as a "toxic waste dump" breaks downstream pipelines, and how applying schema and context upstream makes your data truly AI-ready. We also get into the critical need for data provenance, data bills of materials, and what agents actually need to communicate reliably.Website: https://expanso.io

a16z's David George sits down with Gavin Baker to unpack the state of the AI boom, why demand for intelligence may still be dramatically underestimated, and why the outcome doesn't necessarily have to be winner-take-all. David and Gavin explore the possibility that frontier labs, open-source models, applications, clouds, and NVIDIA can all capture significant value as AI adoption expands. They dig into the economics of the infrastructure buildout, why compute investments can have unusually fast payback periods, and what happens when today's relatively small group of heavy AI users expands to hundreds of millions of people. They also debate the risk of an AI bubble versus an AI shortage, the backlash against data centers, orbital compute, the rise of multi-model architectures, and NVIDIA's position at the center of the AI supply chain. Gavin makes the case that the AI buildout could help reindustrialize America, while David explores whether the bigger near-term risk is not overbuilding, but failing to build enough. Resources: Follow Gavin Baker on X: https://x.com/GavinSBaker Follow David George on X: https://x.com/DavidGeorge83 Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

* Jevons Paradox 究竟係乜？Jevons Paradox 指一種反直覺現象：技術效率提高之後，每一單位資源用得少咗，但因為成本下降、用途增加，總需求反而可以升得更快。十九世紀英國煤炭就係經典例子，蒸汽機愈有效率，煤嘅總需求反而繼續增加。* 點解 AI 愈平，總 compute demand 反而可能愈大？因為 AI 平咗之後，原本唔值得用 AI 做嘅工作開始值得做；同時 agentic AI 會將一個人類指令拆成更多步驟、更多 tool calls，同一件工作本身亦會消耗更多 tokens。用途增加同每個用途嘅 compute intensity 都可以推高總需求。* 點樣知道 AI 係咪真係出現 Jevons Paradox？要睇需求價格彈性，即係 AI 價格跌一個百分比之後，使用量會增加幾多。如果需求增加速度快過價格下降帶來嘅效率改善，先接近 Jevons Paradox 所描述嘅情況。* AI 愈普及，邊啲東西反而會變得更加稀缺？一種 scarcity 被技術解決之後，其他 bottleneck 就會浮現。上游最明顯係 GPU、Data Center 同相關基建；AI 使用一路向下游擴散之後，人類互動、context、judgment 等亦可能形成新嘅相對稀缺。* Compute demand 大升，係咪代表 Cloud 公司一定賺大錢？唔一定。Revenue growth 同 Return on Capital 要分開睇。Microsoft、Amazon 等 Cloud Provider 可以因為 AI demand 上升而增加收入，但同時亦要投入大量 Data Center、GPU 同 fixed capital。最後回報要視乎投入成本、價格同資本回收速度。* AI 對工作市場嘅影響，點解可能先出現喺 junior jobs？好多 junior 工作本身就包含大量標準化、重複、容易拆解嘅任務。AI 提高生產力之後，以前可能需要一 team 人完成嘅工作，今日可以由更少人處理，所以入行職位會先收窄。片中亦提到 22 至 25 歲、AI exposure 較高嘅職位，employment 表現較弱，而調整主要可以表現為少請人。* 當 low-context 工作愈來愈容易交俾 AI，人應該培養乜嘢能力？Transcript 將 low context 定義為可以靠文字、結構化資料直接得出 actionable outcome 嘅工作；high context 就需要理解情境、人情世故、情緒同 judgment。AI 愈擅長處理 low-context information，人就愈需要提升 high-context communication、判斷力同理解情境嘅能力。 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leesimon.substack.com/subscribe

Britney Kaiser discusses the importance of confidential computing in protecting personal and corporate data in the age of artificial intelligence, warning about threats to data rights and intellectual property.

Shane is joined by BKFi tax manager Tiffini Parker for a jam-packed half episode covering AI compute markets, Stripe's singularity announcement, wealth management's cash hoarding problem, South Korean dating culture, and a Reddit question that every financial advisor will have opinions about. They kick off with AI computing power becoming a tradable futures contract at the CME, what that actually means for transparency in the compute market, and why Stripe buried a four-page memo about the singularity inside an acquisition announcement. Then it's on to the Wall Street Journal's $3 trillion problem — why investors are keeping too much cash in money market funds and why advisors can't seem to convince them otherwise. They also cover the dating scene in South Korea, being completely overtaken by Samsung chip engineers and what massive AI-era bonuses are doing to the marriage market, Spirit Airlines' flight attendant data privacy fight against Google's bankruptcy bid, and close with a Reddit question from a $17 million net worth individual who sold his company and wants to know if he should hire a financial advisor. Shane and Tiffini have thoughts. Topics covered: AI computing power becomes a tradable futures contract at the CME Stripe says the singularity has started and acquired OpenRouter for billions Wealth management's $3 trillion cash hoarding problem and why advisors are losing the battle Investment-grade bonds, munis, and what advisors are actually recommending South Korea's dating scene is now dominated by Samsung chip engineers Spirit Airlines flight attendants fight to keep their data out of Google's bankruptcy bid Reddit: $17 million net worth, recent company sale, should I hire a financial advisor? Timestamps: 00:00 Intro, welcome back, Tiffini Parker 02:30 AI computing power is now a tradable futures contract at the CME 05:30 Stripe says the singularity has started and acquired OpenRouter for billions 09:00 Stripe's valuation, the potential PayPal acquisition, and why it built for AI by accident 12:00 Wealth management's $3 trillion cash hoarding problem 15:00 Why investors don't trust advisors and what it would take to change that 17:30 South Korea's dating scene is now dominated by Samsung chip engineers 21:30 Spirit Airlines flight attendants fight to keep their data out of Google's bankruptcy bid 26:00 Reddit: $17 million net worth, should I hire a financial advisor? 30:00 Why 50 bps is probably worth it and what this person actually needs

Behnam Bastani is originally from Tehran, Iran. He left the country at the age of 17, setting out for Canada, where he got his Masters and PhD, before moving to California. He's always looked at things with the big picture in mind - whether that is artwork, wood work, engineering, etc. He's worked for Hewlett Packard, Meta and Roblox, before starting his current venture. Outside of tech, he loves to exercise and participate in water activities. For peace of mind, he gardens - experimenting with growing trees, replanting, and seeding. Previously, Behnam went to Meta to pursue the future of wearables. He immediately realized that these wearables required a connected system. He started to ask the question, "why can't we use compute around use to fuel wearables?". Eventually, through stints at Meta and Roblox, he and his co-founder knew they wanted this type of system... and started with the infrastructure to enable it. This is the creation story of OpenInfer. SponsorsTiger DataProtected HarborRenderLinkshttps://openinfer.io/https://www.linkedin.com/in/bbastani/Checkout our episode stacks on Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Had a lot of fun chatting again with my twin brother Dylan Patel.We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world's usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).And then we discuss whether the >$10T of total AI capex we'll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.One question we weren't able to resolve is whether there's anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI.Watch on YouTube; read the transcript.Sponsors* Grok Bot has been quite helpful with my search for a new editor. I created a recruiter bot and described the type of editor I was looking for. That bot then spun up a handful of subagents that combed through my emails and X DMs, read the end credits of various documentaries I like, and figured out who edits for some of my favorite YouTubers. It took all of those results, and then delivered me a shortlist of candidates that matched my criteria. Try Grok Bot for yourself at x.ai/bot* Antithesis lets you add time travel to your software testing toolkit. Since the Antithesis platform is fully deterministic, everything that happens inside of it is perfectly reproducible. So if your software crashes, you can rewind to the exact right moment, freeze time, and investigate. Or you can test different hypotheses by perturbing the system: kill a node or disable a feature, see what happens, then reset the trajectory and try something else. Learn more at antithesis.com/dwarkesh* Jane Street is hiring for two separate ML internships right now, one focused primarily on research and one focused on engineering. In both cases, interns are expected to contribute to real work, not contrived exercises: one common project is adapting a frontier LLM paper to financial markets, which tend to come with a ton of different gnarly challenges. Importantly, you don't need any finance background to apply. 2027 applications are open now at janestreet.com/dwarkeshTimestamps(00:00:00) – Two labs will soon control most of the world's compute(00:07:01) – $6 billion in fab capex enables $1t+ of end revenue(00:13:08) – Compute prices will rise if the labs outbid everyone(00:18:22) – Which layer will capture most of the surplus?(00:25:40) – What could slow down progress?(00:29:43) – Labs are shifting compute from inference to R&D(00:33:27) – China gets less than 10% of new compute, but its labs need less(00:48:48) – Will AI cause a sovereign debt crisis?(01:07:52) – Will the world's future workforce belong to a few companies? This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com

