Podcasts about gpu

Hyperscalers are spending more than $750 billion a year on AI infrastructure, and much of it is for physical hardware that needs financing. That's creating a compelling opportunity for asset-based lenders who can underwrite real collateral and contracts rather than picking technology winners.   On this episode of Disruptive Forces, host Anu Rajakumar speaks with Sean Hinze of Neuberger's Specialty Finance team. Together, they discuss: Why hyperscalers prefer off-balance-sheet financing and what that means for private credit How to underwrite GPU deals when chip technology evolves every two years Why power is the new bottleneck — and what a 68-gigawatt US shortfall means for lenders Where the strongest relative value sits today across chips, power equipment, and fiber How to separate hype from opportunity in a crowded space      This communication is provided for informational and educational purposes only and nothing herein constitutes investment, legal, accounting or tax advice, or a recommendation to buy, sell or hold a security. Information is obtained from sources deemed reliable, but there is no representation or warranty as to its accuracy, completeness or reliability. This communication is not directed at any investor or category of investors and should not be regarded as investment advice or a suggestion to engage in or refrain from any investment-related course of action. Neuberger is not providing this material in a fiduciary capacity and has a financial interest in the sale of its products and services. Investment decisions should be made based on an investor's individual objectives and circumstances and in consultation with his or her advisors. All information is current as of the date of this material and is subject to change without notice. Any views or opinions expressed may not reflect those of the firm as a whole. Neuberger products and services may not be available in all jurisdictions or to all client types. This material is not intended as a formal research report and should not be relied upon as a basis for making an investment decision. The firm, its employees and advisory accounts may hold positions of any companies discussed. This material may include estimates, outlooks, projections and other "forward-looking statements." Due to a variety of factors, actual events or market behavior may differ significantly from any views expressed. Investing entails risks, including possible loss of principal. Indexes are unmanaged and are not available for direct investment. Past performance is no guarantee of future results.   Use of Artificial Intelligence Tools. Neuberger may utilize AI tools in its business operations to improve operational efficiency and for assistance in research and analyzing data among other uses. AI tools are dependent on historical data, consequently, if the content or analyses that AI applications assist Neuberger in producing are or are alleged to be deficient, inaccurate, or biased, a client account may be adversely affected. Additionally, AI tools used by Neuberger may produce inaccurate, misleading or incomplete responses that could lead to errors in Neuberger's and its employees' judgement, decision-making, investment research or other business activities, which could have a negative impact on the performance of a client account. The application of AI in investment processes, research, or analysis is evolving and subject to limitations, including data quality, algorithmic biases, and interpretive errors. AI outputs should not be relied upon as the sole basis for investment decisions. No assurance is given regarding the accuracy, completeness, or timeliness of information generated by AI.   This material is being issued on a limited basis through various global subsidiaries and affiliates of Neuberger Berman Group LLC. Please visit www.nb.com/disclosure-global-communications for the specific entities and jurisdictional limitations and restrictions.   The "Neuberger" name and logo are service marks of Neuberger Berman Group LLC.   © 2026 Neuberger Berman Group LLC. All rights reserved. M-003297  

Is the open model GLM-5.2 really Opus 4.8 level?

What happens when the biggest AI companies in the world borrow hundreds of billions of dollars to build infrastructure before the demand is fully proven?In this episode of Corporate Finance Explained, we unpack the corporate finance behind the AI boom and explore how Amazon, Microsoft, Meta, and Alphabet are funding one of the largest private capital investment cycles in modern history. With projected AI infrastructure spending approaching $700 billion, the real story is not the technology itself. It's the debt, capital structures, and financial risk sitting beneath the headlines.We break down how hyperscalers are using project finance, special purpose vehicles (SPVs), private credit, and long-term power contracts to build massive AI data centers at unprecedented speed. Along the way, we examine the growing debate around GPU depreciation, AI infrastructure economics, and whether today's AI buildout resembles past capital cycles like railroads and telecom networks.

Patrick Moorhead and Daniel Newman return from a packed week of travel, covering HPE Discover 2026 and Pure Accelerate hosted by Everpure. They break down the government-forced shutdown of Anthropic's Mythos 5, the Apple-Intel foundry signal, the xAI-Cursor acquisition, and whether enterprise AI spending is actually contracting or simply concentrating. Episode 309 of The Six Five Pod covers the week's events, market moves, and the structural questions that follow. The handpicked topics for this week are: Anthropic Mythos 5 Forced Shutdown: The U.S. government issued a 90-minute compliance window and a worldwide kill switch on Anthropic's Mythos 5 and Claude Fable 5 models, forcing them offline across all geographies. Patrick and Daniel examine what this means beyond the immediate headlines: model access has entered the same geopolitical variable set as semiconductor export controls, and every enterprise CIO now has a new on-premises infrastructure argument on the table. The shutdown also surfaced an unexpected counterpoint from the cybersecurity community, which argued that Mythos 5, operating in a defensive capacity, was itself a protection layer against the use of adversarial models. Anthropic's decision to revoke access globally rather than implement citizenship-based authentication reflected both the 90-minute timeline and the practical impossibility of real-time identity verification at scale. (The Decode) HPE Discover 2026: The Agentic Infrastructure Story: Six Five Media spent multiple days at HPE Discover in Las Vegas, live-streaming coverage that drew more than 30,000 viewers across the event. Patrick and Daniel break down HPE's most complete agentic stack story to date, covering its networking-led compute approach, expanded NVIDIA and Broadcom silicon partnerships, autonomous networking through Marvis, and Juniper's integration into the AMD Helios interconnect as a path into hyperscale deals HPE previously lacked access to. (The Decode) Pure Accelerate 2026 and the Everpure Data Primacy Pitch: At Pure Accelerate, Everpure made its clearest case yet for a data intelligence layer designed to reduce token costs in enterprise AI workflows by operating across any storage vendor, any enterprise application, and without being hard-coded into the underlying array. Patrick and Daniel assess the value proposition and the proof burden separately: the concept is differentiated, particularly against Snowflake and Databricks, in that Everpure does not require its own storage hardware, but the company still needs to demonstrate ROI at scale and earn permission to compete in a market where data platform players have already established category positioning. (The Decode) Apple and Intel: The 18AP Signal and What It Sets Up for 14A: The announcement that Apple will manufacture chips with Intel sent Intel's stock up roughly 10%. The hosts parse what that deal likely looks like in practice: 18AP as a test drive for lower-risk logic-layer parts, with the more consequential milestone being a potential M7 SoC on Intel's 18AP process. The underlying driver is the TSMC capacity constraint, with Samsung logic deals picking up across the industry for the same reason. The real inflection point that Patrick notes is 14A: if Intel's backside power delivery process reaches risk production and scales to iPhone volume by 2028, the strategic weight of the Apple relationship will fully materialize. (The Decode) xAI Acquires Cursor for $60 Billion: Elon Musk's xAI acquired Cursor for $60 billion using equity inflated by SpaceX's IPO run-up, a move Patrick characterizes as buying market position in a category where xAI arrived late, having missed the window on thinking models and tool calling. Cursor brought $4 billion in ARR, 7 million monthly active users, and 50% Fortune 500 penetration into the deal. The open question remains whether xAI can convert that installed base into a durable enterprise AI stack or whether it remains primarily a GPU capacity provider selling at well above neo cloud market rates, with the Google-SpaceX deal drawing additional scrutiny as a related-party transaction preceding the IPO. (The Decode) The Flip: Is Enterprise AI Spending Contracting or Concentrating? Patrick takes the position that enterprise AI is entering a rationing phase, pointing to Accenture's bookings decline, Microsoft cutting developer access to cloud code, Uber blowing through cloud licenses, and the emergence of AI cost management as a venture category as converging proof points. Daniel argues the opposing case: dollar volume is growing even as project counts fall, hyperscaler CapEx guidance continues to accelerate across Microsoft, Google, Amazon, and Meta, and what reads as contraction is the market moving from subsidized pilots to production deployments tied to measurable P&L outcomes. Both agree the hard ROI era is arriving, and the real debate is whether that transition reads as discipline or deceleration on the way in. (The Flip) Fed Chair Kevin Warsh's First Meeting: New Fed Chair Kevin Warsh held rates steady in a unanimous decision but delivered remarks that the market viewed as hawkish, sending the S&P lower and two-year yields up 16 basis points before a partial recovery the following day. Patrick and Daniel note the structural signal beneath the reaction: Warsh is establishing the Fed's independence from political pressure while also signaling an intent to move away from survey-based data that arrives three to six months stale, in favor of more real-time economic inputs. Daniel draws a direct line to the kind of forward-looking data infrastructure that firms like Palantir, Databricks, and Snowflake are positioned to provide at the institutional level. (Bulls and Bears) Iran-Israel-U.S. Developments and Oil Below $80: A Memorandum of Understanding between Iran, Israel, and the U.S. briefly sent oil below $80 and signaled a potential opening of the Strait of Hormuz, though by the time of recording, reports were already emerging that the situation may be reversing. Patrick and Daniel keep it brief: the market has largely looked through the geopolitical noise, rallying through the period of conflict, and the oil price signal matters more to the macro environment than the diplomatic specifics. (Bulls and Bears) Accenture Earnings — The Services Layer Faces the Agentic Reckoning: Accenture beat on earnings but missed on revenue. The company reported a bookings decline of 2%, trimmed its 2026 revenue guide by 3-4%, and saw its worst single-day stock reaction in years. Patrick and Daniel use the result as a structural lens rather than a single-quarter data point: agentic AI and enterprise technology vendors are absorbing exactly the work that large professional services firms have historically owned, and the market is beginning to price that displacement ahead of the labor data catching up. Patrick flags this as the canary in the coal mine for the global services industry broadly. (Bulls and Bears) SpaceX IPO Volatility and Valuation Reality: The SpaceX IPO debuted at $135, surged above $210 on its first day of trading, and finished the week around $181. At its peak, the company briefly surpassed the market capitalizations of both Amazon and Microsoft before pulling back. Patrick and Daniel unpack the gap between the premium investors are assigning to Elon Musk and the company's underlying fundamentals. Despite generating roughly $50 billion in annual revenue, SpaceX remains unprofitable, and upcoming lock-up expirations could introduce meaningful volatility, particularly on the downside. Patrick points to long-term comparisons with Amazon and Tesla, while noting that many retail investors are still near break-even. The discussion explores how much of SpaceX's valuation is based on future potential versus current performance—and how much room remains for investor expectations to reset before fundamentals catch up. (Bulls and Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode  US Government Forces Anthropic to Disable Claude Fable 5 + Mythos 5 Worldwide — First-Ever Federal Shutdown of a Commercial Frontier AI Model; 90-Minute Compliance; EU + UK Sovereign-AI Talks Accelerate https://www.anthropic.com/news/fable-mythos-access  HPE Discover 2026 — Neri Bets the Company on Networking as the AI Control Plane; Juniper Integration Operational; Vultr Standardizes on HPE + NVIDIA https://www.crn.com/news/networking/2026/hpe-ceo-antonio-neri-five-boldest-statements-from-hpe-discover-2026 Everpure - Pure//Accelerate 2026 — First Conference Under New Name; "Data Primacy" Vision; Data Stream Built on NVIDIA AI Data Platform; Data Intelligence GA https://www.prnewswire.com/news-releases/everpure-unveils-data-primacy-architecture-for-the-ai-era-302803097.html  Apple's Chip Supply Chain Realigns in One Week — Intel 18A-P Enters Risk Production June 16; White House Confirms Apple-Intel Foundry Deal June 18 (INTC +9% to Record $135); Cook Says iPhone/Mac/iPad Price Hikes "Unavoidable" on RAM Crunch https://www.investing.com/analysis/appleintel-chip-manufacturing-deal-reshapes-foundry-race-200682398 SpaceX Buys Cursor for $60B All-Stock Four Days After IPO — Largest Developer-Tooling Acquisition Ever; Cursor at $4B ARR / 50%+ Fortune 500; Musk's xAI Loses the Code War, Buys the Winner https://www.cnbc.com/technology/ The Flip Are enterprise AI budgets contracting — is the procurement boom ending and the rationing phase beginning? FOR: Yes — Accenture cut its guide and bookings declined today; Uber blew through AI budget in months; Meta killed its leaderboard. https://www.businesswire.com/news/home/20260618029271/en/Accenture-Reports-Third-Quarter-Fiscal-2026-Results AGAINST: No — AI infrastructure capex is accelerating; enterprise demand is supply-constrained, not budget-constrained. https://ca.investing.com/news/stock-market-news/stifel-raises-jabil-stock-price-target-to-460-on-ai-growth-93CH-4698089 Bulls & Bears MACRO — FOMC Chair Kevin Warsh's Inaugural Meeting: Unanimous Hold at 3.5–3.75%, Statement Stripped of Cutting Bias; Dot Plot Flips to a 2026 HIKE at 3.8% Median; Warsh Refuses Own Dot; Worst Fed Day for a New Chair Since 1994 https://www.cnbc.com/2026/06/17/fed-meeting-today-live-updates.html  MACRO — Oil Cracks Below $80: Brent $78 (3-Month Low), WTI $75; US-Iran 14-Point MoU Signed at Versailles; Strait of Hormuz Reopening; IEA Projects 5.05 Mbpd Supply Glut in 2027 https://finance.yahoo.com/economy/policy/articles/oil-plunge-below-80-already-174253019.html Accenture (ACN) Q3 FY26 ACTUALS — EPS $3.80 Beats $3.70 (+9% YoY); Revenue $18.72B Slight Miss; Bookings DECLINE −2% to $19.3B; FY26 Guide Trimmed to 3–4% Local; Stock −13.3% Open; $9B Cybersecurity Acquisition Push https://www.businesswire.com/news/home/20260618029271/en/Accenture-Reports-Third-Quarter-Fiscal-2026-Results  SpaceX (SPCX) Post-IPO Trading Action — Melt-Up to $225.64 Tuesday Intraday Briefly Surpasses Amazon at $2.85T; Round-Trips to $192 by Wednesday Close on Fed Hawkish Pivot; Morningstar Fair Value $62 (~69% Implied Downside) https://www.cnbc.com/2026/06/15/evercore-isi-says-landmark-spacex-ipo-could-reignite-bull-market-send-sp-500-to-9000.html  

Brad's out of town this week, so Will welcomes Expedition: Handheld and The Full Nerd's Adam Patrick Murray to run down the current state of the handheld gaming console market. We talk about Intel's new GPU-first handheld processor, the current state of x86 emulation on ARM handhelds, the pros and cons of the Analog Pocket, and a bunch more!  Make sure you check out Adam's work on Expedition: Handheld and The Full Nerd! Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Gary Shapiro has spent decades at the center of the global consumer technology industry, leading the Consumer Technology Association (CTA) and building CES into one of the most important stages for innovation, policy, and deal-making on the planet.In this first episode of 2026, Gary joins Charlie, Rony, and Ted to preview CES, unpack the explosion of AI across every category, and deliver unusually blunt takes on tariffs, China, manufacturing, and U.S. innovation policy. He explains how CES has evolved from a TV-and-gadgets show into a global platform where boards meet, standards are set, and policymakers, chip designers, robotics firms, and health-tech startups all collide.In the News: Before Gary joins, the hosts break down Nvidia's $20 billion “not-a-deal” with Singapore's Groq, the stake in Intel, and what that combo might signal about the edge of the GPU bubble and the shift toward inference compute, x86, and U.S. industrial policy. They also dig into Netflix's acquisition of Ready Player Me and what it suggests about a Netflix metaverse and location-based entertainment strategy, plus Starlink's rapid growth and an onslaught of “AI everything” products ahead of CES.Gary walks through new features at this year's show: CES Foundry at the Fontainebleau for AI and quantum, expanded tracks on manufacturing, wearables, women's health, and accessibility, plus an AI-powered show app already fielding thousands of questions (top query: where to pick up badges).He also talks candidly about his biggest concern—that fragmented state-level AI regulation (1,200+ state bills in 2025) will crush startups while big players shrug—and why he believes federal standards via NIST are the only realistic path. The discussion ranges from AI-driven healthcare and precision agriculture to robotics, demographics, labor culture, global supply chains, and what CES might look like in 2056.5 Key Takeaways from Gary:AI is now the spine of CES. CES 2026 centers on AI as infrastructure: CES Foundry at the Fontainebleau for AI + quantum, AI training tracks for strategy, implementation, agentic AI, and AI-driven marketing, and an AI-powered app helping attendees navigate the show.Fragmented state AI laws are an existential risk for startups. Over 1,200 state AI bills in 2025—including proposals to criminalize agentic AI counseling—could create a compliance maze only large incumbents can survive, which is why Gary argues for federal standards via NIST.Wearables are becoming systems, not gadgets. Oura rings, wrist devices, body sensors, and subdermal glucose monitors are starting to be designed as interoperable families of devices, with partnerships emerging to combine data into unified health services.Robotics is breaking out of the industrial niche. CES will showcase the largest robotics presence yet, moving beyond factory arms and drones to humanoids, logistics, social companions, and applied AI systems across sectors.Tariffs, alliances, and AI will reshape manufacturing. Gary is skeptical of “Fortress USA” strategies that try to onshore everything, pointing instead to allied reshoring (Latin America, Europe, Japan, South Korea) and the long-term role of AI-powered robotics in changing labor economics and global supply chains.This episode is brought to you by Zappar, creators of Mattercraft—the leading visual development environment for building immersive 3D web experiences for mobile headsets and desktop. Mattercraft combines the power of a game engine with the flexibility of the web, and now features an AI assistant that helps you design, code, and debug in real time, right in your browser. Whether you're a developer, designer, or just getting started, start building smarter at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

Last Friday, the U.S. Department of Commerce forced Anthropic to shut down Fable V for the entire world. One government, one decision, zero global access. Is this the definitive case for decentralized AI?Jake Brukhman (Coin Fund), Jesus Rodriguez (Sentora), and Haseeb Qureshi (Dragonfly) debate the hottest topic at the intersection of crypto and AI: whether frontier AI can and should be decentralized — or whether we're repeating the same mistakes as decentralized storage.What you'll hear: why the government hand-picked who gets access to Mythos (and it wasn't Anthropic's call), whether consumer GPU swarms can realistically compete with data centers, what's really happening with on-chain hacks in 2026, and Haseeb's most controversial take: the world's most powerful AI should be treated like a nuclear weapon, not a public good.No easy answers. No consensus. Just the most important debate of 2026.

