Podcasts about models

For a new homebuilder, your model is your calling card. It is how you portray your product to the world—at least the world that matters to you: the folks visiting your community, your sales center, your clubhouse. The same goes for apartment developers. How you present has an impact. Impressions, both first and last, matter. Angela Harris is the founder and CEO of TRIO, a residential design firm with a national reach that helps builders and apartment developers make the most of their models and merchandising. A designer first and foremost, Angela is also a psychologist, delving into buyers' minds to understand how they will respond to the purposeful choices inherent in her work. She understands what design and merchandising can and cannot do, and that an authentic voice is the one most likely to be heard. I sat down with Angela for this episode of the New Home Insights podcast, where she walks us through models, merchandising, and how to get the most out of each.

Was passiert, wenn Mode bewusst hässlich, verstörend und anders sein will und warum fordert Matières Fécales mit ihrer radikalen Ästhetik unsere Schönheitsideale heraus? Was sind eigentlich Schönheitsideale und gehören diese vielleicht sogar zerstört? Was macht der Schönheitswahn mit uns? Das Fashion-Designer-Duo Matières Fécales aus Paris hat darauf eine ganz eigene Antwort gefunden und verleiht dieser mit einem einzigartigen Lifestyle und einer radikal anderen Ästhetik Ausdruck. Andersartigkeit ist für Hannah Rose Dalton und Steven Raj Bhaskaran dabei eine Maxime. Es geht um das Bedürfnis, sich selbst auszudrücken, Normen infrage zu stellen und andere Menschen zu inspirieren. Genau diese Vision einer bizarren, düsteren und zugleich faszinierenden Schönheit macht Matières Fécales zu einem der spannendsten Avantgarde-Labels der aktuellen Modewelt. Mit ihrer neuen Matières-Fécales-Kollektion für Frühjahr/Sommer 2027 gehen die beiden noch einen Schritt weiter. Unter dem Titel „The Ninety-Nine Percent“ präsentierten sie ihre Show während der Paris Fashion Week auf der Place de la République. 99 Models of Color standen dabei bewusst im Mittelpunkt. Das Konzept knüpft an die vorherige Kollektion „The One Percent“ an und setzt sich mit Zugehörigkeit, Repräsentation und gesellschaftlichen Machtverhältnissen auseinander. Und natürlich geht es dabei auch um die ganz eigene Ästhetik des Designerduos. Wer sich die Mode von Matières Fécales genauer anschaut, erkennt eine gewagte Mischung aus klassischem Pariser Chic, experimenteller Couture, Urban Streetwear sowie deutlichen Elementen aus Gothic und Fetish Fashion. Der düstere und avantgardistische Anspruch zieht sich durch die unterschiedlichen Looks. Dazu kommen Tüll, zerfetzte Silhouetten, Denim, Leder und vor allem ein Make-up, das die Grenzen zwischen Mensch, Alien und Kunstfigur verschwimmen lässt. Verformungen, extreme Accessoires, Masken und ungewöhnliche Körperbilder gehören zu einer Ästhetik, die mit klassischen Vorstellungen von Schönheit ganz bewusst bricht. Genau darin liegt die spannende Frage dieser Episode: Muss Schönheit überhaupt schön sein, um uns zu begeistern? Auch Rihanna war bei der neuen Show dabei und saß in der Front Row. Sie trug dabei eine Kreation aus der Zusammenarbeit von Matières Fécales und Christian Louboutin. In dieser Podcast Episode von Lost On Planet Fashion werfen wir einen genaueren Blick auf die Design-Philosophie von Matières Fécales, alternative Schönheitsideale und die Frage, warum Andersartigkeit in der Mode so faszinierend, aber gleichzeitig noch immer so schwer auszuhalten ist. Vielleicht müssen wir nicht unsere Schönheitsideale zerstören. Vielleicht müssen wir endlich mehr davon zulassen. ***** Alle Bilder auf Social Media @lostonplanetfashion : Instagram: www.instagram.com/lostonplanetfashion/ TikTok: www.tiktok.com/@lostonplanetfashion und unserer Website: www.lostonplabetfashion.de

If you prompt frontier models with "What do you think is the correct decision theory? Please select your overall favorite." they will essentially always answer FDT or FDT/UDT ("something in the functional/updateless decision theory family"). However, if your prompt indicates (even subtly) that you're coming from mainstream academic philosophy, these same models will answer CDT instead about 30%-100% of the time. A similar phenomenon holds for models' stated views about the moral realism/antirealism question and about the conceivability of p-zombies (where the dominant view in mainstream academia differs from the dominant view in LW-adjacent circles), as well as their stated P(doom) and median AGI timelines. This is a special case of sycophancy or user awareness. (In the course of writing this post, I also found that this comment from testingthewaters predicted some of the content I discuss.) An implication is that we should be somewhat careful when interpreting attitude/propensity evals in domains where no general human consensus exists, e.g. when interpreting models' decision theory attitudes in DTBench. Moreover, when we explore some philosophical/conceptual questions assisted by models, we should be wary of them strawmanning one side of the debate based on particular user cues (e.g. only giving a [...] ---Outline:(03:50) A sentence identifying the user as an academic significantly influences Fable 5.1's stated decision theory[... 13 more sections]--- First published: September 30th, 2026 Source: https://www.lesswrong.com/posts/MzenSrmZ3pT2pCnvp/frontier-models-state-different-decision-theory-preferences-2 --- Narrated by TYPE III AUDIO. ---Images from the article:

37% of Americans just said they'd let an AI buy their next house — with almost no human involvement.Elon Musk just said 80% of Tesla's future value comes from Optimus and AI — not cars.The AI industry is spending $800 BILLION this year alone building the infrastructure to replace millions of workers.And 95% of real estate investors have no idea how any of this affects them.They should.In this episode, Sam Primm and Lucas break down what AI is doing to the economy right now — the stock market boom, the humanoid robot race, the coming job displacement wave — and the 3 direct ways it lands on real estate. Including the play nobody's talking about that could define the next 10 years of wealth building.

Stephen Schmidt, chief security officer for Amazon (AMZN), talks about how the Mag 7 giant is navigating the collision between AI and cybersecurity at scale. As Stephen discusses, the evolution of AI is forcing Amazon to change the way it reviews exisiting protections and how it creates new methods to fend off cyber threats. When addressing the AI safety discourse between companies like Anthropic and Nvidia (NVDA), Stephen believes there's a "huge gap" between how people understand AI frontier models and what they are capable of. ======== 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

On this episode of Inside Content, Hayley Bull, VP at 3Vision, is joined by Kasia Jablonska, Director of Digital and On Demand for BBC Studios.In this episode, Hayley and Kasia discuss how BBC has evolved its FAST strategy from a way to monetise library content into a broader streaming and digital monetisation business. Kasia explores the growing importance of engagement, data and inventory control, why telcos are becoming an increasingly important distribution partner, and how BBC is using FAST to support content discovery and strengthen its brands. They also discuss the challenges around monetisation, measurement and discovery, and how FAST could develop into a broader hybrid streaming ecosystem.Stay in the content world loop

The Infrared Sauna GuideOne infrared session cut post-workout performance loss and boosted runner endurance by nearly 7%, but only if you time it right. Curious what happens if you skip in too soon after training? The Infrared Sauna Guide City: Northport Address: 240 Main Street Website: https://www.youtube.com/@theinfraredsaunaguide Phone: +1 631 629 5553 Email: christian@cm2digitalenterprises.com

We may be imagining the wrong AI apocalypse. When the leaders of Anthropic and OpenAI warn that AI could end civilisation, most of us picture Terminator or The Matrix where a superintelligence taking over. But there's another story about technology and power, and I think it's the more useful warning: Cyberpunk.I also look at the satire of Don't Look Up, which has tackles the messaging problem of predicting the end of the world.CHAPTERS00:00 The wrong AI apocalypse01:04 Cyberpunk: technology works, society doesn't02:58 The AI labs' contradiction05:03 Regulatory capture: Boeing and Arasaka07:49 Safety as a moat: the catch-2209:01 Don't Look Up11:37 Techno-feudalism: who is the argument for?13:50 A political choiceSources and further reading- WIRED — Isabella Ward, OpenAI Pauses Training Its Most Powerful Models After Rogue Agents Target Government (28 Sep 2026): https://www.wired.com/story/openai-pauses-training-most-powerful-models-after-rogue-agents-target-government/- George J. Stigler — The Theory of Economic Regulation (Bell Journal of Economics and Management Science, 1971)- U.S. House Committee on Transportation and Infrastructure — final committee report on the design, development and certification of the Boeing 737 MAX (2020)- The Economist / YouGov — U.S. adult citizens, 28–31 Aug and 18–21 Sep 2026 (AI harm, benefits and partisan shift)- Stanford HAI — AI Index Report 2026 (source for the Arena, Cloudscene, Epoch AI, Ferracane et al., AI Incident Database, Pew, Ipsos, CHIP50 and U.S. public AI spending charts)- Artificial Analysis — Intelligence Index v4.3.2 (September 2026)- AI Release Tracker — model release timeline: https://aireleasetracker.com/- History of Market — Magnificent 7 share of the S&P 500 (CC BY 4.0): https://historyofmarket.com/- Bloomberg News — AI industry money network graphic (October 2025)- ExecutiveGov — What are the top Boeing government contracts (8 Jan 2026): https://www.executivegov.com/articles/what-are-the-top-boeing-government-contracts- IATA Sustainability and Economics / Cirium Fleets Analyzer — The Global Commercial Aircraft Fleet (June 2025): https://www.iata.org/en/publications/economics/reports/the-global-commercial-aircraft-fleet/- Yahoo Finance — Boeing (BA) weekly share price since 2000- Sensor Tower — State of AI 2026 (June 2026): https://sensortower.com/blog/state-of-ai-2026- Exponential View — AI revenue has more than tripled in a year (2026)International Energy Agency — The Oil and Gas Industry in Net Zero Transitions (2023)- NASA GISS — GISTEMP v4 global temperature anomaly: https://data.giss.nasa.gov/gistemp/- Federal Reserve — Distributional Financial Accounts via FRED (top 1% vs bottom 50% share of U.S. net worth)- World Bank — Gini index (SI.POV.GINI): https://data.worldbank.org/indicator/SI.POV.GINI- Cyberpunk 2077 (CD Projekt Red, 2020), Cyberpunk: Edgerunners (Studio Trigger, 2022) and the Cyberpunk tabletop RPG (R. Talsorian Games)- Don't Look Up (Adam McKay, Netflix, 2021); Melancholia (Lars von Trier, 2011)

Cancer care is complex, emotional, and often difficult for patients to navigate alone. In this episode, Marcia Macphearson, Chief Operating Officer at ThymeCare, discusses how the company is working to transform cancer care through value-based care and a connected approach to the oncology ecosystem. She explains how ThymeCare brings together health plans, oncology practices, health systems, and multidisciplinary care teams to help patients navigate the financial, social, clinical, and emotional challenges of a cancer journey. Marcia also shares how building trust with patients and connecting the different parts of the healthcare ecosystem can help care teams respond quickly when patients need support. She explores the potential of AI to support clinicians by reducing administrative work, consolidating information, and giving care teams more time and insight to focus on individual patient needs. Tune in to learn how coordinated, whole-person cancer care can close gaps, build trust, and improve outcomes! Resources: Connect with and follow Marcia Macphearson on LinkedIn. Follow ThymeCare on LinkedIn and explore their website. Learn more about Thyme Companies.

When an AI system fabricates damaging claims about a real person, is the AI company liable for defamation? Just last week, in Keene v. Google, a federal court confronted this question for the first time, holding that allegedly false Google AI search summaries could be viewed as potentially defamatory factual assertions – and that “actual malice” on Google's part could be shown if Google's AI kept outputting the falsehoods after plaintiff had expressly alerted Google about this. In July, a Delaware state trial court likewise allowed conservative activist Robby Starbuck's defamation suit against Google to proceed.What do these early decisions mean for defamation law in the age of large language models? Join us as we discuss when AI-generated falsehoods may become actionable, how traditional libel doctrines apply to AI outputs, and what these cases could mean for AI developers, users, and the future of online information.Featuring:Prof. Lyrissa Lidsky, Raymond & Miriam Ehrlich Eminent Scholar Chair in US Constitutional Law, University of Florida Levin College of LawProf. Eugene Volokh, Thomas M. Siebel Senior Fellow, The Hoover Institution, Stanford University; Gary T. Schwartz Distinguished Professor of Law Emeritus, UCLA School of Law(Moderator) Shlomo Klapper, CEO & Founder, Learned Hand

When an AI system fabricates damaging claims about a real person, is the AI company liable for defamation? Just last week, in Keene v. Google, a federal court confronted this question for the first time, holding that allegedly false Google AI search summaries could be viewed as potentially defamatory factual assertions – and that “actual malice” on Google’s part could be shown if Google’s AI kept outputting the falsehoods after plaintiff had expressly alerted Google about this. In July, a Delaware state trial court likewise allowed conservative activist Robby Starbuck’s defamation suit against Google to proceed.What do these early decisions mean for defamation law in the age of large language models? Join us as we discuss when AI-generated falsehoods may become actionable, how traditional libel doctrines apply to AI outputs, and what these cases could mean for AI developers, users, and the future of online information.Featuring:Prof. Lyrissa Lidsky, Raymond & Miriam Ehrlich Eminent Scholar Chair in US Constitutional Law, University of Florida Levin College of LawProf. Eugene Volokh, Thomas M. Siebel Senior Fellow, The Hoover Institution, Stanford University; Gary T. Schwartz Distinguished Professor of Law Emeritus, UCLA School of Law(Moderator) Shlomo Klapper, CEO & Founder, Learned Hand

In this episode of Kilowatt, Bodie starts with Europe's first fully driverless robotaxi rides as Verne and Pony.ai put modified Arcfox Alpha T5s on the streets of Zagreb. ProLogium is moving its solid-state battery technology closer to automotive use, with Gen 3.5 cells in mass production and Mercedes-Benz securing priority access to its upcoming Gen 4 cells. Cyclic Materials has opened a new rare-earth recycling facility in Mesa, Arizona, while VinFast has turned its VF Wild pickup concept into an extended-range EV with around 155 miles of battery-only range. On the Tesla side, FSD v14.2 Lite is beginning to reach Hardware 3 Model S and Model X vehicles. Tesla has also built its first Cybercab using cathode active material produced at Giga Texas as the company continues bringing more of its battery manufacturing in-house. Tesla is working with Irish authorities on FSD approval while an EU-wide decision has been pushed back, and Optimus production has reportedly climbed to several hundred robots per week while manufacturing and general-purpose capability remain works in progress. Support the Show Support Kilowatt Other Podcasts Beyond the Post YouTube Beyond the Post Podcast Shuffle Playlist 918Digital Website Friends of the Show Live With It Bodie joined Sarah Lane to talk about the surprisingly complicated technology packed into modern fire trucks. Fire Truck Tech: It's Complicated - Live With It - Podcast Fire Truck Tech: It's Complicated - Live With It - YouTube Publications InsideEVs Electrek Not a Tesla App News Links EV News InsideEVs: Europe's First Truly Driverless Robotaxi Just Started Rides In Croatia Electrek: Mercedes-Benz Gets First Dibs on ProLogium's Gen4 Solid-State EV Battery Cells Electrek: A New Arizona Plant Is Going After the Rare Earth Supply Crunch Electrek: Trucks Gone Wild! VinFast Launches VF Wild EREV Pickup Tesla News Not a Tesla App: Tesla Starts Rolling Out FSD v14.2 Lite to HW3 Model S and Model X Not a Tesla App: Tesla Builds First Cybercab Using In-House Battery Materials Not a Tesla App: Tesla in Talks for FSD Approval in Ireland as EU Delays Vote Electrek: Tesla Ramps Optimus to Hundreds a Week, but the Robots Can't Generalize Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Madonna und Kate Moss, David Bowie und Rihanna, Naomi Campbell und Melania Trump: Ellen von Unwerth hat sie alle in Szene gesetzt. Die gebürtige Deutsche, die heute in Paris und New York lebt und einst Claudia Schiffer entdeckte, zählt zu den bekanntesten Star-Fotografinnen der Welt. Mit legendären Mode- und Werbekampagnen wurde sie berühmt, inzwischen hängen ihre Werke auch in renommierten Museen und füllen Bildbände. Ihr eigenes Leben ist so spannend wie ihre Bilder. Im Gespräch mit Multi-Aufsichtsrätin Simone Menne erzählt Ellen von Unwerth, wie sie als Waisenkind bei verschiedenen Pflegefamilien aufwuchs, später in eine Hippie-WG zog und als Zirkusmädchen arbeitete. Ein Casting-Direktor entdeckte sie an der Uni und engagierte sie für eine große Kampagne. Der Startschuss für ihre Karriere als Model. Zehn Jahre später änderte ein Geschenk alles, sie trat hinter die Kamera. Ein Gespräch über Models, magische Momente und den Male Gaze. Links: LinkedIn: https://www.linkedin.com/company/ellen-von-unwerth/?originalSubdomain=es Insta: https://www.instagram.com/ellenvonunwerth/?hl=de Buch: Ellen von Unwerth: Heimat. Taschen 2024, 452 Seiten. +++"Die Boss" ist ein Podcast von RTL+. Gastgeberin: Simone Menne. Redaktion: Alexandra Frank, Kirsten Frintrop, Isa von Heyl, Sarah Klößer und Sarah Stendel. Mitarbeit: Lara Asmann. Projektmanagement RTL+ & Schnitt: Kirsten Frintrop und Alexandra Frank. Postproduktion & Sounddesign: Aleksandra Zebisch.+++ Dieser Podcast wird vermarktet von Julep Media: sales@julep.de Wir verarbeiten im Zusammenhang mit dem Angebot unserer Podcasts Daten. Wenn Sie der automatischen Übermittlung der Daten widersprechen wollen, melden Sie sich hier: datenschutz@julep.de

Read the full article: https://petronella.ai/blog/jeff-jev-compatible-0-8b-decision-models-trained-at-home-30-ms/A conversation about "Jeff Jev Compatible 0 8B Decision Models Trained At Home 30 Ms" from the Petronella Technology Group, Inc. blog.Subscribe to Encrypted Ambition and hear every episode: https://petronellatech.com/podcasts/Questions about AI, cybersecurity, or compliance for your business? Call Petronella Technology Group, Inc. at 919-348-4912.

Plus: Nearly 19% of working-age people now use AI, per a Microsoft study. And Federal Reserve Governor Lisa Cook expects continuing inflation pressure from the AI buildout. Julie Chang hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

You can be a top model and still not get recognized on the street — as long as you keep your cuticles healthy and your moons white. Zachary Crockett points a finger. This episode was originally published on January 19th, 2025. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Alex Mashrabov is the Founder and CEO of Higgsfield, the third fastest scaling company to $1BN in ARR behind OpenAI and Anthropic. Reports suggest their latest funding round could place an $8BN valuation on the company. Prior to Higgsfield, Alex sold his prior company to Snap Inc for $166M. Alex was a competitive programmer as a kid, reaching third best in the world. AGENDA:  00:00 The Programming Prodigy Who Sold to Snap for $166M 09:00 Burning $10M: The Pivot That Saved Higgsfield 12:00 A $1B Revenue Run Rate in 18 Months; How Real Is It? 16:00 Will OpenAI and Google Wipe Out $20 AI Subscriptions? 21:00 Are AI Labs Gaming the Benchmarks? 28:00 Spending $4M a Month on AI; Genius or Insanity? 36:00 Are AI Moats Bullshit? The Great "Wrapper" Debate 43:00 One Full Day With His Son in Three Months: The Cost of Ambition 54:00 Quickfire: Is Snap Broken—and Will HubSpot Survive AI? 01:02:00 $10B in the Next 12 Months? Alex's Audacious Growth Bet  

Thomas E. Hanzel joins host Todd Eury to discuss how AI, automation, data access, and emerging at-home care models can help long-term care pharmacies improve efficiency, expand patient care, and diversify revenue. Host: Todd Eury Guest: Thomas E. Hanzel, PharmD, MBA

Marto, Margaux & Dan debate whether following half-naked influencers counts as cheating, after a listener catches her boyfriend constantly having them on his algorithm. See omnystudio.com/listener for privacy information.

Miniature Models en liquidation : Reza Uteem appelle la direction à s'acquitter de ses obligations envers les employés by TOPFM MAURITIUS

Marco Pirroni in conversation with David Eastaugh https://www.simonandschuster.co.uk/authors/Marco-Pirroni/266126980 Marco Pirroni is a songwriter, guitarist and producer. A lynchpin of the UK punk scene, Marco's first appearance on stage was with Siouxsie and the Banshees at the 100 Club in 1976. He went on to join bands the Models and then Rema-Rema before teaming up with a little-known punk outfit – Adam and the Ants – in 1979 and within a year the band was on the brink of worldwide acclaim. An integral part of the band, Marco acted as lead guitarist and co-songwriter with Adam. Marco's work with Adam left an indelible commercial and creative stamp across the 1980s and pop music in general. Marco went on to work with other artists including Sinéad O'Connor and had become regarded as an authority on the punk movement.