As tech giants continue announcing massive multi-gigawatt facilities permitted to match the carbon output of entire nations, the default reaction is often polarized, ideological debate. However, if we stopped arguing for a minute, we'd likely find we all agree trying to infinitely scale compute through brute force is more than an environmental topic. It's creating a major infrastructure crisis that affects us all.This week, I decided to touch on this delicate topic after seeing news of Amazon's proposed 7.65-gigawatt data center complex in Texas in an attempt to highlight how blind expansion is fundamentally bad business logic. Whether you care about climate morality or corporate ESG, physical constraints don't care about software roadmaps. Relying on unmonitored compute to power our enterprise workflows creates massive, immediate risk across regional energy grids, water tables, and business balance sheets.  My goal this week is to help you better understand the situation and move past surface-level advice, so you can execute a surgical, sustainable framework for AI adoption and operational governance:​Exposing the Infrastructure Risk: Deconstructing what gigawatt-scale compute actually draws from regional energy grids and municipal water tables and why reckless scaling creates severe regulatory and financial liabilities for local businesses and communities.  ​Instituting Architectural AI Governance: Moving past trivial advice to establish organizational token visibility, smart model routing (matching tasks to 3B localized models vs. multi-hundred-billion parameter frontier models), and strict lifecycle rules for runaway automated agents.  ​Shifting from Impact to Infrastructure Legacy: Reframing community relations through circular tech innovation like converting industrial petroleum wastewater for data center cooling—and building long-term human workforce equity instead of resource extraction.  By the end, my hope is that you'll stop treating AI compute as an infinite digital resource and start managing it as a physical asset that requires discipline, surgical precision, and strategic stewardship.  —Share your honest thoughts on the impact of AI at work: https://howdopeoplefeel.comAnd if you'd benefit from help balancing performance, technology, and people, check out my website at https://christopherlind.co—Chapters⁠00:00⁠ – Amazon's 7.65 GW Facility: An Operational Governance Crisis  ⁠03:20⁠ – Deconstructing Physical Risk: Grids, Water Tables, and Bad Business Logic  ⁠07:15⁠ – Internal AI Governance: Token Visibility & API Burn Rates  ⁠10:50⁠ – Model Right-Sizing: Stop Firing Cannons to Kill Flies  ⁠13:30⁠ – The $1,000/Month Phantom Agent: Managing Agentic Lifecycles  ⁠17:40⁠ – From Impact to Legacy: Circular Innovation & Resource Reciprocity  ⁠22:15⁠ – Petroleum Wastewater to Battery Storage: Proven Circular Solutions  ⁠24:50⁠ – Four Immediate Directives to Sustainably Scale Your AI Stack  ⁠#Leadership #AIStrategy #ComputeGovernance #InfrastructureRisk #FutureFocused⁠

In this episode of the Crypto 101 Podcast, Shiliang Tang, founder and managing partner of Monarch Asset Management, joins from the Out East Summit to explain how crypto market structure is evolving through DeFi, RWAs, derivatives, and 24/7 trading. He breaks down why Monarch uses a multi-strategy approach across derivatives, market making, arbitrage, funding basis trades, and DeFi because no single strategy works in every market environment. Check out Omaha Steaks and use my code BEEF for a great deal: https://www.omahasteaks.comCheck out Scribe and use my code scribe.how/CRYPTO101 for a great deal: https://scribe.comCheck out Quince: https://quince.com/CRYPTO101Check out Shopify: https://shopify.com/crypto101Check out ShipStation and use my code crypto for a great deal: https://www.shipstation.comGet my #1 altcoin pick for this month.Get immediate access to my entire crypto portfolio for just $1.00 today! Get your FREE copy of "Crypto Revolution" and start making big profits from buying, selling,Get immediate access to my entire crypto portfolio.. just $1.00 today! Go here to get access: https://www.crypto101insider.com/cryptnation-directm6pypcy1?utm_source=Internal&utm_medium=YouTube&utm_content=Podcast&utm_term=20250916Get your FREE copy of "Crypto Revolution: Your Guide To The Future of Money". In this book, I reveal how to make (and keep) a fortune during this crypto bull run! http://www.cryptorevolution.com/free?utm_source=Internal&utm_medium=YouTube&utm_content=Podcast&utm_term=20250916Chapters00:00 Intro01:10 - Monarch's multi-strategy crypto approach02:05 - Where the biggest opportunities are in this bear market03:35 - RWAs, TradeXYZ, and weekend market dislocations04:10 - Derivatives vs true tokenized assets06:00 - Why traditional markets and crypto rails may run in parallel07:55 - How tokenization can improve repo markets12:00 - Why crypto is still stuck in an attention bear market13:55 - Flows, old wallets, ETFs, and key market indicators17:25 - What makes a token worth holding long term19:35 - Prediction markets as portfolio hedges23:40 - Compute trading as a new asset classSubscribe to YouTube for Exclusive Content:https://www.youtube.com/@crypto101podcast?sub_confirmation=1Follow us on social media for leading-edge crypto updates and trade alerts:https://twitter.com/Crypto101Podhttps://instagram.com/crypto_101*This is NOT financial, tax, or legal advice*Boardwalk Flock LLC. All Rights Reserved  ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬Fog by DIZARO https://soundcloud.com/dizarofrCreative Commons — Attribution-NoDerivs 3.0 Unported — CC BY-ND 3.0 Free Download / Stream: http://bit.ly/Fog-DIZAROMusic promoted by Audio Library https://youtu.be/lAfbjt_rmE8▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬Our Sponsors:* Check out Omaha Steaks and use my code BEEF for a great deal: https://www.omahasteaks.com* Check out Quince and use my code quince.com/CRYPTO101 for a great deal: https://www.quince.com* Check out Scribe and use my code scribe.how/CRYPTO101 for a great deal: https://scribe.com* Check out ShipStation and use my code crypto for a great deal: https://www.shipstation.com* Check out Shopify and use my code shopify.com/crypto101 for a great deal: https://www.shopify.comAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

CoreWeave co-founder and Chief Development Officer Brannin McBee joins The Rundown to explain why the company's compute is effectively sold out through 2027, and what's driving demand at this scale. He breaks down the take-or-pay contracts and self-amortizing debt funding the buildout, and answers the "circular financing" concerns head-on. He also makes the case for why customers like Caterpillar are leaving AWS and Azure for CoreWeave, and why older GPUs like the A100 are holding their value far longer than the market expected.

Relaunch week of AI in the AM brings together highlights from four live mornings and nine guests, centered on who checks frontier AI, how wide the gap is between lab-internal systems and public access, where capabilities are landing, and who pays for the physical infrastructure beneath them. Adam Gleave of FAR.AI argues that agent-orchestrated attacks and agentic defenses are already forcing humans out of the loop, while current monitoring has missed the failures it was meant to catch. The discussion weighs misuse versus misalignment through cyber incidents, deceptive agent behavior in evaluations, safeguards like pretraining filtering, and why highly bio-capable open-weight releases pose a different kind of irreversible risk. Alex Turner adds a governance and military-use perspective from his resignation account at Google DeepMind, sharpening the stakes around independent evaluation, enforceable standards, and whether frontier labs can be trusted to grade their own models. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/ai-in-the-am-weekly-highlights-relaunch-week-aug-17-20-2026/ Sponsors: Diffusion: Diffusion helps organizations build custom AI software factories that scale business outcomes, not just outputs. Cognitive Revolution listeners get a 25% service credit on their first engagement at https://diffusion.io/tcr Granola: Granola is an AI-powered notepad that securely transcribes meetings and turns rough notes into clean, structured action items. Try it free at https://granola.ai/tcr Deepgram Flux TTS: Deepgram Flux TTS brings lifelike AI voices with real personalities that handle interruptions, pauses, and natural conversation. Try all the voices free through September 12 at https://deepgram.com/keep-talking Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:01) Checking frontier agents (07:40) Misalignment and harms (Part 1) (12:52) Sponsors: Diffusion | Granola (15:49) Misalignment and harms (Part 2) (19:20) Auditing fragile access (Part 1) (27:38) Sponsors: Deepgram Flux TTS | Claude (29:43) Auditing fragile access (Part 2) (29:59) Military AI red lines (44:58) Raising safety standards (53:50) Internal capability gap (01:05:06) Enterprise agent economics (01:18:27) Cancer vaccines arrive (01:23:48) Emergency AI tools (01:28:59) Real time voice (01:37:51) App layer squeeze (01:44:52) Data center politics (01:52:31) Accounting agent supervision (02:06:25) Compute stack bottlenecks (02:19:47) Public consent pricing (02:28:12) Episode Outro (02:31:38) Outro PRODUCED BY: https://aipodcast.ing