By Doug Green “Running a business, running a network, is really about making good decisions. And to make good decisions, you have to base that on good data.” In this episode of the Technology Reseller News podcast, Doug Green speaks with Jezzibell Gillmore, General Manager and Vice President, Service Provider at Kentik, about how AI workloads, rising data volumes and infrastructure complexity are creating new operational challenges for service providers. Gillmore describes Kentik as a network intelligence company that uses NetFlow, SNMP, synthetic testing, streaming telemetry and data enrichment to provide actionable insights for organizations that rely on networks to run their businesses. As networks generate more data than humans can easily interpret, Kentik helps service providers understand what traffic means, where it is coming from, where it is going, and how it affects customers, performance and profitability. The conversation focuses on the growing infrastructure demands associated with AI. Gillmore says the industry is preparing for a significant rise in AI-driven traffic, particularly east-west traffic between servers and data centers. While the full impact has not yet arrived, service providers are already seeing signs of what may be ahead as GPU deployments, data center power demands and high-capacity interconnect requirements continue to grow. Gillmore notes that service providers will face pressure not only from higher traffic volumes, but also from the physical realities of network expansion. Adding capacity is not always as simple as turning up another wavelength. Providers may need to plan new fiber routes, obtain permits, expand conduit capacity and manage the long timelines associated with physical infrastructure. The podcast also explores where service providers are likely to encounter operational blind spots. Gillmore says resiliency is moving from a “good to have” to a mission-critical requirement. At the same time, traditional observability tools were built for an earlier era and may not provide enough visibility into encrypted traffic, hybrid cloud, east-west AI traffic, GPU-to-GPU telemetry and increasingly complex routing environments. For Gillmore, the shift is from passive observability to actionable network intelligence. Traditional tools may show what happened over the last 30 days, but AI-era networks require near real-time insight that can help operators make better decisions immediately. She also points to a growing skills challenge. Many of the engineers who helped build the internet are retiring, while newer engineers may be strong in automation and code but have less deep operational experience. Machine-assisted insight can help bridge that gap by giving teams clearer guidance and better context. For service providers, the message is clear: AI-driven demand will require better visibility, stronger resiliency and more intelligent operations. Gillmore says providers should begin by identifying gaps in their networks and evaluating how network intelligence can improve efficiency, customer experience and business value. Learn more at kentik.com  

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

Many people said GPU compute would become a commodity. The opposite happened — and a new category of "neoclouds" is now racing to build the physical backbone of the AI boom. Stephen Balaban, co-founder and CTO of Lambda, explains why the conventional wisdom was exactly wrong, why we're still massively underbuilding compute, and what it actually takes to stand up a gigawatt-scale AI factory: land, power, cooling, networking, and a financing stack most people have never heard of. We go deep on the physics of how energy becomes tokens, NVIDIA's real moat, why a 2023 GPU can lease for more today than the day it shipped, and Stephen's provocative vision of "neural software." Plus the wild Lambda origin story — from a facial recognition startup to a camera in a baseball cap to a near-billion-dollar cloud business. This is the state of AI compute in 2026, from inside one of the companies building it.(00:00) — Cold open(01:21) — Why GPU compute was never a commodity(02:45) — The H100 price index and what it gets wrong(04:02) — The real moat: technology or financing?(05:57) — Winner-take-all, or room for many neoclouds?(06:48) — Are we overbuilding or underbuilding AI compute?(09:26) — What if AI gets 10x more compute-efficient?(10:44) — The real bottleneck: land, power, and shell(11:38) — The backlash against data centers — and the misinformation(15:00) — Opening the hood: from photons to tokens(17:11) — Extracting more value from the same chip(19:26) — Frontier inference and distributed training, explained(23:26) — What actually drives compute cost(25:21) — Lambda's chip stack and the NVIDIA relationship(26:17) — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat(28:59) — Networking, storage, and the one-click cluster(34:46) — Renting vs. owning, and full vertical integration(36:24) — How global is Lambda? Does location still matter?(38:44) — The financing stack: off-take agreements, SPVs, and credit(41:16) — Why a 2023 GPU leases for more today(42:36) — A futures market for compute?(43:54) — Origin story: facial recognition, Perceptio, and Apple(47:03) — The Lambda hat and Dream Scope(48:59) — The $60K bet that became a cloud business(52:00) — Holding the team together through the hard times(54:30) — Bringing on a new CEO; Stephen as CTO(57:33) — Matching xAI on high-velocity deployment(59:29) — "AI won't write software — it will become the software"(01:01:30) — Neural software vs. vibe coding(01:04:25) — Do agents change the compute layer?(01:06:14) — Self-assembling software inside Lambda(01:08:18) — Gigawatt-scale AI factories(01:08:57) — One person, one GPU(01:12:04) — Hot takes: overrated and underrated in AI

This Open Source Startup Podcast episode has our co-hosts Robby and Tim in conversation with Dr. Felipe Huici, CEO of Unikraft - the compute layer for sandboxes, AI agents, or any workload with VM-grade isolation. Their open source, also called unikraft, has 4K stars on GitHub and provides a next-generation cloud native kernel. This episode explores how Unikraft is building infrastructure for the next generation of AI agents, arguing that agents should run in virtual machines rather than containers. The conversation focuses on the unique requirements of agentic workloads: fast startup times, the ability to pause and resume state, strong isolation, and efficient resource utilization at massive scale. Unikraft's technology enables lightweight virtual machines that can start in under 10 milliseconds, helping companies reduce latency, lower infrastructure costs, and run large numbers of ephemeral agents on minimal hardware. The discussion also covers emerging AI infrastructure needs such as checkpointing, branching, headless browser automation, and GPU access.The podcast also traces Unikraft's origins from an academic research project to an open-source Linux Foundation initiative and, eventually, a startup founded in 2022. The conversation examines customer adoption, the role of Unikraft as foundational infrastructure for AI platforms, competition and collaboration within the agent ecosystem, the future of GPUs and virtualization, and lessons learned from building a company in the rapidly evolving cloud and AI infrastructure market.

NuNet is building a decentralised compute and orchestration network where people can contribute spare CPU, GPU, RAM and other resources, while developers and organisations can deploy workloads across available infrastructure. In this episode, Peter talks with Jennifer from NuNet about the new NuNet Appliance and why it matters for making decentralised compute more practical for everyday users.The conversation covers how NuNet matches the right compute to the right job, how the Appliance lowers the barrier to onboarding devices, and why use cases like n8n automations, private AI agents, edge AI, Cardano SPO infrastructure and web deployment workflows are a natural fit for the network. Jennifer also explains NuNet's zero-trust security model, pricing approach, organisations, ensembles, deployment templates, and how NTX fits into orchestration fees.If you have spare compute, want to run private AI workloads, or are building in the DePIN and Cardano ecosystem, this episode gives a practical look at how NuNet is moving from concept to usable infrastructure.Key Takeaways:- NuNet is a decentralised compute and orchestration platform that lets people contribute spare compute and lets workloads find suitable resources automatically.- The NuNet Appliance is designed to make onboarding CPUs, GPUs, RAM and other compute resources much easier for non-expert users.- NuNet can support broad workloads, including n8n automation, private AI agents, Qwen-based LLM deployments, edge AI, web builds and Cardano SPO infrastructure.- The network uses a zero-trust model where machines are cryptographically identified and verified at each interaction.- Compute pricing is designed around stable currency values, with automatic conversion into NTX rather than forcing users to price workloads directly in a volatile token.- NuNet organisations can let other DePIN projects bring their own communities and native tokens while still using NuNet's orchestration layer.- Ensembles and templates are intended to simplify deployments so users do not need to manually understand every YAML configuration detail.- NuNet is open source, with docs, GitLab, Discord, Medium and X available for people who want to try the network or contribute.Links & References:- NuNet — Compute Orchestration for a Decentralized World: https://link.learncardano.io/eGKGuZ- What is NuNet? | NuNet Documentation: https://link.learncardano.io/rHu2E4- x.com: https://link.learncardano.io/NIhPKR- https://link.learncardano.io/Tlu7wNWebsite: https://link.learncardano.io/bQ68RcX/Twitter: https://link.learncardano.io/3a1QtvDisclaimer: This content is for educational purposes only. Nothing constitutes financial advice.DISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

Technieuws Fable 5 (Antrophic) komt uit - én weer niet eigenlijk. GPS heeft een geheime boodschap Europol rolt een een netwerk van illegale streamers op Iemand bouwde een eigen FM-radiostation met een Pi Zero en shellscripts, te vinden op Github Ik denk erover GPU's gaan te verhuren Deep dive Wat is het FTI festival?

Anthropic pulled the plug on its Mythos / Fable 5 model after the U.S. government raised concerns, and IREN has completed its acquisition of Nostrum for 490 MW of capacity in Spain. Welcome back to The Blockspace Podcast! Anthropic and Uncle Sam are trading blows again, with the frontier LLM company pulling its recently released Mythos / Fable 5 model after whistleblowers said the model's guardrails were bypassed. Lygos Finance's CEO Jay Patel joins us for his reaction to the news and the market rally with a reported, imminent peace deal coming for the Iran War this week. For other news, we cover IREN's closing its acquisition of Nostrum, which will give it a 490 MW foothold in Spain for AI data center development, and the EPA's stance that it won't regulate AI data centers. Check out Dimetrics, the AI industry's Bloomberg terminal. Track financial metrics and news for AI stocks, GPU rental prices, state-by-state data center pushback, and more with the compute industry's most powerful dashboard. Subscribe to our newsletter to receive updates for all of our shows and content.

美鳳姐天天喝的【補體素優蛋白EX】✅222增肌*關鍵：20g蛋白質、2倍**BCAA及維生素D✅義大利摩洛血橙：促進新陳代謝忙碌也能輕鬆補給，趁少年要保養

The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. Hosted by Sonya Huang, Sequoia Capital

Peter ringer fra Goldmans hedgefondsymposium i Cannes, med en 220 meter lang yacht utenfor vinduet som egentlig er et cruiseskip.Det blir bildet på uka: SpaceX noteres til rundt 500 ganger omsetningen på 4,7 milliarder dollar, og en kryptobørs ved navn Hyperliquid hadde priset selskapet riktig før det i det hele tatt åpnet. Peter forklarer hvorfor en slik pris ankrer alt annet, og hvorfor det eneste vi egentlig vet, er prisen.Pluss: jakten på skjulte signaler i data, der snøfall på Røros kan henge sammen med minnebrikker i Taiwan, og hva du faktisk får for én time GPU-compute til 60 dollar. Og en avtale mellom Trump og Iran som fortsatt ikke er signert.00:00 Goldman i Cannes og yachten som egentlig er et cruiseskip00:13 SpaceX-noteringen og Hyperliquid: børsen som priset den før åpning00:17 SpaceX til 500 ganger omsetningen, og prisen som ankrer alt annet00:27 Det eneste vi vet er prisen: forward P/E forklart00:32 Trump, Iran og avtalen som ikke er signert00:38 Ukens tall: Nikkei, DRAM og Europas dyreste strøm00:42 Snøfall på Røros og minnebrikker i Taiwan: jakten på signalet00:56 425 000 dollar eller 60 i timen: hva én time med compute gir deg01:03 Å styre systemer du ikke selv forstår: avmystifisering av finans01:12 PE-likviditetspremien, Charlie Biellos SpaceX-undersøkelse og ukens sitat Hosted on Acast. See acast.com/privacy for more information.

Cosa succede quando il problema non è più trovare lo storage, ma trovare le GPU? E quando i dati diventano così grandi e strategici da condizionare dove e come possono girare le applicazioni?In questa puntata di Cloud Champions Andrea Saltarello incontra Alessandro Dellavedova, Senior Sales Engineer di Qumulo, per una conversazione che parte dal suo percorso professionale e arriva a uno dei temi meno raccontati del cloud: il ruolo dello storage come abilitatore di flessibilità.Si parla di ricerca oncologica, dati non strutturati, cloud ibrido, scarsità di risorse, FinOps e sovranità del dato. Attraverso casi concreti e aneddoti dal campo emerge una prospettiva diversa da quella abituale: non lo storage come semplice repository, ma come strumento per spostare workload, ridurre vincoli architetturali e adattarsi a un mercato in continua evoluzione.Una puntata dedicata a chi vuole capire cosa succede quando infrastruttura, dati e cloud devono confrontarsi con problemi molto reali.

SpaceX debuted today after a $75B IPO raise, closing with a $2.11 trillion market cap, and Anthropic is searching for 1 GW to host its own GPU clusters. Welcome back to The Blockspace Podcast! SpaceX's historic IPO came and went today, marking a day of firsts that saw the company close the largest IPO ever at a $2 trillion valuation, making its founder Elon Musk the world's first trillionaire. Nakamoto's Brandon Bailey joins us to discuss the IPO and the current state of the AI stock market and bitcoin, plus his project Dimetrics, a Bloomberg-esque terminal for the data center space. In other big news, Anthropic has reportedly entered into 12 letters of intent to rent 1 GW+ of data center space for its first-ever self-owned GPU clusters.  Check out our latest report, “What's a Megawatt Worth?” where we quantify the trillion dollar opportunity for bitcoin miners venturing into the AI sector.  Subscribe to our newsletter to receive updates for all of our shows and content.

IPO price. $135Retail. Process. Allocation. ConfirmationGavin Baker on 4th largest cloud ahead of Oracle. Jensen likes to give GPu's to people that can use themBrad Gerstner on how “smart” people lose money. Price target lower by $20.

There are two great forces reshaping the world of energy today. The AI boom and the wave of investment in new data centres have sent power producers scrambling for generation capacity to meet soaring electricity demand. At the same time, the severe disruption to shipping traffic through the Strait of Hormuz has put security of supply at the top of every importer's agenda. In this special episode, recorded at Wood Mackenzie's Gas, LNG and the Future of Energy Conference in London, host Ed Crooks speaks with three guests about what these twin pressures mean for gas. They discuss demand for gas for power, the sources of supply that could provide energy security in volatile times, and plans for tackling the increased greenhouse gas emissions that could result from increased consumption.First, Ed sits down with Neal Kalita, senior director of global energy management at NTT Global Data Centers, one of the world's largest data center developers. Neal explains why "speed to power" is a priority, and why gas plays such a key role in providing the reliable 24/7 firm capacity hyperscaler clients require.Relying on gas as a key component of the power generation mix means managing a complex set of issues around supply security, demand management and long-term investment. Neal explains how NTT thinks about commodity risk, the trade-offs involved in power supply agreements, and why on-site gas generation may be not just a bridge solution but long-term infrastructure for the electricity system. He highlights the key drivers that are changing the data centre industry, including rising GPU power density, AI-driven volatility in load, and climate-related grid reliability concerns. He also discusses NTT's participation in a demand response programme run by Voltus, which helped stabilise the grid when Winter Storm Fern hit Virginia in January.Next, Ed hears from Keith Shoemaker, Chief Commercial Officer at Coastal Bend, which is developing a new LNG liquefaction project at Corpus Christi, Texas. Coastal Bend is aiming to have the first project in the US to integrate carbon capture and sequestration into its design. Combined with the procurement of upstream gas with low methane leakage and flaring, that should make for the lowest carbon-intensity LNG in the world, Keith says. Crucially, the project can match competitor prices without charging a green premium. The US 45Q tax credit will cover the operational spending (Opex) for the transport and sequestration of the carbon, and costs will be kept down by using brownfield maritime infrastructure that is already in place. Regulation will still be essential in creating a market for lower-emissions LNG. Keith sets out an idea for making that work in the EU: linking the new Methane Emissions Regulation with the Carbon Border Adjustment Mechanism to create an "avoided carbon" currency that LNG importers could use to offset CBAM fees on other products such as cement, steel and fertiliser. That way, the methane regulation would change from a stick to a carrot for the LNG industry.Kristy Kramer, Head of LNG at Wood Mackenzie, closes the episode by assessing how the three trends of AI demand, energy security and decarbonisation fit together. She discusses the big question: has the conflict on the Middle East changed the world completely, forever. It may play out like the Covid pandemic. Huge changes were predicted, and although there were some permanent impacts, in other areas the world has gone back to the way it was before. Politics will change from week to week, or even from hour to hour, but geology and economics don't, and over time the fundamentals will reassert themselves. Kristy and Ed reflect on what that means for the future of energy. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we debrief several industry events I went to last year, including Supercomputing, KubeCon, Stack, the AI Infrastructure Show, and the Red Hat AI Infrastructure Summit. We dive deep into some observations from the shows and what they tell us about the gaps and fractures in how we are working to build AI infrastructure. We focus on how observability is being used for evaluation, tuning, performance issues, GPU dropouts, and cluster management, while anomaly detection and root cause analysis remain less common, and we note that networking is still underserved. We also get into the shift from building clusters to observing and fixing them after deployment, especially for agentic systems, and we end by highlighting the need for observability across application, identity, networking, and infrastructure layers. Transcript: https://otter.ai/u/y6FNvERJRe_8qnmAgVlmvd6kwb8?utm_source=copy_url

PHP Podcast – June 11, 2026 Guest Hosts: Sara Golemon, Elizabeth Barron & Holly Schilling Eric and John are out this week — Sara, Elizabeth, and Holly take over. Here’s what they covered: PHPVerse Recap PHPVerse just wrapped up, and Elizabeth was there in Amsterdam. The format is unusual — all speakers are flown to one location, but the audience is entirely virtual. It was a class act: professional TV crew, studio lighting, and a makeup and hair team on site. Around 2,500–3,000 people watched the live stream. Everything was broadcast as one long block; individual talk segments and possibly the documentary trailer will be cut and released separately. The full stream is available now — the PHP documentary trailer (produced by Jet Breeze, covering 30+ years of PHP history) appears around the 2:24:30 mark. PHP Foundation 2026 Strategy Document Elizabeth and the PHP Foundation released their 2026 strategy document the same day as this recording. The foundation gathered community input across numerous conversations and conferences, synthesized it into findings, and has now published a plan for the rest of the year. Key themes: repositioning PHP’s public perception (which Elizabeth calls a solvable problem), creating six special interest groups, and launching an Onboarding Initiative to build a real on-ramp for new PHP developers. Elizabeth’s view is that the two things giving her the most hope for PHP’s future are the passion and expertise of the community, and how good the language itself has gotten. Visit thephp.foundation to read the full document. The Onboarding Initiative One of the six special interest groups the foundation is launching is specifically focused on bringing new developers into PHP. Goals include creating a true learning path (not just a reference manual that assumes existing knowledge), improving educational resources, and potentially working with the php.net website to improve the first-time experience. Holly made the point that PHP’s barrier to entry is genuinely lower than almost any other language — the Hello World program is 11 characters — but that story isn’t being told outside the PHP bubble. New developers are turning to JavaScript as a first language and running into minified spaghetti instead of something approachable. AI Writing PHP — And PHP as a Second Language Holly built the entire PHP Tek conference app backend in Laravel without writing a single line of code herself — AI-generated throughout, which she reviewed and approved. The code held up to peer review at the conference with only minor style nits. She ran it on PHP 8.3 and used modern standards throughout (one piece of feedback: stop using empty()). The consensus: AI models write good modern PHP because of the vast amount of open source PHP they were trained on. The caveat Sara raised is worth thinking about — how much of that training data is PHP 4-era code and WordPress 3 repositories? Either way, Holly’s case for PHP as a second language is strong: low ceremony, low boilerplate, readable syntax, and it’s a language where you can do something useful in minutes. PHP’s Reputation Problem (and Why It’s Fixable) The group dug into PHP’s perception gap — the mismatch between how good the language actually is and how it’s perceived outside the community. Holly’s experience as a mobile developer who recommends PHP to others: the pushback is immediate (“isn’t that slow?”, “isn’t that dead?”). The benchmarks don’t support that reputation — PHP outperforms Python on most comparable workloads — but data alone doesn’t shift perception. Elizabeth’s point is that this is primarily a storytelling and coordination problem, not a language problem, and that the foundation’s repositioning work is exactly aimed at closing that gap. The community has the passion. It just needs to tell the story outside its own bubble. PHP Polling API RFC Sara walked through the RFC for a new Polling API in PHP (wiki.php.net/rfc/poll_API). The short version: PHP currently has five or six different ways to do I/O multiplexing (watching multiple streams and acting on whichever one is ready first), and which one works depends on the OS, available extensions, and PHP version. The Polling API proposal creates a single, unified interface that abstracts all of that. The immediate beneficiaries are async frameworks like Amp PHP, ReactPHP, and Revolt, which currently have to maintain multiple backend implementations to cover different environments. The bigger picture: this is a building block on the path toward true async PHP, likely contributing to something more complete in PHP 9.0. Most app developers won’t use it directly — but the libraries they depend on will. RFCs are all listed at wiki.php.net/rfc. PHP.net: Do As We Say, Not As We Do Sara, who has contributed to php.net, copped to the state of the codebase: some of it dates to the PHP 3 era, there are functions.inc files, and it is very much “do as we say, not as we do.” The historical reason is that php.net used to rely on community-administered mirrors (r-synced servers running everything from PHP 5.1 to 5.6 simultaneously), so modernizing the code was impossible without controlling the runtime. That’s changed with CDN-based load balancing — they can now control what PHP version runs on php.net — and the code has been getting better. But it’s a slow process. PHP Podcasts Past, Present, and Future Holly asked about the PHP Town Hall podcast (Ben Edmonds and Phil Sturgeon), and the group did a quick tour of PHP podcast history. The PHP Roundtable — originally started by Sammy, taken over by Eric — has produced about three episodes. Sara and producer Joe are planning to take it off Eric’s hands and actually do it properly. And Elizabeth announced that the PHP Foundation is launching a new podcast: tentatively called PHP at Scale, hosted by Ben Marx, focused on telling the stories of organizations pushing PHP to its limits. No launch date yet, but there’s already a queue of interested guests. Next Week’s Show — Moved to Wednesday Sara will be on a boat off the coast of Galicia on Thursday, so next week’s episode is moving to Wednesday. Guests will include Paul Reinheimer and (hopefully) Sean Coase — two veterans from PHP’s podcasting past. Elizabeth is going to try to make it work around the Canadian Grand Prix. Mac Mini M4 for Local LLMs Holly picked up a refurbished Mac Mini M4 (16GB RAM, 512GB storage) specifically to run LLM models locally via Ollama. Apple Silicon is a solid choice for this because the unified memory architecture gives the neural cores access to far more RAM than a discrete GPU setup. Sara is waiting for the M5, which is reportedly not coming until fall — and is already resigned to spending too much on it when it lands. Links from the show: PHP Foundation — 2026 Strategy Document PHP RFC: Polling API PHP RFC Wiki — All RFCs Under Discussion Amp PHP — Async framework ReactPHP — Event-driven async PHP Revolt — Event loop for PHP php.net website source code (github.com/php/web-php) PHP Architect Discord Guest Hosts: Sara Golemon Based in Lisbon, Portugal PHP core contributor; code contributor via the Curl project (which means she technically has code on Mars) Elizabeth Barron Executive Director, PHP Foundation Based in Germany Holly Schilling Primary mobile developer; built the PHP Tek 2026 conference app Based near Chicago, IL Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – Joe and the team are available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ PHPScore Put Your Technical Debt on Autopay with PHPScore Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Note: Next week’s show is on Wednesday (not Thursday) with guests Paul Reinheimer and Sean Coase. Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post The PHP Podcast 2026.06.11 appeared first on PHP Architect.