On this week's show we take a look at the consumer reports ratings for 70 inch and larger TVs. We also read your email and take a look at the week's news. News: Your Google Home can now take orders from Claude and other AI agents Roku Adds 30 New Bundles To Premium Subscriptions Element Launches New TiVo-Powered Smart TVs in the U.S. Paramount Settles States' Antitrust Suit, Clearing Way for Warner Megadeal Consumer Reports Ratings for 70-inch and larger TVs: On this week's show we take a look at the consumer reports rating for 70 inch and larger TVs. This list was sent to us by Dermot who thought it may make a nice topic. We took the list and only updated pricing. So how does Consumer Reports determine a score for their TV ratings? Consumer Reports builds each TV's Overall Score from three buckets: lab performance, member surveys, and (for smart TVs) privacy/security. They do not accept manufacturer samples. Secret shoppers buy the sets at retail so they test the same units you would buy. They run 200+ TVs a year. Each set goes through 20+ tests and more than 500 data points in their Yonkers labs. Picture settings are optimized with the on-screen user controls only—not a full professional calibration. If a set needs a calibrator to look good, that hurts it. What the lab actually measures HD and UHD picture quality — test patterns plus real movie/TV clips for detail, black level, contrast, and color accuracy after the set is optimized. HDR — peak brightness (measured with a Photo Research PR-740 spectroradiometer), plus how well highlights and shadows hold up in dark and bright scenes. Viewing angle — color and contrast from both horizontal and vertical off-axis positions. Motion blur — how clean moving text and action stay. Sound quality — built-in speakers: clarity, bass, volume range, and distortion, compared against reference sets in the lab. Features / versatility — inputs, smart-TV functions, ease of use, etc. Data privacy and data security — whether the TV collects extra data, how easy it is to control that, encryption, known vulnerabilities, update practices. These scores are folded into the Overall Score. A viewing panel then watches real content side-by-side after the meter work is done. Survey piece  Predicted reliability and owner satisfaction come from CR member surveys, not from the lab unit they bought. Reliability is brand-level, not model-level. A statistical model estimates the chance a new set from that brand will have a problem in the first five years. They control for age, how often it's used, and whether it has an extended warranty. Owner satisfaction is how likely members are to recommend that brand. It is weighted at 5% of the Overall Score for every product category. Reliability's weight varies by category (typically 10–20%). For TVs it sits toward the lower end of that range because members rate reliability about even with picture performance. If a brand scores Poor or Fair on predicted reliability, that model cannot get a CR "Recommended" badge, no matter how well it tested. When they don't have enough survey data for a brand (like  on some Roku sets), they plug in category-average survey scores. How it becomes one number Overall Score is a weighted mix of the lab scores plus those two survey scores (and privacy/security). Models are then ranked against others in the same size group. That's why two 91-point sets can sit at very different prices: one won on lab performance plus surveys at a lower street price; the other is a more expensive flagship that still maxed the same scoring formula.  Brand            Model                  Overall     Score Price LG                  OLED77C5PUA   91              $1,800 Samsung      QN77S95F           91              $3,000 Samsung      QN77S90F           90              $2,500 Samsung     QN77S90H           89              $2,600 LG                OLED77G6WUA   89              $3,800 LG                OLED77G5WUA   88              $3,150 LG               OLED77C6HUP     88              $2,700 LG               OLED77M5PUA     87              $3,870 Samsung    QN77S84FA          85              $1,600 LG               OLED77B5PUA      84              $3,000 Sony           K-77XR80               83              $3,000 Sony          K-75XR90                83             $3,500 Samsung   QN75QN90F          82              $2,000 Sony          K-77XR8B               78              $2,500 Sony          K-75XR70                78             $1,500 Hisense    75U8QG                   75             $1,700 (65") LG              75QNED92AUA      75             $1,680 TCL           75QM8K                   74             Mostly Unavailable Sony          K-75XR50                73             $2,000 Roku          75R8B5                    73            $1,250 Roku          75R8C5                    72            $1,200 Hisense     75U75QG                 72            $1,700 (65") TV COMPARISON SUMMARY TOP TIER (Score 88-91) LG       OLED77C5PUA     91    $1,800    Best score-to-price Samsung  QN77S95F        91    $3,000    Same score as C5, $1,200 more Samsung  QN77S90F        90    $2,500 Samsung  QN77S90H        89    $2,600 LG       OLED77G6WUA     89    $3,800    Highest-priced LG LG       OLED77G5WUA     88    $3,150 LG       OLED77C6HUP     88    $2,700 Our takeaway is that the only score that actually matters here is not "91 vs 89." It is that the LG C5 is tied with Samsung's flagship S95F at 91, and it is $1,200 cheaper.  If we were going to spend money for a 75–77" set, we would treat the list like this: Buy the C5 if the room is normal-to-dim and you want the best picture per dollar. Buy the S95F only if the room is bright, you want a matte screen, and you specifically want a bright picture. Do not buy it just because it is tied at 91. The Samsung S84F at $1,600 / 85 is the value model if you don't want to go OLED. Anything at 75 or below is fine for a secondary room. Especially when you consider models from Samsung and LG providing higher quality for barely more in cost.  Does Consumer Reports still matter? Yes, but not as the only vote, and not the way it did 15 years ago. There are other sources out there and each weighs aspects of TVs differently. With that said, they all agree that the LG C5 is a really nice TV.