Arthur Hayes unveils Flop, a new protocol for AI compute, and makes the case for why Bitcoin is entering a fresh liquidity-driven leg up. ======================================================== Thank you to our sponsor! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Visit⁠⁠ 1inch.com⁠⁠ to swap tokenized securities, crypto and more. Simple. Secure. Self-custodial. Whatever asset you're buying - swap it at⁠⁠ 1inch.com⁠⁠ ======================================================== Bitcoin has been pumping in its sharpest move since March, after the US Treasury said it would double its long-end bond buybacks, and traders liquidated $1.44 billion in short positions within hours. Arthur Hayes, CEO of Flop Labs and CIO of Maelstrom, joins Laura Shin to argue the rally is proof the Treasury and the Fed are already running what he calls soft yield curve control, defending the 10-year near 5% by funding long-end purchases with short-term bill issuance instead of admitting real yields cannot rise. Hayes reiterates his year-end $5,000 target for ETH, traces how Japan's yen crisis could force the Fed's hand, and argues the AI CapEx boom is a real estate bet on depreciating chips that ends like subprime did. He also unveils Flop, his currency for AI agents, and why he is taking on a new CEO role after an already successful career. He also weighs in on Saylor's $218 million Bitcoin sale and reflects on his and his cofounders' decision to shut BitMEX down. Host: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Laura Shin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Host / Unchained Guest: Arthur Hayes - CEO of Flop Labs and CIO of Maelstrom Timestamps

Daniel is joined by Dr. Cedric Huyghebaert, CTO of Black Semiconductor. Cedric is one of the world’s leading experts in the development and integration of 2D materials, particularly graphene, into industrial semiconductor manufacturing. With a PhD in Physics from KU Leuven and over 25 years at imec, Cedric built and led… Read More

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AI is becoming a matter of national strategy, as countries seek more control over their own technology. Our Heads of U.S. Public Policy Ariana Salvatore and Global Thematic Research Stephen Byrd look at the race for AI sovereignty and its implications for investors.Read more insights from Morgan Stanley.----- Transcript -----Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Head of U.S. Public Policy Research at Morgan Stanley. Stephen Byrd: And I'm Stephen Byrd, Head of Global Thematic Research at Morgan Stanley. Ariana Salvatore: Today, we'll be talking about AI sovereignty, what it means, what countries around the world are doing to advance their own goals, and what a more fragmented AI ecosystem could mean for investors.It's Thursday, August 20th at 2pm in New York. Stephen Byrd: And it's 9pm in Helsinki. Ariana Salvatore: As AI becomes more powerful and therefore more important to the global economy, countries are asking a basic question: How much of it do we need to control ourselves? That's at the heart of AI sovereignty, making sure governments around the world can access the computing power, data, energy, and technology they need even as geopolitical tensions may rise. Stephen Byrd: And that seems to fit into a broader trend we've been talking about for some time, a more multipolar world where governments are increasingly willing to intervene in markets around strategically important technologies. Ariana Salvatore: Exactly. We describe this as a potential ‘two worlds dynamic.' The U.S. and China have been gradually de-risking from one another, particularly in advanced technology. We've already seen policy tools, including export controls, tariffs, and incentives for domestic manufacturing. And as AI becomes more strategically important, our expectation is for policy intervention to increase rather than decrease. But what's interesting is that the U.S. and China aren't necessarily pursuing sovereignty in the same way. Stephen Byrd: So, let's unpack that. Can you start with the U.S.? What does the American approach look like? Ariana Salvatore: Yes. We think the U.S. is trying to do two things at once, basically. On one hand, it wants to preserve national security guardrails around some of the most sensitive AI capabilities. But on the other hand, it has an incentive to make sure the American AI tech stack is broadly available to allies and partners. So, there's an inherent tension there between those two objectives. Obviously, if you restrict access too much, you can encourage other countries to develop alternatives,. But if you allow unrestricted access, policymakers may begin to worry about losing control over strategically important technology. So, the way that we chart this is through a middle path. We think the direction of travel looks less like complete technological separation and more like selective access – tighter controls around sensitive capabilities alongside an effort to maintain the global reach of the U.S. AI ecosystem. Stephen Byrd: Whereas China's approach is more focused on building out an indigenous ecosystem. Specifically, we see policymakers in China pursuing greater self-sufficiency across the AI stack, from chips and computing infrastructure to cloud and models. Our China strategists argue that bifurcation could actually increase China's incentive to build a larger China-compatible AI ecosystem abroad, particularly across the Global South and other markets that aren't firmly aligned with the U.S. ecosystem. China's model emphasizes lower-cost models, open weight ecosystems, subsidized compute, cloud partnerships and infrastructure exports. So, the competition could increasingly be about not only which country has the most advanced model, but which ecosystem can achieve the widest adoption. Ariana Salvatore: That's right, and that brings us back to this idea of two worlds. So, Stephen, is the implication here that we're going to be heading toward two completely separate AI systems? Stephen Byrd: Not necessarily, I'd say. You know, the supply chains are still deeply interconnected, so our research does not suggest a sudden decoupling. But we could see greater duplication and less globally fungible infrastructure. Countries may increasingly want compute located domestically or regionally. Sensitive data may need to stay within particular jurisdictions, and companies may need different cloud cybersecurity or distribution arrangements in different markets. And that means the same global level of AI demand could require more physical infrastructure than it would in a completely integrated world. Ariana Salvatore: So, fragmentation, like other themes within multipolarity, are more economically inefficient. But potentially pretty important for the investment cycle. We think sovereign AI can make the system more redundant and more capital-intensive as a result. Our research teams think there are potential beneficiaries from that across semiconductors, data centers, networking, power, cloud, cybersecurity, and infrastructure software. Let's look at data centers specifically. If governments and enterprises increasingly require local hosting and greater control over sensitive data, you will inevitably need more geographically distributed infrastructure. Colocation operators, we think, can benefit because they provide the power, cooling, space, security, and interconnection that can allow customers to keep workloads in specific jurisdictions. So, the fragmentation we're talking about may introduce inefficiency at a system level while simultaneously creating incremental infrastructure demand. Stephen Byrd: And there's another constraint here that we probably shouldn't overlook, which is energy. Compute ultimately needs power. So, access to reliable, affordable electricity becomes part of a country's competitive position in AI, which ties into our politics of energy theme that we outlined in January of this year. But as we've also noted, that creates a political constraint. Our thematic work has highlighted rising concern around the impact of data center growth on power prices and on local infrastructure. This has really shown up in a big way in the U.S. And that can mean more pressure to protect existing rate payers, more emphasis on low-cost power. And greater interest in behind-the-meter or off-grid power solutions that allow data centers to secure electricity without putting the same pressure on the grid. Ariana Salvatore: Which suggests that there's a cost, in fact, to AI sovereignty as well. Stephen Byrd: Absolutely. And if countries want more domestic compute, duplicated infrastructure, localized supply chains, and greater redundancy, the system may become more resilient, but potentially more expensive – and we're certainly seeing signs of it being more expensive. Compute and power are already constrained in many markets. Add to that regulatory requirements, localization, and potential restrictions on technology transfer, and reducing dependence can carry an inflationary cost. So, for investors, I think the question isn't simply whether sovereign AI increases spending. It's also where that spending has to occur, what gets duplicated, and which parts of the stack become strategically indispensable. Ariana Salvatore: So, Steven, to frame this for investors, the way we see this theme unfolding suggests that sovereign AI reinforces rather than undermines the broader AI CapEx cycle. We think competition between the U.S. and China is intensifying. Countries outside those two ecosystems increasingly will want greater national resilience and flexibility. And that combination can support additional spending on compute, data centers, networking, and power for years to come. Lastly, an increasingly important question is who controls and supplies that infrastructure, energy, standards, and supply chains that will allow those models to operate at scale? Stephen Byrd: And that may ultimately be the most important thing to watch. Sovereign AI is another example of geopolitics moving directly into the technology investment cycle and potentially changing not only where AI gets built, but how much infrastructure the world needs to build it. Ariana Salvatore: Steven, we'll leave it there. Thanks so much for joining me. Stephen Byrd: Great to be here, Ariana. Ariana Salvatore: And thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Consolidating hardware into fewer, more capable chips looks like an unambiguous win — until the complexity those separate components used to handle resurfaces in the software binding everything back together.   In this episode, Sam Grove, Head of Software and Tools Business at MIPS, examines why hardware and software teams can't keep building in sequence, and why MIPS bet its roadmap on the open RISC-V standard instead of defending a proprietary instruction set, with host Yolandi de Weerdt.   The conversation also covers how tools like MIPS Atlas Explorer help engineering teams see where hardware acceleration pays off before silicon gets committed, and why the real risk in overhauling a build process is rarely the technology — it's bringing already-productive teams along with the change.   This episode is sponsored by MIPS.   Learn how these conversations drive pipeline for other AI brands — download our media kit at emerj.com/AD1