A Computex 2026 trouxe uma série de anúncios importantes para o mercado de tecnologia, mas poucos chamaram tanta atenção quanto o RTX Spark, a nova plataforma da NVIDIA voltada para computação acelerada por IA em dispositivos locais. Neste episódio do Diocast, discutimos o que exatamente é o RTX Spark, quais problemas ele pretende resolver e como ele se posiciona em um mercado que já conta com soluções como Snapdragon X Elite, Ryzen AI e os novos processadores Intel com aceleração dedicada para inteligência artificial.Mais do que simplesmente lançar um novo chip, a NVIDIA parece estar ampliando sua presença para além das placas de vídeo tradicionais. O RTX Spark combina CPU baseada em arquitetura ARM, GPU com tecnologias derivadas do ecossistema RTX e recursos dedicados para cargas de trabalho envolvendo inteligência artificial. Na prática, isso pode abrir espaço para computadores mais eficientes, capazes de executar modelos de IA localmente, reduzindo a dependência de serviços em nuvem e melhorando aspectos como privacidade, latência e disponibilidade.---https://diolinux.com.br/podcast/lancamento-da-nvidia-rtx-spark.html

This Week In Startups is made possible by:NetSuite - Netsuite.com/TWiSTDeel - Deel.com/TWiSTSquarespace - Squarespace.com/TWiSTTwo days before SpaceX launches the largest IPO in history at a flat $135/share, our VC roundtable drops a scorcher: The top 1% of seed deals might actually be underpriced. Plus: the "Sequoia scam" dual-tranche controversy, tokens-for-equity deals, and whether Claude Fable 5 is a true step function.Tomasz Tunguz (Theory Ventures), Michael Downing (Castalia Capital), and Paige Doherty (Behind Genius Ventures) join Alex to go deep on Seed investing, startup economics, AI spend, and the impact of smarter AI on the founder journey.Guest Links:Tomasz Tunguz: https://x.com/ttunguzTheory Ventures: https://theoryvc.com/Michael Downing: https://www.linkedin.com/in/michaeldowning/Castalia Capital: https://castalia.capital/Paige Doherty: https://x.com/paigefinnnBehind Genius Ventures: https://www.behindgeniusventures.comShow Links:Anthropic's IPO announcement: https://www.anthropic.com/news/confidential-draft-s1-secOpenAI's IPO announcement: https://openai.com/index/openai-submits-confidential-s-1/Bending Spoons F-1 filing: https://www.sec.gov/Archives/edgar/data/2004711/000110465926071170/tm2613674-7_f1.htmSpaceX IPO filing: https://www.sec.gov/Archives/edgar/data/1181412/000162828026040364/spaceexplorationtechnologib.htmBrendan Foody's post on Sequoia: https://x.com/BrendanFoody/status/2063470286515683759Claude Fable 5: https://www.anthropic.com/news/claude-fable-5-mythos-5OpenRouter data on Chinese models: https://openrouter.ai/rankings?view=daySaronic: https://www.saronic.com/MotherDuck: https://motherduck.com/Nox Metals: https://noxmetals.co/Timestamps:0:00 Tomasz Tunguz, Michael Downing & Paige Doherty join2:07 The SpaceX IPO and the IPO window4:22 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!6:30 The new bar: 10x growth (not 3x) to raise a great Series A8:46 Net-new AI budgets9:46 Squarespace: Turn your idea into a beautiful website! Go to https://www.squarespace.com/twist for a free trial. When you're ready to launch, use offer code TWIST to save 10% off your first purchase of a website or domain.11:09 How some founders are outgrowing venture capital11:44 The power pendulum swings back to founders12:46 SpaceX vs. OpenAI vs. Anthropic: Which IPO is most enticing?19:53 Deel - Founders scale faster on Deel. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit https://deel.com/twist to learn more.26:07 Tokens-for-equity, GPU-hours-for-equity & the financialization of compute28:35 Founders airing VC dirty laundry (napping VCs included)29:56 Netsuite - The business landscape is very chaotic right now. That's why you need NetSuite, by Oracle. Get the free business guide Demystifying AI at https://Netsuite.com/TWiST36:38 Claude Fable 5 first impressions: pricing, benchmarks & orchestration45:42 Where value accrues: application layer vs. models vs. private data1:00:06 Nationalization of AI labs: Bernie Sanders, Sam Altman & Trump agree?!1:01:25 Portfolio spotlights: Saronic, MotherDuck, and Nox MetalsSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisGreat TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanis

The Information's OpenAI reporter Erin Woo details the confidential IPO filings of OpenAI and Anthropic, highlighting how Anthropic has eclipsed OpenAI in enterprise revenue. Apple reporter Aaron Tilley and Constellation Research CEO Ray Wang break down Apple's WWDC announcements, evaluating its Siri reboot powered by Google's Gemini models and Nvidia GPUs. Finally, AI finance reporter Dakin Campbell joins to discuss how Wall Street titans Goldman Sachs and JPMorgan are exploring derivatives markets to trade the cost of GPU computing power.Articles discussed on this episode: https://www.theinformation.com/briefings/openai-confidentially-files-ipo-paperwork-plans-separate-employee-share-salehttps://www.theinformation.com/newsletters/the-briefing/apples-cautious-ai-overhaul-openais-ipo-filinghttps://www.theinformation.com/articles/goldman-jpmorgan-explore-new-ways-tame-ai-lending-risksSubscribe: 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/

Networking can be an invisible part of IT infrastructure, but AI is creating demands that make it a critical part of keeping AI application fed with data. Mike Fratto returns to the podcast to discuss both the long haul and local requirements for AI networking with host Eric Hanselman. It's always been important to link chunks of infrastructure efficiently, but AI's voracious need for data has dramatically increased the scope and scale of the need. The risk that any gap in performance or capacity presents is that precious GPU resources will be idled, an increasingly expensive proposition. The realities of AI application architectures is that infrastructure is ever more hybrid, requiring access to repositories of data both on-premises and in various clouds and models scattered across various providers. The need for dynamic connectivity is driven by the rapid evolution of preferences for new models and the diversifying needs of agents to reach new data sources. It's not only forcing network expansion, but it's also driving M&A activity as network providers look to enhance automation in response to customer demands. More S&P Global Content: Compute sovereignty: The strategic importance of digital infrastructure AI won't solve its own energy problem – and that might be fine AI in action: unleashing agentic potential AI infrastructure results in 2025 top expectations, forecast upgraded For S&P Global subscribers: MWC 2026: Agentic AI as the next operating model for networks and network operations AI Infrastructure Market Monitor & Forecast Service providers race to meet surging enterprise demand for AI infrastructure In 2026, the telecom network becomes code Credits: Host/Author: Eric Hanselman Guest: Mike Fratto Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith, Dylan Scheible

DDN CEO Alex Bouzari discusses the need to maximize return on investment in AI, calling it “ROI maxxing.” He explains how DDN's partnership with Nvidia (NVDA) helps optimize GPU usage, reduce idle infrastructure, and improve enterprise returns. Bouzari emphasizes efficiency as the key to sustainable AI investment.======== 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

Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this presentation from Ultimate Partner Live, industry analyst Jay McBain breaks down the monumental macroeconomic shifts rewriting the tech sector in 2026. https://youtu.be/r0qTDyw97Gs As the industry rapidly approaches a $6.07 trillion valuation, driven by massive AI infrastructure investments from Sam Altman and the “Magnificent Seven,” traditional sales and channel models are fundamentally collapsing. McBain reveals how buyer demographics have transformed to an integration-first millennial base, why marketplace ecosystems now command over half of all partner-funded deals, and how a tiny elite of just 1,000 tech service providers control two-thirds of global tech revenue. Learn the exact mechanics behind how Microsoft out-partnered AWS to win 26 straight quarters of dominant growth and how your business can deploy an algorithmic early warning system to capture massive wallet share before competitors even step into the boardroom. Key Takeaways Over half of the Fortune 500 companies vanish every 20 years because their leadership fails to anticipate macroeconomic technological cycles. The true opportunity in the $6.5 trillion AI boom lies not in single vendor products, but in the hardware, software, services, and telecom ecosystem surrounding them. Indirect tech sales are undergoing a structural shift toward direct cloud hyperscaler models driven heavily by Nvidia's core infrastructure client base. Modern business deals are won or lost months before the point of sale based on the average of 6.3 partners surrounding a customer’s environment. Over 51% of tech buyers are now millennials who prioritize software integration capabilities and digital marketplaces over traditional human sales interactions. Tech service economics are pivoting aggressively away from upfront margins toward point-based multi-partner funding across subscription cycles. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Nvidia AI buildout, $7 trillion AI opportunity, cloud ecosystem decade, Microsoft vs AWS growth, multi-partner cloud deals, digital marketplace migration, millennial B2B buyers, B2B tech subscription economics, tokenized micro consumption, tech services wallet share, hybrid cloud infrastructure, 28 customer moments, IT services industry growth, telecom spend breakdown, channel chief strategy, managed service providers MSP, global systems integrators GSI, software integration first, point-based vendor incentives, automated co-selling workflows Transcript JAY McBAIN AUDIO PODCAST [00:00:00] Jay McBain: So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book, but chapter one is always you Blame the CEO. [00:00:13] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. With that, I am incredibly blessed to invite a friend of mine to the stage. I have a quick little side note, like I found an old LinkedIn post from this gentleman from like many years ago, like 20 years ago. [00:00:39] Vince Menzione: And I wasn’t really that nice to you on that LinkedIn post. Like, oh, like this is before Jay became the Jay, that we all know Jay to be j. But he was in the space and I was at Microsoft doing something and he reached out about something. It was kind of rude, Jay. I was like, oh my gosh. I can’t believe. But Jay has been a great friend. [00:00:54] Vince Menzione: When we started the podcast back up, uh, during COVID we started doing podcasts together. When we moved to the studio, Jay was the first person in the studio. He’s always got a spot, uh, at our events. He’s s Spot Art, and, and he’s a great friend and supporter of Ultimate Partner Jay McBain. For those of you who don’t know him, Jay, welcome. [00:01:13] Vince Menzione: Thank you, sir. [00:01:22] Jay McBain: 31 days ago, we landed Artemis two. The furthest humans have ever been away from the planet Earth 57 years ago. We landed on the moon in the 56 years. Between those two moments, the tech industry has been the fastest growing industry in the world. Every single year we moved from the space race to the technology race, and we’re just getting started. [00:01:46] Jay McBain: If you’re old enough, you’ll recognize the mainframe and mini era for 20 years. You’ll recognize a young disheveled Bill Gates showing up in Boca Raton, Florida for, uh, August the 12th, 1981 launch, where Bill thought that every one of us would’ve a PC in our home, and IBM thought they were gonna sell 10,000 of them to hobbyists. [00:02:12] Jay McBain: 1999, a small startup from an executive who just left Oracle in San Francisco named Mark Benioff. A couple of years later, Jeff Bezos went into a boardroom and said, listen, we’ve spent a lot of money building infrastructure to our busiest day, Christmas, black Friday. You’re telling me this stuff sits idle 10 or 20% for the rest of the year. [00:02:35] Jay McBain: Why don’t we rent that out to others? Got laughed outta that boardroom and then got made of fun of on magazine covers. Maybe you should just tend the store, let the adults talk about technology. In March of 2023, our neighbors, our friends, our family saw DeepFakes. They saw poetry, they saw music, and they came to us as tech people and said, did we just light up Skynet? [00:03:03] Jay McBain: Now every one of these 20 year eras, this is the Taylor Swift version of our industry. Every single one of these eras triggers the fastest growing product in history. Today it’s actually Chacha bt first to a billion users. It triggers a new, richest person in the world, bill Gates, to Jeff Bezos. Now, Elon Musk is the first to sign a trillion dollar pay package, and it’s not for car. [00:03:27] Jay McBain: It’s not for cars. It also triggers a most valuable company in the world change. And today that’s nvidia. These are monumental changes in our industry and they’re monumental changes in partnering every single time. And it also links to our customers. If you take a 20 year view of business, one era, and, and think about the AI era, you know, at the start of it here, if you’re to grab the Fortune 500 magazine from 20 years ago and start to flip through it, 53% of the companies in there no longer exist. [00:04:06] Jay McBain: Every 20 year cycle, we lose over half of the biggest companies in the world. These are the companies that have very deep pockets to buy their way outta problems. If you’re not in the Fortune 571% of tech companies don’t make it 10 years. These are the changes that cost industries. There are changes that cost really big companies and the decisions we make, the trends we’re in right now, in 2026 will be written about in the future. [00:04:39] Jay McBain: This new era, a lot of big numbers being thrown around. Vince’s best friend talk about a six and a half trillion dollar AI opportunity, but it’s not Microsoft’s tam. Microsoft is chasing about a trillion dollars of this. And the ecosystem, the hardware, the software, the services, the telecom is gonna make up the rest. [00:05:04] Jay McBain: It is an ecosystem. Every time these big numbers are thrown, the word ecosystem is always thrown around it. Not to be outdone, Sam Altman’s talking about a $7 trillion build out. The world economy this year, the world GDP will be 126. These are material numbers to world GDP, but even better, they’re both larger than our entire industry is today. [00:05:27] Jay McBain: So what took 56 years of the fastest growing industry this year will be $6.07 trillion. Big numbers, but it’s easier to think about it in terms of a dollar that our customers spend in that dollar. They’re gonna spend 25 cents on hardware. They’re gonna spend 25 cents on software. So for anyone that read the memo 15 years ago, that software’s gonna eat the world, there’s still a dollar a hardware to run every dollar of that software. [00:05:57] Jay McBain: And whether you’re thinking humanoid robots or whichever future you’re envisioning, there’s going to be a dollar of hardware to run every dollar of software for the next 20 years. There’s over 25 cents now in IT services, and in many cases, these services are growing faster than the product categories and just under 25 cents in telecom, that’s how it breaks out today. [00:06:19] Jay McBain: And this industry, which took 56 years to get to this point, is gonna double in size in the next three to five years. We already have two and a half trillion of that seven raised and being spent. Part of the reason Nvidia is the most valuable company in the world. Now our industry, uh, you talk about ultimate partnerships. [00:06:40] Jay McBain: Our industry traditionally, and world trade by the way, is 75% indirect. The dealerships, the agencies, the brokers, the resellers, the retailers, the franchisees, the gas stations, the grocery stores, the pharmacies, all 27 industries sell indirect. You gotta think back the last time you bought something direct. [00:07:01] Jay McBain: Well, I bought a Dell from that dude in the nineties. Cool. Well, Dell Technologies is now 60% indirect. Well, I bought insurance. Direct is 15 minutes. Could save me 15%. Well, Geico last year sold more insurance through agencies and brokers than they did direct. This is the world now. We used to be 75% indirect four years ago. [00:07:26] Jay McBain: Then it went to 73.2, then it went to 70.1 and it then it went to 66.7. By the way, marketplace is in these numbers indirect. It’s not marketplace causing this change. It’s one company, Nvidia. Nvidia has seven customers. The magnificent seven, uh, half of them are in the room right now that every morning we wake up to a hundred billion dollars press release about this $7 trillion buildout. [00:07:56] Jay McBain: What’s interesting is indirect sales in our industry is growing by revenue. It increases every year, just not at the pace that this AI build out is happening direct with seven companies. But the reason we’re all here, and I think the core reason that Vince is building this community is this, you know, Microsoft forever has measured and been very vocal. [00:08:21] Jay McBain: About 96% of their deals have partners in them. Kind of who cares, who collects the money. We care about the moments, the 28 moments before the customer makes a purchase. We care about every 30 days forever, because two thirds of our industry, over $4 trillion now is subscription consumption based. Winning a customer today is only winning the first 30 days. [00:08:46] Jay McBain: We care about this cycle. We care about who surrounds our customer. So six years ago, I stood on a big stage and said, you know, we went through a decade of sales. You know, in 1999, you thought you were born to be a salesperson. You’re managing your territory with your gut. Well, a few years later, you were introduced to the science of selling. [00:09:07] Jay McBain: You know, 10 years later you thought as a marketer, you sit around a cocktail party joking with your friends, 50% of my marketing dollars are wasted. I just don’t know which 50%. Really funny. In 2009 until every 58-year-old CMO got replaced by a 38-year-old growth hacker. Coming in with Marketo and Eloqua and Pardot and HubSpot, and 15,505 as of yesterday, MarTech and iTech tools, ninjas in marketing, they wouldn’t let a nickel go through without measuring. [00:09:43] Jay McBain: Now we understand 96% of deals and partners that surround it. No deal is gonna be won or lost in this era without partnering effectively. So we had to have this decade of the ecosystem. One of the ways we’re tracking is by outsiders. You know, Salesforce every year publishes the state of sales and they’ve got, you know, the number one CRM in the world. [00:10:05] Jay McBain: So they get to go talk to all the CROs, all the salespeople in the world. And as of this year, a couple months ago, 94% of every salesperson in every industry in the world uses partners every single day. You wanna see what this number was six years ago. Also, 89% of salespeople around the world don’t think they’re going to club this year without partners. [00:10:29] Jay McBain: So this is a big moment for us, halfway through the decade ecosystem, but we’re only halfway through. We’re starting to understand now at a more granular level. What partnering means. It’s not theory, it’s not flywheels. It’s not really cute. McKinsey slides that we keep showing to our board saying how important partnering is. [00:10:51] Jay McBain: We’re trying to get to the very specific level of the 6.3 partners on average that surround the deal and what they’re doing. How their business model works, and that’s average if I’m working on a public sector deal. I was at a Red Hat conference yesterday talking sovereignty. If I’m in an enterprise or a large public sector deal, it’s north of 10 partners in the deal. [00:11:15] Jay McBain: So we’re starting to understand what used to be this, this, you know, you’ve been the fastest growing industry for 56 straight years. Every single professional services person in every industry has come in to join the fund. Over 90% of accountants are tech services firms. Over 90% of marketing agencies are tech services agencies. [00:11:36] Jay McBain: All of this 250,000 software companies, a million emerging comp tech companies, the half a million VAR that have been in that traditional channel. The managed service providers, all of these 20 different partner types, millions of companies, tens of millions of people competing for 6.3 spots. Around the customer. [00:11:58] Jay McBain: That’s it. Luckily, there’s 141 million global customers to compete for. There’s, there’s some open slots that you can go find, and that’s the point. Our industry never had our own Fortune 500. We always talk to, you know, these partners and GSIs are doing this and SI are doing that. And we never really had a view of capability and capacity or what our own TAM was inside of that partnering. [00:12:25] Jay McBain: And so we set out and we would’ve loved, you know, chat GPT or Gemini or Claude or any of those tools to do this. But there’s one problem in partnering with AI is that it doesn’t know one partner from the next. There’s a big digital sameness problem in our industry that every single partner, whether it’s Larry in the White van or Accenture, with 786,000 employees all say they do all things to all people all the time. [00:12:53] Jay McBain: 98% of them, 99% of them are private companies that don’t share their p and l. You can’t go into Microsoft’s LinkedIn system and find out how many employees, ’cause it’s a block system, it AI can’t see into it. So it just sees, and it’s a great pattern matching. Google, SEO can’t figure out who’s who, nor today can the large language models. [00:13:14] Jay McBain: ’cause all the things they’re trying to match, the transformers are trying to match. It all looks the same. Every tweet, every ebook, every website, every digital history looks the same. So this took us thousands of people hours across two years to do, to dig into every p and l to dig into every dollar of what they’re doing. [00:13:33] Jay McBain: But what was interesting is only a thousand partners in our industry do two thirds of all tech services. When you get into enterprise, it goes up to 80 to 90%. The partners in the middle, in Blue do more tech services. The 30 of them than the 970 partners in white on the outside, the 970 partners in White do more tech services than the next million combined. [00:14:03] Jay McBain: This is our industry in a nutshell. Every time we talk to a a vendor, every time we talk to a partner, every time we talk to a distributor, we’re now talking names, faces, and places. You you wanna talk sovereignty. Yesterday in Atlanta, 90% of sovereign conversations in public sector in the globe is handled by these companies here. [00:14:26] Jay McBain: Forget about how much you do with these partners today. You wanna chase the next column, which is the wallet share. And I was a channel chief for 17 years. I get the weekly report and I see a million dollar partner, another million dollar partner, sorted top to bottom. You don’t know which partners which, which of those million dollar partners is doing 1.2 million in your category. [00:14:46] Jay McBain: They deserve a baseball cap and a front row seat at your event as an MVP. The next partner right next to them is doing 10 million in your category. They’re only doing a million with you. ’cause customers are pulling them into it. Nine times outta 10. They’re leading with your competitor. So I don’t want that list anymore. [00:15:03] Jay McBain: I want the new list, which is showing me those $9 million opportunities. And I as a board member, as A CEO, as a CFO, as a CRO, I wanna see this list. And then I want to talk people, processes, programs, technology. What are we gonna do to go get our fair share of that 9 million? Where’s our lowest hanging fruit? [00:15:24] Jay McBain: How do we double our pipeline? How do we double the size of our company in three years? It’s all right here. Let’s have very specific conversations and move away from flywheels and move around from force multipliers and and things like that in partnering. Let’s figure out how this partner community is surrounded. [00:15:45] Jay McBain: What do 10 million people who have to be smart in front of their customers every single day, what do they read? Where do they go and who do they follow? It’s the law of a few. This is the old Malcolm Gladwell of tipping point 10 million people in the broader channel. A hundred percent of our TAM comes down to only a thousand watering holes. [00:16:08] Jay McBain: 12% of that entire audience. Doesn’t sound like a lot, but it’s over A million. People love podcasts. Number one way they learn the Joe Rogan effect. In our industry, there’s 121 podcasts. These are all public lists. You can go get on my LinkedIn newsletter on canals, oia. But there’s 121 podcasts that drive him forward. [00:16:28] Jay McBain: Really high up on that list, actually number one on the list is ultimate partner, Vince. That’s how I met. ’cause I asked people, 10 million people, you love this. You walk your dog, you drive to work, you listen to podcasts. I’m not the biggest podcast fan. It’s not number one on my list, but it’s number one on theirs. [00:16:44] Jay McBain: They say, you know, you gotta meet this guy, Vince. It’s unbelievable how great these podcasts are. They’re ultimate. [00:16:54] Jay McBain: Then I talked to Vince and said, but Vince, you know, 35% of your community, the 10 million people love to come to events like this one. The hallway conversations, the hotel lobby bar last night. This is what we love to do, especially post pandemic. It’s the number one way we learn. We learn from our peers, we learn from those around us, and, and the learn from the conversations we have here. [00:17:17] Jay McBain: We always remember these moments, you know, years and years later. There’s 352 choices. I’m going to five of them this week in five different cities. It’s a lot of coverage, but again, it’s a tighter li list of how people work. The magazine lists 106 of them associations like Conter. Now the GTIA peer groups, there’s 15 different spheres of influence, but only a thousand places. [00:17:43] Jay McBain: I could walk you through billionaire, after billionaire, after billionaire in this industry and show you how they did this. How did Arne Bellini at ConnectWise? How did Austin McCord at Datto, how did Nerdio become a unicorn? How did threat locker and huntress move away from 6,500 cyber companies and become unicorns over and over and over again? [00:18:05] Jay McBain: It’s only one slide. Unicorns and billionaires are made here, and a lot of people don’t get it. So walking away from Bellevue, a thousand partners, top down, a thousand watering holes, bottoms up. You’ve covered a hundred percent of your tam. You do it better than 10% of your competitor, 10% better than your competitors. [00:18:27] Jay McBain: You win. You carry that on your resume into the next company. You get a bigger job at a bigger pay scale. Let’s just walk through some examples. Cyber 91.7% of it goes through the channel. Huge channel audience. You know, if you’re in MarTech, it’s only 10%, but this one happens to be all channel, but that’s not the story. [00:18:48] Jay McBain: For every dollar that the 6,500 cyber companies are trying to close, there’s $2 in services. Plot twist, the products are grown at 11, the services are grown at 12.6. Your partners are growing faster than you are, and they will continue to for the next, at least five years, probably 10. So when I’m here, five years from now, you’ll hear in me talk about a three to one split in cyber and then a four to one split in cyber. [00:19:18] Jay McBain: Now, when we’re in Miami a couple days ago is CrowdStrike, they’re talking about a $7 and 5 cent multiplier, chasing that two to one up higher. You look at managed services. Here’s a fun story. Managed services. 82% of customers who are man, uh, outsourcing more this year than last year. 650 billion in size. [00:19:38] Jay McBain: This is bigger than the entire SaaS industry. Salesforce, ServiceNow, Workday, Marketo, NetSuite, HubSpot, 250,000. Others. This is bigger. It’s also bigger than all the Hyperscalers combined, not just AWS, Microsoft and Google, but Alibaba and Oracle and everybody down the list. This is a massive market also growing at double digits. [00:19:59] Jay McBain: So these are some big things and obviously we’re watching, you know, week in and week out, quarter in, quarter out, the Battle of Software and Battle of the Hyperscalers and things like that, and who’s growing at what pace and, and how partnering is connecting to all of this. You know, we watched a moment really early in the pandemic where Microsoft started growing faster than AWS and they haven’t stopped since 26 straight quarters. [00:20:27] Jay McBain: And you ask customers and say, you know, does Microsoft have a better product? And in most cases they say no. You know, AWS had a five year head start. Well, did they have a better price? Well, no, actually most cases Microsoft’s more expensive. Well, did did they have better promotion? Was their Super Bowl ad better? [00:20:44] Jay McBain: No, they’re both kind of crap. So you kind of ask the questions of what’s the only difference that could create growth above the leader in the market? Well, it’s place. More of the 6.3 partners are walking into those keyboard room meetings and drawing clouds up on the wall and labeling the Microsoft than they are AWS. [00:21:03] Jay McBain: Very simple. It’s never been about product. The best product in our industry has never won. And now the best way forward is that partnering moment, and this is the moment. So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book. And it could be the book like Kodak, they invented the product that ended up killing them. [00:21:26] Jay McBain: And it’s a woe is me story, but chapter one is always you blame the CEO. How could they not see those trends happening in 2026? How could they, you know, were they blind? Were they stuck in their own, you know, innovation chamber? Innovator’s dilemma, were they stuck in their own boardrooms? Why couldn’t they see? [00:21:46] Jay McBain: Well, chapter two, you, you blame the board. They have fiduciary responsibility, outsider view, and how could they not see it? But really, this is the future right here. If you take this slide and apply it 10 or 20 years from now to every failure and every success, these are the chapters of the book. Your buyer is now a millennial. [00:22:05] Jay McBain: As of last year, the 51% of our market is bought by people born after 1982. Different psychology, different behavior, different journey, different criteria, their integration. First buyers. The buy a product, 80% as good as the next one. If it works better in their environment. 94% of people won’t buy a car unless it has CarPlay or Android Auto. [00:22:26] Jay McBain: New Buyer. You have to be more integrated than your competitors. That’s a partnering story. The 6.3 partners. If you heard cyber, you need some great channel partnerships, but you need the other 5.3 partners as well, the consultants, the advisors, the designers, the architects, the implementers, the integrators, the manner service, all of the other partners. [00:22:44] Jay McBain: You need to know more of them than your competitors do, and have them label clouds with your name in them. You need better alliances. Even if you compete, you only compete in the morning. You’re best friends by the afternoon. You have to be tight with the hyperscalers, tight, with the big SaaS platforms, tight with cyber, tight with distribution, there are layers, seven layers to every deal. [00:23:04] Jay McBain: You gotta be tight in and have better alliances than your competitors. And then it all comes to the 28 moments, which I’m gonna end on, but the go to market of all of this, the co-selling, co-marketing, co-innovation, co-development, co keeping. This is it. Your product has to be good enough that somebody’s gonna renew it. [00:23:21] Jay McBain: Your Super Bowl has to be, you know, ad has to be good enough that people don’t, you know, shame you on social media. Your pricing has to be somewhere in a country mile of the bell curve of what the customer wants to pay. But successor failure is just here and platforms are synonymous with partnering. [00:23:40] Jay McBain: It’s our role now in the decade of the ecosystem to drive our companies forward. Marketplace. It’s probably the most predict, you know, great prediction we ever made. You know, growing at 82% compounded, it’s hard to predict ’cause it doubles almost every year. We were almost exact to the decimal point. Five years later now till 2030, we’re watching a second story, which is more interesting. [00:24:02] Jay McBain: If 96% of all deals have partners inside of them and there’s private offers and multi-partner offers and distributor sellers record all these funding mechanisms or services as a product. As of last week, over 50% of all deals in marketplaces now have partner funding. It means that while money changes hands differently, the respect and the recognition of what partners do is in the deal. [00:24:26] Jay McBain: We think that’s going to 59, but at some point, that’s gonna have to hit 96. ’cause to run the best programs, whether it’s an indirect sale, whether it’s a direct sale, whether it’s a marketplace deal, it doesn’t matter how money changes hands. What matters is we recognize the 6.3 partners. They’re not only making the deal happen bigger and faster, but renewing and enriching that every 30 days forever. [00:24:48] Jay McBain: When we watch, you know, billion dollar clubs and when we read all the press releases and all the hubbub about how fast this is growing and who, which companies are behind all this. When I’m quoted in some of these press releases, it’s because of this. You know, CrowdStrike, you know, brags are a billion dollars in a single year, but inside of that, they’re showing that 91% growth in marketplaces, which is pretty phenomenal for any company to almost double in size every single year. [00:25:17] Jay McBain: What’s more phenomenal is they’re growing the channel piece of it, 3548%. That green part of it is growing. Companies that understand platform and have people and processes and programs and technology to do it are winning. And they’re getting recognition and partners are starting to join the Billion Dollar Club who don’t sell a product, but are also winning at Extreme Scale. [00:25:44] Jay McBain: So talk about those partner 1000 and who are leaning in to win at this level. As well as everything changes, traditional billing moved into subscription models, moved into consumption models. Now we’re being tokenized to death multi it’s, it’s in this mode of micro consumption. There’s no chance there was little chance in subscription consumption that would be resold. [00:26:09] Jay McBain: You don’t buy Netflix from the cable guy in the white van. There’s zero chance when you’re buying tokens at a buck a piece that that’s going through any indirect sale. This continues to grow. Now the tectonic shifts is what happens when money changes hands differently. These old programs that we used to all write hundreds of different boxes, we checked every day on deal reg and trainings and all the other things are changing. [00:26:35] Jay McBain: To this, you’ll get these slides, by the way, in high res, inside of this now is the customer. For the first time ever, 45 years later, we have the customer in the middle of what we do, the 28 moments in green before they buy the seven layer stack and the partners inside it. The implementation. The integration, the managed services in a cycle that never ends, and two thirds of our industry. [00:26:55] Jay McBain: With the customer in the middle, we can now move money around to the different moments. It’s not all landing in front or backend margins or market development funds or new customer bonuses or spiffs. It’s landing where it needs to land. Over 400 companies now, pretty much led by Microsoft 400 companies are in a point system right now and 400 more. [00:27:18] Jay McBain: We’re working kind of behind the scenes to get that announced in the next 12 months. This is a total changeover in terms of how economics work and partners are yelling over half of us. I don’t care. Don’t call me a VAR anymore. Don’t call me an MSP. Don’t call me a regional system integrator. I do the consulting over half the time. [00:27:36] Jay McBain: I do the design, I do the implementations, I do the managed services, and 44% of us are vibe coding. On weekends. We’re not happy. Just on the services side. We wanna join the seven layer tech stack as well. These are partners growing faster than their vendors by understanding this cycle and where to show up and where the money is in ai. [00:27:56] Jay McBain: And the number one thing they’re asking for is not more leads, which they did for 45 years. The number one thing is now recognized for what I do. I’ve never just been a cash register. We’re completely now past this idea of a channel being a channel of distribution, and now a channel being this platform for the future. [00:28:16] Jay McBain: As we lay that on top of ai, the first couple of years of AI has really been consumer driven. The 95% failure rate that MIT reported last year is now 70%. That’s the failure to get from proof of concept to production. That 70 will be 50 by the summer we’re moving now in business, the maturity rates are going up at the end customer and in 88% of cases, that’s because of the channel. [00:28:43] Jay McBain: They’re working with partners. They’re not vibe coding themselves and working in little skunkwork groups. They’re working with partners to make it happen, and it now becomes the partner’s number one growth opportunity. I can grow at 11 or 12% in cyber every year. Compounded I can grow in 10% in managed services. [00:29:03] Jay McBain: You know, those are great double digit growth ’cause my customers are growing at 2.7% and I can go four x my customer, but I can go 10 x my customer if I have the right services built around ai. And this compounded growth rate and that big number in 2 20 32, 267 is what’s got those top 1000 partners obsessed. [00:29:25] Jay McBain: And your companies are leading with ai. Now you need to connect to those AI services. You need to get partners on this scale of growth. And they will be adding your name inside every cloud. They write on every whiteboard, but 82% of partners around the world, you know, we survey 25,000 of them aren’t ready, and they’re blaming vendors for not being ready, and they’re telling them exactly the workshops and the training that they need to get ready for this cycle. [00:29:53] Jay McBain: 82% of our entire partner, tens of millions of people, aren’t ready to grow at 35% and they need our help. Last thing I’ll say about AI is it’s the first time from client server to cloud, edge to cloud that it’s been segment driven. SMB alone has one, you know, six different segments, one to nine, 10 to 24, 25 to 49, et cetera. [00:30:18] Jay McBain: Mid-market into enterprise. No one that runs a restaurant is calling Jensen to buy a GPU to put next to the stove. No one’s calling Sam or Dario or anyone at Anthropic or OpenAI directly. They’re waiting. If you run a restaurant with all the people running around with tablets, you’ve invested in toast or square or clover or one of the platforms to run your business. [00:30:41] Jay McBain: A hundred different things. And you’re gonna wait for toast to work with a hyperscaler and build out the capabilities genetically. So when they see a spike in Uber Eats orders, they automatically place a food order and automatically change the staffing to deliver on it. That’s what the restaurant’s waiting for, and there’s no one calling and having a big a agent conversation. [00:31:03] Jay McBain: But even if you go into hundreds of people in medium sized business, every one of the vice presidents have their tech stack already built. I talked about the marketing person already, but the HR leader has one, and everybody’s got their seven layer stack. They’re not calling to buy a GPU and they’re not calling to, you know, bring in open AI directly or, or anthropic. [00:31:22] Jay McBain: They’re waiting for the platform they built to integrate together ag agenta capabilities. Everybody’s in wait mode up until enterprise and public, large public sector. So we are looking at this market and at 90% of that AI market is run by those thousand companies, and the rest of the millions of partners are helping in terms of how these businesses are gonna change at that level. [00:31:46] Jay McBain: Here’s where I end. You know, the 28 moments used to be a theory. It used to be a flywheel. How do we buy a car? [00:31:55] Vince Menzione: Well, we Google it, [00:31:57] Jay McBain: 81% of us now, 94% of us use large language models. We find out that there’s 365 brands of car. I’d have to test drive one every day of the year to get through them all. So we start narrowing these things down. [00:32:09] Jay McBain: We configure it. We put our rims on it, we color it. We download the invoice price. We download the backend rebates this month, whether I buy it in May or June, we find out what 5,000 people paid for our exact car within 50 miles of us. And then we don’t wanna go to the dealer because we know more than the salesperson, the manager ever will. [00:32:26] Jay McBain: We know what we’re gonna pay within, you know, dollars or cents. Just carvana the car. Hand me the keys. Let’s just forget the whole eight hour back and forth. I’ll get you a deal thing. I’m smarter than you in technology. Our customers are smarter than us, smarter than salespeople. That’s why 75% of millennials don’t wanna talk to a salesperson. [00:32:48] Jay McBain: They want to end digitally, and by the way, they’re not gonna send a fax after 28 digital moments. They’re gonna end on a digital marketplace. This is all demographics. It’s not hard to see where it’s going, but we’re getting into names, faces, places again. What if every dollar of your tam, the board, the CEO, runs around with their big multi-billion dollar number, they’re chasing? [00:33:09] Jay McBain: What if every single deal looks the exact same? This is a deal with AstraZeneca, A real deal, real customer spending millions of dollars. We know it starts in October, it ends in April. It’s a six month cycle. We see what they read, the MQ ls at the beginning. We see the sales demo moments. We see ISV, but we’ve never had the light blue boxes. [00:33:30] Jay McBain: What if we as a team could overlay the 6.3 partners in this deal? And when you find out a couple things. Here’s where I end. In December, five deals were one, three of them by NTT. The person at NTT probably coaches AstraZeneca’s, you know, kids’ soccer team. They probably have a cottage together at the lake. [00:33:50] Jay McBain: For the last 20 years, if the person at NTT worked at Deloitte, Deloitte would’ve run this deal. But Software One and Yash are both there, so we understand that when they were drawing clouds up on the wall in the boardroom in December, this deal was won and lost there. It was not won and lost at the point of sale. [00:34:09] Jay McBain: So what if you knew more about this and could see every dollar in your tam? You had an early warning system that this was happening. Two things jump out at this now that we’re in Bellevue. AWS was touched twice in this deal, directly in the marketing cycle and the sales cycle. AWS lost this deal. Here’s an example of Microsoft winning a deal with Microsoft never being touched. [00:34:34] Jay McBain: For some reason, NTT who won, who won AWS’s partner of the year a couple years ago led with Microsoft, so did Software one, Microsoft’s biggest reseller in Europe, and as did Yash, they all led with Microsoft and without Microsoft, knowing Microsoft took a multimillion dollar deal away from their competitors by winning in December. [00:34:53] Jay McBain: That’s one. Second. These partners didn’t just show up other than soccer and cottages. They didn’t show up in December. It went closed one in their CRM system. Back in the summer, August, September, we already knew AstraZeneca was in market, spending millions of dollars. We didn’t need them to read an ebook or go to an event to find that out. [00:35:17] Jay McBain: We knew it because it was closed one. They’re spending hundreds of thousands of dollars times five in December to know what to do at the end. This is an early warning system that’s better than any MQL, better than any SQL. And if you could give your company these level of view into their pipeline with an early warning system that I can work with those partners for months before they ever show up at the customer’s boardroom. [00:35:44] Jay McBain: This is it. Talk about 47% winners. This takes you from not only surviving the AI era to being a top five platform winner. Thank you very much. [00:36:01] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.