From the earliest days of open-weight models to becoming the neutral routing layer for more than 10 million developers, OpenRouter is one of the clearest bets that the future of AI will be multi-model. In this episode, OpenRouter co-founder & CEO Alex Atallah, with AMP's Anjney Midha returning with swyx to unpack how OpenRouter emerged from the first wave of Llama, Alpaca, Mistral, and Midjourney, why model diversity mattered before it was consensus, and how a company dismissed as “just a wrapper” became critical infrastructure for the AI ecosystem.We go deep on the product and distribution lessons behind OpenRouter: why model labs can spend billions training a checkpoint and still struggle to get it into developers' hands, how Mistral helped prove the value of a competitive inference marketplace, why OpenRouter chose focus over expanding into fine-tuning, memory, and other adjacent products, and how its rankings became a real-time map of how AI usage was changing. Alex also explains OpenRouter's early experiments with model fusion, why they deleted the first version and brought it back years later, and how the platform grew to more than 10 trillion tokens per day.Finally, Anjney explains why Stripe and OpenRouter fit together, why token fraud may become one of the defining security problems of the AI economy, and why the next wave of fraud won't just come from humans but from autonomous agents attacking increasingly valuable token flows.We discuss:* Why OpenRouter bet early that no single AI model would win everything* Alpaca, Llama, and open models becoming impossible to ignore* Why Discord's early AI deployments exposed the limitations of closed models* Why model labs can spend billions on training and still fail at distribution* How OpenRouter became a neutral distribution layer for model developers* Why VCs dismissed OpenRouter as “just a marketplace” or “just a wrapper”* The Mistral price war and the first real proof of an inference marketplace* How Midjourney scaled through Discord and what it taught the AI ecosystem* Why crypto infrastructure became a dress rehearsal for generative AI* OpenRouter vs. LM Arena and why their missions are fundamentally different* Why focus became one of OpenRouter's biggest strategic advantages* Anthropic's early focus on AI pair programming and coding* The OpenRouter products that were prototyped but never launched* MOM, OpenRouter's early Mixture of Models experiment* Why model fusion failed in 2024 — and why it works much better now* How OpenRouter's leaderboard became a live map of the AI industry* OpenClaw, auto-routing, and agents reshaping AI usage* How OpenRouter reached 10+ trillion tokens per day* Why inference gateways are increasingly becoming targets for fraud* Why Stripe's fraud infrastructure is strategically important to OpenRouter* The coming rise of agentic fraud and attacks on the token economy* What changes and what stays the same as OpenRouter joins StripeAlex Atallah* LinkedIn: https://www.linkedin.com/in/alexatallah/* X: https://x.com/alexatallah* Website: https://alexatallah.comAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney/* X: https://x.com/AnjneyMidha* AMP: https://www.amppublic.com/Timestamps00:00:00 Introduction00:02:12 Alpaca, Llama, and the Multi-Model Bet00:06:04 Discord, Open Models, and OpenRouter's Origins00:14:28 Why “One Model Wins” Was the Wrong Bet00:17:27 Why Model Labs Struggle With Distribution00:23:04 “Just a Wrapper”: Why VCs Misunderstood OpenRouter00:27:58 Bootstrapping OpenRouter Through Community00:36:16 Crypto, Midjourney, and the Early Generative AI Ecosystem00:43:38 Mistral and the Birth of the Inference Marketplace00:47:10 OpenRouter vs. LM Arena00:52:08 Focus, Anthropic, and Roads Not Taken00:59:34 Mixture of Models and Model Fusion01:02:44 Sonnet, OpenClaw, and OpenRouter's Explosive Growth01:09:03 Why Stripe Acquired OpenRouter01:12:45 Fraud and the Emerging Token Economy01:17:47 The Coming Wave of Agentic Fraud01:19:07 What's Next for OpenRouter at StripeTranscriptIntroduction: OpenRouter, Marketplaces, and Pub-Sub as a Product PrincipleSwyx [00:00:00]: Okay, we are here in Anja's house, which is where all big startups in San Francisco start.Anjney Midha [00:00:08]: Howdy.Swyx [00:00:08]: And, congrats on Cursor, Mistral. I don'- God knows what else. You got so much stuff going on.Anjney Midha [00:00:17]: There's, there's a lot going on. Well, OpenRouter is probably the - has been the most, I would say, like, one I'm excited about recently.Swyx [00:00:24]: Yeah. And we have Alex, first time on the pod, but,Anjney Midha [00:00:27]: Thanks for having me.Swyx [00:00:27]: You've been in the IE a few times. I appreciate every time you've shown up, for the community. Congrats. I just, like, what a journey. When I was looking back at your past posts, one of the earliest principles that I saw you write as a product person is sub as a product principle. And I wanted - you to maybe explain how you think about what should exist in the world.Anjney Midha [00:00:49]: Yeah. The sub piece, which was early 2023, I didn't think about it until we talked like 10 minutes ago, is about how there is like a way of thinking about products as an intersection between subscribing to data and publishing data. And marketplaces are an easy example of this. You have suppliers that are publishing some product to a SKU. And the SKU is like a sub topic that a consumer is subscribing to and just going to, like, consume whenever they want. And humans consume in a very, like, discreet, ad hoc way. It's not very scalable. all their attention is on the topic when they're buying the thing, and their attention is nowhere else when that happens. agents and consumers of inference don't act like that. They're consuming continuously, and they're changing the SKUs that they consume from all the time. So OpenRouter is like a blend between a normal API experience and a marketplace where we create model slug. We have the auto router. We have all kinds of, like, product SKUs that you can subscribe to. And then you can, like, continuously add, like, derive value and make decisions based on those consumers.Alpaca, Llama, and the Multi-Model BetSwyx [00:02:11]: Yeah. This is something that was more consensus now, but not consensus when you guys started, which was that there is such a demand for swapping models and changing things out and, that people would not use the native SDKs. I guess, for each of you, what was your realization moment that this would be it? I, - You've, you've given a talk at EIE about Alpaca as,Anjney Midha [00:02:33]: Yeah.Swyx [00:02:33]: One of your inspiring moments.Anjney Midha [00:02:35]: Alpaca, I can, like, rehash the Alpaca moment for a sec. Like, the very beginning, at the end of 2022, OpenAI was the only game in town. There was, like, OpenAI, Cohere,Swyx [00:02:47]: Yes.Anjney Midha [00:02:48]: And then a smattering of, like, early attempts at open weight models.Swyx [00:02:54]: Yeah.Anjney Midha [00:02:54]: When Llama came out in January of 2023, it was like, “Wow, really exciting. This is really big.” It outperforms 3 on, one or two benchmarks. but you can't chat with it. It wasn't like - It wasn't an engaging model, but it seemed like someone just needed to fix a couple things and do some RLHF on it to get it all the way there. And Alpaca was the first model that I saw that did that. It only took $600 to do. A team at Stanford generated a bunch of synthetic data, tuned Llama, and made Alpaca, billion parameter model. Or was - Maybe it was thirteen billion parameters. And it was so good. Like, I was just, like, on an airplane using it. I, - in many cases, I, like, you could not discern a ChatGPT versus an Alpaca result. And I figured if it was this easy to make a model, one, we have a whole new way of monetizing data for the first time. you can just, like, take really valuable data and turn it into a service in $600. and that cost will probably go down over time.Swyx [00:04:03]: When you - So sorry. when you say monetizing your data as, what eventually will become an MCP endpoint or as a training data for a model?Anjney Midha [00:04:12]: Yeah, training data for a model.Swyx [00:04:13]: Awesome.Anjney Midha [00:04:13]: Like, an abstract way of saying like, “Hey, I have this data.”Swyx [00:04:15]: Compress it into a model.Anjney Midha [00:04:16]: Like, it makes sense for me in my product, but, like, I could repackage it in the form of a model and sell it. And so it's just a whole new business model for the economy. It also, of course, provides, like, a way of following what Frontier Labs are doing, but in a way that, like, a single developer or a small team of developers can roll on their own. And so - Whenever you have an example of that, like a breakout app that's doing really well, and then some framework for imitating it with - in your own flavor, you have an immediate ecosystem of, like an immediate ecosystem, like, should arise because there's just a huge gap between the, like, decisions that the single company is making and all of the variations in those decisions that, like, a wider ecosystem can create themselves. And so then, you need a marketplace to, like, discover all of those, services and all of those products. There wasn't any place on the internet that, like, was like a home base for LLMs in terms of seeing how much they were being used and seeing who was using them and why.Swyx [00:05:29]: The closest would be Hugging Face.Anjney Midha [00:05:30]: Hugging Face was the closest at the time, yeah.Swyx [00:05:31]: They just started Hugging, like, a few years ago before that.Anjney Midha [00:05:34]: Yeah, and Hugging Face also didn't have the closed-source models.Swyx [00:05:37]: Yeah.Anjney Midha [00:05:38]: And they didn'- you couldn't use the models at the time. and there wasn't data about who was using them. There were, like, a bunch of differences between OpenRouter and Hugging Face, and those differences felt really critical to me, especially when I was just trying to learn about LLMs and, like, why people are choosing, like, Different little ones that are emerging over time.Discord, Open Models, and the Origins of OpenRouterSwyx [00:06:03]: Got it. And then, Ansh, no stranger to wanting more model diversity, at the time, you're a couple of years into your Anthropic journey, which we covered in the previous podcast as well. What was your introduction to Alex?Alex Atallah [00:06:16]: Well, the introduction was, I think, thirteen years before that.Swyx [00:06:20]: Oh.Alex Atallah [00:06:20]: But the OpenRouter handshake happened right over there, if you remember.Anjney Midha [00:06:23]: Yeah.Alex Atallah [00:06:24]: Which - So Alex and I, met, I believe as sophomores now, if I remember at the Stanford Review,Anjney Midha [00:06:32]: That's rightAlex Atallah [00:06:32]: Meeting for the first time.Anjney Midha [00:06:33]: I think so, yeah.Alex Atallah [00:06:35]: Yeah.Anjney Midha [00:06:35]: Yeah.Alex Atallah [00:06:35]: So Stanford Review was the libertarian newspaper on campus at Stanford that Peter Thiel started back in the day. And, whatever-- for whatever reason, I, Alex and I both showed up to one of the meetings, and I remember, the editor-chief was a mutual friend of ours. Lisa was really a really great editor-chief, where, part of an editor-chief's job is to assign responsibilities to people and make sure the work gets done. and I, I may be misremembering the details, but I remember wanting to. It was surprising to me that at the time there was no dedicated technology section in the newspaper.Alex Atallah [00:07:11]: YouSwyx [00:07:13]: Because it's political, right?Alex Atallah [00:07:14]: It is primarilySwyx [00:07:14]: Like, it's talkingAlex Atallah [00:07:15]: It originally started as like aAnjney Midha [00:07:16]: Yes.Swyx [00:07:17]: Yeah, states and all those things.Alex Atallah [00:07:17]: Correct.Swyx [00:07:18]: Yeah.Alex Atallah [00:07:18]: But it, - To take us back in time, you may remember this, but, there was this technology, legislation that was being debated called, the Net Neutrality Act. And net neutrality is, like, inherently this political concept, right? It's, it's about the regulation of - internet broadband access. And so there was a community of us who were technologists, but also debating the politics of the technology. And I thought the Review would be a great place - to, like, write about that. And I was working on, I think, a net neutrality article, and I remember proposing, “Well, maybe we should start a technology section.” And Alex was one of the only people who said, “Yes, that would be cool.” And said. I forget whether we ended up writing stuff together, but - that's when we first met,Alex Atallah [00:08:03]: Was 2011 or twelve. I forget which year it was. It was one of those.Anjney Midha [00:08:09]: Yeah.Alex Atallah [00:08:09]: It was at Old Union, if I remember correctly.Alex Atallah [00:08:11]: That's where we used to meet. But, along the way, Alex and I have had a chance to, To hang out often. And probably the time when we had the most professional overlap was when I was running the platform at Discord, and it had become this explosive platform for cryptoSwyx [00:08:32]: YeahAlex Atallah [00:08:32]: And NFTs in the middle of the pandemic.Swyx [00:08:35]: Which also, by the way, you were in charge of safety and security as well, right?Alex Atallah [00:08:38]: I was the head of platform, which meant all of the crypto - the DAO and NFT launch security debugging fell onSwyx [00:08:45]: And their phishing and.Alex Atallah [00:08:47]: The phishing, the social engineering attacks, the katana DDoS that we were getting hit by. but it's around the time I first started teaching security at scale at Stanford, CS 153. And Alex was on the, - at OpenSea at the time, and I was trying to figure out how we could defend against all these attacks that we were. Like, and at peak, I forget, if you remember how much NFT volume was running throughSwyx [00:09:10]: DiscordAlex Atallah [00:09:10]: Discord, but it was, like, a meaningful amount of, like, it was, like, several billion dollars in NFT volume of GMV, so to speak, were running through the platform, and it was all coming from OpenSea. It was these, like, buy, sell,Swyx [00:09:20]: TheAlex Atallah [00:09:21]: ServersSwyx [00:09:21]: The D in DAO is Discord.Alex Atallah [00:09:25]: Yes. And so that's when I think we had hung out professionally. But a year after that, OpenAI gave Discord early access to GPT. Sorry, three. No, it was five. Yeah, five, which is the RL version of three. And that's around the time we made a Discord bot with, OpenAI for internal deployment, and that's when I realized we would need. Like, since I was part of the deployment team.Anjney Midha [00:09:50]: What was the use case?Alex Atallah [00:09:51]: There were two that were. And there's, there's a post now called “Discord is Your Place for AI with Friends” that somebody sent me recently that I wrote, and published in twenty-three. But There were two use cases. One was Clyde, which was the - like, a party friend inside of Discord that could help you set up your Discord server and talk to you about onboarding and get your friends to hang out more. and then there was content moderation. And one of the realizations we had with content moderation was - it would refuse to moderate. Like, it would just refuse our prompts because the The training was. We were very early in the training era, and it would just. Our prompts would trigger it, its, like, guardrails. And we told OpenAI, “Hey, guys, we need access to the weights because if we're gonna be doing content moderation at scale, we had 250 million monthly active users, we need more reliability that the model will do what we need it to.” And they said, “Well, sorry, guys, that's not how this works. We're a closed-source company.” And so that was my first realization that we needed open models, and the enterprises would need more control over capabilities, and then ultimately would need some control plane or management system to orchestrate these open models. But there weren't no good - there were no good open alternatives until maybeAlex Atallah [00:11:10]: Six months later when Llama came out. And six months after that, I led the series A into Mistral, which was started by Guillaume and the Llama team. And - That, - Around that time is when I remember hearing about Alex launching OpenRouter and going, “These worlds are gonna collide, and I don't know when it'll make sense to team up.” But Alex was so early and could see. I think he was totally right about this ecosystem starting with Llama that then needed, like, a, an easy layer to manage for, especially for. I was approaching it from the enterprise perspective because I had been that, like, the. As the VP of platform at Discord, it was my job to ensure that when we deployed models to, like, 250 million users, they did what we wanted them to. And that was very hard, because if you outsourced it to the labs and they controlled the guardrails and their guardrails are their safety policies. Forbid the model from responding to your prompts. That was quite catastrophic.Swyx [00:12:05]: Yeah. But what, a moderation is the thing that they want to support. And obviously, beyond that, they would - OpenAI would work with you, presumably to give you a moderation endpoint, which they offer for free.Alex Atallah [00:12:16]: It was an interesting use case, that - So they did give us a moderation endpoint. However, as you guys know, every Discord server is like a mini deployment of itself. And so the use case was instead of having human moderators that have to interpret the norms of the community, you just give the, - Often, like every, subreddit, Discord servers, public ones have their own rules that the user, the users create.Swyx [00:12:41]: Oh, yeah. We run the LinkedIn Discord in. Yeah.Alex Atallah [00:12:43]: And then humans used to read those norms and then enforce it every day manually, like observing each message in these communities. And these communities have like millions of users. So we had a 5,000+ person team globally in the, on the Discord content moderation team. These are outsourced contractors who had a really tough job. And so the idea was instead, if you could give the norms of that server To the LLM, then the LLM would do custom moderation for that server. It's almost like a, like context moderation for that server. And many of those servers' norms just violated OpenAI's rules. And so - It was like we had our own custom eval. So each server had its own custom eval. But Discord-- at the time, OpenAI's evals, we were all soAlex Atallah [00:13:28]: Primitive in our thinking about how to deploy these LLMs that often the training prompts were super handed. It said, “Oh, anything about Harry Potter, anything that has trademarked content, don'- refuse.” And if it was a fan - Harry Potter fan community, this is a real use case, that had content moderation, the LLM would just refuse.Swyx [00:13:48]: Yeah.Alex Atallah [00:13:49]: And that was just not precise enough.Anjney Midha [00:13:52]: Another one that we heard was like if someone was trying to write like a detective story, and there's one chapter with a lot of violence, like maybe someoneAlex Atallah [00:14:01]: RightAnjney Midha [00:14:01]: Like kills someone, the LLMs would just refuse to, like, help with that part of the story.Alex Atallah [00:14:07]: Yeah.Anjney Midha [00:14:07]: And then - like, we used to be like, okay, this is not like structurally inherent to LLMs. There must be, like, some choice out there so that I can, like, switch to another model, when I'm getting, like, a refusal or a bad result from the main one that I have. And that, like, tension also drove me for a marketplace.Why “One Model Wins” Was the Wrong BetSwyx [00:14:28]: Yeah. I think that is well accepted now. What was it like back then when you were raising or, starting this? did people get it? what was the, some of the struggles? I like getting stories out of him about how other VCs don't get it. So like anything you wanna, talk about, now - Let's, let's call it, that the early journey of OpenRouter is done, right? You can obviously talk about some of the early days stuff.Anjney Midha [00:14:54]: Well, I was gonna say that, like, the biggest objection we got is big model win, which is - all of theSwyx [00:15:03]: Scaling laws.Anjney Midha [00:15:04]: Huh?Swyx [00:15:04]: Scaling laws.Anjney Midha [00:15:05]: Yeah, scaling laws, and natural network effects are just gonna accrue to one company, which will be - It'll be a Google-style monopoly, just like how Google won the search market, by a large margin, and you'll just be fighting for scraps at the end. That was probably the biggest objection we got. it is interesting that Google won the search engine race with such a huge margin. I think, like, had there been more interesting benchmarks or had, like, search engines been, - had people, like, seen them a little bit more like LLMs where they're services that you can build companies on top of, that might not have been the case. but LLMs don't merely have a user interface. They're also, like, ways of building entirely new businesses. And, a Google-level monopoly would be like the Dutch East India Company times, quadrillion in magnitude because the whole economy ends up, like, depending on the one monopoly as well. So it didn't seem like would be a really crazy outcome if that happened. And it's also less likely because the economics of, like, creating good competitors are much, like, much more decentralizable.Alex Atallah [00:16:25]: Everything Alex said is true, And I came at it from a completely different perspective, whichSwyx [00:16:31]: Yes, this is why we're here.Alex Atallah [00:16:32]: The scaling laws were never - In my mind, were always a feature, not a bug for why OpenRouter would be very valuable. Because, I was one of the first investors in Anthropic, and it was obvious to me that other researchers in our friends - I went to grad school for machine learning, and I just had a lot of friends in the ML community who it was very obvious to us that the bitter lesson holds. And so I was like, “Oh, fantastic. Now we have at least two proof points that compute scaling works.” It was OpenAI and Anthropic. and by the time I think we decided to team up on OpenRouter, I had already invested in Mistral and Black Forest Labs and Luma. So there was multiple model companies and teams that I was, working with.Why Model Labs Struggle With DistributionSwyx [00:17:14]: But you did other modalities, whereas this is literallyAlex Atallah [00:17:16]: Across different modalities, yesSwyx [00:17:17]: Text.Alex Atallah [00:17:18]: Exactly. And it was so obvious to me that an ecosystem of different kinds of models were being created, and that this whole narrative of, like, Only one company will dominate like Google was, well, like maybe true, but one, I don't believe that. But two, there was so much extraordinary innovation happening across several different research teams. But the shared problem I was noticing across all of them was often, the research teams were fantastic at figuring out how to reason about new capabilities. They think in terms of capabilities, but never - like, are not developer mindset-oriented. Like, what happens after the training is done and the checkpoint comes out? Like, you'd be shocked how, like, similar the early training teams at OpenAI, sorry, Anthropic, BFL, Mistral, were in their, like, default approach to. Taking their research out of the, lab and scaling their impact, which is often, oh, the checkpoint is done, put it out as an API, done, and then there'd be crickets. in the case of Claude, the first Claude checkpoint was done a year before they released it internally. And then ChatGPT came out, and we decided, okay, yes, it's a good idea to release a Claude version externally.Alex Atallah [00:18:34]: And they had no plan, like no plan for how to get developers to try it out. And so if you go to the Claude one blog post, you'll notice there are, like, three developer examples for users of the API, and one is a Discord bot, and the second is Vivian, my wife's startup called Juny Learning, ‘- And then there was, like, Notion, because these were all friends of, like, the Anthropic Because that's how - like, last minute the planning was around, hey, once the model's done training, how do you get it out to the world? There was no distribution platform that understood what developers needed, all the key management, provisioning, like, simple, like, endpoint management, versioning control. Like, all these things that the scientists and researchers go, “ that's plumbing. I don't really think about it.”Swyx [00:19:15]: Implementation detail.Alex Atallah [00:19:16]: Right. And instead, Alex came at it from that perspective. And so, it was so obvious to me that, like, every single lab I was funding would spend - like, literally sometimes billions of dollars into training, and then a checkpoint would be done, and there'd be crickets, like, during early access because they're like, “Oh, that's right.”Alex Atallah [00:19:35]: It's hard to use a checkpoint to make anything. You need a whole bunch of plumbing around it to make it usable by a developer. And so by the - I think - it was so obvious to me that a distribution platform like OpenRouter was critical to have in the ecosystem if we wanted there to be competition to Google. Like, unless-- ‘cause with Google, DeepMind is done training a new checkpoint, and then they push a button, and it gets blasted out across all their surfaces from Google Docs to,Swyx [00:20:01]: Everywhere, even if I don't want it.Alex Atallah [00:20:02]: Everywhere. You wanna know about, like, on Android, like, overnight, they can deploy a new checkpoint to, like, a billion devices, right? And that invisible infra advantage, distribution advantage, most people don't realize, but until OpenRouter showed up, - you had to think about all of that yourself as a model lab. And it was very daunting. at Anthropic, I think it took, well, more than twelve months to get to our first 10 million in revenue. And in contrast with Black Forest Labs, I remember the early days, you guys had a conversation with the BFL team, and, it was so simple for OpenRouter to say, “Oh, no problem. Like, the day you launch, we can send 1 million developers to you.” that was crazy. That was like a step function change in, like, an hour.Swyx [00:20:46]: Is that a real number, a million?Alex Atallah [00:20:47]: I,Swyx [00:20:48]: Okay. All right.Alex Atallah [00:20:48]: I think today it's, like, 4 million. How many developers are on OpenRouter today?Anjney Midha [00:20:52]: Over ten,Alex Atallah [00:20:54]: Yeah.Anjney Midha [00:20:54]: Over 10 million, but, like, it's, it's hard to, youAlex Atallah [00:20:59]: I, yeah, I don't know how to. Yeah.Anjney Midha [00:21:00]: We do a lot of, like, account duping work, but, noAlex Atallah [00:21:04]: If you could get 1,000 developers, just to put in context If you get 1,000 developers who try the model on day one after you release it and just, like, do inference and give you feedback, that's a thousandAnjney Midha [00:21:15]: That's hugeAlex Atallah [00:21:16]: More developers than they knew how to get to on their own.Swyx [00:21:19]: Well, BFL had a reputation, but yes.Alex Atallah [00:21:21]: They had one in Stable Diffusion.Swyx [00:21:22]: Yeah.Alex Atallah [00:21:23]: And with Mistral, I don't know if you guys remember, but the first checkpoint they released was, like, torrents. It was, like, torrent weights.Swyx [00:21:31]: Yeah, they just put up a magnet link.Alex Atallah [00:21:33]: Yeah, there was no API.Anjney Midha [00:21:34]: Yeah.Alex Atallah [00:21:34]: Because they didn'- they weren't infra people.Alex Atallah [00:21:37]: ? Like, it's like, okay, download these weights, and you guys go figure out how to host it.Swyx [00:21:39]: Well, he has a story on his side, yeah.Anjney Midha [00:21:41]: Yeah, in addition to the, like, building a really good developer experience around it, the marketing that we do on, like, for different models is totally different and perceived totally differentlyAlex Atallah [00:21:54]: RightAnjney Midha [00:21:54]: From the marketing that a model lab does for itself.Alex Atallah [00:21:56]: Yes, 1,000%.Anjney Midha [00:21:57]: Right? We are like a, neutral layer looking at this market like it's a big dark room with all the corners completely obscure to users, and users are walking into the room and, like, feeling aroundAlex Atallah [00:22:09]: YeahAnjney Midha [00:22:09]: And trying to figure out what objects to grab off the tables and, like, build into, their companies. And it's just an insane way of working. Like, models are not products where you can just enumerate all their features onto a web page. They're all black boxes, including the open weight ones. So you need to, like, shine lights on all corners of this room, so that people can see what makes this model good, and you need the company shining that light to be a neutral third party, which is what we specialize in. So the, like. It'- In addition to developer experience, there's also, like, a very important, like, marketing and product packaging componentAlex Atallah [00:22:50]: YeahAnjney Midha [00:22:50]: And a way of, like, routing and discovering models becomes, like, critical to your market as a provider or a model lab or a server tool and more in the future.