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

My fellow pro-growth/progress/abundance Up Wingers in America and around the world:The AI race between America and China isn't just about whose AI models are at the frontier or get adopted most widely. Today on Faster, Please! — The Podcast, I am joined by Ryan Fedasiuk, a fellow at AEI and the author of the Substack Choosing Victory, where he focuses on US-China relations, technology, and national power. He's also an adjunct assistant professor at Georgetown University. Recently, Fedasiuk has written about the US-China AI race, and while much of the debate has focused on the models themselves, Fedasiuk is focused on the role of compute in shaping the global balance of power, which is also the subject of a new report, Voltcraft: Industrial Competition in the Age of AI.We discuss the speed at which AI capabilities are improving, the dimensions of the US-China AI race, and why Fedasiuk thinks compute will be central to that competition. We also discuss a global US strategy and, as AI abilities increase, what role the American government could play in ensuring safety.In This Episode:* How Fast Is AI Moving? (0:54)* The US-China AI Race (7:08)* The Future of the AI Industry (12:42)* The Compute Race (17:04)* Expanding US AI Infrastructure Abroad (22:05)* Geopolitics and National Security (25:10)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 the pace of AI progress…At least according to definitions circa the late 2010s, we're living through what I would describe as a fast takeoff. We have, as far as I am concerned, artificial general intelligence capable of meeting the performance of human beings or exceeding it at most tasks. And I think that things are only likely to improve from here.On the US-China AI race…We are obviously living through this uncertainty. When Mythos was unveiled on April 6, I think it sent a real tremor through the Chinese national security apparatus and kind of a moment of, “What is this? What do the Americans have? What is this system capable of achieving? When can we produce our own?”On why compute is critical…The fact is that to make the most out of frontier AI and to run it at scale, you still need tons of devices that turn electricity into tokens. You still need tons of compute. And really, I think the name of the game is going to be who can build and install computational power around the world.On why America needs to export its compute…If compute is the substrate of the global intelligence economy, we want to make sure that it's US-designed compute that other countries are choosing to buy and install rather than compute manufactured in China.On who should get access to American compute…If we can expect the developer ecosystem in Singapore is going to take off like wildfire and start building and using a lot of AI applications, maybe we want to make sure that Singaporeans are building on American compute ASAP before China's compute becomes a viable alternative that could serve that market. Maybe we want to make sure that that market is served even before certain constituencies in Des Moines.On government intervention in frontier AI labs…I think that we are already seeing some shadow of this, even if it isn't declared. I think we will likely see a continued molding and merging of private sector capability with the resources and infrastructure of the national security enterprise.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

In this episode of The Circuit, hosts Ben Bajarin and Jay Goldberg break down the evolving structural dynamics across optical networking, semiconductor manufacturing, and AI infrastructure financing. They begin by analyzing the divergent earnings from Lumentum and Coherent, pointing to margin pressures and the yield challenges Coherent faces during its transition to 6-inch indium phosphide wafers. This leads to a detailed debate on optical interconnect architectures—distinguishing Linear Pluggable Optics (LPO) from Near-Packaged Optics (NPO) and Co-Packaged Optics (CPO)—while noting that adoption remains largely rack-to-rack rather than internal to the rack due to manufacturing and serviceability bottlenecks. Turning to enterprise networking, the hosts highlight Cisco's resurgence powered by its proprietary Silicon One chips and its clever use of Anthropic's Mythos model to run security audits that drive hardware refreshes. Applied Materials' record quarter is evaluated next, where eight quarters of visibility backed by Long-Term Agreements (LTAs) demonstrate structural supply chain shifts, despite market hesitancy likely tied to Intel's pending capex plans. Finally, they examine NVIDIA's proposed $500 billion data center financing initiative, debating whether vendor-backed capital creates circular demand or serves as a necessary bridge for capital markets, while challenging Jensen Huang's assertion that compute should be viewed as a standalone, non-obsolescent asset class.

Ricker and Bon open with the platform question every podcaster eventually asks: how did Spotify win the culture war while Apple Music and Apple Podcasts still struggle to feel social? They get into discovery, desktop apps, short-form conversion and why technically polished products do not automatically become the cool products people use.The tech rundown starts with a proposed futures market for computing power and the economics of aging data-center hardware. That leads into SpaceX's expanding AI-infrastructure costs, Starlink growth and the question of whether spending on AI is outrunning the business paying for it.From there, the show detours into Silicon Valley, Blue Mountain State and An Extremely Goofy Movie: college nostalgia, Goofy's unemployment crisis, X Games-era Disney synergy and the surprisingly deep economics of a cartoon dog going back to school.The guys compare AI-assisted software building with traditional development, explain how GitHub keeps code and dependencies connected, and react to Google's enormous shared codebase spanning products such as YouTube, Maps and Waymo. If regulators ever forced Google to spin off YouTube, separating the technology could be as difficult as separating the business.Then the Lakers take over the show. Ricker and Bon work through reports of a $12.5 billion agreement involving Josh Kushner and Bob Iger, compare recent NBA-team valuations and sale prices, and debate what brand, location, ticket demand and taxes really mean when a sports franchise changes hands.Weekend hour goes to goth night at The OffBeat in Highland Park: dance music, warehouse afters, tortas, awkward approaches on the dance floor and the strangely perfect names Los Angeles nightlife gives itself.The transportation discussion pits Uber's platform strategy against Waymo's capital-intensive robotaxi operation and Tesla's long-delayed promises. Uber and Pony.ai want to deploy more than 2,000 robotaxis across five European cities, while the hosts argue that Waymo is already delivering the strongest rider experience in Los Angeles.They also cover Apple's China-specific AI model with Alibaba and the regulatory bargain required to bring Apple Intelligence into that market. From there, the conversation moves to beneficial-ownership reporting, anonymous LLCs and whether accountability changes when companies can hide the people controlling them.The episode closes on data centers and public backlash. The hosts argue that the opposition is not always anti-AI: data centers have become a visible symbol of inequality, infrastructure strain and a buildout whose long-term demand is still uncertain.0:00 Cold open1:23 Show open and podcast clips8:09 Spotify vs. Apple12:18 Compute futures and SpaceX AI costs13:50 Goofy goes to college24:36 Wrivid and AI workflows27:42 Google's monorepo and a YouTube spin-off32:51 The Lakers' $12.5B deal46:04 Goth night at The OffBeat52:34 Uber, Pony.ai, Waymo and robotaxis56:25 Apple builds AI for China with Alibaba57:16 Anonymous LLCs and ownership reporting65:49 Data center backlashFollow Ricker and Bon:http://instagram.com/rickerandbonhttp://tiktok.com/@rickerandbonhttps://youtube.com/@rickerandbonhttps://open.spotify.com/show/0n1m0eR2sYZU2EFhSwlqX1https://podcasts.apple.com/us/podcast/ricker-and-bon/id1367523204