Olvídate de hacerle preguntas genéricas a ChatGPT; hoy vamos a ver cómo sacarle partido real y práctico a la tecnología para solucionar problemas cotidianos y quitarnos de encima la fatiga de decisión diaria.Seguro que te suena la película: post-its en la nevera, hojas de cálculo que se quedan desactualizadas y el clásico "¿qué cenamos hoy?" que acaba en improvisación o en una compra desorganizada. Para evitar esto, he diseñado un ecosistema de agentes basados en cuatro cajas de herramientas que llamamos MCP (Model Context Protocol). Estos protocolos permiten que la IA no solo responda preguntas, sino que interactúe de forma directa con mis datos y aplicaciones externas.Te explico de forma muy sencilla las piezas que componen este sistema:El RAG Semántico para las recetas: Tengo una base de datos vectorial con unas 1.700 recetas cargadas en PostgreSQL mediante pgvector. La clave es que no busco platos por coincidencia exacta de palabras. Si le digo que quiero "algo rápido y ligero con verdura", el sistema realiza una búsqueda semántica, entiende lo que busco y me propone las mejores opciones. Todo esto se procesa de forma económica mediante OpenRouter sin necesidad de tener una potente GPU en local.Los Skills y SQLite: Los "Skills" definen los procesos exactos que debe seguir el modelo. Le he marcado unas pautas sencillas: platos únicos mediterráneos para comer y cenas ligeras. Toda esta información se gestiona en una base de datos SQLite muy ligera.Lógica difusa en la lista de la compra: El asistente es capaz de agrupar ingredientes similares. Si dos recetas piden tomates en formatos distintos (por ejemplo, "tomates a granel" y "100g de tomates"), la lógica difusa los unifica bajo un mismo concepto para evitar duplicados en la lista de la compra, organizando además los productos por pasillos o secciones (como frutería o carnicería).Typst para exportar a PDF: Para ver el menú en una tablet o imprimirlo para la nevera, utilizo Typst, una alternativa moderna a LaTeX que me genera unos documentos PDF impecables en cuestión de segundos.Además, te cuento cómo puedes montar todo esto en local de manera gratuita con Ollama, y aprovecho para actualizarte sobre mis andanzas de vuelta al "cacharreo" puro en Linux: desde mis experiencias recientes con el editor Helix y "mkdr" (mi renderizador de Markdown para terminal), hasta "podcli", una pequeña utilidad para exprimir los feeds de podcast desde la consola.Espero que disfrutes de este episodio tanto como yo montando todo este tinglado. ¡A cacharrear!Capítulos del episodio:00:00:00 Agentes de IA que de verdad nos facilitan la vida00:01:42 El ejemplo práctico: Automatizar nuestro menú semanal00:03:51 La fatiga de decisión y por qué la disciplina humana falla00:05:38 Mi caja de herramientas: 4 MCPs (Model Context Protocol)00:06:58 Buscando comida con IA: El RAG semántico de 1700 recetas00:08:45 Búsqueda híbrida y embeddings económicos sin usar GPU local00:10:00 Simplificando las comidas: El papel de los "Skills"00:11:58 Organizando la base de datos de manera sencilla con SQLite00:13:31 Lógica difusa: Evitando duplicados en la lista de la compra00:15:23 Creando PDFs bonitos con Typst (la alternativa moderna a LaTeX)00:17:03 Demostración en directo: Generando el menú de la semana00:19:12 Automatización total: Generación automática de menús con Cron00:20:19 Revisión del menú, las recetas y la alternativa local con Ollama00:23:12 De vuelta al "cacharrero" de Linux: Helix, mkdr y Podcli00:24:51 Próximos episodios: Instalación desde cero a producción de Hermes00:25:38 Despedida y cierre del episodioMás información y enlaces en las notas del episodio

Description: Nate hosts a hardware‑heavy patch day as Wendy upgrades her main workstation from a Ryzen 9 3900X to a 5950X, experiments with 3D‑printed retro ITX cases, and shares updates on her MOVA V50 robot vacuum and UniFi travel router. Matt tunes an HP Omen Transcend 14 with OmenCTL and gives an MSI Trident 3 a GPU transplant, while Nate resurrects a retired Dell R740 into a TrueNAS‑powered “Franken‑NAS” built from leftover 16 TB drives and budget SFP+. Show Links: Wendy Ryzen 9 5950X vs Ryzen 9 3900X https://cpu.userbenchmark.com/Compare/AMD-Ryzen-9-5950X-vs-AMD-Ryzen-9-3900X/4086vs4044 3D artist profile (retro ITX cases) https://www.cgtrader.com/designers/sgw32 Retro-style mini ITX PC case https://www.printables.com/model/1225304-retro-style-mini-itx-pc-case ITX llama retro mini ITX case https://www.printables.com/model/1165579-itx-llama-retro-mini-itx-case Amiga-style mini ITX case https://www.printables.com/model/1351873-amiga-style-mini-itx-case MOVA V50 Ultra Complete Robot Vacuum https://us.mova.tech/products/mova-v50-ultra-complete-robot-vacuum UniFi Travel Router https://store.ui.com/us/en/products/utr Matt OmenCTL (HP Omen control utility) https://github.com/yunusemreyl/OmenCtl Sky Break (delisted game archive) https://archive.org/details/sky-break_delisted Nate TrueNAS https://www.truenas.com/ Rockstor https://rockstor.com/

Google acaba de lanzar una herramienta que puede cambiar la forma en la que usamos inteligencia artificial: Google AI Edge Gallery, una app que permite ejecutar modelos Gemma directamente en tu dispositivo, sin depender de la nube. En este episodio pruebo modelos locales en Mac, analizo cómo funcionan con imágenes, texto y audio, comparo el rendimiento usando GPU y CPU, y te muestro algo clave: esta IA puede trabajar offline, de forma privada y gratis. Pero no todo es perfecto. También aparecen limitaciones importantes: respuestas inconsistentes, problemas de contexto, diferencias frente a Gemini en la nube, límites en transcripción de audio y una experiencia que todavía parece estar en desarrollo. La gran pregunta es:¿Estamos viendo el futuro de la inteligencia artificial personal, corriendo directamente en nuestros celulares, iPads y computadoras?¿O todavía es apenas un experimento prometedor? En este video te muestro pruebas reales, resultados concretos y mi opinión sincera sobre lo que Google acaba de poner sobre la mesa.

In this episode of Tank Talks, Matt Cohen and John Ruffolo unpack the latest leaked details around Canada's national AI strategy, including a proposed Canadian Tech Growth Fund that would take direct equity stakes in AI startups and scale-ups. John pushes back on whether creating yet another government-backed fund solves the real problem or simply adds more confusion to an already crowded funding landscape.The conversation then moves into the AI capital arms race, where Anthropic, OpenAI, SpaceX, and Alphabet appear to be racing toward public markets and massive equity raises at the same time. Matt and John unpack Anthropic's reported path toward a late 2026 IPO, Alphabet's massive $80 billion equity raise to fund AI infrastructure, and why even companies with enormous free cash flow may be rushing to secure capital before debt markets tighten further.The episode closes with what Matt calls the “fugazi” layer of the AI boom: complex GPU financing structures, off-balance-sheet debt, SPVs, and Michael Burry's criticism of NVIDIA's xAI-related financing arrangement. From Canada's AI strategy to Alphabet's infrastructure spend to opaque AI financing models, the core question is clear: is this the beginning of a new AI-driven market cycle, or are the biggest players trying to raise capital before the music stops?Canada's New National AI Strategy & Tech Growth Fund (00:52)Matt introduces leaked details of Canada's expected national AI strategy, including a new Canadian Tech Growth Fund that would take direct equity stakes in AI startups and scale-ups, along with additional funding for the AI Compute Access Fund.Direct Investment vs. Backing Canadian VC Funds (05:02)John argues that government capital may be more effective when deployed through BDC, EDC, and Canadian venture funds, rather than direct government selection of startups. The concern is that direct investment could create political complications and distort private capital markets.Anthropic's $65B Raise and Potential 2026 IPO (09:02)The conversation shifts to Anthropic's massive fundraising round, reported $900 billion pre-money valuation, and potential late 2026 IPO path. Matt frames it as part of a broader wave of trillion-dollar AI and space-related public market activity.The IPO Race Between Anthropic, OpenAI, and SpaceX (10:04)Matt and John discuss whether the IPO window is reopening or whether the biggest private companies are rushing to get out before capital markets become less forgiving. John speculates that Anthropic may want to reach public markets before OpenAI captures investor attention.Alphabet's $80B AI Infrastructure Raise (12:18)Matt outlines Alphabet's reported $80 billion equity raise, including a private placement to Berkshire Hathaway, a public offering, and an at-the-market equity program. The raise is positioned as fuel for Alphabet's unprecedented AI infrastructure build-out.The AI Infrastructure Cold War (14:41)Matt argues that hyperscalers like Google are proving that frontier AI economics are fundamentally different from prior technology waves. John compares the AI arms race to baseball owners escalating salaries because no one can afford to fall behind.Michael Burry, NVIDIA, xAI, and “Fugazi” GPU Financing (16:01)Matt breaks down Michael Burry's critique of NVIDIA's GPU financing structure involving Valor, xAI, Apollo, Athene, and an SPV. The arrangement raises questions about revenue recognition, asset ownership, credit risk, and who ultimately carries the liability.The Real Question: What Happens When the Music Stops? (17:55)The episode ends with Matt and John questioning how these layered financing structures will play out as AI CapEx continues to explode. From public markets to SPVs to off-balance-sheet risk, the AI boom is starting to look less like a clean growth story and more like a capital market stress test.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

Chief Fixed Income Strategist Vishy Tirupattur takes a look at how credit markets are adapting to fund the new phase of AI capex.Read more insights from Morgan Stanley.----- Transcript ----- Welcome to Thoughts on the Market. I am Vishy Tirupattur, Morgan Stanley's Chief Fixed Income Strategist. Today – The critical question behind the AI-driven capex cycle that is front and center for markets year to date. How is credit market financing this ecosystem evolving? It's Wednesday June 3rd at 2 pm in New York. When we first discussed the role of credit markets in financing the AI and data center build-out around the middle of last year, the direction of travel was clear. Realizing the transformative potential of AI requires unprecedented levels of capex. What has really surprised us since is the scale and speed of that spending, both of which have exceeded our expectations by a wide margin. The upward revision to capex expectations has been dramatic. A year ago, we projected the combined capex of the five large hyperscalers at roughly $450 billion in both 2026 and 2027. After the first quarter earnings reports, Morgan Stanley's internet equity analysts, led by Brian Nowak, now expect hyperscaler capex of roughly $800 billion in 2026 and $1.2 trillion in 2027. One data point really captures the surge in the underlying demand for compute. According to OpenRouter, the global weekly token usage, which is a key proxy for compute, has risen by roughly 350 percent since early January, increasing from about 6 trillion tokens to 28 trillion tokens. Credit channels for financing this capex have not only been broader and deeper than we anticipated, spanning public and private markets, but have seen remarkable in the structural innovation that is blurring the lines between public and private markets. Over $200bn of public AI-related issuance across the different credit channels has happened just in the first five months of this year. We had previously assumed unsecured issuance would be limited by the scale of the largest non-financial issuers, confined to investment grade credit only, and largely USD denominated. Instead, some hyperscaler issuance has now far exceeded even the largest telecom names; funding has expanded well beyond USD into EUR, GBP, CHF, JPY and CAD markets. The issuer base has also broadened to include data center REITs and neoclouds, particularly in the high-yield market. The scope of financing has also widened beyond the data center shells themselves. GPU financing, which we assumed would be funded entirely through equity capital, has begun to migrate into credit markets. Funding is now coming through broadly syndicated loans and asset based financing, with ABS structures not far behind. Structural innovation illustrates how rapidly the credit ecosystem is adapting to the complexities of demands of AI-driven capex. Financings that combine elements of project finance, tranching, and residual value guarantees, along with high-yield issuance backed by hyperscaler guaranteed leases – these are innovations that we have never seen before. These structures have expanded the investor base, reduced the funding frictions, and further blurred traditional boundaries – between both corporate and project finance, and public and private credit markets. At the same time, physical, operational, and political constraints are beginning to shape the pace and the composition of the AI infrastructure build-out – and, by extension, the demand for financing. Grid access, power generation equipment, skilled labor, and permitting delays are emerging as significant constraints. These are compounded by political and regulatory frictions at the local, national, and international level. As power availability becomes a gating factor, the AI build-out is likely to pull energy infrastructure financing more tightly into the orbit of AI infrastructure financing. The clear takeaway is this. The capex requirements underpinning AI infrastructure are expanding exponentially, and with them the role of credit markets in financing this build-out. Along the way, there will be winners and losers, periods of adjustment, and a range of physical, financial, and political constraints that shape outcomes on the margin. But the broader trajectory is certain. The scale, duration, and strategic importance of AI infrastructure investment mean that financing of this will remain a defining theme for credit markets and credit investors for years to come. Thanks for listening. If you enjoy the podcast, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Dave Airlie, a Distinguished Engineer at Red Hat, speaks with host Gregory M. Kapfhammer about Linux kernel maintenance. After over-viewing the scale and structure of the Linux kernel, they dive deep into the review and validation of kernel patches, drawing on examples from the GPU subsystem. After discussing the features and benefits of the Linux kernel's maintenance model, they also explore kernel maintenance best practices and the supporting tools for these practices. Dave and Gregory also discuss topics such as the integration of Rust code in the Linux kernel and the ways in which AI-driven code review are influencing kernel maintenance.

SUMMARY: After the first successful AI IPO of 2026, we dig into what makes the Cerebras WSE architecture unique in the market for fast inference. GUEST: Andy Hock, at Chief Strategy Officer at Cerebras AISHOW: 1033SHOW TRANSCRIPT: The Enterprise AI Show #1033 TranscriptSHOW VIDEO: https://youtu.be/ed2nVbOtZiASHOW SPONSORS:OutShift - “Scaling Out Superintelligence”  The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:OpenAI announces 750MW partnership with CerebrasCerebras and AWS partnershipCerebras announces IPOTopic 1 - Welcome to the show. Tell us about your background, and what you focus on today. Topic 2 - For anyone that's not familiar with Cerebras, give us an overview of the company, and especially an overview on the Cerebras technologies (e.g. Wafer-Scale Engine).Topic 3 - Cerebras' WSE architecture is different from many of the GPU or GPU-like architectures in the market today. Centralized vs. distributed architectures always have their tradeoffs. Walk us through the technical and economic value of the Cerebras architecture.Topic 4 - Congratulations on the recent IPO (raised $5.55B). Let's use that as a point in time vs the previous planned IPO. How has the market changed in that timeframe, and how has the Cerebras position changed? Topic 5 - Cerebras (today) offer both WSE hardware, and Cerebras Cloud (API) - very different GTM paths. Can we expect both of those to stay top priorities, or have the market dynamics shifted such that the priorities shift more towards the WSE business - as we're seeing OpenAI, AWS and other engagements announced?Topic 6 - Is Cerebras a training and inference company, or are the economics of inference significantly different enough that it needs to be the sole focus of the company (for now)? Topic 7 - How much effort is it for any company to add support for the Cerebras chips if they have previously been using other architectures?Topic 8 - An IPO is a major milestone for any company, but the markets will now look for your future story. How do you see the AI market evolving over the next 2-5 years, and what are some things that people aren't understanding yet about how it will evolve?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

There is growing demand for time with GPUs, the chips that power artificial intelligence. AI companies need those chips in order to keep their models up and running. And to do that, they can reserve time with a GPU. Now, there's interest from Wall Street in creating a futures market for this AI compute time, essentially treating it like a commodity. Marketplace's Stephanie Hughes spoke with Liz Hoffman, business and finance editor at Semafor and host of the “Compound Interest” podcast, who recently wrote about this.

There is growing demand for time with GPUs, the chips that power artificial intelligence. AI companies need those chips in order to keep their models up and running. And to do that, they can reserve time with a GPU. Now, there's interest from Wall Street in creating a futures market for this AI compute time, essentially treating it like a commodity. Marketplace's Stephanie Hughes spoke with Liz Hoffman, business and finance editor at Semafor and host of the “Compound Interest” podcast, who recently wrote about this.

The energy transition conversation focuses on what connects to the grid. Far less attention goes to whether anyone is coordinating what those assets do once connected. AI training runs swing hundreds of megawatts in seconds as GPUs checkpoint and restart a profile that looks like a generator tripping offline. At distribution level, millions of inverter-based resources create localised variability that overwhelms individual circuits even when aggregate models look healthy. The planning tools in use today were designed for neither problem.Host Bridget van Dorsten is joined by Kay Aikin, CEO and Founder of Dynamic Grid, energy engineer, grid architecture advisor to the DOE-supported GridWise Architecture Council, and contributor to the UN Environmental Program's building decarbonisation work. Kay unpacks what an AI training facility actually does to the grid with full GPU load for hours or days, then a drop to ten percent in seconds during checkpointing. She talks about how at the scale now planned, the Stargate project in Texas alone could represent ten percent of ERCOT disappearing in four seconds. The behaviour is stochastic and cannot be modelled with traditional statistical tools. At distribution level, virtual power plants responding to wholesale signals without circuit-level visibility can create competing oscillations, the kind of emergent dynamics that contributed to the Spanish grid failure.The proposed fix is an AI controller at the substation, sending price-based signals and flexible operating envelopes to large assets and VPP operators, giving them twenty-four-hour forecasts and real-time circuit visibility. Total cost: under a hundred thousand dollars installed. The reason it isn't everywhere is cost-of-service regulation. Utilities earn returns on deployed capital, so a million-dollar transformer replacement is more profitable than software that eliminates the need for it.Without new approaches, rebuilding the US distribution grid could cost up to ten trillion dollars by 2040. Kay is developing grid utilisation metrics with regulators in Maine, Virginia, and Maryland to incentivise extracting more from existing infrastructure. The episode closes on the need for distribution system operators and the affordability death spiral that looms if the structural incentives don't shift. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI infrastructure is breaking the old data center model.In this episode of Liftoff with Keith, I sit down with Lukas Gentele, CEO & Co-Founder of vCluster Labs, to unpack what it really takes to operate GPU infrastructure at scale in 2026.As AI workloads explode and neoclouds race to meet demand, Lukas and his team are building the operational backbone for modern AI clouds — from managed Kubernetes and tenant isolation to automated node provisioning and GPU lifecycle management.We discuss:Why traditional data center assumptions are collapsing under AI pressureWhat's fundamentally changed since the VMware eraHow an early partnership with CoreWeave shaped vCluster's trajectoryAnd the one mistake AI cloud operators are making right now that could hurt them over the next 18 monthsIf you care about AI infrastructure, GPU economics, hyperscaler strategy, or building category-defining platforms — this conversation is essential.Sponsor Info: We are strategic business advisors with decades of leadership experience and a proven track record of driving businesses' growth. We specialize in creating custom-tailored strategies to introduce your company, drive growth, build leadership teams, and ensure companies implement appropriate compensation programs. Our mission is to utilize our expansive network to benefit your company https://www.compass-strategic-advisors.com/ Connect with Lukas Gentele: Website: https://www.vcluster.com/ LinkedIn: https://www.linkedin.com/in/gentele/ Subscribe for more founder insights and hit the bell for notifications! Follow us on our channels for exclusive startup content and behind-the-scenes insights from interviews like this one. Spotify: https://open.spotify.com/show/3cFpLXfYvcUsxvsT9MwyAD?si=f5a14e779777487d Apple Podcasts: https://podcasts.apple.com/ca/podcast/liftoff-with-keith-newman/id1560219589 Substack: https://keithnewman.substack.com/ Newman Media Studios: https://newmanmediastudios.com/ LinkedIn: https://www.linkedin.com/company/liftoffwithkeith For sponsorship inquiries, please contact: sponsorships@wherewithstudio.comFrom the Host: A special shout-out to our Great Host of the Ignite Studios: https://www.ignitegtm.com/ and Producers of AI Infra5 @Plug and Play World, HQ in Sunnyvale, CALiftoff is sponsored by a strategic consulting firm and the M&A specialists at Compass Strategic Advisors - https://www.compass-strategic-advisors.com/ and The GTM Firm - https://www.thegtmfirm.com/