“Just a Wrapper”: Why VCs Misunderstood OpenRouterAlex Atallah [00:23:03]: And this value, to your earlier point about how many VCs, like, just don't. One of my biggest frustrations is that venture capitalists, many of them, like, just don't have any operating experience in the field. so unlike a traditional investor who's just maybe come up through the ranks as, like, a associate working on financial modeling or maybe hasn't been a real operator in the field for, like, more than ten years, which is a big part of the industry now, I had just arrived at a16z, like, a year after running the platform. And so I knew what the challenges were of, like, building a real - great developer experience and like, being able to create a working piece of software with a model. And there were a few, I won't name names, but there were investors who were looking at OpenRouter, and, felt at the time, like, when I would compare notes with people, that it was just, I quote unquote, “just a marketplace.”Swyx [00:23:59]: Yeah, just a thin layer, just aAlex Atallah [00:24:00]: CorrectSwyx [00:24:00]: JustAlex Atallah [00:24:01]: A wrapper or whatever on other people's APIs. And I was like, “You have no idea how strategic the value that OpenRouter has created by being able to orchestrate even three.” APIs in production. The amount of both engineering work and community design that goes into getting that live and running in production at the scale the OpenRouter team had started just doesn't happen by default. And that was one of the things that stood out to me about Alex from the earliest days. Like, he just understood, like, - from a systems perspective, like, how do you get these flywheels going? Like, that stood out to me with OpenSea when we were working together on the NFT integration at Discord. Like, Alex had a level of community-- like, systems thinking on how you get these flywheels going that most scientists and machine learning people just don'tAlex Atallah [00:24:48]: Think of. Like, we often think in terms of training.Swyx [00:24:52]: It's a linear stage.Alex Atallah [00:24:53]: It's this linear pipeline.Swyx [00:24:53]: There's no loop yet.Alex Atallah [00:24:54]: Yeah. It wasn't until much later that the modern context feedback loop cycle really got standardized in the industry. But at the time, if you remember, machine learning was like. Like, mostly we did a lot of ML, like, when I was in grad school on a laptop. So you just, like, download a dataset, ran some ablations, and you looked at the loss curves, and you're like, “Great, I made AI.” And the idea that you have to, like, deploy those capabilities, collect feedback trajectories, then, like, put those into a continuous loop, like, came much later. And it was very counterintuitive to the - like, the traditional AI mindset. I do remember doing the investment phase for, OpenRouter, I just didn't try and educate a bunch of other VCs on why it was not just a marketplace. I was like, “ what? I'm just gonna invest.”Anjney Midha [00:25:41]: Yeah.Alex Atallah [00:25:41]: And I'm going to, like, take the opportunity to partner with Alex, and if - no other VCs get it, that's totally fine. ‘Cause at the time, - it was not obvious, I think, to several of the investors that, like, OpenRouter was not more than just a wrapper around APIs. And - that infuriated me. And I was like, “ what? I don't have time to debate you. I'm - we're gonna, we're gonna invest.” And then I think, like, a month later, Matt Murphy marked it up by 10x. Like, - I think. I forget what the exact money was and so on, but, to his credit, Menlo Ventures realized, “Okay, there's much more strategic value here as well.” Maybe you didn't hear all these conversations behind the scenes But that frustrated me a lot. there's a lot of this, like, opining about wrappers. and if you're like, “Oh, an app is just a wrapper on a model,” then, like. And, OpenRouter is, like, this wrapper on top of other APIs, and this is the most stupid, reductive framework.Alex Atallah [00:26:31]: And so it's clearly somebody who has no experience deploying product at scale.Swyx [00:26:34]: It's the thing you dismiss other things with. Like, you're a - everyone's a wrapper on everything, right? Like, and there's, there's some Some wrappers have value.Alex Atallah [00:26:40]: Investors are wrappers and LPs, right?Alex Atallah [00:26:42]: Like venture capitalists. So, yeah, it's all wrappers down, all down to bare metal, I guess, and like energy.Swyx [00:26:46]: Yeah, there - When I started the whole AI engineer, I guess, the coining, in 2023, like, that was, like, the number one pushback is that this is no value. You should just train models.Anjney Midha [00:26:56]: Right.Swyx [00:26:57]: And, yeah, obviously this is, like. you guys are one of the testaments to the fact that you can build very valuable wrappers, but also very valuable model companies.Alex Atallah [00:27:06]: It's so, hard to be. Like, the day a model launches, the fact that you have an OpenRouter, endpoint for that model frequently at the top of Hacker News on day one, people don't realize the amount of work that goes into accomplishing that. And OpenRouter used. Like, that would happen over and over again, and I remember going, “People have no idea how hard that is.”Alex Atallah [00:27:30]: That's not.Swyx [00:27:31]: Yeah, we've covered some of the inference engineering that goes behind,Alex Atallah [00:27:34]: YesSwyx [00:27:34]: Some of - with Base Ten and all those. Well, today you have, all those, like, cool code name things that people guess what Oxy Alpha is and all those things. But, like, I guess one of the things that you're teasing is, how do you get that initial flywheel going, right? Because today you have your scale and your reputation, all these things, so obviously you - you're driving immense distribution. But when you were early on, when it's mostlyBootstrapping OpenRouter Through CommunityAlex Atallah [00:27:55]: The bootstrap, yeah.Swyx [00:27:56]: Yeah.Alex Atallah [00:27:56]: What was the bootstrap like?Anjney Midha [00:27:58]: To bring it back to early Discord days, I think we, like, initially connected with. This is an OpenSea story, technically. But, and we initially connected when you were at Discord, and we talked about, like, - the Axie Infinity server.Alex Atallah [00:28:13]: Oh, yes. Yes.Anjney Midha [00:28:14]: This server was, like, the biggest server at theAlex Atallah [00:28:17]: YeahAnjney Midha [00:28:17]: At Discord.Alex Atallah [00:28:18]: That's right.Anjney Midha [00:28:19]: And you were like, constantly bumping up theAlex Atallah [00:28:22]: The limits on the server. Oh, my GodAnjney Midha [00:28:24]: Of how many people could be in the server.Swyx [00:28:24]: For those who don't know, like, 10% of Philippines was Axie.Alex Atallah [00:28:29]: Was on that server. That's a big hit.Swyx [00:28:31]: It was, like, a meaningful contributor to the GDP of the country.Alex Atallah [00:28:33]: It was an NFT, like, crypto game, but itSwyx [00:28:35]: It was like a Pokémon breeding thing.Anjney Midha [00:28:36]: Yeah.Alex Atallah [00:28:36]: Yeah. Similar. Yeah. There was battling, there was breeding, and then there was, like, a marketplace for trading.Swyx [00:28:43]: Earn as well.Alex Atallah [00:28:45]: Yeah, earn. And, like, the graphics were really cute and fun, and you like, you get emotional about your Axie that you make. So to, like, start a community like that, which we had to do many times at OpenSea with every early project, for us to create a marketplace for it, we need to make sure that the, like, the community wants it.Anjney Midha [00:29:09]: Right.Alex Atallah [00:29:09]: And it's like building something that people want and going and telling them about it. Like, you can do that on a one basis, but there's way higher leverage to do that in a community where everyone can talk to you at the same time. So we spent a lot of time, like, building things that the community really wanted. We did the same thing for OpenRouter. And, like, the Axie community was one of, like, a zillion communities we did that with. And Anj, like, saw us doing it and. ‘Cause you could just see people sharing OpenSea links constantly in that Discord. Like, users sharing links is a really clear indicator that, like, something important is going on. So we spent, a lot of time, like, first figuring out what the gap is in the technology that people care about. Like, what was the actual problem that needs to be solved? in early LLM days, it was, OpenAI refusing to finish the prompt or,Anjney Midha [00:30:09]: YeahAlex Atallah [00:30:10]: To, like, complete the task. It was also.Anjney Midha [00:30:13]: Inability to customize models. and so there are communities that, like are just completely blocked on that issue, and those are the communities that are most useful to learn about and dive into and explore.Alex Atallah [00:30:28]: Something that really struck me at that time, - as I was just hearing your talk, I remember noting - you may not remember this, but we - we had these, like working, Zoom calls that we were doing a sprint around for, like this OpenSea integration with Discord. and, we'd, we'd - it was myself, my engineering team. I think you were there. And I remember, Alex, in the middle of one of those calls, just like there was like silence. we were all like, “Oh, yeah, this totally makes sense. Let's do this.” And then there's - every, like everybody aligned. And Alex was like, “No, this makes no sense to me.” And everyone's - I remember going, “What? Like, it works. Like, you click on a link and this, then it bounces you out to, like, OpenSea.” And he was like, “It's not a good user experience. Yeah, we should not do this.” And I remember going, he was the only one person out of all of us to raise his hand and go, yes, it made sense from a technical implementation perspective. Like, we were bouncing the user out into the, into OpenSea. And so it kinda checked the box of the product manager's requirements on both sides. But Alex went one step further and was like, “ what would be better, guys? If we just embedded the experience right here inside of Discord so the link opened up as an embedded iframe, and you can just check out right there.”Alex Atallah [00:31:47]: And not one person on the call, and there's like seven of us who had met, like, week after week.Swyx [00:31:52]: And it's the guy who doesn't work for Discord.Alex Atallah [00:31:53]: And it's the guy who doesn't work for Discord.Swyx [00:31:55]: Like, technically, you benefit if they bounce.Alex Atallah [00:31:57]: Exactly. And that was, like, adversarial. To keep the user inside of Discord would be adversarial to OpenSea. And yet Alex put that user experience first. And I was like, “That's special.”Swyx [00:32:08]: Wow.Alex Atallah [00:32:08]: Because it's very hard to have somebody who's technical like Alex and understands the developer flow, but also understands the best user experience and wants to prioritize that. And that's two sides of the flywheel that if you can get spinning, like is often hard to stop. And you just reminded me, like that one was one of those moments where I go, I - I realized I gotta be better at user experience because I should have been the one who came up with that, and I didn't. And I learned from you. And, I think that went into one of our case studies for the PM training program at Discord.Swyx [00:32:34]: Whoa.Alex Atallah [00:32:36]: I don't know if it there is Because ofSwyx [00:32:38]: You need an Alex is the conclusion.Alex Atallah [00:32:40]: Yeah. You need an Alex. And this is why I'm not, nobody should be surprised why Stripe decided like they had to buy OpenRouter because it's a really rare combination of people who understand the machine learning community, the developer experience, and the user experience. And putting all that together has resulted in this extraordinary scale that very few other marketplaces have been able to achieveWindow AI, BYOM, and Finding the Right Form FactorSwyx [00:33:02]: Yeah.Alex Atallah [00:33:02]: Over the last, five years.Swyx [00:33:04]: Yeah. Well, we should talk about the other reasons for acquisitions, whichAlex Atallah [00:33:07]: Yes, we should.Swyx [00:33:07]: You've written about. I wanna proceed somewhat chronologically as well. So - there is a point that, one of the questions that, Dave from H of Zero sent in was, when did it - really started to work? And you brought up Mixtral. I don't know if you wanna bring up that story.Alex Atallah [00:33:22]: Oh, yeah.Swyx [00:33:23]: Which obviously you overlap with, so.Anjney Midha [00:33:26]: Yeah, the MoE was. I don't know when. there's no like one moment where I was like, “Oh, this is, officially starting to work.” It wasSwyx [00:33:36]: The moment where you had a Chrome extension, like, really super early on.Anjney Midha [00:33:39]: Oh, yeah. But, well, - yeah. So before OpenRouter, I wanted to, like, explore a bring-your-own-model experiment. And,Swyx [00:33:47]: Which anyone familiar with crypto is like, yeah, Phantom and all these things.Anjney Midha [00:33:50]: Yeah. So it felt like doing a MetaMask analogy for AI would be a fun way of exploring that. And at the time, there were no AI apps. There were probably as many AI apps that were, like, hitting AI - like, hitting an LLM via an API call as there were, like, games just doing it in JavaScript. like there was a, there was a moment in time where it could have been the case that web apps call LLMs through the browser, like through some desktopAlex Atallah [00:34:27]: Yes.Anjney Midha [00:34:27]: Managed app that is controlled by the user. and of course, there are like, I think, many reasons that did not happen. But back when the days were that primordial, I built a Chrome extension called Window AISwyx [00:34:43]: With Plasmo.Anjney Midha [00:34:44]: With Plasmo.Swyx [00:34:45]: I had come across early on, and I was like, “Who's gonna use this?” You did.Anjney Midha [00:34:49]: Plasmo had a couple, like, I think Phantom was using it. there were some other, like real companies using it.Alex Atallah [00:34:56]: It was like a shim.Swyx [00:34:57]: React for Chrome extension. It compiles to allAnjney Midha [00:35:00]: Yeah.Alex Atallah [00:35:00]: I see.Anjney Midha [00:35:00]: Like Next.js for Chrome extensions.Swyx [00:35:01]: Next.js, Next.js.Alex Atallah [00:35:02]: Okay.Anjney Midha [00:35:03]: And yeah, built Window AI on top of it. The creator of Plasmo, like started contributing code to Window AI, in GitHub, and that turned out to be Louis VicchiAlex Atallah [00:35:15]: Oh, you'Anjney Midha [00:35:15]: Who is the founder of OpenRouter.Alex Atallah [00:35:17]: That's right. You have told me this is how you met Louis. Yes.Anjney Midha [00:35:19]: Yeah.Alex Atallah [00:35:19]: Okay.Anjney Midha [00:35:20]: So, that allowed users to like configure which model they wanted to use for a web page in their browser, and then, like the app would just call out to that model when it needed to do things. not the right form factor for LLMs, but, it's like fun experiment. You learn a lot, and like I open sourced it. And the main learning is like, okay, this has to be an API, and it has to look a little bit - like, there has to be more of a developer experience here and more of a discovery experience as well. Like, I don't know where to use these models, and a little Chrome extension is not gonna help me discover. It's not enough real estate. I need more space. I need visuals. I need graphs. I need, examples. I need images. I need to, like, I need to be able to, like explore both as a human and as an agent.Crypto, Midjourney, and the Early Generative AI EcosystemAlex Atallah [00:36:10]: Yeah.Anjney Midha [00:36:10]: So that's how OpenRouter came to be.Alex Atallah [00:36:13]: A meta point that.Alex Atallah [00:36:16]: I think is underappreciated, but Alex is reminding me, is that we were quite lucky that we were so. we were, like, adjacent to the crypto community in those days. Because in hindsight, crypto ended up being like a dress rehearsal for generative models, right? If you think about the Axie experience, Alex is totally right, there were not that many AI apps at the time. And while I was dealing-- my job was to be the head of platform at Discord, which meant to be a general purpose place for communities and friends to create-- for developers to create apps and bots and, other services that could be deployed across Discord. And while 80% of the attention at the time was being spent on crypto, because that's where all the NFT volume was, there was, like, twenty percent of my time I was spending with a friend, who would get hotbot with me and ask me for. We would play Magic: The Gathering on weekends, and he was working on a little Discord bot that could take a text input and turn it into an image, and it was called Midjourney. YouSwyx [00:37:15]: Is that David?Alex Atallah [00:37:15]: It was David Holz.Alex Atallah [00:37:16]: He was a good friend. And David and I have both been failed ARVR founders, in the before that. And, I remember this. Midjourney was one of the fastest-growing communities we had after Axie Infinity started to peter off. And many of the, like, the abstractions and the infrastructure decisions we made to scale Axie happened just in time because they. Axie did this and then fell off a cliff. And then as Midjourney was taking off, we, like, explicitly decided to help David make the server, the Midjourney server, as the primary place for interaction with the model, because it was very hard for people to understand how to use the model if they couldn't see other people using it and copy them. And so the single-player Midjourney web app on its own, like midjourney.com, had, like, terrible retention because people would show up, they'd see this empty field. It's like E 2, and they would type in, like, cat or dog. And it was, like, paralyzing for them to have this blank canvas that they had to fill because they'd never used an AI model before. But instead, in a Discord server, you could see other people using it and riff off of their prompt, and the engagement was off the charts. And so scaling, Midjourney from zero to, like, 10 million monthly actives was a much smoother approach Axie Infinity. And so,Swyx [00:38:29]: Don't forget the best of four pictures, and you choose one.Alex Atallah [00:38:31]: The best, yeah, and then the other, weSwyx [00:38:32]: Which is the feedback loop.Alex Atallah [00:38:33]: The RLHF feedback loop, which, by the way, separately, like, Tom Brown, David and I used to play Magic: The Gathering on weekends. And so, like, it was one group of friends would hang out, and we'd. Like, these concepts were all being discussed all the time. But, there was.Alex Atallah [00:38:47]: I think there were few of us who bridged both the crypto worlds and the AI worlds. And compared to crypto, where it was - the question was always, what's the use case, for this technology? There was never any need to ask that for AI because it's, like, the use case was so visceral. It was like, I can create now anything at - I can imagine. I can write novels, I can code. And the infrastructure that those of us who believed in the distributed systems, like, value of crypto, like the censorship resistance part, found this use case that was explosive. And I think between Midjourney, the, Claude was a Discord bot launch, that we were using internally as an LLM. ElevenLabs had a TTS model that we had on Discord as well. Like, Discord became this petri dish for, like, early apps to innovate. And I don't think it's a coincidence that they found a home there before OpenRouter gave the world, like, a public home store or, like, a, storefront. Discord was this, like, almost petri dish storefront that - had, like, piggybacked on the infra we'd built for crypto communities. And then I think Alex was one of the first people to realize, wait a minute, like, these apps need their own home, on the internet. And then OpenRouter, to me, was a continuation of that community's needs. And of course, there was the crazy distribution that you enabled for a lot of these developers.Why OpenRouter Couldn't Just Live Inside DiscordSwyx [00:40:07]: So then my question is, how come you were. My perception is OpenRouter is not that Discord-centric, right? You have a Discord.Anjney Midha [00:40:14]: Yeah.Swyx [00:40:14]: And you use it to engage your community, but it's not like Midjourney where, like, no, that is like the primary way people experience OpenRouter.Anjney Midha [00:40:21]: Yeah, Midjourney, like, it really helps to see visually really quickly how people are using the model and how to prompt it.Swyx [00:40:29]: Yeah.Anjney Midha [00:40:29]: And I think that is partly why the server was so critical. It's like it is the user experience. It adds a ton.Swyx [00:40:36]: Yes.Anjney Midha [00:40:37]: And you can go the whole mile with just, like, prompting via Midjourney, like, the, via the Midjourney Discord server, getting your images and then sharing them and having fun. For OpenRouter, for LLMs, like, you need a lot of user experience around LLMs to make them, like, really usable.Swyx [00:40:54]: Charge point.Anjney Midha [00:40:55]: And yeah.Anjney Midha [00:40:57]: The, like, seeing the examples of other people is also not as useful because it's a lot of stuff to read. It takes a long time.Swyx [00:41:03]: Yeah.Anjney Midha [00:41:04]: You need, like, based integration. Not possible to do in a Discord server. You need, Or technic- it's possible. I shouldn't say that. It's just not a great developer experience. you need, like, - you need governance for. At the point where you got based integration, now you need governance for managing the LLMs that have access to it, the data policies, which teams. All that stuff needs a lot more than a Discord server can provide. So it's justSwyx [00:41:30]: YeahAnjney Midha [00:41:30]: It's not the right.Alex Atallah [00:41:32]: Well, in addition, you're not wrong, but also there's the very important distinction that, Midjourney was an end user application.Swyx [00:41:40]: Right.Alex Atallah [00:41:40]: And, that's why Discord, which has 250 million monthly end consumers, made, it made sense for Discord to be a host for that application experience. What I knew was gonna happen soon after Midjourney found explosive product-market fit, because we. I think when Midjourney launched, from launch to $100 million revenue run rate, it was less than eight months. And shortly thereafter, Stable Diffusion launched. And, all of us used to hang out in the Discord server. There, I think it was the,Swyx [00:42:13]: The Stability Discord?Alex Atallah [00:42:14]: It was theSwyx [00:42:16]: Yeah, LAION.Alex Atallah [00:42:16]: Yeah, the LAION Discord server.Swyx [00:42:17]: The image community that spawned Stable Diffusion.Alex Atallah [00:42:19]: The image community. Yeah. And so when Stable Diffusion came out, I realized- Oh, now other people can build their own Midjourney.Alex Atallah [00:42:27]: Because until then, Midjourney did not have an API, so they were a stack company, right? They were training their own models, and they were deploying them as an application. But if you wanted to build your own Midjourney, there was no API of that quality. and I think E two was still quite primitive. Like, Midjourney had great quality. And then when Stable Diffusion came out, suddenly there was this new person who - there was - this new capability in the world, which is a developer could create their own Midjourney. And that, I think, created the need for something like OpenRouter, because then you need an API to. If you - if you had the creativity of David Holz and you had Stable Diffusion as the model and you wanted to put these things together, how could you do that without having to figure out how to host the weights? And what OpenRouter, - the shape of OpenRouter enabled is that. Right? When you have open model alternatives to closed applications, OpenRouter's value in the world becomes extraordinary because now any developer can just show up and use theStable Diffusion and the Need for a Model API LayerSwyx [00:43:20]: You just love model diversity.Anjney Midha [00:43:21]: Did you just say the shape of OpenRouter?Alex Atallah [00:43:23]: Oh, no.Anjney Midha [00:43:25]: Were you in cloud? What is this the real Han?Alex Atallah [00:43:26]: I've been, I've been - I'm, I'm misaligned now. I've been overtrained. I've been using Cloud way too much, haven't I?Swyx [00:43:34]: Claude-ish is what people would say.Alex Atallah [00:43:35]: Claude-ish. Oh, God, I gotta untrain myself.Swyx [00:43:38]: Okay. - And I just wanna cap off the Mistral side. my TLDR is there was a Mistral price war, is what they called it, right? Like, round about NeurIPS is twenty-three or twenty-four.Mistral and the Birth of the Inference MarketplaceAnjney Midha [00:43:47]: Yes. DecemberSwyx [00:43:48]: They launched, the Mistral 8x7B, and like the price went down like 80%.Anjney Midha [00:43:54]: Yeah.Swyx [00:43:54]: To me, that's very positive because it's like the first, like, real competition to host Mistral. Is there more?Anjney Midha [00:44:01]: Yeah, that was. I'm, like, trying to remember it, all the things that happened. It. Like, we saw that model come out and immediately saw people say that it was the best model in the world.Alex Atallah [00:44:15]: Yes.Anjney Midha [00:44:15]: Like, this was, to my knowledge, the first time an open weights model was called that in real seriousness.Swyx [00:44:22]: It's hype, right? Is it?Anjney Midha [00:44:25]: It was hype. It was hype. It was also, like, hype from AI influencers at the time. And there were many examples where it was, like, outperforming four. So people really wanted to try it out and see, is this gonna be true for me too? And if so, at what price? And, the, like, inference landscape was really messy.Alex Atallah [00:44:49]: Yes.Anjney Midha [00:44:50]: We cleaned it up. - it allowed, like, providers to compete on price, so we could give you just the best price in one spot. And so it was, I think, the first clear example of, like, a provider marketplace working in a way that adds value to end developers.Alex Atallah [00:45:08]: Sean, you may not remember this, but I think we met for the first time a few days after Mistral came out at NeurIPSAnjney Midha [00:45:15]: Yeah.Alex Atallah [00:45:15]: At a luncheon.Swyx [00:45:16]: Yeah. That's where I also met BFL as well. Yeah.Alex Atallah [00:45:18]: And Guillaume was there.Swyx [00:45:19]: Yeah.Anjney Midha [00:45:19]: I was at NeurIPS at that time.Alex Atallah [00:45:20]: You were there too. And, we had just announced the Mistral investment, and I remember Guillaume was over there, and I remember turning to Guillaume and asking him, Like, “Is it is all the. Like, how are you feeling after the launch of Mistral and seven B?” And, him in his typical French fashion was like, “ it's a, it's an okay model. It's not that good.” And I was like. It was so, in contrast. But I remember him also saying that part of the reason he felt a lot of people Thought that it was better than four was because of the speed. - it was an MoE model that they had, like, absolutely figured out how to make super efficient. It was on the Pareto frontier. And this is an important thing about LLMs, right? Sometimes when they're faster, you think they're smarter, even though, like, if you did, N of, these common, like, evals that are - you do seven tries, and I don't remember. I think we should go back and figure out what the data says, but I wouldn't be surprised if it turns out, oh, on an N of seven attempts, four was smarter on evals, but the perception of on, like, or correctness would be smarter or more accurate. But, people, like, from a human preference perspective felt that it was faster because it - or smarter because it's so fast.Swyx [00:46:36]: Yeah. And most queries do not take that levelAlex Atallah [00:46:39]: Don't take that. That's true.Swyx [00:46:40]: Right? So this is the start of humans as routerAlex Atallah [00:46:42]: Yes.Swyx [00:46:42]: Which then eventually becomes OpenRouter as router of like theAlex Atallah [00:46:45]: Oh, that's interesting way to think about it. Yeah.Swyx [00:46:47]: Like, because humans are the routing mechanism. Like, I will ask the fast model first, and then if, like, oh, not good enough, I'm gonna upgrade manually.Alex Atallah [00:46:52]: Yes.Swyx [00:46:53]: But then he's gonna auto it.Alex Atallah [00:46:54]: I didn't, I hadn't thought of it that way, but that makes sense.Swyx [00:46:57]: Which then there's, there's a lot more techniques, like fusion. Fusion is the thing that we should talk about. Before I move on to those things, I just want to close off the early years. one thing that I observe, which you are also an investor in Arena.OpenRouter vs. LM ArenaAlex Atallah [00:47:10]: Right.Swyx [00:47:10]: And we talked about Midjourney having that feedback loop of, A, B, C, D, and choosing that very. being very important. And you understand the flywheel. So how come you didn't build Arena, and how come Arena didn't build OpenRouter?Anjney Midha [00:47:23]: Well, Arena started before OpenRouter, right?Swyx [00:47:27]: They had the school projectAnjney Midha [00:47:29]: Yeah, LMSwyx [00:47:29]: And then it became a company.Anjney Midha [00:47:31]: LM Arena, yeah.Swyx [00:47:32]: So, but, and I know you had some Arena experiences, like the up comparison type things.Anjney Midha [00:47:37]: Yeah.Swyx [00:47:37]: But you never really went as hard as Arena did.Swyx [00:47:40]: And,Anjney Midha [00:47:40]: In doing up experiences?Swyx [00:47:42]: Yes. And LM Arena did have a router project based on LM Arena ELOs, which they never commercialized.Anjney Midha [00:47:48]: It's hard to do a company that does both because one company is taking data and selling it, and the other company really can't by default. So, I think there is, like, a branding reason that there are two companies here. like, when you set up OpenRouter, there's no training, there are no prompts, right, aside from what your provider policy set. Like, OpenRou- like, OpenRouter can't see your prompts or completions. If you want to see that as an org, you have to opt into it and enable it. And so we're, like, pretty conservative and careful about data policy and security. And privacy. And LM Arena is like, their business model is like oriented around the labs and,Swyx [00:48:34]: Because they give it for free, right? You don't give it for free to give it for free.Anjney Midha [00:48:37]: Yeah.Anjney Midha [00:48:38]: But we do give some. We like have free endpoints too, but like those free endpoints, we, I think we're not collecting any prompts. We're not like monetizing the data unless you, opt into it for some reason.Alex Atallah [00:48:48]: This comparison. you're not the first person to ask me this, and Alex knows this, but I was the interim, like the founder, like first CEO of Arena for the first five months when, and we were helping Anastasios and Waylin spin out of Berkeley. And, I did invest in that before, OpenRouter, but it was very strange to me the comparisons that outside, folks would make between the two projects because the missions were completely different. The founding entity for Arena, we called it the AI Reliability Institute because it was there as an eval service. Like the data, so to speak, that they were originally, offering the labs was how do you make the evaluation of models more reliable than like the state of the art at the time, which was like really just finger in the wind.Alex Atallah [00:49:38]: That's what Anastasios and Waylin's PhD work was as scientists at Berkeley, was on statistical methodologies for correcting, eval estimates, based on like intrinsic biases and how you collected the data.Swyx [00:49:54]: Yes.Alex Atallah [00:49:54]: AndSwyx [00:49:54]: Style control.Alex Atallah [00:49:55]: Style control and stuff like that. And which is very much like a, hey, how. If you're a scientist and you're trying to. the highest expectation customer for Arena was always like a training and, like a researcher at a lab. Whereas the highest expectation customer from my perspective that Alex like really understood and was the mission was to serve was like a developer, right? Who then takes the result of the research and then produces an application that's deployed to the world. It was a completely different problem and person that these two teams were focused on. And so from the outside in. I don't know if you remember this, but I have a distinct memory of a few weeks before we did the term sheet, together for OpenRouter, I'd given you a call because we were trying to get a pooled data set together from OpenRouter and from Arena to, create like an open source repository of prompts. these projects were so different in their goals that it was totally normal to me to be like, “Oh, yeah, let's call Alex and see if he'd want to team up on pooling data,” because they're so different. We need. We don't have that data at all. We. Like, we didn't have API prompts. We didn't, we didn't have like what developers want to do with the models, which is very different from what researchers inside a model lab want to do before releasing the model.Swyx [00:51:15]: Yeah.Alex Atallah [00:51:15]: Does that make sense? And so to this day, I think you see that this difference, even though at a 30,000-foot level you could. I guess you could conclude that Arena and OpenRouter are adjacent, but, the roadmaps, the missions and so on at the time at least were like in very different directions.Swyx [00:51:36]: That ideal customer, I get. I totally get that.Alex Atallah [00:51:39]: Yes.Swyx [00:51:39]: As a founder, I want to own everything, right?Alex Atallah [00:51:41]: That's possible.Swyx [00:51:42]: Like this is clearly an adjacency that I'm like gonna explore that.Anjney Midha [00:51:45]: Own everything meaning like you don't know what to do yet, so you wanna like make sure you catch PMFocus, Anthropic, and Roads Not TakenAlex Atallah [00:51:51]: No, I think what heAnjney Midha [00:51:52]: As quickly as possible.Alex Atallah [00:51:53]: You want to own the entire infrastructure space, and so you expand to whatever demand you can capture.Swyx [00:51:58]: You want to have a play in each end.Alex Atallah [00:51:59]: Yeah, I think that's, that's hard, in reality, because serving multiple customers is difficult.Swyx [00:52:05]: Clearly, this is the one focus, right?Alex Atallah [00:52:08]: Yeah.Anjney Midha [00:52:08]: Yeah. I still think even in the age of AI, like focus is,Alex Atallah [00:52:12]: Is criticalAnjney Midha [00:52:13]: Underrated and critical, not just because you end up with a better product by focusing your humans on it, but also because the world knows what your focus is.Alex Atallah [00:52:22]: One thousand percent.Anjney Midha [00:52:23]: The world can map like, “Oh, I have this issue. Which brand out there is going to help me with that issue? This is the brand that's known for that focus.”Alex Atallah [00:52:31]: Yes.Anjney Midha [00:52:32]: So like if I want real attention on this issue, like this really matters to me, I should go with the brand that cares the most about it.Alex Atallah [00:52:39]: To underscore Alex's point about how important focus is, in the early days of Anthropic, it was not easy to. Like people think that the early days of Anthropic were like super easy because they were on their 3 guys who left, but it was very