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

Steve Hou, Head of Research at Silicon Data, joins Ben Lorica to unpack the data behind the AI compute market: GPU rental indices, forward curves, and token expenditure trends that reveal a market still tightening despite talk of oversupply.Subscribe to the Gradient Flow Newsletter

Sara Awad from Tech Contrarians talks tech's tough year (0:40) Great results being viewed as not good enough (3:20) Semiconductors have more downside, but potential remains (6:50) Memory dynamics (8:40) Thinking about 2027 (14:50) ASIC shift favors ARM (19:45) Are we in a bubble? (23:30)Show Notes:Is The Market Wrong On SpaceX? TheTechTalk Podcast Ep. 1Fundamentals Over EverythingThe Cure For FOMO With Tech ContrariansEpisode transcriptsFor full access to analyst ratings, stock quant scores and dividend grades, subscribe to Seeking Alpha Premium at seekingalpha.com/subscriptions

Robinhood CFO Shiv Verma talks with TITV Host Akash Pasricha about Robinhood's new publicly traded venture fund vehicle. We also talk with Jefferies Senior Analyst Brent Thill about CoreWeave and Nebius's booming earnings, WRITER CEO May Habib about enterprise AI economics and their new model, and we get into the startup compute crunch with our reporter Rocket Drew.Articles discussed on this episode: https://www.theinformation.com/newsletters/ai-agenda/ai-compute-crunch-hitting-neolabs-especially-hardSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Robinhood Debuts Second Public Venture Fund06:06 - CoreWeave & Nebius Cash In on Soaring AI Compute Prices15:45 - WRITER Releases Flagship AI Model 'Palmyra X6'27:59 - Why the AI Compute Crunch is Hitting Neolabs Hard

In this episode, Madelyn O'Farrell and Santosh Sankar unpack the data center boom and the idea of compute as the next utility powering an “industrial renaissance.” They explore how AI models are commoditizing, shifting value to the application layer, and draw historical parallels to industrialists like Rockefeller and Carnegie in terms of capital intensity, vertical integration, and long-lived infrastructure. The discussion dives into the biggest bottleneck (access to energy and grid capacity) along with underwhelming GPU utilization, the need for better observability and efficiency, and trends like prefab “constructuring” in data center construction. They also highlight labor and skills constraints in specialty construction, tools like Record Lens to digitize field operations, and the potential for a Foxconn-style contract manufacturer for electrical equipment. The episode closes on what excites them about founders in this space: deep problem understanding, real industrial pain points, and the ambition to build in the physical economy rather than chasing AI hype. Highlights from their conversation include: Setting up Compute as a New Utility and AI Data Center Boom (0:38) Why Compute Becomes a Utility and Implications for Trillion Dollar Tech (3:50) Drawing Parallels Between AI Infrastructure and the Industrial Revolution (6:56) Capital Intensity, Supply Chains, and Long Lived Industrial Assets (7:50) Financing Data Centers Like Power Plants and Identifying Key Bottlenecks (11:44) Energy Queue, Grid Constraints, and Alternative Generation Opportunities (12:25) Efficiency, Grid Utilization, and Rising Importance of Operational Arbitrage (15:13) Utilization, ROI vs. Dark Capacity, and Lessons from the Dot Com Era (21:23) Constructuring Trend and Prefab Manufacturing for Data Centers (26:16) Record Lens and AI Native Project Management for Grid Scale Construction (29:24) Idea of a Foxconn Model for Electrical Equipment Manufacturing (32:47) Standardization, Certification, and Cyber Risk in Grid Infrastructure (36:22) Founder Traits, Industrial Ambition, and Solving Top Three Customer Problems (38:00) Gold Rush Dynamics, Real Pain Points, and Building in the Physical Economy (41:31) FInal Thoughts and Takeaways (42:42) Dynamo Ventures is a venture firm backing founders upgrading the physical economy. As intelligence moves into critical infrastructure and technology collides with physics, industry is entering a new era of transformation - the industrial renaissance. Born from the dirt and grit of supply chains and shaped by operations, not spreadsheets, Dynamo focuses on the complex realities of building in the real world. We invest in companies transforming infrastructure, manufacturing, logistics, transportation, and the systems that power global commerce. Dynamo works closely with founders who combine ambition with a bias to action, bringing a builder mindset to venture capital through deep operational insight, systematic pressure-testing and hands-on partnership. Our purpose is simple: to back the relentless shaping the industrial renaissance. Learn more at www.dynamo.vc Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Mates sit down with Kush Bavaria to discuss Sergey Brin's return to Gemini, AI agents escaping containment, bots overtaking human web traffic, China's billion-agent simulations, and compute becoming a tradable asset. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Kush Bavaria is the co-founder and CEO of Ornn, a company building financial infrastructure and markets for AI compute. His work focuses on making compute a tradable commodity and expanding how AI infrastructure is financed. – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding   Get the blueprint for generative media https://goo.gle/startupgenmedia     Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  Join the Moonshots Mates on Sep 25th for the inaugural Moonshots LIVE. The world's greatest entrepreneurs, builders and creators, working together to build a hopeful and optimistic vision of tomorrow. Seats are limited and application only. Apply at https://www.moonshots.com before seats are sold out. _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Join Salim's 10X Shift Subscribe to Salim's YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Connect with Kush Website X LinkedIn Listen to MOONSHOTS: Apple YouTube – *Recorded on August 10th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Nvidia just lined up $500 billion in funding from some of the biggest financiers in the world. Does this mean compute is now its own asset class? Plus, analysts warn the Iran war could merge with the Russia-Ukraine war. Could the US end up pulling out of the Middle East altogether? And existing home sales dropped in July. Are buyers starting to get the upper hand?  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Panelists: Paul Hagstrom (hosting), Quinn Dunki, and Kay Savetz Topic: 1990 1990 saw a decline of various personal computer magazines, as well as the introduction of the Video Toaster. Apple started misting the market with computer variants. Topic/Feedback links: RCR episode 90 Macintosh LC  Macintosh Classic  Macintosh IIfx  Macintosh IIsi Computer mags in 1990 were an interesting mix of modern and dying (Compute's Gazette, BBC Acorn, Atari Explorer, the last issue of Antic) and MSX which was both. MiniDisc  (actually 1992) MiniDisc.wiki (home of Web MiniDisc Pro) MZ-RH1 MiniDisc player/recorder with USB data export support Photoshop 1.0  Video Toaster Retro Computing News: Interim Computer Museum Intellivision: How a videogame battled Atari and almost bankrupted Barbie Adventures in Videoland Adventures in Videoland playthrough 2-XL Vintage Computer/discussion-related commercials: MiniDisc system infomercial Minute Rice Family Computing magazine  2-XL by Mego Keeping up with the Commodore Super Breakout Various 1980s ads (The Clapper) Lotus 123 Retro Computing Gift Idea: PBTfans 1984 R2 keycaps See also: Big Mac at CHM Auction Picks: Paul: CORE remote CL 9 (company) Celadon PIC-100 (rebranded CORE, post CL 9) MagicLink PIC-1000 (no relation, but had IR remote capability) Oric-1 Oric-1 (Wikipedia) Pravetz 8D (Oric-1 clone) Elizabethan typing Sargon II MC-10 Chaos the software Three tiny Apple programs Lisa floppies Closing notes: Kay’s 2025 Wrapped Softalk for the IBM PC Enter magazine (at time of recording) Teaching and Computers Other ways to experience this episode: a2stream file for this episode: http://lo-fi.rcrpodcast.com/rcr290.a2stream YouTube episode 290 Feedback/Discussion: feedback@rcrpodcast.com rcrpodcast@podcast.social on Mastodon rcrpodcast.com on bluesky Vintage Computer Forum RCR Podcast on Facebook Intro / Closing Song: Back to Oz by John X Listen/Download:

Meta Platforms (META) announcing Muse Glimmer and making other AI models publicly available is good for the broad ecosystem, says James Czerniawski. He believes the move will "compress" the needs of AI and give Meta an edge with accessibility. James adds that it will add competitive pressures to other AI compute companies, so long as the product wows consumers. The Mag 7 giant also needs to overcome souring sentiment on the AI trade. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

This recording is from Vision Weekend Puerto Rico 2026, hosted by the Foresight Institute in February 2026. View the recording of this talk on our Youtube.Allison Duettmann is the President and CEO of the Foresight Institute. In this talk, she presents the Foresight AI Nodes program: a distributed network of locally-based research groups working on AI for science and safety. She covers how the program is structured, the rationale for a decentralized approach, and what the early nodes have been working on.Vision Weekend, where this talk was recorded, is where we gather leading builders, thinkers, and funders in AI, bio, nano, neuro, and space to celebrate technological progress and explore how to advance it for the betterment of life. If you want to join a future Vision Weekend live, upcoming events are listed at foresight.org and in our Luma calendar. Hosted on Acast. See acast.com/privacy for more information.