Scott Hanselman talks with Omar Shorbaji from the Anyscale engineering team about how Anyscale on Azure scales Python AI workloads from a single notebook to thousands of CPUs and GPUs. Built on Ray, the most widely adopted AI compute engine, Anyscale gives you a unified runtime to build, train, and serve, running directly on Azure Kubernetes Service without the complexity of managing Kubernetes. See a live demo that fine-tunes a vision-language-action robotics policy, with the metrics you need to push GPU utilization higher. Chapters 00:00 - Introduction 00:52 - Ray and the Anyscale platform 03:11 - Start of demo: Workspaces 04:38 - Running a job and viewing utilization metrics 05:24 - Choosing the right scale 06:53 - Abstracting Kubernetes on AKS 08:53 - Wrap up and where to learn more Recommended resources Learn Docs Anyscale on Azure Connect Scott Hanselman | Twitter/X: @SHanselman Anyscale | Twitter/X: @anyscalecompute Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

I'm excited to work with Microsoft once again as the presenting sponsors of the AI Engineer World's Fair! We'll streaming live from MS Build today for a special crossover pod with our friends at No Priors and the one and only Satya Nadella. However we did not hold back with this interview - we asked all the burning questions about uptime and Copilot that we know you have in your minds. Lets go!For almost two decades, GitHub has been the home of software, where both open source and closed flow, through commits, pull requests, reviews, actions, etc.This ecosystem flourished as open-source maintainers and contributors would continue shipping code for the benefit of the community. However as coding agents began to ship mass quantities of code - growing 1400% in 2026, it marked a new era that was both extremely exciting and challenging for GitHub.While these agents help more people ship more projects, they also significantly increase the floor of how much code is shipped, how often it is shipped, how many people commit code, and basically orders of magnitude multiples in every dimension of GitHub infrastructure:Now GitHub inevitably experiences more pressure on their infrastructure which was originally designed around human developers moving at human speed. This has resulted in a very publicly notable uptime story:So it begs the question of whether current systems around code can absorb what AI produces. Can CI/CD keep up when every idea becomes a build? Can open source maintainers survive floods of AI-generated slop contributions? Can GitHub preserve the human social contract of software while becoming the operating layer for agents?Which brings us to the perfect person to answer these questions: GitHub COO Kyle Daigle. In this episode, he joins swyx to unpack what happens when AI doesn't just autocomplete code, but starts changing how companies operate, how open source works, how pull requests get reviewed, and how GitHub itself has to scale. We go deep on GitHub's internal AI workflows: micro-skills, WorkIQ, MCP, Slack, Teams, email, Copilot workflows, the new Copilot desktop app, CLI, cloud agents, and how Kyle uses agents to look backwards across company context before deciding what to do next. Kyle also reflects on GitHub's history building webhooks, APIs, Actions, npm, Dependabot, and Semmle, why the AI era is breaking GitHub in new ways, how Actions became a general-purpose compute layer, and what Copilot becomes after code completion.Full Video PodWe discuss:* Kyle's expanded role across GitHub* How AI got Kyle coding again after years in leadership* Why GitHub rolls out AI through existing workflows instead of forcing new tools* WorkIQ, MCP, Slack, Teams, email, and GitHub as company context* Why massive “mega-skills” are giving way to small, atomic micro-skills* How AI changes summarization, communications, marketing, and analyst work* Why former developers in leadership may have a unique advantage in the AI era* Kyle's “15 agents on Saturday” workflow* How Kyle built an AI-generated executive presentation for CRO/CFO teams* Why AI changes the chief of staff role without removing the human work* GitHub Actions, webhooks, arbitrary code execution, and secure agent compute* The npm acquisition, supply-chain security, 2FA, and token invalidation* Slop forks, vendoring, and whether AI agents change dependency management* What pull requests become when most PRs come from agents* Prompt requests, vouching, AI review, and trust in open source* What counts as a “developer” when AI lowers the barrier to building* GitHub Spark, low-code, and why GitHub refuses to hide the code* 14x commit growth, Actions load, databases, monorepos, and availability* Copilot's evolution from completion to CLI, desktop app, cloud agents, and SDK* Context, memory, rules, and making GitHub “act like Kyle wants it to act”* Ambient AI, OpenClaw, enterprise security, and the new operating system for agents* What swyx should ask Satya Nadella about Microsoft's AI futureKyle Daigle* LinkedIn: https://www.linkedin.com/in/kyledaigle* X: https://x.com/kdaigleTimestamps00:00:00 Introduction00:03:36 Why AI Got Kyle Coding Again00:07:04 Running GitHub with AI: WorkIQ, MCP, Slack, Teams, and Skills00:15:39 The Golden Age for Former Developers in Leadership00:17:31 15 Agents on Saturday and AI-Generated Executive Work00:20:20 How AI Changes the Chief of Staff Role00:21:45 GitHub's History: Actions, npm, Webhooks, and Open Source00:28:45 Slop Forks, Vendoring, and AI Dependency Management00:33:57 Pull Requests, Prompt Requests, and Trust in Agent-Generated Code00:41:21 GitHub Stars, 200M+ Developers, and the New AI Builder Wave00:45:15 GitHub Spark, Low-Code, and Why GitHub Still Shows the Code00:47:38 GitHub's Hardest Era: 14x Growth, Reliability, and Scale00:59:21 Actions as the Compute Layer for CI/CD and Automation01:02:04 The State and Future of GitHub Copilot01:08:24 Ambient AI, Background Agents, and the Future of the SDLC01:13:09 OpenClaw, Enterprise Security, and the New OS for Agents01:18:03 Build Announcements, WorkIQ, FoundryIQ, and Microsoft Context01:21:41 What Should swyx Ask Satya?TranscriptIntroduction: Kyle Daigle's Expanded Role at GitHub and MicrosoftSwyx [00:00:00]: We're here with Kyle Daigle, COO of GitHub. Welcome.Kyle [00:00:07]: Hey, thanks for having me.Swyx [00:00:08]: You're not just CEO of GitHub. People know you as that. You have a new role.Kyle [00:00:11]: So I have an expanded role now. I've been working at GitHub for thirteen years and doing all things developer. Joined as a developer myself. And now, I'm also responsible as the CMO of Developer for Microsoft. And so all the kind of learnings and passion for developers and how we work with them and how we communicate and how we bring our products to market, we're also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product or would like to have a sort of similar experience that they've had with GitHub over the years. So it's a different role in some ways, but it's also just building on the experience that I've had at GitHub of just sort of tell the truth, be authentic, show people how to use it and then let the products speak for themselves. Now just doing that with, all of Microsoft.Swyx [00:01:09]: We'll be releasing this in conjunction with Build. You got lots of stuff planned, and we can sort of touch on that whenever it's appropriate. I think one of the interesting things is I rarely meet a COO who's also a CMO. I think you're a very outward facing and you're very confident publicly. That's rare. Do you actually view yourself as COO? What's What is your thing?From GitHub Developer to COO/CMO: Building the Platform and Operating GitHubKyle [00:01:33]: I think for me, it's been funny. The titles have always been, a— have always felt a little strange to me. I joined GitHub as a developer? I wrote so much of theSwyx [00:01:46]: Let's bring that up. You wrote the back ends?Kyle [00:01:48]: I was going through, I was going through, some old photos, when folks were talking about how things were being built or how there was a build GitHub. I built, webhooks and worked with teams building the API, built the platform layer. Anything that integrated with GitHub, up until really twenty eighteen, I built or ran the engineering teams. And that's kind of where my the beginning of my passion always was helping people build things, deliver them to, their customers. And so being a developer, building for developers was always super unique. In a— I think as my role expanded, it became my ability to talk to not just developers, but also enterprise customers or business leaders and have this translation layer. And then through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in two thousand and eight. But all that expertise of running remote teams, doing it well, became this sort of bigger role, ultimately turning into the COO role of how do we operate GitHub in the way that GitHub's always operated after the Microsoft acquisition. And kind of so on from there. So like for me, I think the— I've, I still code. I love coding but the problem has always been, people. It's a much harder problem to both support our own employees, a harder problem to communicate to developers and enterprise buyers what we're building why it matters, ‘cause those are two very different messages. And so getting to work in the mix of COO, CMO, also just being a dev, I think is what's kept me at GitHub for so long.AI Workflows for Leadership: Commits, Retrospectives, and ContextSwyx [00:03:40]: Apparently, you have— your commits have gone up. What's this? What's going on?Kyle [00:03:45]: Rui's called me out pretty aggressively. So I think— as you can imagine, right, you can see my normal era of being a dev In the twenty thirteen, twenty fourteen era, and then moving into management, and then ultimately the COO role. I think what you see there is me, really getting back to coding thanks to AI. I— similar to, attaching problems between how to market and how to operate a business and how to code, I find, building agents and workflows that are connecting very disparate problems to be what's driving this. So that's, some of it's writing software. A lot of it is, connecting a ton of a different data sources to, help me out. But that is completely me really diving in on the AI side in trying out our tools, trying out everyone's tools, But building for me, building for the non-technical leader, though I'm technical and how we're, able to use these tools more than just the simple, call and response that I think a lot of the non-technical, your employers, you have to get— you have to use AI, and so everyone uses, ChatGPT or Copilot or Claude or whatever. To really get into, how is this going to help me out, it— I find that it's not the I need to write a blog post, I need to those simple examples. Helping people find the workflows of, “Okay, I need you to go through all the PRs today. I need you to go through everything that we've posted online. I need you to go through what we did the last three months. Go through all of my Obsidian notes for any mentions of this then go through my transcripts at work.” We use, Teams, so, using WorkIQ, go call that MCP server, grab all the transcripts, go through all the Slack, and then build me out the plan of, what this week's messaging actually was. That's something that was, impossible because for me, I find AI in a what most of this launch here is actually, less building forward. It's actually, a recursive loop backwards. I'm always looking at what had happened first. Go back through the week and tell me what we did, what worked, what didn't work? And then tell me in the next three or four days-What would you tweak based on this sort of like looking backwards and then looking ahead a little bit? I find that to be so much more valuable, especially for like non-technical, because that retrospection is actually LLMs are very good at that. Like finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or just like a short period of time. Is all a bunch of apps that I've built and launched a bunch of, internal tools. I use the new, GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it's running workflows for me. It's just a ton of different stuff and of course, it all ends up on, it all ends up on GitHub.Swyx [00:06:47]: Of course. That's where, that's where, stuff is hosted. Man, there's so much to ask you. I was going to leave the how do you run a company with AI thing at the end. I have to ask one— double click one thing. You said, you are looking back at the week. You're, you're understanding what happens. When you say we That's three thousand people. How?Rolling Out AI Internally: Skills, CLIs, and Company ContextKyle [00:07:09]: I think when we started rolling out AI internally beyond engineering, right? One of the things that I was really, passionate about is like we have to do this in a way where no one has to change how they work. I don't want to have to teach you a tool. I don't want to have to teach you something new. And so for us, we tried out a few tools. Most of them don't work because I got to get you on board? I got to teach you how to use it. What we've actually ended up doing is we've built like a set of skills internally. We have we each have our set of skills, and we've just been distributing even to the non-technical folks, the CLI. And then effectively, we're just giving it access to like read about everything that we're writing. So that's for us, that's usually GitHub, Teams, Email, and Slack. So Teams for, video chat, generally speaking.Swyx [00:08:03]: Teams and Slack?Kyle [00:08:04]: so we use Teams for video communication, but we don't use it for chat. W-we— GitHub for a long history, right? We're alwaysSwyx [00:08:13]: Also SlackKyle [00:08:14]: Talking about ChatOps and like everything is built into Slack. Like every command, every flow.Swyx [00:08:18]: So even though you have been acquired for I don't know, eight years nowKyle [00:08:22]: we stillSwyx [00:08:23]: You still use Slack?Kyle [00:08:23]: it's a purpose-built tool for us, and I think the reality is that moving off of it would be so bluntly expensive? Simply because all the tooling is, baked in with that paradigm. And they both have their pros and cons but they don't work the same way at all. We still use a bunch of different tools Because it's the purpose-built tools that We need. And thenSwyx [00:08:47]: Well, the same doesn't go for the rest of Microsoft, presumably.Kyle [00:08:50]: like the like various teams like operateSwyx [00:08:53]: They make their own decisionsKyle [00:08:54]: Various ways. I think it just matters what you're trying to what you're trying to do. But we do we do work across kind of every tool that we use, and then by giving everyone access to all of that context and the new WorkIQ MCP server, which is quite cool if you do live in the M365 like world. I can ask it all these backwards-facing questions, and it's incredibly important for our teams that are working remotely. There's a lot of stuff you miss when you're not in an office, and we are spread out all over the world. So most of that is looking back. And then we post, we post either auto-automatically into GitHub issues or discussions, these sorts of like findings or like our industry reports. Like what's happening this morning, today, yesterday. A little automation gets run. We'll use the app. We might use GitHub Actions like with, our agentic workflows just to go do that run, and then we push it into GitHub, and w-we keep having a conversation. So usually for us, it's about that sort of like looking back, looking forward on the non-technical side. And then of course for a lot of those folks, it's also building an app, pushing it to GitHub pages or pushing it somewhere to host it et cetera. But it's just like enabling everyone with that power of it's going to take me a week to figure this out. Instead, we're going “Okay I built a skill. Let's put it into a repo. We'll all share that skill together, and then we'll use the CLI or now the app-” “just to run it.”Micro Skills vs. Mega Skills: How GitHub Uses AI at WorkSwyx [00:10:26]: All right. I think, I think we're going straight into like the team management and productivity thing. I think a lot of people are getting various levels of LLM psychosis. How do you manage the bloat of skills? Like everyone Has their thing, and they're Like trying to promote it to the rest of their peers in their org, right? And obviously, whoever becomes a skill influencer internally becomes like an AI leader, right? Of sorts. I assume you have those.Kyle [00:10:50]: like I think we haveSwyx [00:10:52]: And I assume it's a mess a Yeah.Kyle [00:10:54]: there's like I— like I think the reality is there's two pieces. Like first is I think that we're ending the era of these like massive, beautiful, perfect skills that are just like not any of those things. ‘cause for a while, right every tweet every day is like go download the skills, the perfectly managed thing to do this entire workflow. And I think that like what we've found and what— I was just with my team, this week, and we were talking about the skill side, and we're really talking about these like incredibly micro skills that are just doing one thing for us very well Versus a skill that's going to do I said, that full report. That doesn't really exist on our side anymore. It's usually how do— like a single skill that's going to identify the most important marketing information given any MCP server. Like this is the most important thing. Less about stitch a bunch of tools together and have it produce this mega output because then weeks go by, months go by, things change, and you want to tweakSwyx [00:11:58]: It's brittleKyle [00:11:58]: Your mega skill and you're screwed? You can't do that. And so now we're really just talking about the Legos we're using and just letting the instruction book be something we're all putting together. Whereas I think a lot of AI skills for a while have been that mega instruction book style.Swyx [00:12:15]: I've, thought a lot about Postel's law. I don't know if that's a term that is, means things to folks. It's the idea that you should be liberal in what you accept and strict in what you output, right? And I think that's like a good framing principle for skills. This is my skills, obviously on GitHub. I feel like everyone should have like how like some repos In GitHub are special repos? I feel like we should sort of reify the slash skills and everyone like give it some kind of special presentation. Anyway, so, yeah, this is one of those like download Download anything, transcribe anything, and then you can string together the atomic skills that do one thing well Into like some kind of orchestration skill that calls other skills. I assume, does that match?Kyle [00:12:56]: I like I think so. I think that theSwyx [00:13:00]: Summarize anything.Kyle [00:13:01]: Like I think the- For me, summarizing something for I do communications and PR and analyst relations and marketing and customer activities, and so my summarize everything is very different for each one of those like Contexts. What ‘Cause if I'm summarizing something for an analyst, that's a very different thing than, probably how I'm going to summarize something for like a customer meeting or an engagement. So that's I think like the difference when we're talking about the like the tools I might use on Saturday or the skills I might use on a Saturday when it's just for Kyle. Yeah, those are kind of like they have an atomic actual tool underneath or maybe skill, and then Kyle cares about X. But I think when we're talking about work and enabling the the marketers, communicators there, it's the atomic, this is what good summarization is, and then this is what I care about as for marketing for communications For whatever. And that I think is like the interesting matrix problem when we go from like a developer set of concerns to all kinds of different professions, is that what that word means to me is different than it means to you is different than it means to the analyst or the salesperson, and that's where I think the matrix mess is that we're starting to like still starting to find. It's about these mega skills but they're all just slight permutations, but those permutations are really important. It's the difference between someone reading this and going “Did AI make this?” what Or “This makes total sense, and I would expect this when I'm giving a briefing to Gartner,” or like whatever else.Swyx [00:14:37]: I think the beauty of it maybe is that you don't have to be that careful about what goes in there. It doesn't have to exactly fit as long as it like roughly is contained in there. I used to complain about plugin hell, basically. Like when you have a framework and then you have a hundred things that you need to integrate, everyone does like the GitHub used to be bloated full of these things. And now we don't need them anymore ‘cause now you just use skills.Former Developers in Leadership: AI as a Creation MultiplierKyle [00:15:00]: And like I think the most magical thing is the just that like I can just also crack it open. Like Like yes, I could go like change the how the plugin is coded, or like I could go do that now with AI, but I think there's just something more magical about getting a response back and being “That's not right,” and then you just crack the skill open, you just type English words and it's different. That building block is just, I think very unique. Once I get everyone to kind of understand how to best how to best make those changes to get the most power out of them.Swyx [00:15:36]: Is there a— you have a your peer group that Of people like you. Is there a common framing for Something I'm feeling is, which is true, is that is this a golden age for former developers who are now in leadership? Because you can wield the tools, you would know the right words, you're maybe not too close to the details. Doesn't matter. But like you're more effective than someone who doesn't come from that background.Kyle [00:15:59]: I think that like the secret has always been your ability to identify patterns and solve problems, and I think that for folks that like myself that don't code day to day anymore, that has made me successful as a developer, made me successful as a COO and now CMO. And so now that I have access to get and write code, I'm now applying that sort of like pattern finding and problem solving, and I know enough still about how to then go and say, “Oh, I want to make an app, but I don't want to break into jail or create something that's not going to be able to work or to be deployed scale or whatever.” that ability to apply all that additional business knowledge and still code I think is what makes that so interesting to me. Slightly different than I think some of the other like technical leaders that became business leaders and now are going back to their apps and updating them. Good for them? But I think the more, much more interesting thing is, well, now I have this whole new set of expertise over ten plus years. Why not take that and use that as a developer with these AI tools? So I definitely think that makes me more powerful, but I think that's true for like every dev as well. Most of the dev friends I still have also have some other underlying skill and passion. There's really talented, very kind of linear computer science software devs, absolutely. I just find that the folks that came from a different career, went to school for something else, went off and did this random thing, and then became a software dev, or were a dev, did a random thing, came back. Learning that extra set of information, learning those extra skills, and now having the power of an AI where I can crank up fifteen agents on Saturday while my kids are doing lacrosse, That's like really powerful. And I think it gets me back to that feeling of like creation, and it's very hard to replicate that in most other senses? That first time you build an app and you click it and you show someone that's magical. And so being able to do that not just in code, but across all kinds of different assets that's, that's huge. We were doing we're doing our every year we do our revenue planning. We talk about okay, what is it going to look like for next year? And of course as you imagine, there's, slideshows everywhere talking about what are we going to talk about, what's the narrative, et cetera. And so as you said I'm “Okay, well, I could probably just like build something to build this and then that way I don't have to go build the whole spreadsheet or I have to pass it to my team.” So we went through this process, and I got all the information and used the skills I mentioned. I built like a little app just to make it so I could look at some of the information in a SQLite database, more easily. And I ultimately built this entire presentation without touching any of it and I was “Okay, I'm just going to present this to our CRO, the CFO, their teams,” without mentioning I'd built it with AI. I like built a skill to make it look very much not AI driven. Just not pretty.AI-Generated Presentations, Human Taste, and the Changing Chief of Staff RoleSwyx [00:19:03]: Like a design. Yeah.Kyle [00:19:03]: Not pretty. But just like very clearly not AI. Kind of like don't do anything interesting.Swyx [00:19:08]: That's, yeah, that is valuable.Kyle [00:19:08]: Just go Exactly. We did the whole thing through. It used my notes from Obsidian, it used all the context I mentioned before, the plans, and Never came up once that it was AI generated.Swyx [00:19:20]: It didn't matter.Kyle [00:19:20]: Never once. D It didn't matter. And so now I takeSwyx [00:19:23]: This is a toolKyle [00:19:23]: I can take that tool and go, “Look, I don't want you to go build slideshows.” They're just helping us share information with each other. If this thing can do it With a little bit of crafting from you and then we can look at it together, awesome. There's no value in all that extra work. I think that the ability to, make it look humanly bad and and build a little app to, manipulate the data I think is part of, that upside for devs that are now in leadership roles. Because, the thing that I feel like I said before, this that's all a people, that's all a people problem. I know if you've used a coworker or not to build a slide deck, unless you spent a bunch of time to not do it.Swyx [00:20:07]: I know, but like it was so, I think there's a certain charm to just being blatantly AI. ‘Cause I think that you're well, you're just honest about There may be mistakes here that I cannot vouch for. So how much value is there? But anyway I think, actually the real question I want to ask is, there's a— You were a chief of staff To Thomas. And in the pre-AI world, the that job would've been a chief of staff job of like Can you prep me these slides and all that? And now you do it yourself.Kyle [00:20:35]: I still, I still have a chief of staff. Because, the difference is it's sort of the discussion every time we have some sort of technology evolution is it's not that the jobs the roles don't all go away, they just change? And so yeah, I don't have someone spending all their time building out slides for me and presentations ‘cause I don't need that anymore. But now I need that person that is able to go and find all the different connections between humans in those discussions to help me find out, okay, I should be meeting with this group and this team, and they have an opportunity, and I'm going to be in San Francisco today, I'm going to be in Seattle tomorrow. Those sorts of human connection aspects are still incredibly valuable and has always been a big part of that chief of staff role. But now just like chiefs of staff are not opening up, letters to process, they're doing emails. What It's the same thing. And now they're, they're not building out as many of these presentations because they have the the ability to have a AI take it on for, and share that with me and great. Let's keep moving ‘cause it's allowing us to go faster and make better decisions more quickly.Swyx [00:21:45]: Awesome. Well, so we can dive into more sort of, Productivity insights as you go. I did want to do a little bit of a brief history of colleague and hub. Because, we started here. And then you also involved the NPM acquisition. I did, I do want to touch upon that. And then more recently, I just want to bring up to present day where we're having uptime issues Which transparently we've already Addressed publicly, but we'll, we'll discuss in the pod. Did I miss anything? Like what, any other major highlights? Obviously, it's, it's a lot of years to cover.A Brief History of GitHub: Webhooks, Actions, Acquisitions, and Platform EvolutionKyle [00:22:15]: No the I think one of one highlight was right before the acquisition closed in twenty eighteen, I got to launch the first version of ActionsSwyx [00:22:27]: OhKyle [00:22:27]: At GitHub Universe. So it was OSwyx [00:22:29]: They're that young?Kyle [00:22:30]: It was October of twenty eighteen, I think. Yeah. Yeah.Swyx [00:22:33]: Gee, Jesus.Kyle [00:22:34]: I got to I was the engineering leader on that project and got to launch that. And then, yeah, we did acquisitions of NPM you said, Semmle, Dependabot Pul Panda a whole bunch of things. That was a bigSwyx [00:22:47]: Pul Panda.Kyle [00:22:48]: Abi is doing well.Swyx [00:22:51]: DX. Holy crap.Kyle [00:22:52]: Did well on DX. I and like that was a that was the big shift, after the acquisition. I had to join the sort of business side.Swyx [00:23:00]: So I need to hit you on some of these things ‘cause you were there. Right? And how often do I get to talk to someone who was there? But yeah, Actions. Is that the number one source of security issues on GitHub?Kyle [00:23:11]: Oh, sh I think that the number one source of, security issues is probably like all, the literal code in everyone's like underlying repositories. I would say back further than that is, if you remember I had to show in this graph was this is, I'm, didn't say this before, this is ultimately webhooks.Swyx [00:23:30]: You yeah.Kyle [00:23:31]: Like circa whatever it was.Swyx [00:23:32]: It says Hookshot in there.Kyle [00:23:32]: I forget. Yeah. Yeah, Hookshot's in there. And so like back then, it says GitHub Services. Do you see, it says Hookshot FE for front end, and then it says GitHub Services. GitHub Services back in the old days, right? You we had a repository that was Ruby code, and you could write any Ruby code in there, and then we would execute that On your behalf As a service, and then that way if an if you were trying to integrate with something, it didn't we would run it for you.Swyx [00:23:57]: And of course no containers ‘causeKyle [00:23:58]: No, ‘cause it wasSwyx [00:23:59]: Well, no containersKyle [00:24:00]: Twenty fourteen. And so there was some isolation obviously, but it was mostly the separations on the server level. That's like an example as long as the very old version of Pages, which ran on its own containerization infrastructure, not on Actions.Swyx [00:24:15]: Which like all-time great product.Kyle [00:24:16]: Pages powers the internet at this point to some degree. Those were places where like clearly there were no like issues like to my knowledge. But it was those things where I'm looking at and going “Okay, well we can't be running arbitrary Ruby code,” like on everyone's behalf. Then containerizing all of that up intoUh into actions now where yeah the containerization, is r-really good. The pinning most folks aren't pinning it the like to a particularSwyx [00:24:48]: ImagesKyle [00:24:48]: Sha, et cetera like their workflows, and so that's a big that's a big place Of pain for folks if they're just doing similar to any dependency management, just V1 or newest or latest, I think. But, that journey from that day to “Okay, we're just going to run all this arbitrary code, and, it'll basically be okay,” to now, no, we have, really good containerization. We have a new, underlying, ag-agent, containerization, service. It's like we're using it under the hood. It's through Azure. They recently announced it. The Azure, Dev Compute, but it's, very fast, very fast compute to be able to, spin up your own cloud agents, or whatnot. We're using it under the hood for some parts of the new,Swyx [00:25:36]: Microsoft Dev Box?Kyle [00:25:37]: No. Dev Compute, yeah.Swyx [00:25:41]: Hmm. Not finding it just yet.Kyle [00:25:44]: Oh, it's, it's in there somewhere.Swyx [00:25:46]: All right. Well, we'll cut that out.Kyle [00:25:47]: Sorry. But with, Dev Compute, you can, run, really fast, spin up really, small VMs really quickly, so you're doing a tool callSwyx [00:25:58]: Same conceptKyle [00:25:58]: Just do it containerize exact-exactly. So we're using that so definitely