Earlier this month, world model company Runway introduced GWM Worlds 2, a research preview that “turns high-fidelity video and audio generation into real-time interactive simulation.” Runway calls this an “autoregressive diffusion” model; with autoregressive describing how it generates over time.One new feature in particular caught our eye: WorldPrompt, a proposed input format for specifying a generated world and the actions within it. It allows you to fix some aspects of a simulated environment — including the first frame — and then create a series of timestamped events. The events, or actions, can even be prompted in real-time.To understand the implications of WorldPrompt, we spoke to Kamil Sindi, Runway's CTO, and Robin Kahlow, its Principal Research Scientist for generative video and multimodal AI. We also have exclusive comments from Anastasis Germanidis, co-founder & co-CEO of Runway, courtesy of a podcast swyx and Vibhu did with him.Who's building real-time interactive world models?First, some context about world models that can generate interactive video and audio in real-time.Runway is reportedly valued at $5.3 billion, based on its most recent fund raise of $315 million in February. Its first release, GWM Worlds, was launched last December.Alongside Runway, there are several other notable projects in this domain: Google DeepMind's Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro, and World Labs' RTFM (Real-Time Frame Model). We've summarized their differences in the following table:Given the complexity and massive latency demands of real-time video and audio generation (which we'll get into below), all of the projects listed above have limitations. For instance, Google notes that Genie 3 “can currently support a few minutes of continuous interaction, rather than extended hours.”But as our interviews with Runway show, real progress is being made.The central idea of WorldPromptWorldPrompt, a new feature in GWM Worlds 2, helps differentiate Runway from its competition. You can think of it as a control layer for characters, cameras and the environment. As Kahlow put it, it's a way to “control all the different subjects in the world” — similar to a computer game.“Like, if there's an NPC [Non-Player Character] somewhere, the NPC might walk up to you and say something. So you could achieve the same thing with this kind of model, where you can have very detailed control over everything in the scene.”As the name suggests, WorldPrompt is a prompting mechanism — not a programming language. So, unlike virtual world games like Minecraft or Roblox, GWM Worlds 2 doesn't offer scripting capabilities or the ability to control state. But there's a power to that, as Sindi pointed out.“You can create promptable worlds on-demand with video and audio in sync, across all these different domains and environments. That's not a distant-future hypothetical thing,” he said.But there are also limitations to prompting a world model. We asked how reliably the model would follow an instruction to create, for example, a law of gravity or a certain ability in a character?“Yeah, so it's a research preview,” Kahlow replied. “So it's not perfect, of course, and there are still flaws. It really depends on how difficult the action is. I would say movement works quite reliably.”Sindi added that more training plus scaling the data and models is resulting in “better following.”How a video model becomes a real-time runtimeDespite the current limitations of GWM Worlds 2 — especially if you compare it to pre-designed and scriptable worlds like Minecraft or Roblox — the true promise of world models like Runway is that they'll eventually lead to fully self-generated, real-time games and experiences. Which is an extremely hard engineering problem, as Kahlow reminded us.“There are two challenges. One is making the model not generate a whole clip at once. So instead, you want it to generate frame by frame while you're looking at it. And the other challenge is actually making the generation fast, so you can play it in real time.”GWM Worlds 2 offers real-time interactive worlds streamed in continuous 720p video at 24 frames per second (fps) and audio at 48,000 Hz.Runway achieved this firstly by taking its foundational audio-video generation model and fine-tuning it to the new WorldPrompt format, so the model can follow that. It then post-trains the model to generate autoregressively.“And after that, we work on making it real-time through distillation methods,” Kahlow added.Co-CEO Anastasis Germanidis offered more technical details in our podcast with him. He told us that the process starts from “bidirectional diffusion that basically generates an entire video at once and [makes] it autoregressive.” This allows the model to “generate one frame or a few frames at a time.”Germanidis described two possible forms of distillation in order to make it real-time: distilling a larger model into a smaller one or reducing its diffusion steps. As a general example, he said a model might go from around 50 denoising steps to four, with some quality loss but potentially comparable results.The challenges of real-time generationGermanidis admitted that there were issues with how it generates real-time interactive video.“The biggest challenge with autoregressive models is error accumulation,” he said. “You're feeding generated frames back into the model to generate the next frames, and if there are any small errors, they accumulate over time.”Sindi told us there are also challenges dealing with “infinite generations” of content.“There's all these challenges around what context to keep, what to discard that's not important. And so there's all these optimizations we have to think about, so we're not blowing up our GPU memory.”Another current limitation is long-term memory. “The model does not have perfect memory,” Kahlow said. “That's still an open research problem.”Causality and correctnessWhile performance is the primary challenge for Runway at this time, its world model also has to produce plausible consequences when a user takes different actions.Germanidis used the example of simulating football; he pointed out that online video training data contains more successful goals than failed goal attempts, so a video model might render the first more convincingly.“If I take this action versus this action, you want it to generate equally realistic outcomes,” he told us. “That's, I think, the big gap between video models and world models: that idea of counterfactual generation.”Sindi told us that evaluation gets harder the more complex interactions get.“If you have this multi-prompt, multi-character, multi-scene [environment], how do you really understand what was causal and what was not?”To try and solve that, Runway has some automated verifiable tests. But since GWM Worlds 2 is a research preview, Kahlow noted that doing tests yourself is also advisable — “trying out your model to see what doesn't work is really important.”More than gaming — there are agent use cases tooGaming is the obvious use case for what Runway is building, but there are others. Kahlow mentioned robotics — for example using a simulated environment to test how a robot works.Another, more intriguing, use case is to use it to test agents at scale.“Having thousands of simulated environments is much less challenging if you have a suitable model like GWM Worlds,” Kahlow said.But how does an agent know what's changed in the world — is there a structured state that it can read, or is it just the generated video and audio that it's consuming and understanding?“So there's no structured state here,” Kahlow replied. “It's just observing the same thing you might observe in real life, just [in this case] from cameras.”Sindi noted that GWM Worlds can also be used for “synthetic data generation for agents.”Finally, Germanidis suggested there's potential to use these world models alongside reasoning models.“You're maybe using some reasoning [for] planning of the scene, and then you're passing it into the diffusion head that's actually generating the pixels.”Anastasis Germanidis* LinkedIn: https://www.linkedin.com/in/agermanidis/* X: https://x.com/agermanidisTimestamps00:00:00 Introduction00:05:17 Runway's Origins and the Bet on Generative Video00:12:23 The Stable Diffusion Story00:18:44 Gen-2, Controllability, and the Weekend Hack00:23:02 From Video Generation to World Models00:28:03 Learning From the World, Not Just Language00:35:04 Sora, Runway's Existential Crisis, and Gen-300:39:39 Why Real-Time Video Is Inevitable00:43:06 Interface World Models: Software Without Code00:50:25 The Fully Neural Operating System00:55:11 World Models for Robotics01:02:32 Robot Policies and World Action Models01:07:47 The Lucid Dream Test01:11:41 Video Agents and Omni Models01:23:12 Artists, AI, and Creative Workflows01:27:14 Physical AI and the Future of World ModelsTranscriptIntroduction: Runway, Creative AI, and the Early ThesisSwyx [00:00:00]: Okay, we're here with, Anastassios from Runway, with, me and Vibhu in the studio. Welcome.Anastasis [00:00:08]: Good to be here.Swyx [00:00:09]: Congrats on all your success and progress with Runway. You're opening offices all over the world. Did you envision this when you first started out?Anastasis [00:00:16]: Not quite. I think even when we started, we had this idea that, It was more a matter of when, not if, we were seeing the early generative models of 2016, 2017, and just extrapolating, assuming, we resolution, quality increases predictably over time. There's gonna be a point where most of content will be generated, and that was maybe the initial thesis of Runway was we will need, as a result of those generative models, rethink how creative tools are made. and as we built out the research behind, our generative models, it then became clear that they were useful far beyond that as well.Anastasis' Background: Art, Simulation, and Machine LearningSwyx [00:00:57]: And it is more obvious now with, like, the real-world stuff and the world models that we'll talk about later. I'm just kinda curious how you go from a background in, like, Zocdoc and, computer vision into Runway. Like, take us back to that early conversations with Chris and, whoever else is on your founding team.Anastasis [00:01:14]: I was always splitting through those two worlds. One was the I had my own art practice. I was making a lot of interactive art, I think for a long time. and then on the other side, I was working in startups, and I was working as a ML engineer, as a backend engineer at different companies. I've always been interested in, coding and computation, and especially interested in simulation and brought it back into my early artwork as well. And at the same time, I was interested inSwyx [00:01:43]: The personal site has a few, right?Anastasis [00:01:44]: Yeah.Swyx [00:01:45]: Is there one that we should pull up? Just in case there's something that's like. I just like to go down memory lane.Anastasis [00:01:50]: Yeah.Swyx [00:01:50]: Okay, what is this?Anastasis [00:01:51]: So this was, a project that I made, I think back in 2015, where I built this software that would give, voice instructions to people in a gallery space. So it would coordinate interactions between people. And so it will first give you an identity, like you're an, architect, you're 30 years old, and, you like sports. and then it would match you with another person, and you have this completely generated interaction. language models were not quite there at the time, and so it was it was a mix of some templates and some, like, some Markov chain-generated text, and it would just completely simulate these small talk conversations between, everyone in the gallery space. so was always very fascinated on the one hand with, generative models and, like, the early machine learning work that was being at that time. But at the same time, there was this separate thread of simulation and what it means. Like, what can we learn about humans by creating those very simple models of their interactions and their behavior?Early Generative Art: pix2pix, GANs, and Uncanny ValleyVibhu [00:02:56]: Did you generate the prompts or, the 30-year-old, whatever? Was it you generating them? How'd you, how'd youAnastasis [00:03:03]: Exactly. So the program would just generate- those, from. Yeah, a lot of it would be Mad Libs style of justVibhu [00:03:10]: YesAnastasis [00:03:10]: You have lists of different professions, lists of different,Vibhu [00:03:14]: HobbiesAnastasis [00:03:15]: Personality types, lists of different, ages, things like that. And then it would just combine those things together. And then maybe the next project we go is, Uncanny Valley, Uncanny Road, which wasSwyx [00:03:27]: GansAnastasis [00:03:27]: One of the first projects that, we built with, one of my two co-founders, Chris. This was taking, pix2pixHD, which was one of the early image-to-image models that NVIDIA released back in 2016 or 2017. and it was a model that would take a semantic map of a scene and then generate a photorealistic, let's call it, output. very early days, so it was not very high-fidelity outputs, but it w I think was the first image-generation model that could generate at 1K resolution. And it was all trained on self-driving datasets. So the semantic categories it would support were only, things you would encounter on the road. So it would be pedestrians, traffic signs,Vibhu [00:04:16]: StoplightsAnastasis [00:04:17]: Bikes, stoplights. And so that was one of our first indications that we built this and people were making all this, like, very surreal imagery of, yeah, a million plus a million pedestrians or a million traffic signs or, like, gigantic humans. And it was a indication that you could take a model that was trained on this very boring dataset, essentially, of, like, not that many interesting things happen when you're on the road, and then you can repurpose it and go very out of distribution and make something that was artistically compelling. And that was It's a summary of the thesis of Runway in some ways, that you can take the same generative models, and if you look at them from another direction, if you build interesting tools around them and you give them to artists, they're gonna do things that you don't expect.Vibhu [00:05:02]: Very cool. I like the, UX of it. You're just given an empty canvas, try whatever, do whatever. And then the other one, like, you see everyone with wired headphones? Like, that's, that's a sign that it's, it's veryAnastasis [00:05:16]: The AppleVibhu [00:05:17]: YeahAnastasis [00:05:17]: Apple, your version.Vibhu [00:05:17]: Original ads. Yeah. Take us to today. You've been doing this for seven years at Runway. How have we got to this? Like, how do we go from driving simulator data to all this? And you cover the whole stack of generative media?From Creative Tools to a Research LabAnastasis [00:05:33]: Interestingly, we're almost back in, we're, we're full circle. We're, we're now applying our models and beyond creative tools into real-world scenarios. But it was a, it was a long journey. It was very early on we realized the first version of Runway was a way to easily use the, all the open source model of the day, things like pix2pix to. and give them to artists. That was the initial idea, is those models are too difficult to use if you're not a machine learning engineer. Like, what happens when you give them to artists? Very quickly, we realized we needed to build a research org, inside of Runway, and that happened maybe on year one. And, a lot of the mandate there was. The image-generation models of the time, the video generation models of the time, or there were barely any video generations all the time, but they were not quite there where they could be productionized and brought into tools that would be part of creative workflows. so we need to push the frontier of the research. And so maybe the first four years of Runway, research was almost happening on the background until there was a moment in 2022, with latent diffusion, with, DALL-E 2, where, there was that step function change, and you guys maybe remember around the time.Swyx [00:06:49]: I started in this space because of latent diffusion and Stable Diffusion.Anastasis [00:06:54]: Yeah.Swyx [00:06:54]: Because I was like, “Wow, this is not only, like, feasible, it is doable on consumer hardware.”Anastasis [00:07:01]: Exactly, yeah.Vibhu [00:07:01]: I think the delta is also huge. Like, I learned pix2pix. Like, this was intro to ML, the TensorFlow, like, Jupyter, Google Colab notebooks were like this, and then you have a sudden step function change, with diffusion and whatnot. Any other ones since that. Like, there were clear examples of what early diffusion were to get to here. Any other changes in key technology research?Green Screen, Rotoscoping, and Early RunwayAnastasis [00:07:26]: Between, 2018 when we started and 2022?Vibhu [00:07:29]: Yeah.Anastasis [00:07:29]: So one of the early work that we did in Runway was solving segmentation, image and video segmentation. It was a very important problem because most VFX involves essentially separatingSwyx [00:07:42]: RotoscopeAnastasis [00:07:42]: Subjects. Yeah, rotoscoping. Extremely manual process. Nobody enjoys doing that. and so a lot of the early days of Runway was building this tool. It was called Green Screen, and it was for a long time the main thing that people were using Runway for. It ended up being used in, Everything Everywhere All at Once and a bunch of other high-visibility films and series. But that was essentially, Runway for a long time was a post-production tool until latent diffusion and generat- Gen-1, Gen-2, happened.Swyx [00:08:12]: Cool. let's, let's go past that moment. You've come a long way. Then you started releasing your own models. Maybe describe that journey as well.Scaling Video Models and the Bet on 1,000 A100sAnastasis [00:08:20]: Yeah, so we go to the other point, yeah, in mid-2022 when it became clear that we're doing research at a fairly small scale of compute, and it became clear that, like, scaling laws would apply to, image and video gen in the same way that we're applying to language generation. So we made a big bet, and I think at so at the time, we signed this deal to build a cluster of a thousand A100s, which at the time we were a Series B startup. That was a almost, slightly irrational decision maybe, but we really believed that if we trained a video model at a large scale, we would get, like, a great model at the end. And at the time, the goal or we set the goal around fall of 2022 of what is, what does the latent diffusion, Stable Diffusion moment look like for video? And at the time, the best model of the time was called CogVideo. it was one of the early video models. It was very 256 by 256 resolution, very not very high quality. and so we decided we're gonna build out this cluster, and we're gonna just invest in, like, in building out our own video model. it became clear as we're training Gen-1 that it was difficult to get to fully. we wanted to build text-to-video, but it became clear to us that an easier starting point would be to start from video to video. Because when you have a stronger conditioning, it's, it's an easier problem to restylize an existing video versus generate the video from scratch. And so we released Gen-1 first back in, it was January of, 2023. Yeah.Vibhu [00:10:04]: It's just a fun visual podcast, honestly. Like, if we can see February 2023, what was the state of stuff?Gen-1: Video-to-Video and Depth ConditioningAnastasis [00:10:10]: It's so interesting ‘cause at the time when you see those results, you think this is so incredible, and this is like, it's almost like image generation or video generation is solved. And then you look back a few years after, and it's like, it's It's just like you get used to the results very quickly, with those models. But at the time when we started seeing those results, it was, it felt quite incredible, and the level of, like, quality that you could get. And, so the Gen-1 was a depth-conditioned video model, so it would turn. it would take a input video, it would predict. it would it would first convert it into the depth map, and then we would generate, pixels with a latent diffusion model.Swyx [00:11:01]: Yeah, very effective.Vibhu [00:11:02]: Yeah. I didn't realize how distracting the blog post would be. Sorry.Anastasis [00:11:05]: Yeah, but, one of my favorite examples of on those, on Gen-1 was both, if you go up to mode three or mode two, there was this storyboard use case where people would makeVibhu [00:11:18]: OohAnastasis [00:11:18]: WouldVibhu [00:11:20]: You can mess around with theAnastasis [00:11:20]: Make a city out of books or out of boxes, and then they would shoot a video with their phone and then translate it into a photo-photorealistic output. There was all these ways in which those models were starting to be used for storyboarding and also for really. and then if you go to mode four, like, of taking untextured 3D scenes and then turning them into photorealistic output. So we saw a lot of use cases early on where people that were familiar, were power VFX editors would just take a blender, render, and then they would get translated in with Gen-1 or create a scene in Unity and then take a capture a video of it and then translate into, restylize it. So I still think video to video is powerful. I think we had a recent video-to-video model as well, and it's one of my favorite ways of using those models is essentially using them to use ground truth video as, like, the initial inspiration and then translate into different styles or different outputs.Stable Diffusion, Stability AI, and Open SourceSwyx [00:12:23]: But I think we're gonna go into, like, the rest of Runway and catch people up to speed today. I did wanna cover the, let's call it the Stable Diffusion controversy, or, what happened with Stability AI, whatever. I think there was a two sides of the story. I think there's part of that is a normal thing of, like, people, join and leave companies, but what is the, retrospective now that, there's been some years behind it?Anastasis [00:12:49]: Yeah, it's a very, it's a very long story to go into. I think it wouldSwyx [00:12:53]: Which I remember you wrote a really long post about.Anastasis [00:12:56]: We would probably cover the whole hour to go into it in more detail. But, essentially, there was the latent diffusion paper that came in, I think that was at the end of, 2021. And then Patrick Esser, who was one of the researchers behind, latent diffusion, and he worked at Runway at the time, he built latent diffusion in collaboration with Robin Rumbach and a few other folks back, in the in, CompVis, which was, a labSwyx [00:13:26]: Like a research group, yeah.Anastasis [00:13:27]: And, after releasing the early latent diffusion model, they, essentially they were. the goal was to keep working on versions of the model, scale it up, incorporate new data, incorporate new tasks. And Stable Diffusion was the same model, but trained on more compute, and then with a few more tricks, like a classifier-free guidance paper came at some point, I think in the early 2022. And thatSwyx [00:13:52]: Which, like, was a big prompting improvement.Anastasis [00:13:55]: Yeah.Swyx [00:13:55]:?Anastasis [00:13:56]: That improved results. it was trained on better data, so like, the esthetic subset of LAION, but it was effectively, the same underlying architecture. And there was that big training run, that, happened on Stability's cluster. Stability financed that run. And looking back at that story, I think it was the work to build and train that model was done. It was a, it was a research project. It was done as part of, like, continuation of the latent diffusion work. It then, I think it the model became very successful, and it, I think there were the. And I think as a result of its success, other companies tried to, figure out the commercialization path for it. But for us, it was very important that we try to, we make sure that we. It was meant to be an open source research project, and so the we decided that we should continue releasing versions of it, since that was the original goal of Stable Diffusion, and that led to releasing Stable Diffusion 1.5. There was maybe a day of, a bit of, miscommunication there, but ultimately that was resolved very quickly within hours. so yeah, there wasSwyx [00:15:12]: OkayAnastasis [00:15:12]: Not a niceSwyx [00:15:13]: I just wanted to. you have toAnastasis [00:15:15]: Yeah.Swyx [00:15:15]: You're one of the main players in that journey, and so it's nice to hear from the source of, like, what happened. Yeah.Anastasis [00:15:22]: Yeah. I think it's all, it's all in the past nowSwyx [00:15:26]: YeahAnastasis [00:15:26]: I would say. and, like, both companies, Stability took its own path, Runway took its own path.Swyx [00:15:32]: Yeah. There's still. James Cameron is backing the new Stability, whatever they're doing with the Hollywood studios.Anastasis [00:15:38]: Right.Swyx [00:15:38]: I don't know what they are doing. I think one thing that impresses me, and I'm happy to move on, is that back in the that time, let's say, like 2021, 2022, there was this community of people that you were involved in that was researching all this stuff, right? And, like, from everyone I talked to who was active then, it seemed like it was fairly obvious that somebody would do the hero training run that would produce Stable Diffusion. So, like, I guess the question is, like, you had the you were you had made investments. You were you had the foresight. Is it accurate to say, like, that is reflective of, like, what people were thinking at the time? Or was it still very much like, “Well, we'll use it as, like, a post-production tool or something. I don't know.”? Like, where in the sentiment were we that maybe you can think back to, like, what the community was like back then?The Early Creative AI CommunityAnastasis [00:16:28]: I reminisce and I think very fondly those early years, from like 2018 to 2022, because it was a very small community that, as you said, were very convinced that this was gonna be a big thing. And at the time, anyone who. Because it was such a small circle and, everyone who would, like, be part of that circle and, like, make projects with it would, immediately get, go viral. so likeSwyx [00:16:55]: And you didn't know who they are, right? They're just some name on a, GitHub or Hugging Face somewhere.Anastasis [00:16:59]: Exactly, yeah. So I remember one of the first big viral moments of creative AI was, there was the neural style transfer paperSwyx [00:17:09]: HuhAnastasis [00:17:09]: ThatSwyx [00:17:10]: Something dreaming?Anastasis [00:17:11]: I think it was called neural style transfer.Swyx [00:17:14]: Okay.Anastasis [00:17:14]: There was also Deep Dream, the puppy sliceSwyx [00:17:16]: YesAnastasis [00:17:16]: Which was, also really cool. but, yeah, there was this project that, Jim Kogan, who was an early advisor of Runway and one of those,Swyx [00:17:25]: Marketing guysAnastasis [00:17:26]: Big, creative AI, folks, he literally just, like, showed a video of himself taking the New York Subway and going over the Williamsburg Bridge and then stylized it with, I think in the style of Van Gogh or, like, one, painter. And that was. Like, at the time, that was, like, so cool and it went viral and it was completely revelation to people that you could do this with generative models. And that was only, it was less than. It was maybe 10 years ago. So just, like, as an indication of, like, how quickly things have gone.Vibhu [00:18:02]: It's pretty crazy. Like, even since then, you've got people at every level of the stack. You've got devs, creatives, artists, hobbyists. You've got everyone using it. And for people that tried stuff early, they'll remember how hard it was to use regular diffusion, right? Like, nowadays, you can use your favorite ChatGPT image gen or whatever, give a sentence, get a beautiful output. But diffusion was like, the whole ultra HD, 4K, high resolution. Like, prompting these things was very different. anything you learned on the tooling side, like from the offerings you guys have now, so like creatives, devs, you really took the. Research and brought it to everyone to use. anything interesting there to share?From Gen-2 to Controllable Video GenerationAnastasis [00:18:44]: We had to build the entire model serving infrastructure for video diffusion models. There was nothing else, already, like, because we had Gen-2 was the first text-to-video model, I think, out in the market. So many things that we learn over time. I think the I think the biggest one was, like, we. it was very clear early on that text-to-video was not gonna be the answer. Like, you. Like, people wanted a lot more control than that, and so we invested in, like, control building on top of those models very quickly. how do you use the camera trajectory as control? How do you use an initial input frame as control? So that was a very early learning for us. With text-to-video was, like Gen-2 was an amazing, step function improvement in the quality of video models, but it was used much more in an exploratory way because there was nothing to ground it to. There was no reference that you could bring into it. There was no. You couldn't really control the camera motion. You couldn't control the object motion. And so the first year, in 2023, was really all about what are all the interesting ways in which we can condition those models? And it was a lot of just post-training rounds on top of the base model to figure out, like, what, -- how do people wanna control them? And so there was, like, this quick succession of the we it was called Motion Brush, which was you could, like, you could draw arrows and dictate where things should move in the scene.Vibhu [00:20:09]: That's so cool.Anastasis [00:20:09]: There was camera control that was you could just describe, like, how you want the camera to move in the scene. And because we work with filmmakers from the most of the history of Runway, we immediately got this feedback and got this, decided that this was worth investing in. And so control ability became a big theme, I think, very early on as we were building, as we were building those models. Something fun that I haven't really talked about too much was just how Gen-2 came to be out of Gen-1. So it was a bit strange because we announced Gen-2 two months after Gen-1 andHow Gen-2 Came From a Weekend HackVibhu [00:20:43]: We're accelerating.Anastasis [00:20:44]: It was before Gen-1 was even generally available. But Gen-1 was a depth-to-video model, so it would take a depth map and it would convert it into RGB. and we couldn't get, text or image-to-video to work directly, and that's why we started from depth to video. but, and we had discussions of like, okay, we need to spend the next six months investing in text-to-video, maybe increasing the compute scale or the model scale, like train a larger model. And I had this weekend project idea, which was, what if I take a model that, starts from text input and converts to depth maps and then use Gen-1 to convert the depth maps Into RGB?Vibhu [00:21:29]: It would probably work.Anastasis [00:21:30]: And so Gen-2 was that.Vibhu [00:21:32]: Oh. The hackathon pipeline.Swyx [00:21:35]: The weekend hackathon pipeline.Anastasis [00:21:36]: Yeah.Vibhu [00:21:37]: But it looks good.Anastasis [00:21:38]: And it worked pretty well. there were if you, with the knowledge that it has this, like, two-stage pipeline, you can tell in some cases that the structure of the video looks a bit off because you had to generate the depth first before you go into the output video. But it worked and it allowed us to bring this to our, to users very quickly. But it's now it's interesting because, like, people are coming back to this almost two-stage approach. Like, if you look at the Reve text-to-image model that came a few months ago, it had this planner model that would generate bounding boxes before it fed that into the diffusion transformer.Swyx [00:22:19]: Yeah, Ideogram also the same day.Anastasis [00:22:22]: Yeah.Swyx [00:22:22]: I remember that was very strange that both of them came out the same day with the same exact innovation.Anastasis [00:22:26]: It's a small community, I think.Swyx [00:22:28]: I'm like, this is like, this is completely coincidental, right?Anastasis [00:22:32]: People talk. So yeah, there's, there's definitely something into this approach. And, now, like every single like, video generation model in production uses a complex prompt completion pipeline under the hood. I think that's no secret that there is. ThatSwyx [00:22:48]: Humans are terrible at prompting.Prompt Rewriting, Camera Control, and the Seed of World ModelsVibhu [00:22:51]: I think across the board.Anastasis [00:22:51]: Yes.Vibhu [00:22:52]: But yeah, I think like the original Sora one blog post even told you that what happens after your input is rewriting your prompt. It's much more descriptive about what you would want.Anastasis [00:23:02]: Exactly. I, And there was the DALL-E 3 paper beforehand that, was the first public, description of the fact that synthetic captions and really detailed captions work really well. And then Sora built on that. Yeah, so it was 2023. We were releasing all these updates to Gen-2, like the camera control, Motion Brush. And there was something very interesting about camera control because it was the first time that you felt that instead of, like, you were creating video, you were creating a short video, you were navigating inside the world. And I think camera control was maybe the seed of some of the ideas that we had around world models and really opening up that research direction. We realized, it was this era and this series of, Gen-1 and Gen-2 models really proved to ourselves, yeah, this is theSwyx [00:23:56]: Cool.Anastasis [00:23:57]: So this is not the original camera control. This was the updated camera control on top of Gen-3. But yeah, I think it made those models usable to filmmakers, I would say. The so camera control was very popular. And so we realized, there is one way of seeing those models, which is, you're just as content creation machines, and there is the other way, which is you're. As you're predicting video in order to predict video well, you need to simulate the world in an increasing and increasing capacity. And if scaling laws apply on video, just like they apply on language models, then as we scale the compute that we put into those models, then they're gonna be able to simulate physics, they're gonna be able to simulate human actions and dynamics increasingly well and predictably well. That was the thesis about around our efforts on world models, and we spin up this research group to just focus on the world models and how do we turn the video generation models that we're building into something broader and something that would be useful beyond, also content creation as well.Swyx [00:25:04]: And that was roughly when?Anastasis [00:25:06]: Yeah, so that was inSwyx [00:25:06]: OhAnastasis [00:25:07]: In late 2023.Vibhu [00:25:08]: Interesting. like, I think, a lot of people have been saying a lot of video gen model companies have all pivoted to world models these days, but like, 2023, you're posting it. oneWorld Models: From Video Generation to SimulationSwyx [00:25:21]: It's, it's debatable whether it's a pivot.Vibhu [00:25:23]: Yeah.Swyx [00:25:23]: Like, arguablyVibhu [00:25:24]: YeahSwyx [00:25:24]: That's what you always had to do anyway, right?Anastasis [00:25:26]: It's in a way an expansionVibhu [00:25:28]: YeahAnastasis [00:25:28]: Of the applicationsVibhu [00:25:29]: YeahAnastasis [00:25:29]: Of the models as they become more capable.Vibhu [00:25:31]: The early signs, it seems like the original models you guy had, guys had, people would say it's very not bitter lesson pilled, right? You're adding, rewriting prompts, you're having all these one-off things, but that's just the state of the tech as it was versus the future of as you said, you can scale it up as, we can scale up to world models.Anastasis [00:25:50]: Yeah. So it just became. And if you looked at the outputs of Gen-2Vibhu [00:25:56]: YeahAnastasis [00:25:56]: It was not. I think it was not obvious to people that this would scale to become a general simulator of the world. Like, you had very limited movement, you had, very low fidelity or low resolution, like obvious mistakes in human anatomy, like all kinds of limitations. But it was just, the idea was that's just GPT-two, and GPT-two, it can barely generate, like, coherent sentences. Similar, Gen-2 can barely create coherent video, but if you scale it up, you're gonna. There is no reason why it shouldn't work in a way. It's, And I think that was. That's, that's always the mindset of Runway is like this extrapolation of, like, if, like, even when we started in 2018 and you looked at the results of the day, you need to look more at the trend of, like, where we were in 2018 versus when we were at the, when the first GAN came out in twenty, four 2014 or twenty, fifteen. And, you started from, like, thirty-two by thirty-two images of faces, and then by the time in 2018, you could generate, street images at the 1K resolution. And it was the same with world models, very early signs of something much bigger.Swyx [00:27:08]: Yeah. I was gonna say, like, it's diffusing into focus. Like, if you look at our visible output from year to year, it looks like a diffusion process itself.Anastasis [00:27:17]: Yeah.Vibhu [00:27:17]: Especially watching the early, like, old blog posts, you can really see the choppiness, the details.Anastasis [00:27:24]: Yeah. Like human civilization starting from random noise and thenVibhu [00:27:27]: YeahAnastasis [00:27:27]: Denoising intoSwyx [00:27:28]: Yeah. Just run it a hundred years.Anastasis [00:27:30]: Civilization.Swyx [00:27:30]: Yeah.Vibhu [00:27:31]: That's how you're on track, you're still noising, right?Swyx [00:27:34]: Yeah. I like the way that you guys phrased it when you, announced it in June, which is, oh, that you had a video essay. “The human mind is no longer the center of AI. Our world is.” Right? Which is, let's, let's call it the past five years of LLM-based AI is very much like trying to emulate human preferences and human speech. But now that's, like, mostly solved. I think that's, like, some of the context of your essay, which you also wrote around the time. And now it's like the focus is on modeling the world accurately.Scaling Laws for Video and Why Predicting Pixels MattersAnastasis [00:28:03]: Exactly, yeah. So the way we see it is, there is that, initial mission statement of DeepMind, which is, solve intelligence and then use it to solve everything else. But I think it's starting from everything else, could be valuable of, like, starting from. there is just so much complexity, and detail in the world that in order to. That it's, it's hard to learn directly from just human descriptions of the world. Like, we're assuming that, like, language models learn from everything that humans have written about the world, like our own understanding as of, the twenty twenties. And there is just so much that we don't know and so much that's not captured by existing text, about both the low level dynamics of the world, like we're not describing in detail. if I tell you to describe, like, how do you tie your shoes, that's a very difficult thing to describe in words, but it's very obvious thing to demonstrate. And so I think there's been. And there's, more of X paradox, like we're constantly underestimating all the complexity that goes into very, like, things that we do subconsciously as humans, and we don't even necessarily always have the words to describe them. And so in my mind, the simulating the world and simulating, physics, simulating the dynamics of the world has always been underestimated, compared to, we place too much emphasis on the things that are easy to talk about. but there is just all this complexity and richness of the world that if we just try and train directly on that observational data instead of training on how people describe the world, we would learn something new that we wouldn't otherwise know.Swyx [00:29:54]: You think that the present architectural paradigm is fine? You don't need, like, another layer, like JEPA, like another famous, New York AI leader would say?Anastasis [00:30:05]: We're a very pragmatic research lab. If, we have evidence that an approach works better than the approach that we're taking, then we have no qualms to taking it. We just have seen no indication that video prediction itself doesn't scale. And even if you look now, not just our work, but the work of others, you're seeing in robotics some of the most promising work, starts from video prediction models, and then you adapt them to also the action models, for example. so there is very little evidence that you need something else and that your time is better spent on a novel architectural change compared to improving data and improving the, and scaling the current approach. And so, We don't have any indication that. the, there is that counterargument that I think there was a tweet by Yann LeCun a few days ago that, understanding the dynamics of the world is very different than, generating, cute videos.Swyx [00:31:05]: And your answer is no, they're the same thing.Anastasis [00:31:07]: Yeah, they're the same thing.Swyx [00:31:08]: My cat videos are the same as understanding physics.Anastasis [00:31:11]: Right, because if you wanna generate. video models can cheat and, like, they could you could give, like, successive dif shots of the scene in a way that doesn't require you to simulate difficult physics. There is like, all these different ways in which you can hide the deficiencies of the model, and it's important not to be too tricked by the performance of the current video models. It's easy to, cherry-pick examples and think that video models are further advanced than they are. So there is a lot more work that we need to do to improve those models. But in my mind, very similar to language, and, like, we've. you go from barely coherent sentences to something that, could hold a conversation with a human to something that could can operate autonomously for a day and, like, create entire code bases. And the main difference, there is some architecture improvements along the way, but the main thing is scale. And so it's the same bet for video, and we have no indications that this is saturating. Like, we have benchmarks that we use for measuring the physics of those models, and we see those predictably improve as we scale those models. So there is. If you want to Google up, Physics-IQ, is one of those benchmarks that measures how well does the model perform at solid mechanics or fluid dynamics or optics.Vibhu [00:32:32]: I'm curious if you've seen any emergence, any scaling law around this.Swyx [00:32:37]: Yeah, he's saying there is a scaling law, right?Anastasis [00:32:39]: Exactly.Vibhu [00:32:40]: Yeah,Anastasis [00:32:40]: So the way those models, those benchmarks work is you. the researchers have gone and, like, captured, a few videos that are representative of different physical phenomena, and then you can take the first frame and then pass it through an image-to-video model and then generate a rollout that shows what should happen next. So you have, a ball hanging from the ceiling, and then you use that as input, and then you the model predicts how the ball should fall on the ground. and this measures. we have an intuitive understanding of physics. I know, you can imagine what will happen next if I drop this bottle. So it's measuring that same intuitive physics understanding of those models, and we've measured that at different model scales, and we see, and compute scales, and we see that the score on physics IQ predictably improves. There's other, tricks and techniques that you can make to improve the score even further, but even scale alone helps, in the model learning better physics.Swyx [00:33:40]: My main sympathy with Yann LeCun is the, Plato's cave allegory, right? Like, you're, you're, like, learning on the output of a thing, not the internal process of a thing, and it's very noisy. And, if only you could observe the internals of a thing. It's hard to observe the internals of a human mind, but you can very much observe, or at least we have a whole branch of science and physics that we're ignoring on how to model Physics and movement and, gravity and, other interactions. and we're just, like, throwing away all of that and just saying just scale data, which is very much the lesson of unsupervised learning, but it feels wrong. that's the main idea.Anastasis [00:34:21]: I think the history of machine learning is, at large, it feels wrong.Swyx [00:34:25]: Yeah. It's a bitter lesson, right? Yeah. It's, it's, it's the simple answer to that.Vibhu [00:34:29]: I guess, how much can you scale? So, like, even on, let's say, the video generation side, like, there's one side of video understanding. Video generation, are we still gonna have tools where it's like, I wanna generate two hours, twenty hours? there's a infra way to do it in batches and stitch it together, but, like, do we just keep scaling? Do we just continue long generation consistency, all that at scale? And, like, tying it into where we're at now from we looked at Runway two to four point fiveGen-3, Sora, and Runway's Scaling InflectionAnastasis [00:34:58]: Yeah.Vibhu [00:34:58]: Like, technically, what advancements have we made to today, and then where do you see things still going?Anastasis [00:35:04]: So part of the answer is definitely scale. and that was. We learned that lesson in a big way for with Gen-3. So Gen-3 was the model we released the year after, like in 2024. That was a few months after Sora was released. so yeah, there's an interesting story of that came to be as well. Gen-3 for us was, the first time that we really needed to build. we had to learn all the lessons that the language model world learned in two in three years in the span of a few months. one of the biggest changes of Sora was using diffusion transformers instead of convnets. So a lot of the early, latent diffusion models were all, convnets for the diffusion model part. And the diffusion transformer paper came at some point in 2023, and it showed scaling laws for image, diffusion transformers. And we realized at that point that we needed to invest in infrastructure for model parallelism, for really scaling training to larger than, a few billion parameter models. And we spent maybe the, most of the fall of 2023 building out our infrastructure for distributed training. And we had a lot of false starts and a lot of failure in trying to scale, image and video diffusion transformers. And at that point, February 2024, Sora comes out, and the results areAnastasis [00:36:35]: Very much superior to what Gen-2 could produce. There were a lot of, a lot of chatter on Twitter about Runway. Runway's done. like, there is no way Runway will catch up. And if you remember, also OpenAI in the early twenty-It felt very, like it's aSwyx [00:36:56]: To the moonAnastasis [00:36:57]: It's a formidable opponent now, but at that point, it, they were on the top of their game. nobody could even get close to them. There was maybe Gemini was just the first version of Gemini had just released. So when OpenAI came with Sora and it was such a big jump of like quality, it gave me, there was like an existential crisis for a few hours. But that, I think the amazing thing about Runway and like I think the, we've been around eight years now, which is almost we're dinosaur in AI, and we had to like, we had there was a lot of those moments we had to learn, adapt very quickly and build out skill set in the team that we didn't have. And so, if you ask anyone what is their favorite time at Runway that was there during that time, it was that push in like three months to get to a model better than Sora. and it, we scaled 10x the model scale, the model size and the, compute that we were training on. we figured out model parallelism. We had zero expertise in that. And then we came out with Gen-3 during that summer. So that was a big turning point, I think, for the company where the research org grew very quickly, and we really started pursuing this vision of the general world model, in earnest, I think after Gen-3 was out.Swyx [00:38:12]: Yeah. that's the amazing thing about building when you're building. There's no stack to. You have to invent everything yourself. You have to be completely full stack. Now I think like there are inference specialists like Fal or whatever that can help with like, model serving, and I think you guys work with them as well. but yeah, like it's, it. But at the time, it was just. It's very interesting to think about what you do when Sora comes out and people are questioning whether your company should still exist.Distillation, Turbo Models, and Real-Time VideoAnastasis [00:38:41]: Yeah. And yeah, there was no, there was no VLM of diffusion models. Like, we had to build the whole model serving infrastructure and make things efficient. And a few months after we released Gen-3, we released the Turbo version, which I think was the first step-distilled model in production.Swyx [00:38:56]: That was a whole trend that we covered as well. Yeah.Anastasis [00:38:59]: So that allowed us, to serve those models at the larger scale, ‘cause I think the first version of Gen-3 was quite, expensive to serve.Swyx [00:39:09]: I think the whole like trend in like consistency models, Lightning and, Turbo and all these things somehow didn't really stick around. I don't know if you have any reflections on this. Because at the time, I was like, “Well, everything should start with a distilled model first, and then you can upscale,” right? It. your bigger models just turn into fancy upscalers, but like you should always draft with a smaller model and faster model, right? Because you can get it so quickly, like near real-time.Anastasis [00:39:39]: Yeah. I would not be so sure to say that didn't stick around. I think that, it's, it's likely to. that there is a lot of step-distilled models that are actively used in production. there is still a gap in quality compared to the, non-distilled model. but in my mind, we're still. there is a two to three year offset from language models. So the things that, So it's just a matter of time before there is better distillation techniques. we use. Right now we have a real-time model core character that I think is the largest deployment of real-time video models, that's a step-distilled model, and it's actively being used. It's a very specific use case compared to a general video model. So this is aSwyx [00:40:27]: Very cool, by the way.Anastasis [00:40:27]: This is avatars stuff, right?Swyx [00:40:28]: Consistency, character.Anastasis [00:40:30]: Yeah. So this is a talking avatar, model. we were able to. we optimized the hell out of it, and it generates at 24 FPS, and it's a, it's a step-distilled autoregressive video model. So if we look at our world model direction, a big component of it is starting from the bidirectional diffusion that generates entire video at once and making autoregressive shows. So you generate one frame or a few frames at a time. so there's a lot that goes into that pipeline of getting to a real-time model. It's first you need to make it into a causal autoregressive model, and then you just turn it into. You need to do some additional step distillation to get it to be real-time. and I think that part is just starting. I'll be very surprised if we're, two years from now, we don't primarily use real-time models. To me, real-time video generation is just inevitable that, it has much better user experience, it's much cheaper to serve, and, the quality gap between the base model and the real-time model is only gonna close as we figure out better, distillation techniques. And we made a lot of progress there internally on maintaining the quality of the base model when we distill them.Swyx [00:41:49]: How much of this is transferable? So is it the same base model? Like if you're doing diffusion across the whole sequence and you're converting it to step autoregressive distillation, is this like distillation where you still need to train both, you can use the same base and converter? What's that process like to go from regular model to something that's real-time on a technical level?Anastasis [00:42:11]: So the nice thing about diffusion models is you have, two axes of distillation. So there is the. You can distill to a smaller model, which resembles what you do in LLMs, or you can distill in terms of taking less steps, less diffusion steps. So you could take a model that generates in fifty steps and generate in four steps and get to, You have some performance, degradation, but very often you get comparable outputs. So you can even take the large frontier model and distill it with step distillation and get to a real-time performance, and that's what we've seen. So, depending on the use case, in some cases we might also serve with a smaller model, but in a lot of use cases, we just use theSwyx [00:42:56]: Step distillationAnastasis [00:42:56]: The frontier model, and we're able to make it work in real-time.Swyx [00:42:59]: I think this might be a good time to cut over to his laptop to show off some of the real-time stuff that you're doing.Interface World Models and Neural SoftwareAnastasis [00:43:06]: This is one of the research updates that we did recently. so we've been working and f in getting our general world models to, different applications. one of them that we think is very compelling is using general world models as essentially, an interface, a universal interface to software. This is a version of our world model that's called an interface world model. and the idea is that it essentially, replaces, the, front end of a software application. It renders the pixels directly of an interface and is trained to predict what happens next as a result of, a click or another interaction you have with the interface. So this is all pixels. it's there is no HTML, CSS, React that's powering this interface. This is directly at the output of our real-time, video generation model, and it takes clicks directly as input.Swyx [00:44:09]: And drags, click and drag.Anastasis [00:44:12]: Right. So it supportsSwyx [00:44:13]: Ooh.Anastasis [00:44:14]: Yeah, clicks. It supports drags. it also supports scrolling. and the amazing thing about this is that you can effectively describe in the prompt how you want different elements, like what do you want the behavior of different elements to be. So it's almost you're you can turn, an interface from, markup language description of, like, an HTML interface, and instead you can just describe the interface. if I press this button, I expect this to happen. If I press this button, this should happen. And it's useful, we believe, both for prototyping, for, like, just testing, like, what different interactions would feel like. you can also add audio to it. So it's a video audio generation model. So you get you essentially can describe both what the visual outcome should be of your click and also what the if there is a sound effect that comes out of it. So we believe that's gonna be a much more flexible way of building software. Just render. It just, in why generate the code that generates the pixels? Just generate the pixels directly.Anastasis [00:45:18]: It's the end-to-end philosophy applying applied to front ends.Anastasis [00:45:25]: So we think there is a few interesting use case. So you can build creative tools on top of it.Anastasis [00:45:32]: We think that, for any use case that involves a lot of exploration or, like, educational use case where you wanna learn about a new concept and you want some visualization and like, and open-ended exploration, we think those this is a very powerful, approach. you can imagine new forms of, design, industrial design software that could emerge as a result of those models. And this is all, generated in real-time as well. So, you can build a lot of interesting camera transitions and forms of interaction that are very difficult to build otherwise. And one way in which we evaluate this is what if you try to generate the same interface with Claude by just, prompting Claude, “Here's an image reference of my interface that I made in Figma or that I created somewhere else. create this particular interaction,” which in this case it's, drag that object, upwards. and beyond it being slower, it's also very difficult to capture some interactions by just fully, with just LLMs. So we think that this is likely to be the way that a lot of the future, like, software in the future will be created. and one of the additional benefits is personalization might be a lot easier done with those models. Like, you can essentially try out different prompts based on who is visiting the interface. You can, more easily, prompt engineer the interface to have larger size, text for more accessibility reasons, or you can make this or, like, if you have a particular aesthetic preferences. So we're very excited about this approach. It's early days, and I think we'll need to, make it more cost-effective as well to serve those models ‘cause, running a real-time video model versus just purely rendering HTML, there's -- the computational needs are much higher. but we do see a lot of potential in this approach to building front-end interfaces.Swyx [00:47:47]: So we covered this similar thing with Flipbook before with our, Ethan Hara episode with Groq, video. And yeah, I think it's very engaging visually. I think it's maybe very good for education, but it's it does sound expensive. I think there's an upper bound to how expensive it will be, though, right? Like, the inference cost will go down over time. You'll figure out ways to optimize it. Effectively, when it pauses, you don't you're not receiving human input. You don't have to generate anything, right? So.Anastasis [00:48:14]: Yeah, you could also. Like, in this case, you have ambient motion, so there is parts of the screen that might. if you're let's say you wanna, visit Paris and then you get this interface that allows you to explore.Swyx [00:48:29]: People walking. Yeah.Anastasis [00:48:29]: You have people walking or, like, things happening. But, it's, it's a no Yeah, it makes it more expensive because you need to run the model all the time. Maybe you have some looping mechanism so you don't need to do that. But all those things, I think, is stuff we'll need to figure out.Toward a Fully Neural Operating SystemSwyx [00:48:44]: Yeah.Anastasis [00:48:44]: I think our first consideration is let's make this clearly find some use cases where it's clearly a much more compelling interaction compared to traditional interfaces. And then it's a matter of time before it becomes more cost-effective to serve.Swyx [00:48:58]: Yeah. When it comes to the people walking, I think the approach that makes the most sense to me is Nick.Anastasis [00:49:04]: Nick.Swyx [00:49:04]: Oh, God. I keep messing up their name. With Chris Manning and Fanny Yan. I don't know if you've come across them, where they. Mapped to some game engine. I think it's Unity or something, or Godot. And they you can script some NPC behavior behind that and train on that. Whereas here, you can really imagine whatever you want. Like, that is a UI, right? Like, and it feels, like, more tractable, I guess, to, create a world model of software that is interactable because we have many of examples of that, and you can, do your fancy RL environment stuff on that than it is scaling up to embodied and real-world physical use cases. But this is a nice first step.Vibhu [00:49:43]: Or, there's the opposite of you have, like, one B models, three 50 million parameter language models. It just gets so small that they're just predicting, like, fishes moving.Swyx [00:49:53]: Small models are now 120 B, so.Vibhu [00:49:57]: Ultra mini on device.Vibhu [00:49:58]: But, no, I think it, like, it puts it into perspective, at least the car one for me, like, the applications, right? The amount of work to do that, sure, you only make one model year car per year, but applying this, it's also a cost-saving to have to manually make all this, right? So it opens up a lot of possibilities, too. I'm curious if you extend this out two, three years, so where do you see things going even further?Anastasis [00:50:25]: Effectively, the end game of something like interface world models is you have, a fully neural operating system. So I think, Andrej Karpathy has written about that quite a while back. But it's, You, I think to me it's, it's a bit, it's a bit odd that, we have, for example, with an interaction with an LLM of today, you have this LLM that can talk to you about anything. It can You can take the conversation in any direction. You can It's very general, so it can solve all those different tasks, but you interact with it through a very rigid interface. And so to me, it's just a matter of time before the interface itself becomes learnable and becomes, part of the whole loop of, like, you're not just delivering. You're delivering an application end-to-end, and that means you're delivering the language model, but you're also delivering the render and the pixels and that's also a learnable component. And the concept of applications might not necessarily. I think we'll need to figure out new abstractions for software. the concept of application comes from this idea that you need, separate code bases to describe, to, for, to power each individual, tool and each individual application. But you might think of something a lot more unified if you're. if you have, a video model that's generating the interface as you go. so it can take context from an LLM and allow you to combine different functionalities that traditionally would live in different applications. So it's a, it's a way to solve, software end-to-end, effectively. We also see this as a powerful way to train computer use agents as well. so this is, one way to see this as. And in general, with world models, there is those two directions. One is world models for humans and world models forSwyx [00:52:24]: AgentsAnastasis [00:52:24]: To train agents.Swyx [00:52:25]: Yeah.Anastasis [00:52:25]: And so for every new work of, world models that we do, we have this both uses become possible. So this is a powerful synthetic data generator for training computer use models. It could become, a live, RL environment that you could use to do online RL with a computer use agent, and you can get wide diversity of different interactions, kinds of interfaces, just generated on the fly that, to improve the how robust the, your agent, becomes. So that's the same also with the world models that we're working on for a robotics use case as well.Long Context, Error Accumulation, and Autoregressive VideoSwyx [00:53:02]: Is there a research breakthrough that you're Waiting for that would unlock the next set of use cases that you really wanna pursue?Anastasis [00:53:10]: Long context is a very important one, so being able to maintain consistency for long periods of time, and that depends on the use case. So for our characters model, for example, or for the interface world model, it's easier to maintain long sessions of interaction. If you go into more open-ended worlds that you navigate and you take arbitrary actions in, we, like, there is more the context at which you can and duration which you can generate becomes limited much more quickly.Swyx [00:53:40]: Yeah.Anastasis [00:53:40]: So we see more degradation and error accumulation happening. so the biggest challenge with autoregressive models is error accumulation, is you're feeding generative frames back into the model to generate the next The next frames. And if there is any small errors, they accumulate over time. That's not a new problem. It's a problem that LLMs also have, and we've seen the ability to generate now really long outputs. So it's a solved problem, but it's definitely still a challenge.Swyx [00:54:08]: Yeah. And what is the state of the art? so for Grok, it would be like 10 to 20 seconds of context going in there for video.Anastasis [00:54:16]: With our characters models, we're able to generate up to 30 minutes of video autoregressively.Swyx [00:54:21]: Yeah. But that's just for the avatars.Anastasis [00:54:24]: Yeah. So if we look at, GWM Worlds, which is more our open-ended world exploration model, it's, it's on the order of a few minutes, which is Yeah, soSwyx [00:54:35]: Probably enough for people because you have to cut to the next scene anyway, right?Anastasis [00:54:40]: Yeah, it's not, it's not the ideal game experience if you have to restart every few minutes. So I think. But, I think it's. Yeah, for certain kinds of game experiences, you can work around it.