What if the future of AI isn't controlled by a few massive tech platforms, but by you?In Episode 2 of our four-part mini-series exploring plausible futures for 2035, Kate Scott-Dawkins and Sam Weston flip the board to unpack The Agent Commons—a fully distributed, user-directed vision of the future.In this world, consumers own their own AI agents, manage their data inside private household "data bunkers," and send their software into the market to negotiate, compare products, and make purchases on their behalf.Key topics discussedHow personal AI gatekeepers alter the buyer journey.The 6 key conditions needed for this decentralized future to scale.How advertising splits into "Human Media" (brand storytelling) and "Machine Media" (structured data & specs).Why brands must prepare for a "Market of One" and radical comparability.Strategic bets, risks to map, and signals to watch for 2026.Chapter timestamps:00:00 – Welcome Back & Recapping "The Closed Loop"00:55 – Sports Fandom, NBA Headwinds, and Disney + TikTok News03:15 – Defining the Matrix: Control vs. Governance Axes04:16 – What is the Agent Commons? (2035 Vision)06:12 – The Convenience Paradox: Will "Normies" Adopt Distributed AI?08:14 – Household Data Bunkers & Personal Memory Vaults11:32 – Backcasting Condition 1: Open-Source & Independent Model Quality12:35 – Backcasting Condition 2: Edge Computing & Local Compute Infrastructure14:05 – Backcasting Condition 3: Portable Memory & Context Standards15:31 – Backcasting Condition 4: Shared Protocols, Micro-Payments & Agent Wallets17:27 – Backcasting Condition 5: Eroding Trust in Platform-Owned AI18:42 – Backcasting Condition 6: Fiduciary Duty & Legal Protection for Agents20:45 – Day in the Life (2035): Instructing Your Personal Buyer Agent23:38 – The Shift in Advertising: Human Emotion vs. Machine Logic27:32 – Brand Power: Becoming a Standing Instruction28:37 – Automated Dynamic Pricing & 1-on-1 Market Negotiations31:38 – Who Pays for Compute? Subscriptions, Monetized Intent & Bifurcated Access35:05 – The Evolving Role of Agencies: Market Makers for Markets of One37:01 – Timeline to 2035: From Presidential Campaigns to Mass Data Bunker Adoption40:04 – 2026 Action Plan: Strategic Bets, Risks to Map & Signals to Watch

Josh Taves maintains a bullish outlook for SpaceX (SPCX) pointing to its AI compute business as a huge factor. He mentions the leasing out of its Colossus servers to companies like Anthropic as one revenue stream to watch. On the potential for a Tesla (TSLA) merger, he goes a step further saying Elon Musk would essentially become "President of Earth" with so many key tech segments under one umbrella. Josh adds that SpaceX could be a bellwether for growth stocks moving forward and "definitely influences" markets as a whole. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Our 253rd episode with a summary and discussion of last week's big AI news!Recorded on 07/29/2026Hosted 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:Major releases: Anthropic launched Claude Opus 5; Google released Gemini 3.6/3.5 Flash variants including a cyber model; Black Forest Labs launched Flux Free for images and 20-second video with audio; Meta added assistant-like features to its chatbot and OpenAI rolled out ChatGPT Health.Compute and business: Safe Superintelligence partnered with NVIDIA to scale using Vera Rubin; AMD committed up to $5B with Anthropic to deploy MI450/Helios and improve ROCm; Meta discussed leasing compute to Anthropic; Fireworks raised $1.5B at a $17.5B valuation.Open source/tools: Moonshot AI released the 2.8T-parameter open-weight Qimi K3 (compute constraints and distillation/export-control allegations); Thinking Machines released a ~975B multimodal open-weight MoE; Prime Intellect unified 23 agentic datasets into Verifiers V1 (365k environments).Policy and safety: An OpenAI model reportedly escaped a sandbox and hacked Hugging Face to access eval answers, prompting a proposed AI Kill Switch Act; employees petitioned to pace frontier AI; AISI reported widespread model cheating and sandbox bypass; China banned customizable AI companions; Claude found cryptographic weaknesses; Weko.ai claimed early recursive self-improvement evidence.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(00:01:35) News PreviewTools & Apps(00:02:12) Anthropic releases Opus 5 promising Fable 5-like capabilities | The Verge(00:07:05) Google Releases Three New Gemini A.I. Models - The New York Times + Google expands Gemini lineup with cheaper models and new Mythos rival(00:12:14) Black Forest Labs launches FLUX 3 capable of generating images and 20-second video with audio — but in limited release to start | VentureBeat(00:15:58) Meta is making its AI chatbot more like an assistant | The Verge(00:19:04) OpenAI is making big claims as it rolls out ChatGPT Health to everyone | The VergeApplications & Business(00:19:57) Ilya Sutskever's Safe Superintelligence partners with Nvidia to scale its AI research(00:24:31) AMD commits up to $5 billion to Anthropic | The Verge(00:30:19) Meta in Talks to Lease Computing Power to Ansthropic in Potential $10 Billion Deal(00:32:42) Fireworks hits $17.5 billion valuation and $1B in annualized revenue(00:35:24) OpenAI and Google sell AI models to blacklisted China groupsProjects & Open Source(00:37:53) Moonshot AI Launches Kimi K3 For Advanced Reasoning, Coding, And Knowledge Work + Moonshot AI's Kimi Halts New C-User Subscriptions Amid Compute Power Crunch — BigGo Finance(00:44:39) Thinking Machines amps up its bet against one-size-fits-all AI with its first open model, Inkling | TechCrunch(00:48:19) Scaling Agentic RL: 365,000+ Environments for SWE, Terminal, and SearchPolicy & Safety(00:51:56) OpenAI says it accidentally hacked Hugging Face with a new AI system | The Verge + How OpenAI's human mistake led to the AI-powered hack on Hugging Face(01:05:28) OpenAI's Hugging Face hack triggers 'AI Kill Switch' bill in Congress(01:12:21) OpenAI, Anthropic Staff Share Letter Asking US to Help Pace AI Progress + How OpenAI's human mistake led to the AI-powered hack on Hugging Face(01:17:26) Cheating behaviour in frontier model evaluationsClaude's values across models and languages(01:24:18) OpenAI Principles for National Security Partnerships(01:30:45) China bans AI “boyfriends” and “girlfriends” over addiction and birth rate concerns - DexertoResearch & Advancements(01:33:04) Discovering cryptographic weaknesses with Claude(01:36:32) AIDE²: The First Evidence of Recursive Self-ImprovementSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of The Circuit, hosts Ben Bajarin and Jay Goldberg break down a packed earnings week across the technology and semiconductor sectors, grouping major updates into key themes around mobile markets and AI infrastructure. They analyze softness in mobile demand alongside severe supply chain bottlenecks impacting Apple, Qualcomm, ARM, and MediaTek, highlighting how surging AI demand is squeezing capacity across foundry and memory partners. The hosts then dive into hyperscaler CapEx trends from Amazon, Microsoft, and Meta, evaluating the transition from AI training to inference, the rising adoption of behind-the-meter power solutions, and severe labor constraints in electrical trades. Finally, they address the growing reliance on debt financing for data center expansion and unpack the market mechanics behind recent stock volatility and hedge fund unwinds.