moving that direction to protect us from every every piece of code that we're ultimately running.Swyx [00:26:07]: look, that grows into the full SDLC? Code hosting was just the start and and then it's grown beyond that. Let's talk about NPM may-maybe ‘cause I think that's also, a very major point in the industry. I do think, it was looking for a home. It was, kind of struggling as a business, right? I don't know, I don't know how you would characterize that whole acquisition and how itNPM, Package Security, and Keeping the Internet RunningKyle [00:26:33]: like when we were talking to the team, I think the big thing for the both of us was to find a way to keep NPM, which was basically powering the internet then and way more so now to some degree running. Keep it going keep continuing to scale. It was having scaling problems, if I recall, back at that time. They were doing some rewrites. ItSwyx [00:27:00]: that's cute compared to now.Kyle [00:27:01]: Well, that's the thing is like when I'm talking to folks now, there's there's so many more underlying uses of NPM than there were back when we had them join in with GitHub. But that was ultimately the goal. It was really okay, we used to have pages. We have, the world's code. Let's make sure that we can keep NPM running well for the world. And we put a bunch of time and investment into fixing some of the underlying backend, changes, some of which we talked about some of the manifest work, et cetera. And then now, really trying to bring the the security posture of NPM up to speed. But, it is a unique challenge in that every move that we make to make it more secure will break a lot of people. And security is paramount. And also, we take it very seriously. We're, the any time that we have a problem with GitHub or we make a change that makes us more secure but hurts, there's, a snow day for developers or a really bad fire that they have to go put out. And so we've, have changed the 2FA policies. We've changed the way the tokens work. When we find tokens that have been exposed or potentially, exposed, we invalidate them, andSwyx [00:28:22]: I love that feature in GitHub. Yeah, it's greatKyle [00:28:23]: That creates issues, but, the but that's the thing is we're trying to push the community, forward without necessarily, doing something that is going to break the contract that's been for 15 years or close to it or some amount of years on NPM.Slop Forks, Vendoring, and the Future of Open Source Supply ChainsSwyx [00:28:43]: I think the— So now we're talking about, open source and publishing. And I think there's something here with what people are calling slop forks, which, I think Malta from Vercel is doing. And, part of me thinks, well, the way to get past any vulnerabilities, we just, let's just get rid of the concept of NPM. And we only publish source code. And anytime you want to import it you have your coding agent look at it and then adapt whatever subset you're going to use into your vendor it. But, the AI vendor it. Is that realistic? I don't know. Is it— Will that solve all our security issues? I don't know.Kyle [00:29:24]: I don't think it'll solve I so Mitchell was just talking Mitchell Hashimoto Was just talking about this today, and I think that I-in some ways, it's all all things, old or new again? Yeah, absolutely vendoring everything. Like I do I do remember twenty thirteen, twenty fourteen.Swyx [00:29:42]: This is Yeah. Let's, we must return toKyle [00:29:43]: That's what is We were vendoring everything. We were having actual discussions around, or at least I remember we were “Should we take this full thing?” “Why is this so big? We only need this one file.” And so I do think there's something true there where having either taking only what you need or the dependencies just getting incredibly small over time, I think will help to some degree, but it's not going to solve the fundamental problem, I don't think, because the vulnerabilities in an agent looking at them, there's time and time again, there's a million different ways in which we can convince an agent that this thing is, secure or not and pull it in. Or we can do static code analysis or runtime testing to say whether the code works or not. That is, I think, the step that needs to continue to be, invested in. The question is just on, how much scope. Should it be this enormous project that I'm pulling down, or should it be this piece? Either most companies are running some amount of security checking on the on the packages that they're bringing in or vendoring. That I think won't change. That's like what advanced security does to some degree, Socket does some degree. Like everyone is doing a piece of that. How we each do that like especially when we're talking to enterprise customers, is just like very different. No there's no one wants one single way to do it. And I think that's always been GitHub's, unique position in the world. I talk a lot to maintainers, I talk a lot to folks about this. It's we're— we rarely start like a process and a practice and like push it onto the community. We usually wait for the sort of like RFC process socially or literally, everyone agreeing, and then we'll cement something in. Because otherwise we'reMaintainers, RFCs, Vouching, and the Social Layer of TrustSwyx [00:31:35]: That fits your role in the ecosystem, yeahKyle [00:31:36]: We're GitHub. Yeah, we don't want to shape the whole thing. We want it to be figured out. But like how do you balance that like sort of Role in the industry to keep everything as secure as is possible and make sure that you're you're not going to be compromised as a human, ‘cause that's usually how it all happens. And Not not create a process or lock us into a flow that you're not going to or like Mitchell's not going to or other open source projects aren't going to like. That's always been a tricky balance for us, and I think that's something that we haven't talked about enough is we're not going to be able to fix everything for everyone in a way that everyone is going to like. So tell, help us, tell us what is working. When Mitchell was talking about, the Upvote, the upSwyx [00:32:22]: I was going to bring up his thing. Yeah.Kyle [00:32:23]: I forget what it Yeah. When he's talking to us, I was chatting with him and talking to him about this and I put it on Twitter and we talked to, also over DM, was “We're going to keep working.” but I think the important thing is I do actually want to hear what isn't working for you. And as, be as specific and clear for your project as is possible. And to every piece of credit over the many years that we've known each other through the industry, he's always done that and I appreciate that ‘cause there are places that we need to fix up, and we hear from him, and we'll fix up just like we do all other kinds of maintainers. But that that process between making those types of improvements and being more secure and like creating, I forget what he calls it's not the proof process, not the claims process. Do what I'm talking about? He has that he his projects have a way for you to kind of like,Swyx [00:33:13]: VouchKyle [00:33:13]: Vouch. Thank you. Yeah. He has like the vouch system for saying, “Hey, you should accept my PRs.” That's beenSwyx [00:33:20]: I just built this into GitHub. I don't know.Kyle [00:33:22]: Well, see, but that's the thing is that you say that and like he and his community really likes this and then I'll go talk to other maintainers and other maintainers, globally, and they're “No, this doesn't work for me.” And that is the tension, but also the kind of beauty of GitHub, depending on which way you look at it is we want to help maintainers, so we create all these tools to let you have more control over how much you take in from AI and PRs. But you can also use this. What You can go use this project, and if it takes off and becomes the kind of mostly standard, then yeah, we probably wouldn't enforce it but we would add it in because that's the flow that we tend to do?Swyx [00:34:02]: I hear a lot of people don't know the history of the pull request. And like like that's how, that's something that GitHub standardized basically.Kyle [00:34:08]: Yeah. It was a very messy process Like beforehand, and now the we have the benefit of it being the process? And now we have to go and Figure out the next best process or what adaptations change, or what does a pull request look like when eighty percent of your PRs are just coming from your agents and not From other devs?Swyx [00:34:31]: Do you like the prompt request idea from Peter?Kyle [00:34:34]: like I think that for each like each idea I think has its merits. I'm not, I'm not avoiding saying anything good or bad, but I feel like I've seen a version of we have that we have entire Thomas' store. Take all the assets of what you've built and put that in. I think that's got great ideas. There's all these various permutations of the PR flow, but I think the reason why there's not a single answer is ultimately we're trying to codify trust. We're trying to say “Okay, if Sean reviews this I'm going to trust it because you're Sean or you're the senior dev or you're the whatever.” And right now, when we are working in a flow where an agent writes code and another agent reviews code and then Kyle goes and looks at it the trust is kind of diffuse. And most of the tools that we're talking about are talking more about verification flows. We have more assets to look at, so I can probably say whether this is a good PR or not. But that still doesn't solve, I think, the human problem of I'm looking at a PR and I want to know if I can trust it. And we're still, we still tend to use human signals for that? Mitchell approving it or Kyle approving it or whatever. And so I think that's, I think that's why most of these options haven't really solved it is because, it's a social problem ultimately. It's a it's a human problem to review it and agree. Or you fully trust the tool and you're imbuing that tool with full trust Which I think in some cases that absolutely exists.AI-Generated PRs, Trust, and the Waymo AnalogySwyx [00:36:08]: And so like in the same way that there will be a tipping point in society when we don't allow humans to drive anymore Because machines are measurably better than Than humans. I'm looking for that tipping point, right? Like Mythos is ridiculously expensive. Someday we'll have Mythos on a desktop. I don't know. Will, does that change the equation?Kyle [00:36:30]: I think it's more I took a Waymo here, and I was on my phone and not looking around at all. There are other, self-driving, vehicles that I would not trust while, staring at the road. And I think that trust is something that isSwyx [00:36:48]: Is this a Zoox thing? What is itKyle [00:36:50]: I think that is both. I think that is both. LikeSwyx [00:36:53]: There's Zoox in this robo taxi. That's it. It'sKyle [00:36:56]: Well, depending on what level Of self-driving. But, my point is sort of that I think part of that is I strongly believe that's, a mixture of verifiable proof. Like how many accidents, how much data, and so on, and the human aspect of how I feel when I'm in this car, what it tells me, et cetera. And so that's why I think some of the like Some of these some of our AI tools tend to, imbue me with more of that feeling of trust, even if the data says this is 100% accurate. I feel like it takes more time for us to go, “Should I trust this or not?” And that's in the soft sense of, startups with high agency, weekend projects, and open source. And then there's enterprises and regulated industries and everything else, and that is an even harder problem to go solve because even when it is fully verified, not only do you have to have trust from the humans on the team, you probably have to have trust from multinational,Swyx [00:37:55]: Oh my GodKyle [00:37:55]: Multi governments around the world and regulating agencies. And so that's where I feel like until we tip over to your point on the sort of like human EQ side of it. I feel okay this feels okay I've been proven enough. Then the ball will start to roll a lot faster, where we'll end up getting to the “Okay, we can trust this,” and feel good about it in the Most difficult of cases.Reputation, Sponsors, Stars, and Bot Activity on GitHubSwyx [00:38:18]: If human trust is the thing that matters, I feel like GitHub as the developer social network could maybe do more there. Like vouchers are one system But, we have star counts, and then we have Contributor rights, and that's it. And I feel like there should be more in that space. I don't know if there's any other design decisions there.Kyle [00:38:37]: I think that one of the places that we don't really expose right now in this sort of way is, some degree of like hard trust and support, which would like for me is like sponsors is a good example of that.Swyx [00:38:49]: Ah.Kyle [00:38:49]: It like costs you something. To prove that I believe in your project and I trust you To some degree or I want to support you at the very least.Swyx [00:38:56]: Solve payments for open source. Why not?Kyle [00:38:58]: I think that I think that like as we keep moving forward, right, there's more and more projects where I'm, adding more and more dollars into sponsors personally because I want to like support them, but I also like know of I've probably never met them in person, but, I know of enough of their work that I want to support them. I think the thing that I don't love about stars or commit counts or anything else is ultimately, even with all of the various, abuse and de-spamming and deduplication work that we do or anti-abuse work that we do, these are all, not active social signals. They're passive ones that are ultimately gamifiable. And you may trust me, but another open source maintainer may not. And on what heuristic should you be, trusting me? That I think, is kind of where some of our thinking is right now. What signal from me is most important to you? You— If you can define that potentially, honestly in an agentic workflow that's what we see some of these open source projects do, where you have GitHub actions, and then you have like an agentic workflow that's calling AI, and you're setting these rules. Like if Kyle has submitted and gotten accepted PRs across any given project and has a social handle tied to his account in GitHub, and that social account's older than a certain amount. Really complex measures that matter to you ‘cause most open source projects have that heuristic built into their heads, if not written down in the contributing guidelines. You could take that and then go apply that and then just say, “Oh, we're not going to accept this PR.” Building something that is, I think, malleable to everyone's needs, is a little bit better, rather than going “Hmm, this account's too young.” Because what happens? The attackers just go and go and create a multitude of accounts, and they wait Until it ages up. Needs to have a certain amount of stars. That's how star inflation happens. Need to have a certain amount of reposSwyx [00:40:46]: Oh my God. YeahKyle [00:40:47]: With PRs. They all just create repos and submit PRs to each other, and then they come in and do something nefarious. And so, it's hard. It's hard to find the measure. So I think we're, we're looking more at how can we provide you tools so you can kind of choose what's best for you. And of course, we'll give you some standards. But the trust vector, gets down to I don't know, some version of like human digital ID like everyone's been talking about. Like how do I prove that it's meSwyx [00:41:13]: Give me your eyeballsKyle [00:41:14]: On the internet. Give me your eyeballs. Exactly.Swyx [00:41:18]: The I got to keep moving on Topics, but obviously I can go all day on this stuff because, I've been involved in GitHub and open source My entire professional career. Stars. Very superficial. Everyone knows it. But I think time to one hundred thousand stars is the fastest I've ever seen. Like people just reached that in I don't know, months. And then like at the same time I don't trust it right? Like how many of these are real or bot or like whatever. I don't know how to ask this but like what can we do about it? LikeKyle [00:41:49]: JustSwyx [00:41:49]: Is stars broken? Is stars fine?Kyle [00:41:51]: I think that there's kind of two, there's like two pieces. Obviously we're constantly like trying to find ways in which like your users are producing spam, which would, I would include like be like only doing star gamification. When we find them, we pluck ‘em out and we,Swyx [00:42:08]: But it's like a Whac-A-MoleKyle [00:42:10]: It's a hundred percent like a Whac-A-MoleSwyx [00:42:11]: There's no wayKyle [00:42:11]: Now, powered by AI to be helpful. But I think more so what I'm seeing is, a lot of the like fastest time to X tends to be because we're now inviting so many more people into like software development on GitHub That like the zeitgeist is just swarming? And it'sSwyx [00:42:32]: It's not just developers anymoreKyle [00:42:33]: And it's not you and I. Like like however you want to say like what a developer is it's not just folks who have been coding for a very long time. It's folks that have maybe started coding or only joined in since the AI era. And nowSwyx [00:42:44]: what's the latest Octoverse number? I know eighty million was my lastRem- member that a number of developers on GitHubKyle [00:42:50]: Oh, we're over 200 million now.Swyx [00:42:53]: Okay. Well, so you see?Kyle [00:42:55]: Like over 200 million developers now.Swyx [00:42:56]: But it's not developers, right? It's, it's people with a GitHub account.What Counts as a Developer in the AI Era?Kyle [00:43:00]: So, so this is, this is the biggest debate that I would say, everyone loves to have at GitHub at this point. From my perspective, right, I think that there's, there's clearly a difference between, professional enterprise developer and then developers. But I think that I think that the idea that we should be I don't know, splitting hairs or segmenting developers in the early era of software development is, not worth our not worth the time. SoSwyx [00:43:29]: When you get into gatekeepingKyle [00:43:31]: 100%Swyx [00:43:31]: What is a developer?Kyle [00:43:31]: 100%. ‘Cause I wasn't a developer when I started writing code? I was going toSwyx [00:43:36]: Oh, no. I made— I cloned a thing, seven years before I learned to code. And then I and then I wrote about my learning to code journey, and people Just called me a fraud ‘cause I had a GitHub account. And I'm “Well, no, I just use GitHub, but I don't know-” “I didn't know what I was doing.”Kyle [00:43:49]: I I remember that. I remember those sets of posts, and like that's, that's b******t. So I fight very clearly on the line of, if you create code, if you have an idea and you create it into some way of, I'm, I'm going to run it and use the app right now, you may still use AI in that moment, but that's okay. At some point you're going to do the next thing. You're going to create a big— You're going to have to learn about this database. You're going to fix a bug, whatever. We're all on some same journey, and those people are also hearing about the great new agent skill package or a new CLI tool or a new whatever. And those projects are going up because you want to be a part of this moment, just like I wanted to be a part of the Ruby community when Ruby was popping off when I started becoming a developer, and now I can just click the star button. And so I think that yes, there's clearly some amount of like spamming and game gamification that we're working against, but I really think we're just seeing this whole new cohort of folks that are moving from technology to technology because they're not working on a 20-year-old software application. They're working on a side app that they built on the weekend for their friends or for their new idea or whatever. And that's how you see these enormous charts going up and to the right with With stars.Swyx [00:44:59]: I think something that's remarkable is the persistence or, that GitHub extends to those folks. Usually when I see platforms go into a new audience, they usually have to, have like a second platform with a different name that wraps the main platform. But somehow GitHub has been able to sort of persist and extend, and it's friendly and whatever? So it's, it's nice.Spark, Low-Code, and Always Showing the CodeKyle [00:45:19]: I that's partially why I think as we've tried to move into I don't know, more like low-code-y things. We so we started working on Spark as like a way to, build an app and run it. I think that the reality is that we anytime we try to, kind of put even a veneer on top of it without when we put a veneer on top of something, we still always show you the code. That's kind of like a tenant. We're never going to, hide the code from you ever, because whatSwyx [00:45:52]: Why would you?Kyle [00:45:52]: That's, yeah, that's the whole point? However, I think that what we learned with things like Spark is that really the value of Spark for most devs is, easy runtime. And you may have a runtime or a host that you're going to use for that or you just build something and run it but, the package of making that even more simple isn't really needed for folks that are trying to build software and not just trying to build, an app, which is, slightly different, a slightly different goal. So I want to get you in, I want to get you comfortable. I think the best thing for me as, someone that did not traditionally come into software dev way back, I want anyone to be able to breach that chasm and not be in the I don't know, I feel like we're, we're still in an era of, STEM. I've got a 12-year-old and an eight-year-old, and it's “We got to get ‘em into STEM,”? Over and over. And I like I do, I do the things that good parents do. I was “Oh, you want to do coding?” “Yes, I want to do coding.” Do coding classes. But now they're just not afraid of doing software. And that's, I think, the thing that's honestly kept me at GitHub for so long. Anyone should be able to go and build a thing, just like I can go change a light switch in my house. I'm not going to go into the breaker box ‘cause I'll probably kill myself? But, I can go change that light switch. Everyone should be able to go and say, “This fricking app doesn't do what I want. I want it to work like this.” And that I think, is what's kind of kept us all connected with GitHub through the years and some and during the easiest of times or in the hard times because of that opportunity of, we're the home for all developers, and we want everyone to be able to have that feeling that we've had of, had an idea, I created it and holy s**t here it is.Swyx [00:47:37]: Here it is. All right, I'm going to try to do more spicy questions.GitHub's Hardest Scaling Moment: Growth, Agents, and UptimeKyle [00:47:42]: Great.Swyx [00:47:42]: Is it an easy time now or a hard time?Kyle [00:47:45]: Oh at GitHub? It's a hard time. Like, it's a hard time and also, I was just with my team and I said, “This is also, the best and most exciting time that I think I can remember at GitHub.” BecauseSwyx [00:47:57]: Best of times, worst of times. It's never oneKyle [00:47:59]: ‘cause we've we were talking about Octoverse reports and, usually we do an Octoverse report once a year, and we look at the numbers, and we say, “Oh my goodness.” I was at Universe in October saying, “This was the fastest year of growth that we've ever had,” right? And now we're doing more in a month than we did in a year last year.Swyx [00:48:20]: You're talking about PRs.Kyle [00:48:21]: Commits.Swyx [00:48:21]: Commits, yeah.Kyle [00:48:22]: PRs. Kind of like you name it by roughly every measure that we're looking at, there's some amount of sort of growth that is much bigger, and that is breaking our system in new ways, not old ways. Like webhooks were always notoriously, unreliable over the years?Swyx [00:48:38]: Whose fault is that?Kyle [00:48:39]: not anymore mine, but for a period of time, I'm sure you could pull up a tweet that was “It was me. I'm sorry.” but, now, that got rewritten at a scale level that is still working and is not having problems today. Now what we're finding isn't just the isn't the-The simple stuff that folks are on the sometimes on Twitter or on the internet are “Hey, why is this like this?” Sure. There's absolutely silly problems that we shouldn't exist. But now we're talking about, unique, novel permission problems that happen only at a scale across all different objects or whatever, that now we have to go rewrite this underlying system. And so it's, there are problems that yeah, caught us off guard, which I think I said. Like the growth is astronomical, but also we're making such material progress in that I'm excited once we're once we've kind of like reimagined the underlying foundation layer, or pieces of it at least, what's going to be possible when it's not just all of us and all the new people that are being developers and all of their agents and all the tools like working together. Because that'll still happen in that in that GitHub tool, that GitHub community. But it's a it's a hard day anytime we can't give you what you're looking for. We have the same problem internally. We operate through github. Com. Of course, we have backups when things go down and whatnot for our own operations but we feel it too. If it's not working it's not working for us, and that's kind of like the promise of dogfooding for GitHub. It's always been true. We're using the same tool you're using. We're not using a super secret version. We and so we also need it to be great for us for our customers of course for open source. And now an exponential growth of agents, Doing it too.Swyx [00:50:32]: I wanted to load for audio listeners who maybe haven't seen your tweets, whatever. So one billion commits in twenty-five. Now it's two hundred and seventy-five million per week on pace for fourteen billion this year, if growth remains linear. Is that still the pace? I don't know. It's been aKyle [00:50:48]: it's, it's speedingSwyx [00:50:50]: Roughly.Kyle [00:50:50]: It's still speeding up.Swyx [00:50:51]: It's, it's April, so yeah.Kyle [00:50:51]: Exactly. This was in April.Swyx [00:50:53]: All right. So basically you have fourteen x growth, right? Year on year on year. And I think that's a scaling issue. I think, I'm going to like try to really steel man this thing. People have experienced fourteen x growth. They haven't had your downtime. And that's like— C-can we go dig into that? Why? Like what's the— what broke? What are we doing to fix it? Like just anything for the community to reassure them.Why GitHub Reliability Is Breaking in New WaysKyle [00:51:18]: so there's a Like I was saying, there's a couple different places that we've seen the growth issues. Some of the growth issues, which is why we're t— I was talking about pushing hard on more CPUs is in actions in particular. More tools, more agents, more PRs mean more builds, more builds mean more CPUs. And so we are expanding through not just our data center, but obviously we were talking about moving to Azure and moving to, adding an additional cloud compute because we simply need more CPUs. Not as much GPUs. We definitely need GPUs too, but now CPUs are becoming a factor.Swyx [00:51:53]: It's very CPU heavy.Kyle [00:51:54]: Underneath the hood when it comes to some of the underlying services, we've been breaking up over the years our database infrastructure, so that way we have, more cognitive separation between our the various services. The place that we continue to have pain is in, permissioning. And so right now m-many of our permissioning layers sit into a database that we like internally call MySQL One, and old Hubbers will know what I'm talking about. And so we've been pulling things out of MySQL One for many years, because like and we use we use Vitess and we use other technologies to shard and we do it as one bigSwyx [00:52:31]: Famous thing, PlanetScale was born from this andKyle [00:52:32]: A hundred percent. Sam Old Hubber and friend. And so finding these opportunities to like break this out and then do that globally. The other thing that I think is interesting and both a unique opportunity and tricky is we also run everything I just talked about in a black box container with GitHub Enterprise Server for people that work on-prem. So we take everything I just said, and we also do it on-prem, and we also do all of that and we do it in a data residence setup for customers that need to have their data in a single location. Each of these has the unique characteristic around how we're sort of storing that data in MySQL or in a permissioning setup. That's where some of these outages have oc-occurred, where you're seeing it more like across the board rather than just like the one pieceSwyx [00:53:17]: Filling the databaseKyle [00:53:17]: Isn't quite working. Exactly. And so part of it is that. I think there's been some other places where agents are much more or more projects appear to be moving towards monorepo versus we were going the other direction for many years in the industry. Repos were smaller, but there were more of them, and now we're seeing the opposite. Repos are bigger, and there's, not fewer of them per se ‘cause there's new growth, but, we're just seeing many more big repos. Big repos, big monorepos have always had, a unique performance problem. Because each one, is slightly different if, particularly if the underlying blobs are incredibly big Inside the repos. And so we've done a ton of work that you pro— like most people haven't probably experienced, unless you're in this case of the monorepo. But that Git, infrastructure layer improvement does help the overall, system because, many of the improvements that make monorepos work better make all repo infrastructure work better. And so, I could kind of keep going down the line where it's another thing where we're moving out of, We're changing how we do j I'll just say job queuing for lack of a better, explanation changing the underlying technologies there.Swyx [00:54:32]: I spent two years being a job queuing guy, so.Kyle [00:54:34]: And so it's kind of a little bit of a little bit of piece by piece, and it's mostly because as we were— as it was built, we built everything in a way that assumed, I guess in some ways that the size of the pipe of work was going to remain the same. There's just going to be more people coming through each of those pipes. But instead now in places whereA git push was, generally a certain size for example, is now, no longer true.Swyx [00:55:03]: Oh, yeah.Kyle [00:55:03]: OrSwyx [00:55:05]: I push a thousandKyle [00:55:06]: On the average. 100%Swyx [00:55:06]: A thousand line commits like dailyKyle [00:55:07]: Same thing with PRs. Like PRs same thing. And like we've talked about optimizing that and making changes where, and there were technology choices that did not work there? And it got slow, and it didn't It was not fast. It did not do what the users wanted. And so we've been reeling that all out and going “Okay, that's just not right. Let's stop putting good money after bad and do it the do it the right way or the right way now.” So there's It's a it's a lot of things, not quite when I've experienced scale at GitHub historically, it's almost always two options that we've used. We go vertical scaling, particularly with databases, right? And we go horizontal scaling. Oh, we just have more people using this service. Great. We're going to add more servers, and we rack them in our data center, or we use it in a cloud. And now we're sort of in a like diagonal, where like vertical doesn't really work anymore. Horizontal isn't work either because we're all We all have some CPU or GPU constraints in the world now, and now we have to go in and like crack open services that have been running for 10 or 15 years and go, “Okay, the rules of this service have legitimately changed, and now we have to rewrite them.” None of this is an excuse. This is like we're We have to do the work. We have to make it better.Swyx [00:56:22]: actually as an infra guy, I'm “This is like one of the most fascinating scaling challenges I've ever seen.”Kyle [00:56:26]: That's that's, that's the thing that's the thing that it's hard for Like when we weren't talking about it publicly, and I was like I came out, and I was “Hey, I just want to explain what's going on.” Part of it comes from a very old GitHub ethos, which is it's our it's our uptime. It's down. W What I know you're a developer, so you're, you're inclined to want to understand more what's going on. But at the same time us going “Hey, this service didn't, perform the way we expected, and now we have to go change it,” we weren't We're not trying to hide anything from you i