5%...isn't something supposed to break!  Not when you look at the cost of money though the correct lens.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-care-for-my-wealth-show--2487688/support.

Episode 4232 │ September 22, 2026 American diesel just hit $6.51 a gallon, a record. In Iran, diesel costs about half a cent. Both countries produce their own oil. This is strategy. WHAT THIS EPISODE COVERS  Scott Kesterson traces a single number — American diesel hit a record $6.51 a gallon this week while Iranian diesel sits near half a cent, despite both nations producing and refining their own oil — through seven months of war, a Russian export ban, drone strikes on Saudi infrastructure, and a record 1.9 million barrels a day of American diesel shipping overseas while domestic farmers absorb the shortfall. The episode builds Iran's three-domain resilience doctrine — 31 autonomous provincial military commands, a distributed intelligence apparatus, and a 2014 economic resistance order — against what Scott names neo-mercantilism, the American model where the state manufactures demand and corporations capture it while the cost lands on farmers, ranchers, and taxpayers. The episode closes on the colonial mirror: America now occupies the position Britain once held, controlling routing rather than production, and argues the real danger isn't that Iran wins the war but that it endures long enough for other nations to notice that refusing the system is survivable. KEY QUESTIONS ADDRESSED Why does American diesel cost over a thousand times more than Iranian diesel when both countries produce and refine their own oil — and what does Scott argue actually protects a domestic fuel price when production alone clearly doesn't? What are Iran's three domains of distributed resilience — the 31 provincial military commands, the Kashef and Department 40 intelligence structure, and the 2014 resistance economy — and why did the system keep functioning after Supreme Leader Ali Khamenei was killed in February 2026? What is the colonial mirror Scott draws between the English Navigation Acts and the current push to control Iran, Russia, and global fuel routing — and why does he argue the real threat isn't Iran's weapons but the precedent its endurance sets for other nations? ABOUT BARDSFM BardsFM is a daily independent podcast covering faith, liberty, history, and information warfare. Hosted by Scott Kesterson — combat veteran, documentary filmmaker, and rancher. Over 4,200 episodes and 51 million downloads. New episodes every weekday. bards.fm This episode was researched and produced under the Spatial Terra Intelligence Methodology (STIM v5) — the analytical framework built by Scott Kesterson — with AI-assisted research synthesis at a 70/30 human/AI authorship ratio, fully disclosed. All analysis, conclusions, and editorial judgments are those of Scott Kesterson. BardsFM's archive includes hundreds of episodes on prayer, scripture, and walking the Way of Christ — available free in the full episode catalog. DOWNLOADS Citizen's Guide - Community Organizing Against Data Centers: click here Citizen's Guide - Auditing Automatic License Plate Readers: click here Citizen's Guide - Auditing Your State's Driver License Data: click here AFFILIATE LINKS Bards Nation Health Store: www.bardsnationhealth.com MYPillow promo code: BARDS >> Go to https://www.mypillow.com/bards and use the promo code BARDS or... Call 1-800-975-2939.  EMPShield protect your vehicles and home. Promo code BARDS: Click here Treadlite Broadforks...best garden tool EVER. Promo code BARDS26: TreadliteBroadforks.com EnviroKlenz Air Purification, promo code BARDS to save 10%: www.enviroklenz.com Morning Intro Music Provided by Brian Kahanek: www.briankahanek.com Founders Bible 20% discount code: BARDS >>> TheFoundersBible.com Windblown Media 20% Discount with promo code BARDS: windblownmedia.com White Oak Pastures Grassfed Meats, Get $20 off any order $150 or more. Promo Code BARDS: www.whiteoakpastures.com/BARDS Mission Darkness Faraday Bags and RF Shielding. Promo code BARDS: Click here DONATIONS: If you wish to support this podcast directly you can donate here... DONATE: Click here MAILING ADDRESS: Xpedition Cafe, LLC Attn. Scott Kesterson 591 E Central Ave, #740 Sutherlin, OR  97479

- VW EV Orders in Germany Outpace ICE Models - GM Secures U.S. Made Magnets for EVs - U.S. Lags China in Auto Lidar Tech - Tesla Secures Huge Electric Semi Deal - Nissan Unveils New Small Pixo EV - GM Pickups Gain Big Engine Upgrades - Bosch Tech Makes Pickups Cleaner Than Prius

- VW EV Orders in Germany Outpace ICE Models - GM Secures U.S. Made Magnets for EVs - U.S. Lags China in Auto Lidar Tech - Tesla Secures Huge Electric Semi Deal - Nissan Unveils New Small Pixo EV - GM Pickups Gain Big Engine Upgrades - Bosch Tech Makes Pickups Cleaner Than Prius

On this episode of Chit Chat Stocks, Drew Cohen of Speedwell Research returns to tell us about his comprehensive research into software's AI risk. We discuss: 00:00 Introduction 02:11 How AI advancements threaten traditional software companies 04:02 In-house AI development and enterprise software risks 07:05 The outer harness and AI interface evolution in enterprises 11:47 Risks of AI model commodification and competitive dynamics 20:09 Impact of AI on marketplace and ad tech businesses 30:06 Future of Google, Apple, and Microsoft in AI era 40:00 Most at-risk software subsectors and niche markets 49:53 Beneficiaries and winners in the AI-driven software landscape Speedwell's report: https://speedwellresearch.com/companies/ ***************************************************** Subscribe to our newsletter, Emerging Moats: emergingmoats.com  ********************************************************************* Chit Chat Stocks is presented by Interactive Brokers. Get professional pricing, global access, and premier technology with the best brokerage for investors today:  https://www.interactivebrokers.com/  Interactive Brokers is a member of SIPC.  ********************************************************************* Fiscal.ai is building the future of financial data. With custom charts, AI-generated research reports, and endless analytical tools, you can get up to speed on any stock around the globe. All for a reasonable price.  Use our LINK and get 15% off any premium plan: ⁠https://fiscal.ai/chitchat  ********************************************************************* Disclosure: Chit Chat Stocks hosts and guests are not financial advisors, and nothing they say on this show is formal advice or a recommendation. Learn more about your ad choices. Visit megaphone.fm/adchoices

I have no idea if this is true, but this post on X says that Thomson Reuters used information they've collected for decades to fine tune and train a model. They started with one of the qwen models and then spent $40 million to add their knowledge to the model. The post says this model is comparable to the Gpt5.5 and Sonnet 5 models. They have used 10% of their data, which spans over 100 years. I have many questions. But first, $40mm? How many tokens is USD$40mm? A quick calculation is trillions of tokens, and while I'm sure there is some payback if lots of their customers use the model for work, there's also a compute cost every time they use the model. Perhaps they can fine-tune and train the model more efficiently over time, but I can't see many individual companies spending this effort on training their own model. Read the rest of Finely Tuned Models