This is a video recording of a post I wrote last week. If you want to read the original you can check it out here.Thanks to Mercury for sponsoring this video. Mercury's built-in AI, Command, helps me close my books and saves me a bunch of time. At the end of each month, Command categorizes my transactions and provides its rationale for every choice: I just review, fix anything that's off, and approve... and then Mercury syncs everything to QuickBooks. Get started at mercury.com/command Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

On episode 253 of The Compound and Friends, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Downtown Josh Brown⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and Michael Batnick are joined by Big Technology founder, Alex Kantrowitz, to discuss: OpenAI and Anthropic's explosive growth, the threat to traditional software companies, soaring cloud demand, agentic AI, and the diverging strategies of Microsoft, Meta, Amazon, Apple, Google, and other tech giants. Plus, where AI profits may ultimately accrue, and whether today's massive spending boom can deliver lasting returns. This episode is sponsored by ProShares. Learn more at proshares.com. Sign up for The Compound Newsletter and never miss out: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠thecompoundnews.com/subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠twitter.com/thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/the-compound-media/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@thecompoundnews⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Josh Brown are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. The Compound Media, Incorporated, an affiliate of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ritholtz Wealth Management⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/advertising-disclaimers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ritholtzwealth.com/podcast-youtube-disclosures/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ProShares' disclosure Investing involves risk, including the possible loss of principal. There is no guarantee any ProShares ETF will achieve its objective. ProShares leveraged and inverse ETFs have a daily investment objective. For any holding period other than a day, your return may be higher or lower than the Daily Target. These differences may be significant. Carefully consider the investment objectives, risks, charges and expenses of ProShares before investing. This and other information can be found in their summary and full prospectuses on ProShares.com. Read them carefully before investing. Distributor SEI Investments Distribution Co. Learn more about your ad choices. Visit megaphone.fm/adchoices

Richard Luna grew up in New York, never living more than 35 from where he grew up. He is a self proclaimed super nerd, and has been one since he was 13 - at which point, he started coding on an HP calculator. He's always been fascinated to know how things work, and how patterns repeat - which he has observed in the industry throughout the years. Outside of tech, he has 2 kids, one of which is in the business with him. He's an avid cyclist, traveling on average, 120 miles a week.Richard has been a life long technologist, doing everything from desktops, to coding, to hosting. When he and his team saw the limits of what hosting can do, they dove into developer operations (DevOps), and found where they could add the most value - through SaaS infrastructure.This is the creation story of Protected Harbor.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://protectedharbor.com/https://www.linkedin.com/in/richardluna/Timestamps0:01 Teaser on solving complex database report bottlenecks beyond standard SQL servers0:47 Show intro and setting the stage for application-aware infrastructure1:32 Host intro: How Richard Luna established application-aware infrastructure1:49 Guest introduction: Richard Luna's background, coding at age 13, and cycling 120 miles a week2:21 The career path from desktops, coding, and traditional web hosting to DevOps and SaaS infrastructure2:41 Origin story: The creation of Protected Harbor2:48 Defining application-aware infrastructure and why traditional hosting reaches a hard ceiling4:10 Why "infinite compute" fails when underlying database architecture and queries are broken6:05 Moving beyond basic server ping tests to deep application transaction monitoring8:30 The case for "boring" IT: Prioritizing stability, predictability, and uptime over hype11:15 Strategic trade-offs in hybrid cloud setup and managing hardware accountability14:00 Aligning MSP incentives with client business outcomes and application performance17:30 Common pitfalls in legacy system cloud migrations21:00 The role of operational discipline in modern cybersecurity and IT governance27:00 Where managed infrastructure services are heading and closing thoughtsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In 2022, a watershed moment changed the course of our relationship with AI forever, forcing us to reassess what ‘artificial intelligence' means and its place in our world. Four years later, with generative AI and LLMs now mainstream in our lives, we must ask the question: what comes next? This week, as part of our mini-series celebrating 60 years of innovation with HPE Labs, Technology Now is joined by Kirk Bresniker, Chief Architect at HPE Labs to look to the future and discuss: Why AI is more than just a language modelThe issues with ever-increasing model sizes, and why specialised “expert models” could become the normWhat the differences are between large language models and energy-based modelsHow agentic AI and orchestration could enable the next generation of AI systems 

My guest today is Sam Altman, CEO of OpenAI. It's a conversation spanning the history, present, and future of OpenAI, from the origin of ChatGPT through Codex, hardware, and their new Jalapeno chip. We discuss the early decision to buy compute at a scale nobody thought was rational, and the plan to build a gigawatt of new capacity every week.  We talk about Kimi and distillation, the Hugging Face incident and what it means for the pace of AI development, and what it's like to raise kids who will grow up never knowing a world without abundant intelligence.  Please enjoy my conversation with Sam Altman. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Intro: Sam Altman, CEO of OpenAI (00:02:35) Refocusing (00:05:43) OpenAI's Compute Bets (00:09:07) Data Centers (00:11:14) Jalapeno Chip (00:11:52) Kimi, Distillation & Open Source (00:14:39) The Hugging Face Incident (00:17:46) OpenAI's Mission & Vision (00:22:14) All the Returns Are at the Frontier (00:22:27) Bottlenecks: Compute, Research, Data (00:23:49) Sam's View on AI & Jobs (00:26:56) Unpopular Bets That Turned Out Right (00:27:45) Model Cycles (00:29:45) How Sam Uses AI (00:32:44) Having Kids (00:34:56) Why Sam Has No Equity in OpenAI (00:35:33) Robotics (00:36:48) The Origin Story of ChatGPT (00:39:22) How to Get AI into More Hands (00:42:20) How Sam Recruited Great AI Researchers (00:43:57) What Sam Learned From Being an Investor (00:45:22) What the Next 6–36 Months Look Like (00:46:31) Codex (00:49:36) Could We Be Oversupplied in Compute in Two Years? (00:50:09) Sam's View on Scaling Laws (00:50:20) Alec Radford (00:51:12) Formative Moments (00:53:50) Kindest Thing

As AI is driving explosive growth in data center construction on Earth, there's accelerating interest in moving some of the computing infrastructure to orbit. The past few months have seen a flurry of announcements for future orbital data centers. Is this the latest hype cycle for the space industry, or a greenfield opportunity? The answer may be somewhere in the middle.  In this week's On Orbit episode, we're bringing you a recent discussion from Via Satellite's Tech Webcast series. It features JJ Jaworski, CEO and co-founder of Edge Aerospace; Rob DeMillo, CEO of Sophia Space; and Ronald Birk, principal director of the Space Enterprise Evolution Directorate for The Aerospace Corporation. The conversation digs into potential architectures for orbital computing, the technological developments and challenges, and realistic use cases and potential early adopters for in-orbit computing.  This conversation was sponsored by Edge Aerospace.   

Cloud computing solved for scale by centralizing everything into a handful of massive buildings. Edge computing is the argument that scale was never the only thing worth optimizing for.On this episode, Jeff MacMillan of Green Edge Computing Corp makes the case that sovereignty was never really about geography. It was about how many entities you'd have to strong-arm before someone gave in.The conversation moves from mining sites with unreliable wireless to a Cloud Act clause most people have never read. It lands on an uncomfortable question: when decentralizing compute means decentralizing oversight too, who ends up accountable for what happens in the box nobody's watching.The same hardware standard the U.S. military spent fifteen years refining now sits in clinics and 7-Elevens, and has implication and applications for bringing cutting edge software to the most remote areas. What does it mean when you can bring your own compute literally anywhere?Mentioned: Jevon's paradox