What does it take to modernize the systems that keep water flowing, wastewater moving, and nine million New Yorkers served every day?In this episode, we sit down with Robert "Max" Maxfield, Chief Systems Architect at AITHERAS and the architect behind New York City's SCADA modernization efforts for the Bureau of Wastewater Treatment. Max takes us inside the world of critical infrastructure, where downtime isn't an inconvenience, it's a public risk. From managing decades-old industrial systems and balancing modernization against reliability, to defending essential services against cyber threats, Max shares what it really takes to operate technology that most people never think about until it fails.We also explore the realities of AI in critical infrastructure, the cybersecurity challenges facing utilities, the surprising longevity of legacy systems, and how Max's passion for motorcycles, racing, and building machines shapes his approach to engineering. It's a conversation about technology, risk, resilience, and why sometimes the most important systems are the ones nobody notices.Robert “Max” Maxfield is the Chief Systems Architect at AITHERAS, leading the SCADA Modernization Program for NYC's Bureau of Wastewater Treatment. In this role, Max designs and deploys the systems that keep critical water infrastructure operating for nine million New Yorkers. With 20+ years in industrial controls, 27 platform certifications, and prior architect roles on national operations centers and the Doyon Utilities Alaska modernization, Max specializes in the messy intersection of legacy industrial systems, modern SCADA, cybersecurity, and, increasingly, AI. He's been published in Forbes on industrial technology, runs his own GPU lab for local model fine-tuning, and spends his off-hours on custom motorcycles, off-road racing, and drag racing. Equal parts engineer, builder, and pragmatist, Max brings a field-tested perspective on what actually works when the stakes are critical infrastructure.

Scott Hanselman talks with Omar Shorbaji from the Anyscale engineering team about how Anyscale on Azure scales Python AI workloads from a single notebook to thousands of CPUs and GPUs. Built on Ray, the most widely adopted AI compute engine, Anyscale gives you a unified runtime to build, train, and serve, running directly on Azure Kubernetes Service without the complexity of managing Kubernetes. See a live demo that fine-tunes a vision-language-action robotics policy, with the metrics you need to push GPU utilization higher. Chapters 00:00 - Introduction 00:52 - Ray and the Anyscale platform 03:11 - Start of demo: Workspaces 04:38 - Running a job and viewing utilization metrics 05:24 - Choosing the right scale 06:53 - Abstracting Kubernetes on AKS 08:53 - Wrap up and where to learn more Recommended resources Learn Docs Anyscale on Azure Connect Scott Hanselman | Twitter/X: @SHanselman Anyscale | Twitter/X: @anyscalecompute Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.

Reconstructing an Akira Ransomware Kill Chain from Perimeter and Endpoint Logs https://isc.sans.edu/diary/Reconstructing%20an%20Akira%20Ransomware%20Kill%20Chain%20from%20Perimeter%20and%20Endpoint%20Logs/33024 Vaultjacking: One Captured PIN, the Entire Google Password Manager Vault https://phishu.net/blogs/blog-vaultjacking-phishing-the-google-password-manager-vault-in-the-phishu-framework.html From poisoned search results to GPU mining: A cryptojacking campaign abusing ScreenConnect and Microsoft .NET utilities https://www.microsoft.com/en-us/security/blog/2026/05/26/poisoned-search-results-gpu-mining-cryptojacking-campaign-abusing-screenconnect-microsoft-net-utilities/

In this week's episode of Hands-On Tech, Robert asks Mikah for help choosing a new Windows laptop suited for heavy photo and video editing work, including guidance on GPU and VRAM requirements for his specific software stack, as well as advice on whether switching to a Mac is a viable option after a disastrous previous migration experience. Don't forget to send in your questions for Mikah to answer during the show! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsors: outsystems.com/twit shopify.com/hot

Brad's tired of throttling his CPU due to an inadequate heatsink. Will's been spending a lot more time testing PC hardware of late. Between those two things, we thought it was a good time to do a check-in on CPU cooling, and primarily liquid cooling, so we can establish the facts on the ground about modern AIOs and custom loops with an eye toward helping Brad decide what to get. Turns out, there's more to know than ever, and yet it's also never been simpler. We also talk a little about modern air cooling, CPU spikes in Windows, and other stuff! The GamersNexus video on AIO placement: https://www.youtube.com/watch?v=BbGomv195sk Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Download MP3 | Watch Video Episode Full Timestamps: https://docs.google.com/document/d/e/2PACX-1vT44TUsVZDuKxJqgbhZrh6_hEVMU02wcpfzsQ_7Rfei8DkTcgVVBO3S6sKmPIS8v3-gY5vb0P1CDeeJ/pub The Hardest Card Game I've Ever Played  ONE HUNDRED DOLLARS?!  Here's The Thing: It's a Bad Version of The Thing  Pokemon Cards Anti-scalping Tech: Answer Our Riddles Three  Seeing The Matrix on Opening Weekend HIT DIFFERENT Watch full episodes: https://www.youtube.com/@CastleSuperBeastArchive Reggie In The Lab Limited-time Plushie only available this month! https://www.makeship.com/products/reggie-in-the-lab-plushie - Visit http://drinkag1.com/SUPERBEAST to get a FREE AG1 Flavor Sampler and a bottle of Vitamin D3+K2 in your AG1 Welcome Kit! - Head to http://factormeals.com/castle50off and use code castle50off to get 50% off and free daily greens per box - Sign up for your 1$-per-month trial today at http://shopify.com/superbeast - Invincible VS is out now on PlayStation, Xbox, and PC. Docket: PS5 Class Action Lawsuit Targets Sony Over Price Hikes Jess Cox - This Is A Genius Way To Prevent Scalping PlayStation Has Started Revealing Public Player Counts - Insider Gaming Microsoft has confirmed that Windows Update has been downgrading newer GPU drivers that users install manually from Intel, AMD, or NVIDIA websites. Marvel Tokon: Fighting Souls Was Almost A 1v1 Fighter PlayStation CEO Hermen Hulst says single-player Sony games won't come to PC going forward Random Avatar Matches and Avatar Arcade, two brand-new modes for Street Fighter 6, will be added on May 28 alongside the release of Ingrid GenAI In Games? Most Players Just Don't Care, Study Finds Capcom Execs Still Excited About GenAI, Is Still Ramping Up Hiring