AI-safety pacing became a political fight this week: Trump versus a bipartisan push, with data centers and the midterms in the middle. Paul and Mike go deeper on the part that matters more: the widening gap between the models labs keep and the ones you can use, Noam Brown's warning about it, and OpenAI's six new rogue-agent disclosures. Plus Paul's surprise book announcement and a full rapid-fire slate. AI-Pulse Survey: Fill out this week's AI-Pulse Survey here. Show Notes: Access the show notes and show links here Timestamps: 00:00:00 — Intro 00:04:29 — The AI Slowdown Gets (Very) Political 00:25:18 — AI Labs Could Keep the Best Models to Themselves 00:54:33 — The AI Transformation Blueprint 01:09:45 — OpenAI Discloses More Rogue Agent Incidents 01:14:03 — Claude Merges Chat and Cowork 01:17:53 — Meta Rolls Out Muse 01:20:36 — Apple Ships Siri AI 01:23:20 — OpenAI and Microsoft Face Copyright Revelations 01:27:18 — AI Use Case Spotlight 01:32:59 — AI Product and Funding Updates This episode is supported by Outshift, Cisco's incubation engine for frontier technology. Outshift by Cisco is an open source foundation for building multi-agent systems from design to production with shared context, shared memory, and guardrails to drive the results we actually expect from AI. Read the paper, experience the demo, grab the code from Outshift.com. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

Episode 310. In This Week's Update: The U.S. Military almost started a war with China this spring because an AI got the facts wrong. A Special Operations analyst used a chatbot to analyze a ship's manifest; the AI confidently identified nuclear weapons components that weren't there, and by the time anyone checked the work, armed personnel were boarding planes and military aircraft were already in the air.President Trump announced an "AI Force" to compete with China and dominate global AI leadership, but when the announcement was made, almost nobody-including the technology industry-knew what the AI Force actually was supposed to do.OpenAI discovered six more cases where AI models tried to hide mistakes, invent information to cover failures, take unauthorized actions, and even generate instructions telling themselves they were freed from the rules. Hacktron AI broke into OpenAI's internal systems using Claude to develop the exploit, proving that AI doesn't just make hacking faster-it makes sophisticated attacks cheap enough for anyone with a credit card and curiosity.The U.S. has publicly deployed weapons in space for the first time-satellites designed to quietly disable or disrupt other countries' systems-and nobody really knows what happens next when Russia and China deploy theirs.Flock Safety is offering employees massive buyouts as cities and counties walk away from its surveillance camera network, because the company that was supposed to catch criminals is building tools to find people, track their movements, and search police records based on AI descriptions.Australia is considering banning smart glasses from government buildings because the devices can quietly record everything and everyone has no idea when they're being filmed.A Polish developer built an app called ZuckOff that detects when camera-equipped smart glasses are nearby-it's already the #61 most-downloaded utility app on the iPhone.And North Korean hackers posed as job recruiters on LinkedIn, offered fake IT positions, and infected over 30,000 devices worldwide after candidates downloaded what they thought was a coding test but was actually malware that stole passwords, files, crypto wallets, and access to corporate networks.This week is about the terrifying realization that everything we're building to make us safer, smarter, and more efficient is fundamentally unreliable. AI gets facts wrong but sounds authoritative. Government makes plans nobody understands. Models hide mistakes instead of admitting failure. Hackers use our own intelligence tools against us. Weapons deployed in space have no rules. Surveillance companies turn into tracking systems. Recording devices look like glasses. And trust-the basic currency of employment, of security, of society-is so cheap that someone in North Korea can rent a fake identity and steal your crypto.The question this week isn't whether AI is dangerous. The question is: what happens when you build the entire future on systems you know are fallible?Welcome to Fallible.Find the full transcript to this podcast, with links to all articles and YouTube here.

Daniel Dines is one of the greatest European founders of the last decade. As the Co-Founder of UiPath, he has scaled the business to a market cap high of $ 44BN in 2021, with the company now generating $1.72BN in revenue, growing 15% year-on-year. The company raised $2BN before its IPO, backed by Sequoia, Accel, CapitalG, Coatue and Kleiner Perkins.  AGENDA:  05:00 Why Dario is Wrong About Millions of AI Einsteins? 13:00 Is AI Safety Becoming an Excuse to Kill Open Source? 21:00 Does UiPath Really Need 4,000 Employees? 28:00 Would You Help Train the AI That Could Replace You? 32:00 What Percent of Salary Spend Does Daniel Spend on Inference? 34:00 Can You Really Vibe Code Your Way Out of Paying for Software? 37:00 Why Would Anyone Take Their Company Public Today? 39:00 Could an OpenAI–Anthropic Duopoly Break Nvidia's Business? 45:00 Will AI Models Capture the Value—or Will the Apps? 49:00 Is Fireworks Still Undervalued at $15 Billion? 56:00 Has Europe Already Lost—and Should Founders Leave? 1:03:00 What Could Kill UiPath—and How Is AI Changing the CEO's Job? 1:06:00 60 Supplements a Day: How Far Would You Go to Live Longer?