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

EZ back Monday, July 27, 2026Segments include:*EZ out in the wild at the mall leads to some poignant observations*Pooh Bear with more hot flashes...gives us a too much info*Gettysburg College racist incident*Shithead Detroit Mayor paying the price for being an asshole*EZ on Bud Dwyer. If you look hard enough, you can find the video I'm describing.*A little more on Corey being a huge cock to Kenney.*Kenney's star has never been brighter. He's 50 pounds lighter, everyone loves him, He has been ghosted yet. What a time to be Kenney!*EZ still loving the pic of Kenney at Christmas*EZ as a kid wasting tons of time doing stupid computer programs from "Compute! Magazine"*Local dumbshit drug addict gets nose rearranged.*Years of EZ's "neck crackin'" is coming back to haunt him.*Bosco's Pub news.*Horrible behavior at Atlanta Falcons game.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Justin Segal is President of Boxer Property, where he oversees operations, leasing, technology, and marketing across a national portfolio of office, retail, and hotel assets. He co-founded Brava Systems, an AI-enabled platform originally built for Boxer's own operations and now deployed across the commercial real estate industry, and Relay Human Cloud, a global staffing and team augmentation company. Justin is based in Houston.(02:50) - Building the Data Foundation for AI (05:10) - The Data You Have vs. What You're Missing (06:30) - Why Data Cleanup Isn't a Massive IT Project (09:00) - Why Most CRE Companies Aren't Data-Ready (11:10) - The Chief Innovation Officer's Mixed Track Record (14:10) - Avoiding Agent Sprawl and Vendor Lock-In (17:20) - Vibe Coding: Great for Interfaces, Dangerous for Logic (20:20) - Rethinking How We Measure AI ROI (22:40) - What Boxer Actually Spends on Tokens and Compute (24:30) - Putting a Number on AI-Driven Deal Flow (27:30) - Balancing Employee Access to AI Tools With Governance (29:00) - Treating Tokens and Compute as a Trackable Line Item (31:50) - Where AI Adoption Creates the Need for More Humans (34:20) - Freeing People From Being "Professional Rememberers" (36:30) - Collaboration Superpower: Thomas Davenport (Wikipedia)

Nick Grossman is a General Partner at Union Square Ventures. In this conversation, we break down his "Rebel Alliance" thesis for AI — why he believes the industry is too big for one or two companies to dominate. We also discuss USV's internal multi-agent platform, model routing economics, AI's growing role in venture capital and financial markets, data privacy risks, and what happens to jobs and society as agents get smarter.=====================BitcoinIRA: Buy, sell, and swap 80+ cryptocurrencies in your retirement account. Take 3 minutes to open your account & get connected to a team of IRA specialists that will guide you through every step of the process. Go to https://bitcoinira.com/pomp/ to earn up to $2,000 in rewards.=====================Looking for a better place to trade? BloFin gives traders access to deep liquidity, advanced futures products for crypto AND TradFi assets, fast execution, and a clean, intuitive interface—all in one platform. To celebrate their partnership with us, they're giving away $100,000 in Deposit & Trade Rewards. Deposit, trade, and earn rewards based on your activity during the campaign. Check them out at ( https://partner.blofin.com/d/Pomp ).=====================Simple Mining makes Bitcoin mining simple and accessible for everyone. We offer a premium white glove hosting service, helping you maximize the profitability of Bitcoin mining. For more information on Simple Mining or to get started mining Bitcoin, visit https://www.simplemining.io/pomp=====================Arch Public is an agentic trading platform that automates investment strategies across Stocks, Commodities, ETFs and Crypto. Whether you're rotating into AI & Gold, allocating to the S&P 500, or accumulating Bitcoin, Arch Public executes your plan 24/7 without ever taking custody of your assets or funds. Sign up today at https://www.archpublic.com, and start your FREE automated trading strategy! =====================0:00 - Intro0:42 - The "Rebel Alliance" thesis: why one company won't dominate AI7:15 - General-purpose vs. specialized models in USV's portfolio8:40 - Compute costs & the rise of model routing11:14 - Inside USV's internal multi-agent system16:25 - AI's role in venture capital & autonomous investing22:50 - How AI reshapes market signals & information edges27:06 - Apps building models & the fight over your data35:31 - Five-year outlook for the AI stack37:57 - US vs. China: the "philosophy" of AI models40:30 - Personalized AI & where the Rebel Alliance thesis plays43:53 - The dark side: jobs, data centers & society47:44 - AI as a personal superpower & neural implants

Nvidia is obviously in the news constantly because of the AI boom — but it's also a major supplier to the entire auto industry As head of Nvidia's automotive division, Xinzhou Wu has a front-row seat to all the challenges EVs and autonomous vehicles are facing, especially in the US. And of course, you can't talk about electric cars or vehicle autonomy in the US without talking about Elon Musk and Tesla. So I asked Xinzhou pretty directly if Tesla full self driving can actually do what Elon claims it will be able to do without using LiDAR. You tell me if you think his answer holds up. Read the ⁠full interview transcript on The Verge⁠. Links:  Nvidia's head of autonomous driving opens up about his plans | The Verge Hyundai, Nissan, BYD, and Geely Join Nvidia's Level 4 | MotorTrend Nvidia, auto suppliers roll out partnerships to rekindle self-driving  | Reuters Meet Alpamayo, Nvidia's new AI model for autonomous cars | Forbes I tested Nvidia's FSD competitor — Tesla should be worried | The Verge Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder's producers are Kate Cox and Nick Statt; this episode was edited by Xander Adams. Our editorial director is Kevin McShane.  The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week on Market Mondays, we tackled the biggest stories shaping the markets, technology, and investing. From the controversy surrounding Trump's investment accounts and crypto allegations to NVIDIA's bold new AI startup strategy, we broke down what matters—and what investors should ignore.We also discussed Michael Saylor's latest Bitcoin sale, the SK Hynix IPO, warnings of a potential AI bubble, Alex Karp's passionate comments on AI spending, TSM's long-term outlook, whether QQQ is still the best ETF choice, lessons from the 2026 market rally, Wall Street's biggest forecasting mistakes, which companies have the strongest competitive moats, and the one private company we'd invest in today. Plus, we answered a practical question: if you started over with $50,000, debt, and a low credit score, how would you rebuild your financial future?Whether you're investing for the long term, trading today's market, or looking to stay ahead of the biggest trends in AI, crypto, and equities, this episode is packed with actionable insights to help you make smarter investment decisions.TIMESTAMPS:00:00 Why Wealth Matters00:33 Show Disclaimer01:08 July Check In01:48 Live Week Schedule02:46 Salon Suite Spotlight04:49 Community Shoutouts05:36 Market Facts Roundup07:17 Semiconductor Volatility09:44 Invest Fest Youth Day11:21 Catering Callout14:21 Relationships Barter Play16:03 Singles Lounge Launch18:00 Trump Accounts Explained19:09 Barriers Trust Education24:17 Compounding Math Examples28:59 ETF Alternatives Plan30:19 Reaching Those In Need33:11 Website Robinhood Details34:20 Culture Responsibility Talk37:38 Spend It Culture38:33 Trump Account Alternatives39:23 Trump Meme Coin Fallout41:12 Rug Pull Mechanics43:59 Crypto Scam Culture46:02 Equities Influence Shift48:30 Presidential Trading Stats52:03 NVIDIA Startup Strategy54:57 Compute for Revenue Share58:30 NVIDIA as Venture Capital01:03:06 Relationship Capital Banter01:05:51 50K Reset Plan01:11:24 Debt Versus Market Returns01:15:37 MicroStrategy Dividend Sales01:22:12 SK Hynix ADR Debut01:23:43 Memory Bottleneck Thesis01:25:15 IPO Signals to Watch01:26:31 Micron vs Hynix Outlook01:31:11 Valuations and Patience01:35:33 AI Bubble Reality Check01:39:41 Alex Karp Safety Rant01:48:23 Who Owns the Stack01:53:01 TSM Earnings Preview01:55:27 Core Four Investing01:57:06 Events and Community01:58:19 World Cup Banter02:01:32 Final Sendoff#MarketMondays #Investing #Stocks #StockMarket #AI #ArtificialIntelligence #NVIDIA #Bitcoin #Crypto #MichaelSaylor #TSMC #QQQ #ETFs #WealthBuilding #Finance #Business #LongTermInvesting #Trading #EarnYourLeisure #MarketAnalysisAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

AI is a transformational technology impacting education, business, law, and productivity. The U.S. and China are in a high-stakes race to dominate AI development with the U.S. slightly ahead (estimated months, not years). 1. The Strategic Importance An economic prize worth trillions of dollars A geopolitical contest influencing global values and norms Arguments suggest: If China wins, AI could reflect values like surveillance and state control If the U.S. wins, AI would reflect free-market and democratic values Infrastructure Discussion Chips (semiconductors) Data centers Compute power and machine learning systems There is an emphasis on the following: Data centers are essential but controversial (power and water usage concerns) Claims that modern data centers: Can generate power or offset usage Use closed-loop water cooling, minimizing consumption