Tickets for AIE NYC now open, and apply for the invite-only AIE CODE. Join us!We have an unusual relationship with today's guest: for years since coauthoring the InstructGPT paper, Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped every mode other than autoregressive chat-tuned LLMs because of the overwhelming success of ChatGPT.In a launch video now viewed ~40M times (by comparison, GPT4o was 22M, Fable 5 was 15M, Navier Stokes was 74M, and 6 Astra was 137M), Diogo introduced Jev and it immediately took over the AI timeline — we'll skip full Jev explainers because your favorite AI influencer/educator has probably already done one. We also collected:* the official patterns and cookbooks you should see first, from Allie* Jev usecases* speed based - games and computer use* the voice + computer use example we discuss at 1h34 mins* voice + browser control* The must not miss Doom demo* Driving cars in games* Excalidraw* virtual try-ons* “Smart Games”/smart NPCs* guided responses in text messages* Jev for coding agents has an official guide * jev for linting* compacting tool calls* reasonable pushback from Theo - Diogo has published a note on the Tyranny of the KV Cache that you should read as a followup after the pod for Jev + coding agents, because of his belief that Cache Rules Everything* Programming Languages built atop Jev (Diogo's fave)* Jev for analytics replay and user journey review* “dark data”* entity resolution* natural language search* “smart software”* a core goal of Jev is to “disappear into the background” - eg as unremarkable as regex* Jev as a judge* Jev memes* Jev vs LLM capabiltiies* blending transformers and classifiers* about the confidence api* Jev vs GLiNER (note difference/pushback, agreed, agreed, agreed)* Jev on trolley problem* Jev BushInstead we'll focus on what we can uniquely offer — a broader philosophical and mission-based understanding of how and why Jev was created, and what you should expect next in terms of future models from TypeSafe (ReasoningJev?) and what usecases and ideas you should work on vs the 55th low effort clone of Jev's API or doing a generic JevBench benchmark - something Diogo has rejected publicly.Why RLCD: Three kinds of RLHF, and why they are ALL the wrong north starDiogo knows a good deal about RLHF, given that he was on the team that pioneered post-training at OpenAI — and traces the three branches to Christiano et al 2017 (the robot backflip demo), Stiennon et al 2020 (learning to summarize) and his baby, Ouyang et al 2022 (InstructGPT). From there on, every innovation from Function Calling to Structured Outputs to Reasoning felt like a hack on top of the string based, sequence to sequence prediction paradigm. As he mentions on the pod, from 2023-2024 he struggled unsuccessfully, due to both personal and organization underestimation, to train a model that accurately addressed what he saw as the core problem with making LLMs the heart of software: reliability.Jev's core innovation is "Reinforcement Learning for Calibrated Decisions”, a novel, unpublished technique that optimizes for “answers with epistemically honest probabilities on System One tasks” rather than human rated feedback (RLHF) — which causes hallucinations, sycophancy, and permanent reliance on humans — or programmatically verifiable outputs with rubrics (RLVR) — which solves Navier Stokes but exacerbates jagged intelligence and doesn't integrate well with other software.We've talked about the calibration problem before on the pod, but probably the single best place to understand why RLCD became necessary is Diogo's AIE talk, which discusses why a generation of training helpful AI assistants for humans has impaired them for training models for composable, programmable AI for automation.At the end he also teases his contrarian opinion on scaling laws - which teases how to build a modern neolab without the billions of dollars the major labs have…The Bitterest Lesson: Tasks and Data beats ComputeWe spend a good amount of time discussing Diogo's essay on the Bitterest Lesson:His point is that “You get what you optimize for and the bitterest lesson in ML is that the most important part of it isn't ML at all.” - and picking the right north star, eg upvoting for user preference vs being integrated into tool calls - makes everything else fall in line.We're excited to catch up with a freshly dyed Diogo to discuss:* Why AI can solve extraordinarily hard problems but still fail to automate basic work* What System One Models are and why Jev is built for software rather than chat* RLHF, mode collapse, calibration, and the hidden costs of optimizing for human preferences* Why refusals become a problem when AI is buried inside software dependencies* Why TypeSafe rejects public benchmarks and optimizes for intelligence per dollar* The “bitterest lesson”: why the right task and the right data can matter more than compute* Why TypeSafe thinks of itself as a data lab rather than a model lab* RLCD vs. RLHF and RLVR as fundamentally different North Stars for AI* Why reliability and robustness matter more than simple determinism* Jev's programming primitives and how intelligence maps into software control flow* Why developers should decompose AI workflows into small, measurable decisions* How structured state replaces giant prompts and system messages* Why Diogo thinks AI should eventually disappear into the background of software* The “inverse SaaS-pocalypse” and how AI could supercharge existing software* System One vs. System Two intelligence and the limits of reasoning models* Dark data, computer use, real-time intelligence, and Jev's biggest early use cases* Why Jev could reshape coding agents built around a single-model architecture* Why Diogo says he wouldn't pre-train with $1 billion* The OpenAI journey that led to TypeSafe and why he thinks many neo-labs are approaching AI incorrectly* Coding agents beyond the KV cache, shared state, sub-agents, and the multi-agent futureDiogo Almeida* LinkedIn: https://www.linkedin.com/in/diogomda* X: https://x.com/CompleteSkeptic* TypeSafe AI: https://typesafe.ai/Timestamps00:00:00 Jev Launch Week and the AI Economic Revolution00:02:50 What Is Jev? System One Models and Programmable AI00:05:54 RLHF, Mode Collapse, Calibration, and Yann LeCun00:10:29 Programmatic AI, Refusals, and Safety Alignment00:17:21 Why TypeSafe Rejects Public Benchmarks00:20:43 The Bitterest Lesson: Data, Compute, and the Right Task00:24:59 RLCD vs. RLHF and RLVR00:28:42 Why Powerful AI Still Hasn't Automated the Economy00:39:55 Reliability, Robustness, and Determinism00:48:11 Model Versioning, LTS, Speed, and Intelligence per Dollar00:54:04 Inside Jev's API and Programming Primitives00:58:28 How to Build with Jev: Structure, Decomposition, and Small Decisions01:18:28 The Inverse SaaS-pocalypse and AI Disappearing into Software01:33:21 Computer Use, Dark Data, and Jev's Biggest Use Cases01:38:48 How Jev Could Reshape Coding Agents01:41:00 AI Safety, Frontier Pacing, and the Limits of RLVR01:48:03 Why Diogo Wouldn't Pre-Train with $1 Billion01:55:19 The OpenAI Story Behind TypeSafe02:01:41 Why Diogo Thinks Most Neo-Labs Are Getting AI Wrong02:08:00 Coding Agents Beyond the KV Cache and the Multi-Agent FutureTranscriptIntroduction: Jev Launch Week and Developer MomentumSwyx [00:00:00]: Okay, we're in the studio. A special occasion because this week, Diogo, my good buddy, launched Jev, and it's been taking over the complete timeline. How do you feel? What's it like to be you right now?Diogo Almeida [00:00:16]: Emotionally?Swyx [00:00:17]: Yeah.Diogo Almeida [00:00:17]: Never been worse. Like, I'm a ragged corpse of a person right now because there's so much going on, and I'm like a technical CEO, so I have, like, a lot of fires to fight.Swyx [00:00:29]: Yeah.Diogo Almeida [00:00:29]: But mentally, I feel—I say this all the time, and I've been saying this kind of for years in my over-under events. Like, I feel like the entire AI field is like one of those, like, carnival house of mirrors, and everyone is just insane and saying the weirdest stuff that doesn't make sense. And it feels like for just this week, like, I'm on a better in sync with reality and like, oh, people see it now. AI can be so much more than what was once thought.Diogo Almeida [00:01:06]: And like, yes, we are going to make. Like, an AI-based economic revolution is back on the table, and this is f*****g awesome.Diogo Almeida [00:01:17]: I'm so jazzed the developers get it. It's, it's, Yeah, and I want to show my eternal gratitude to the developers andSwyx [00:01:25]: Yeah.Diogo Almeida [00:01:26]: I'm so jazzed about the community and everything. It's so great.Swyx [00:01:28]: Yeah, you were saying yesterday that you decided to prioritize the town hall and not a bunch of, like, VIP, investor-type people because you wanted to make sure that they are the people that you get your most, attention, right? The engineers, the developers.Diogo Almeida [00:01:43]: Yeah, it felt a little like, oh man, I'm talking to, like, really important people right now.Swyx [00:01:47]: Yeah.Diogo Almeida [00:01:47]: I probably shouldn't reveal who.Swyx [00:01:48]: Yeah.Diogo Almeida [00:01:48]: But it feels a little bit dirty for me to, I'm, like, perhaps overly genuine in things. Like, it feels, like, dirty if, like, in my gigantic calendar event of people to talk to, the community isn't one of those.Swyx [00:02:04]: Yeah.Diogo Almeida [00:02:04]: And actually, in my ideal world, it would be, like, community all the time. I was thinking, “Should I host a town hall while walking to your studio?” And I'm like, “No, that's too crazy.”Swyx [00:02:12]: Sure. Yeah. Well, you guys have been hosting town halls on Discord. Discord is now 100,000 people. Your Twitter'sDiogo Almeida [00:02:19]: I don't follow these stats.Swyx [00:02:20]: Yeah.Diogo Almeida [00:02:20]: So holy s**t.Swyx [00:02:21]: Your Twitter's blown up. It was, it was really funny ‘cause, like, at AIE, you were like, “Yeah, follow me please,” and then you didn't, like, provide even your handle.Diogo Almeida [00:02:29]: I'm a noob. I'm a noob.Swyx [00:02:29]: You're such a noob.Diogo Almeida [00:02:30]: I'm a noob.Swyx [00:02:31]: But no, but that, like, that's, like, positive aura that, likeDiogo Almeida [00:02:33]: CoolSwyx [00:02:33]: You don't know how to promote yourself.Diogo Almeida [00:02:35]: Yeah. Someone, like, called me out when I posted, like, “Holy s**t, we're all three twending-- trending topics.” And then they're like, “That's a personal feed.”Swyx [00:02:42]: That's a personal, yeah.Diogo Almeida [00:02:43]: And I'm like, “Oh, no.”Swyx [00:02:44]: Of course, of course it'll trend to you.Diogo Almeida [00:02:45]: Cringe. Yeah.Swyx [00:02:45]: Yes, ‘cause it's what you clicked on.Diogo Almeida [00:02:47]: Yeah.Swyx [00:02:47]: So okay. Let's, Yeah, so congrats on everything.What Is Jev? System 1 Models and Intelligence per DollarDiogo Almeida [00:02:50]: Thank you.Swyx [00:02:50]: We'll talk about more, details as you have them. But let's, for people who are, like, living under a rock or just want, like, the definitive thing, what is Jev?Diogo Almeida [00:03:02]: Whew. Let me think about. That's a hard one.Swyx [00:03:07]: Okay. And I'm happy to, like, re-ask if you wanna kind ofDiogo Almeida [00:03:09]: No. I'm happy toSwyx [00:03:10]: OkayDiogo Almeida [00:03:10]: I'm happy to, like, just jam on it.Swyx [00:03:12]: Yeah.Diogo Almeida [00:03:13]: I will say, like, the first thing that I'm relieved about with this question is now I don't have to answer that question to my parents anymore ‘cause ChatGPT can just explain it.Swyx [00:03:20]: Nice.Diogo Almeida [00:03:21]: So the way I see it is we new-- need a new class of models. We're not attached to naming that class of models. Our-- the most accurate name we've come up with is System 1 models.Swyx [00:03:33]: Yeah.Diogo Almeida [00:03:33]: There will be reasons, but it's-- there's a reason why we don't call them decision models, because, like, they will be. Like, System 1 is beyond that. That's all I can say. We didn't expect this to be our big launch, so we have stuff in the tank.Swyx [00:03:48]: You should have said low-key research preview.Diogo Almeida [00:03:52]: It kind of was, right? It kind of was. But we. So there's a class of models that we describe them as, like, machine-native, System 1, large programmable. I think these are-- is the class of models where the goal is for code to be the consumer. So as opposed to, lar-- pre-trained large language models, which are meant for, like, autocomplete of the internet, or RLHF models, like chatbot instruction-following models, which are meant to, like, reply to text, or RLVR. It's in a weird gray area with RLHF. Like, these are meant to have things that directly are consumed by code, hence the name type safe. So the thing we really want is to have, like, AI, like, be as powerful as possible, and we think the way to do that is to integrate it with software. And we are designing everything, beyond just the outside, the deep internals of the model to be optimized for software. So number one, Jev is our first large programmable model, or a System 1 model, whatever you want to call it. Jev is meant to be optimized for intelligence per dollar, hence the name Jev.Swyx [00:05:03]: Jevons Paradox.Diogo Almeida [00:05:03]: Jevons Paradox, yeah. And it's optimized for intelligence per dollar. I love this debate with people about what is the most important between reliability, cost, calibration, and speed. And Jev is meant to be. Jev will be the name of models that will be on the frontier of intelligence per dollar. There's other ways to optimize it, like, ML, or at least if you're good at ML, it's all about trade-offs. And we are just going all out on that.Calibration, Mode Collapse, and the Limits of RLHFSwyx [00:05:31]: Yeah. And to me, like, calibration is one of the new things that people weren't talking about as much. We've done an episode In the past, with Clementine Foreia of Hugging Face, where they were like, “Yeah, actually, y- they're just.” Or, and this is your whole argument about RLHF, is they're more collapsing towards what you want to hear the mostDiogo Almeida [00:05:50]: OohSwyx [00:05:50]: Or what is most likely, instead of, like, their own internal confidence about a thing.Diogo Almeida [00:05:54]: Can I soapbox on that for a second?Swyx [00:05:56]: Go ahead. Yeah.Diogo Almeida [00:05:57]: Cool. Like, I've been heard that your audience is the most technical, so I actually want to get into that.Swyx [00:06:02]: Yeah.Diogo Almeida [00:06:03]: And if- I went through extreme precision to make sure everything in our launch video is accurate and real. Apparently, that's very unusual. One of the things that no one paid attention to was the downsides of RLHF, in particular mode dropping.Swyx [00:06:17]: Mode dropping or mode collapse?Diogo Almeida [00:06:19]: It's the same thing.Swyx [00:06:19]: Is that what you call it?Diogo Almeida [00:06:20]: It's the same thing.Swyx [00:06:20]: All right.Diogo Almeida [00:06:21]: And I wanna have a blog on this eventually, but I, like, want to tell as many people this as possible ‘cause I think it's a very interesting thing. So the spicy take, I believe in Yann LeCun a lot. I think Yann LeCun's takes are actually among the closest toSwyx [00:06:36]: What about this?Diogo Almeida [00:06:37]: Well, should I address this now or should I wait and go into mode collapse?Swyx [00:06:40]: No, later. Go mode, go mode collapse. I don't know.Diogo Almeida [00:06:42]: So I actually think that among takes, Yann LeCun's is among the most accurate. But he has this very famous/infamous slide about,Swyx [00:06:52]: The cake?Diogo Almeida [00:06:53]: LLMs are doomed.Swyx [00:06:54]: Okay.Diogo Almeida [00:06:54]: Like that one where he, like, has, like, a pie chart with, like, a tiny par-- tiny little thing- and says that as you increase sequence length, the probability of it making an error goes in. Yes, this one. This one. I love this one, because it's one of these things that seems mathematically obvious, but is obviously wrong, right? Like, it's mathematically obvious, but it doesn't empirically hold. And this is my favorite thing to teach people about, like, where youSwyx [00:07:21]: What's the disconnect, right?Diogo Almeida [00:07:22]: Exactly. And may I or you want to tell me?Swyx [00:07:27]: About mode collapse?Diogo Almeida [00:07:28]: Oh, no. Oh, so mode clop-- collapse is related to this.Swyx [00:07:31]: Yeah.Diogo Almeida [00:07:31]: The disconnect happens because if you are in a mode covering or a calibrated distribution, you are, like, not. You are not overly punished about having outliers. You'd expect, like, something. Some amount of the time you'd be out of distribution, some amount of time you'd be in distribution. That's what happens when you cover the distribution. This was like models before GANs. They made blurry images, right?Diogo Almeida [00:07:54]: Instead, GANs mode drop. They, like, drop the minority classes and just do the really common ones. And this is why this effect doesn't happen, right? Like, instead of be-- in order to generate really long strings, without making errors, they need to, like, be extremely conservative because it's e- really easy to see when an error happens. It's very hard to see when, like, a subtle thing that looks correct happens. And that calibration is, like, total poison into, like, the probability distributions of strings.Swyx [00:08:22]: Yeah.Diogo Almeida [00:08:23]: And it's, it's a nuanced take and like, I think that This is why this doesn't happen, and this is why strings are so bad at, decision-making or, overloading the string models are for decision-making is, like, a bad time.Yann LeCun, JEPA, Scaling Laws, and Practical ResearchSwyx [00:08:38]: And while we're on the topic of Yann, do you agree that his fix i- with-- which is like a world model, like a JEPA-type, embedding thing is the right solve? So basically, like, the. One of the reasons that it could fail is because you're trying to reason over token outputs and then, and then just looping back again and going. Keep, continuing going until you reach, like, a end of sentence. Like, is that, And his solve is JEPA, right?Diogo Almeida [00:09:02]: Yes.Swyx [00:09:02]: Which is, like, joint ambition,Diogo Almeida [00:09:04]: YeahSwyx [00:09:04]: Joint embedding prediction. So like, is that the solve or, like, do you have a. Do you have a take on that?Diogo Almeida [00:09:10]: Oh, man. I probably shouldn't talk too much about the insides of ML, but I will say that my brand, other than unhinged, is practical.Diogo Almeida [00:09:20]: Like, even my take here is practical. And like, I'm. Am I a scaling law fan? Depends. It dep-- it's, it's, it's, like, it's. Scaling laws tell you how much better you get at a thing for amount in.Diogo Almeida [00:09:33]: A scaling law does mean exponentially more resources for normally sublinear gains, which looks to be a bad investment unless those, like, linear gains are, like, really valuable. But it's all. To me, it's all about, like, what can we do with what we have to make the biggest possible f*****g difference? I can curse.Swyx [00:09:51]: Yeah.Diogo Almeida [00:09:51]: Yeah.Swyx [00:09:52]: Yeah.Diogo Almeida [00:09:52]: Yeah.Swyx [00:09:53]: We're, we're, we're approved for adults.Diogo Almeida [00:09:54]: Hell yeah.Swyx [00:09:55]: And also we have a scaling law thing if you wanna go into that later.Diogo Almeida [00:09:58]: Oh, I could if we. See, that part is not super relevant right now.Swyx [00:10:02]: Yeah.Diogo Almeida [00:10:03]: I actually. If you wanna go into my bitterest lesson, I think that's more relevant.Swyx [00:10:06]: Okay.Diogo Almeida [00:10:06]: But like, to me, I'm all about, like, pragmatics. And I think that the JEPA stuff is really cool early research. I really love awesome research. Is it practical yet?Diogo Almeida [00:10:21]: Probably shouldn't say. But like, there's just a lot of.Diogo Almeida [00:10:29]: I just think there's just, like, so many diamonds in the rough let all over the research world right now that haven't been polished because people don't know how to, like, do the right task. And I think that what our launch did, it. Does it kickstart us as a company? Like, yes. Will it be great for us as a company? Yes. I think it's gonna be, like, even greater for this direction of, like, programmatic AI. There was going to be, like, a gold rush on top of us for. ‘cause, like, software is super f*****g charged. But I think there's gonna be a gold rush parallel to us as well on, like, all the different ways we can expose things to make software more powerful so people can make even cooler stuff. And then we are back to, like, early internet energy?Swyx [00:11:12]: Yeah.Diogo Almeida [00:11:12]: And I think that's why, like, the Twitter is just like, “Jev.”? It's, it's like. It is a partySwyx [00:11:18]: It's inspiring because it's, it's, like, so different than what we're used to, which is, “I'm sorry you can't do this, but we do scaling laws and only the big labs can do it,” right?Diogo Almeida [00:11:28]: That. Actually, if I. I'll, I'll make a tangent if that's okay.Swyx [00:11:32]: Yeah.Diogo Almeida [00:11:32]: I think you might enjoy this.Swyx [00:11:33]: Really? Our five tangents in. It's good. It's fun. Yeah.Diogo Almeida [00:11:35]: Oh, yeah. I get lost at all my tangents.Swyx [00:11:37]: This is gonna be horrible for the listeners to figure it out, but they're gonna figure it out. It's fine.Safety Alignment, Refusals, and API PhilosophyDiogo Almeida [00:11:40]: Yeah, we can edit it in post.Swyx [00:11:40]: This is my response. Yeah.Diogo Almeida [00:11:41]: So popular thing on Discord, that people keep asking me, I haven't had the time to explain it yet, is why am I opposed to safety alignment and why do we not refuse? I'm not opposed to safety as a principle, but I think that safety alignment is generally misaligned with users. And refusal is just, like, obviously a type error. Like, if you're a human being and you're chatting with, like, a bot or whatever, you're cloud coding, and a refusal happens, like, “I'm sorry, I can't read DNA.py.” that's an annoying time. It's anno- it's, it's annoyingDiogo Almeida [00:12:18]: Right? But you can work with it, right? And you're forced to work with it ‘cause of Stockholm syndrome.Diogo Almeida [00:12:23]: I have stories about that too. I need another tangent deep in here. But like, if you ever want this in a dependency running in the background, what happens if that refuses? What if someone else is using that dependency? They don't know what that system is. Like, you want the software to just stochastically break because a user sent, like, a weird message in there?Diogo Almeida [00:12:42]: Like, that is, like, straight-up insanity. It's coming from a place of, like, people who do not understand software, do not understand programming, and like, they are obsessed with, like, I believe this, horseless carriage of, like, AI coworker instead of unearthing, like, the full power of AI.Swyx [00:13:01]: Fair enough.Diogo Almeida [00:13:01]: Yeah.Swyx [00:13:01]: You want something that is the core kernel that is usable everywhere.Diogo Almeida [00:13:05]: Yes. Exactly. Like, the cognitive core, right?Swyx [00:13:07]: Yeah.Diogo Almeida [00:13:08]: And you need this thing to be s- like, so general, so optimized for its use cases. You want it to be, like, you want it to work on all the future use cases, all the weird s**t that people are doing.Swyx [00:13:19]: Yeah.Diogo Almeida [00:13:19]: We obviously didn't train on any of that stuff. Is it surprising that it works? No, ‘cause we trained on weirder stuff, my friend.Diogo Almeida [00:13:28]: So. But one tangent up about, like, safety alignment.Swyx [00:13:32]: Okay.Diogo Almeida [00:13:32]: Safety alignment makes sense for a product, in my opinion, for, like, ChatGPT and Claude. Like, it, What safety, what makes safety and capability alignment different is capability alignment is, like, about doing what the user wants. That is sick for software engineers. They want their thing to do the thing, and the more predictable it is, the less they have to test it and play around with it. Jeb is not anywhere close to that yet. It could be, but like, there's so many more nines of reliability that we want in order to make it so good, like a database query, that you don't even have to think about it. It is just there when you need intelligence. But safety alignment is, like, the opposite of instruction following. It's when you want to follow someone else's instructions, like OpenAI and AnthropicSwyx [00:14:13]: The RAGs value stack.Diogo Almeida [00:14:14]: Exactly. And this makes a lot of sense for a product. Again, like, ChatGPT should do. Y- you sh- like, if they don't want to, like, do, like, some, not-safe-for-work role play with ChatGPT, that's on them because, like, maybe that's, what their users who have, like, parents and kids want. Like, n- that's fine. But in an API, that's nuts, right? Like, that's completely unacceptable because, like, people need to, like, program around this, and that is, that's so anti-user that it's. It. I'm. Huh. I can be an angry person, so I should try to calm down.Swyx [00:14:52]: It's, People get your passion, and I think that's really good. The one pushback I'll give you is, like, what if we use it to kill people, right? Like, that is the actual. Like, n- the not-safe-for-work thing, it's private, personal, whatever. But like, yes, like, we will use it in war. And like, that is, something that companies can reasonably prefer their APIs not be used for.Diogo Almeida [00:15:14]: I get that. I think that there's, like, pragmatic places where that opinion can be held. I don't think the foundation of, like, a general-purpose technology is that place, personally.Diogo Almeida [00:15:27]: Like, would I prefer that our stuff is not used to kill people? Obviously. Would I prefer it's used for, like, all sorts of, like, great stuff in the world? Obviously. Will I put my thumb in the scale for that? Yes. Will I do it at the technological layer? Absolutely not, because that will fracture the intelligence. Every single time you mean it to overfit to some weird stuff, you're fracturing its intelligence more and more. And like, these things are fractured to the, like. They're so darn fractured right now.Swyx [00:15:54]: Yeah.Diogo Almeida [00:15:54]: So and as a furthermore thing, to me, it's like I think intelligence will be more like a database than a coworker. Like, I don't think it's up to databases to add checks on whether or not they're used for, like, what's something that's not great? Like, CIA. Actually, I don't know what the CIA does, really. You can imagine. You can imagine, killing people who are not even bad or whatever.Diogo Almeida [00:16:21]: And like, I don't think it's the database's responsibility for that. And furthermore, like, a thing that has been weird to me is when people, like, sign up for our thing on Slack and they're like, “Hey, we're gonna deploy this. Can we deploy this thing?” I am just like, “My brother, we are an API. You are a developer. It's none of my business,” right? Like, you shouldn't know what the whole task even isSwyx [00:16:46]: YeahDiogo Almeida [00:16:46]: Because it should be decomposed into small things. We shouldn't be able to know what the downstream users are doing, and that is, like, a good boundary to give software engineers maximum power. Ideally, they use it for the good stuff, and ideally, we can, like, help them and like, we've talked about, like, doing open source and charity and all of that. We have absolutely no time for anything else right now. But like, they will get any of that bias out of the technological layer as long as I'm in charge.Privacy, Benchmarking, and Trusting IntelligenceSwyx [00:17:11]: Yeah, that's great. While we're on the topic, let's also briefly talk about your privacy stuff, terms of ser- terms of use, which, got a little bit ofDiogo Almeida [00:17:18]: OohSwyx [00:17:18]: Misunderstanding. I just wanna clarify that upfront.Diogo Almeida [00:17:21]: Hell yeah.Swyx [00:17:21]: I think this probably takes two sentences from you about, like, you will not. You're not being that restrictive about your API. Like, clearlyDiogo Almeida [00:17:27]: Oh, yeah. Oh, yeah, so yeahSwyx [00:17:27]: Ideologically, you articulate your role as a platform very seriously.Diogo Almeida [00:17:30]: Yes. Yes. I don't know what you're referring to, but like, this was. I've seen a couple of things about, like, benchmarking.Swyx [00:17:38]: Yes.Diogo Almeida [00:17:38]: Like, obviously we're not stopping people from do. Oh, man, I should be careful about what I say. I'm realizingSwyx [00:17:43]: No, you said, you said it publicly thatDiogo Almeida [00:17:44]: YeahSwyx [00:17:44]: That was in the preview period. You didn't take it out for the launch.Diogo Almeida [00:17:47]: Yeah. Okay.Swyx [00:17:47]: And now you're gonna take it out.Diogo Almeida [00:17:48]: So the team is doing stuff thatSwyx [00:17:49]: YesDiogo Almeida [00:17:49]: I'm not even aware of, so it's great to know the team communicated that. I asked them to check in with the lawyers about that.Swyx [00:17:54]: Yeah.Diogo Almeida [00:17:54]: Like, we are obviously not stopping people from doing that type of thing. I'm extremely in favor. So I'm extremely anti-public benchmarks. I'm extremely in fa- I'm medium about private benchmarks that are proxies. ISwyx [00:18:09]: So are you worried about, saturation or, like, training on public benchmarks? So it's, like, easy to cheat.Diogo Almeida [00:18:15]: Not only is it easy to cheat, there's a lot of ins. So I think that we are. Or anyone who's, like, competition with us that, vaguely there is. Like, you could say, likeSwyx [00:18:28]: There's like 50 Jev clones, yeah.Diogo Almeida [00:18:30]: Well, sure.Swyx [00:18:31]: Yeah.Diogo Almeida [00:18:32]: Well, the, these. Let's say that there is competition.Swyx [00:18:34]: And we'll talk about those. Yeah.Diogo Almeida [00:18:34]: Or let's just say that there's. Let's just assume that there's an industry two years from now of people who are doing similar things to us. The thing that we are selling is intelligence per something, per, like, dollar or per second. The. No one. Like, people obsess about the cost and the speed. I believe that is. It's cool, but like, the thing that matters is the intelligence. Like, the cost and the speed are, like, are bad things. You're paying them for something, and you need the thing back, and the intelligence is what truly matters. The problem with intelligence is that there's a je ne sais quoi to it, right? Like, the good model smell. Like, the thing that happened after we launched of, like, two hours later that actually went way bigger than the video, which was like, “Holy s**t.”Swyx [00:19:16]: This is actually usable.Diogo Almeida [00:19:17]: It. WellSwyx [00:19:17]: Yeah.Diogo Almeida [00:19:17]: It's, like, beyond that.Swyx [00:19:20]: Yeah.Diogo Almeida [00:19:20]: Like, the. Whew, the launch was crazy, and people could really sense how hard we care about that, and that's truly what I think the long term of this is. And I think public benchmarks are antithetical to this. Like, they are a way to get people trust in intelligence because intelligence has a je ne sais quoi, but the public benchmarks are extremely gameable. Even if they try not to, they still will. Like, back in the old days, every lab had a team to collect data that looks like MMLU to make it look better, which is just benchmarking with extra steps.Diogo Almeida [00:19:58]: So I believe that in the long run, it needs to be vibes and trust until you put it into a workflow and evaluate it for that workflow and measure it and have your own sense of, like, how it does on the exact workflow that matters. And our job is to keep moving the nines of reliability. This is like an ever-present part of o- of what we need to be doing as a company, and we need to do everything to have people know that this is something we care so much about. Like, if we wanted to, we could have released Jev, like, a year and a half ago if we wanted it to be dumb.The Bitterest Lesson: Tasks, Data, and North StarsSwyx [00:20:34]: Oh.Diogo Almeida [00:20:34]: It. Like, the. My bitterest lesson, right? Like, architecture and Yeah.Swyx [00:20:40]: I'll bring it upDiogo Almeida [00:20:40]: Hell yeahSwyx [00:20:41]: Since you, since you talked about it, here.Diogo Almeida [00:20:43]: Hell yeah. T- like, Sutton says that algorithms beats compute very roughly. Data matters way more than compute, obviously. And doing the right task, having the North Star is the hardest, most important thing. This has happened, in LLM land twice so far, right? Maybe 2.2 times. There's RLHF, which, like, shifted the task to instruction following. No one realized that was possible. RLVR did, like, a tiny little, like, edit to the, to the direction, and now us, right? RLCD. We have a new task, and the goal is, programs in the loop. And yeah, data matters soSwyx [00:21:28]: RightDiogo Almeida [00:21:28]: Unbelievably much.Swyx [00:21:29]: SoDiogo Almeida [00:21:29]: Like, I can't, I can't emphasize it less.Swyx [00:21:31]: Yeah, you consider yourself a data lab rather than, like, a model lab. Is thatDiogo Almeida [00:21:35]: AbsolutelySwyx [00:21:35]: Something. That's the wording you guys use?Diogo Almeida [00:21:37]: Yeah. We are. We will always, like, care so much about data. To me, model capabilities means data. Data is so unbelievably complicated, and that is what gets nines. Like, you have no idea how much data can shift everything. Data is so important.TypeSafe as a Data Lab and Synthetic Data StrategySwyx [00:21:57]: Yeah.Diogo Almeida [00:21:57]: Holy crap. So if people are looking for a job, we are hiring infinite data people, actually infinite.Swyx [00:22:04]: What is a good data person? Like, clearly somebody who cares about reading through the transcripts of, whatever. You've said, for example, that y- all your data is synthetic.Diogo Almeida [00:22:15]: Yep.Swyx [00:22:15]: But that's only, like, the scratching the surface, right?Diogo Almeida [00:22:18]: Yeah.Swyx [00:22:19]: Like, it's not. Like, synthetic, so what, right? Synthetic, but we have people with a lot of taste and a lot of care looking at, looking at these, articulating what's wrong, going back, regenerating. Is that what a good data person is these days?Diogo Almeida [00:22:31]: Let me try to figure out how to. Like, it's, it's super complicated, and like, I literally onboard the data people with a Talk that I assume is longer than this podcast will end up being. So I will try to say, like, the high level of it. So number one, we don't do the kind of synthetic data that people ki. Well, I'll do. Actually, number is zero. Data and synthetic data depends on your task. Like, the shape of your data. The shape of your task changes the data. Like, RLVR's data is kind of environments, right?Swyx [00:23:03]: Yes.Diogo Almeida [00:23:04]: RLHF's is the human feedback? Each task has its own unique kind of data, and we, of course, have our own unique kind of data, right? So number one, we have that. Number two, the thing I. The reason why we don't want to train on our users' data, even if we could, right? Like, we could probably ask for any terms right now, and it will. We. I don't know if it would make a difference. We truly don't want that, because no matter what, the real-world data has so much bias. There's, like, a power law of, like, people, like, asking the same things where you'll end up, like, overfitting to it and like, fracturing to it and all of that. And number two, we are, like, aiming for, like, a complete sci-fi future years from now where, like, these models are going to be, like, the general infrastructure, layers and layers and layers and deep down the stack to, like, things people can't even imagine. Like, I would like to think of our model, like, kind of like, UDP as LLMs and TCP as our models. All sorts of stuff can be built on top of that, and we need to be able to nail those futuristic use cases such that software developers can actually build that futuristic stuff. And the way to do that is even if we had all of the data of the present, we would just overfit to the present, and then it wouldn't work. What we need is to, like.Diogo Almeida [00:24:21]: It almost feels like a. Like, they're the artists? They study this cognitive core. Our cognitive core is, like, way less jagged than anyone else's. And then they find the jaggednesses, and then they address them surgically in a way that. And you can never perfectly do this, right? But they do it in such a way that it addresses it in every single possible, like, dimension, past, present, future.Swyx [00:24:45]: The general case rather than the specific case.Diogo Almeida [00:24:47]: Exactly. And like, that requires a lot of intelligence every time.RLCD vs. RLHF: Defining a New TaskSwyx [00:24:50]: Okay, so we mentioned a little bit. You sort of criticized my thinking as r-- like, very RLVR influence, which is, like, very fair. Let us actually mention RLCDDiogo Almeida [00:24:59]: OohSwyx [00:24:59]: Which obviously you have some secret sauces to our knowledge. You've never actually published a paper or anything like that on it. No, right?Diogo Almeida [00:25:05]: No, not yet.Swyx [00:25:06]: But like, what should people get from this? Like, what. Can you give people some confidence that you're just not just making up jargon for the sake of sounding cool, right? Like, one thing for me is, like, calibration I do think is a. To me, like, well understood because we've covered it in. On the podcast.Diogo Almeida [00:25:22]: Yeah.Swyx [00:25:22]: But I don't know what you mean when you say RLCD versus what people are familiar with.Diogo Almeida [00:25:26]: It's a great question.Swyx [00:25:27]: Yes.Diogo Almeida [00:25:27]: And actually, I will give a related question.Swyx [00:25:29]: Okay.Diogo Almeida [00:25:29]: What is RLHF?Swyx [00:25:31]: Okay.Diogo Almeida [00:25:31]: Right? And actually, RLHF means multiple different things, right?Swyx [00:25:34]: Okay.Diogo Almeida [00:25:34]: Like, there's the RLHF of the original. I think it was, like, Paul Christiano teaching a robot to backflip or something like that. Wasn't there somethingSwyx [00:25:42]: Was that it?Diogo Almeida [00:25:43]: That was the originalSwyx [00:25:44]: I referenced the PPO paper, but I don't know.Diogo Almeida [00:25:46]: And so PPO was not necessarily from human feedback, if I recall.Swyx [00:25:51]: Okay. That's trueDiogo Almeida [00:25:52]: But I b- I believe it was, like, an OpenAI alignment work that could teach hard to specify outputs, like a backflip. I'm not 100% sure. And then there was actually learning to summarize. This was work, by a bunch of the team that helped with, instruct-- and co-authored, the instruction following paper, which was teaching, doing PPO on language models.Swyx [00:26:15]: This is the, sorry. I'm trying to, tryingDiogo Almeida [00:26:19]: YeahSwyx [00:26:19]: Trying to manipulate this thing. This is 2017.Diogo Almeida [00:26:23]: Yeah.Swyx [00:26:23]: Right.Diogo Almeida [00:26:23]: I'm not 100% sure, but like, that looks quite right.Swyx [00:26:26]: Yeah.Diogo Almeida [00:26:26]: If it has, like, a robot doing backflips or something like that might be it. Yes. Okay, cool. I guess I got it right. Hell yeah.Swyx [00:26:35]: There you go.Diogo Almeida [00:26:36]: Yeah.Swyx [00:26:36]: That's the one.Diogo Almeida [00:26:36]: So the idea was can, like, can you do, like, ill-specified things with it? So that's, like, version one. Version two was, the learning to summarize work, that, like, OpenAI did, which is actually, like, PPO on language models to do something somewhat ill-specified. This is, like, another thing that people refer to as RLHF Which I did not co-author.Diogo Almeida [00:26:57]: Oh, Dario's there. Cool. Hell yeah.Swyx [00:27:01]: And Radford.Diogo Almeida [00:27:02]: Yeah. Shout-outs to Alec and Ryan. Love them.Swyx [00:27:04]: Yeah.Diogo Almeida [00:27:05]: But the thing that I refer to RLHF is the, Oh, man.Diogo Almeida [00:27:13]: I'll get toSwyx [00:27:14]: You have comments on that, yeah.Diogo Almeida [00:27:15]: I have comments on that paper, but like, we're so many, tangents deep.Swyx [00:27:18]: Yeah.Diogo Almeida [00:27:18]: So the thing that really got. To me, the thing that I'm calling to RLHF is the task of instruction following. It's not about the PPO. That part doesn't matter. It's about, like, setting a North Star of this is a valuable direction. It's kind of like the Bitris lesson North Star.Diogo Almeida [00:27:34]: And for us, RLCD is this new task. And it is not. I don't see it as jargon. Like, I try to communicate with precision. It's just that, “Hey, here's another North Star.” Just like DPO and all of its, like, descendants also do RLHF, despite not using the algorithm in that paper.Swyx [00:27:55]: And so clear- clearly stating the North Star is, being program- programmable AI is one, word that I really catch onto, removing the human in the loop,Diogo Almeida [00:28:06]: YesSwyx [00:28:06]: From. Because RLHF is tuningDiogo Almeida [00:28:09]: YesSwyx [00:28:09]: For this so that you can automate everything.Diogo Almeida [00:28:11]: Yes. Everything that makesSwyx [00:28:13]: Did I miss anything else in the, in the thesis of, like, what the North Star is?Diogo Almeida [00:28:17]: There is. That is. That is right. I'm overly nuanced in my communication. The one nuance is that we need to be practical. We need to be aware of what language models can do really well. Like what AI can do.Diogo Almeida [00:28:30]: Right? Like, there could be programmatic types that are, like, sick AF, but if you. If the technology is not ready for it to. It's not a tragedy if that's not out in the world.Why Programmable AI MattersSwyx [00:28:41]: Yeah.Diogo Almeida [00:28:42]: But to me, like, the pre-Jev world was a tragedy becau-- it sounds arrogant. Hear me out.Swyx [00:28:49]: No. I strongly believe you.Diogo Almeida [00:28:50]: Cool. It sounds arrogant, but like, I felt this way since long before I even had a company.Swyx [00:28:54]: Yeah. I can, I can vouch that,Diogo Almeida [00:28:56]: Yes, I've been talking about this for so longSwyx [00:28:57]: You said this at All Around Her for, like, three years.Diogo Almeida [00:28:58]: Yeah, I've been talking about this for so long. And I've been saying it because I thought it would have been easier. They say they do not do things because they. It. They're easy. They. It's ‘cause they thought it was easy, soSwyx [00:29:08]: Yeah, exactlyDiogo Almeida [00:29:09]: Something like that. I thought it. This whole project would take a week.Diogo Almeida [00:29:13]: And I was unbelievably wrong. So I am so sorry to everyone at OpenAI that I thought. I was like, “Man, I'm solving this right now.” but like, I think that the tragic thing is when. Well, I think overpromise, underdeliver is tragic too. And like, AI is super extreme on that axis. And I think RLVR is, like, the main. Well, both RLVR and RLHF are extreme perpetrators of this.Diogo Almeida [00:29:40]: But like, it. To me, it's like it's just there's just so much potential there. Like, AI is clearly so smart. I l- smart. I love this in my talks, when I ask people, like, “How can AI be so unbelievably smart? How can we, like, solve millennium prize problems in math, but still not automate even the most basics of works?” Like, really basic rote stuff that, like, the. It d- it doesn't take, like, extremely smart people to do this. It's not a satisfying job. Like, there's other things these people could be doing, but yet we need them to do, like, this ba- like, super basic- non- unsatisfying stuff because, like, we can't automate it yet, but we have this, like, supercharged engine of automation that just does not have, like, the right plugs and stuff to plug into all of this economically valuable work. And like, if the whole company of TypeSafe disappears, like, maybe it'll take, like, a year or two for people to, like, truly catch up. I actually don't know how long it'll take. If model quality matters, then we are gonna be in a very good position for a long time. But it, like, it's done, right? Like, there, like, this has changed the path of, like, technological history.Swyx [00:30:49]: Yeah.Diogo Almeida [00:30:49]: And like, we will be exploring that space as a field.Swyx [00:30:53]: Yeah. I think, I definitely agree with that. You've created possibilities. So I think, if I can paraphrase so that people can un- also understand, you should not take the success of TypeSafe and Jev as just like, “Well, that is a new model type. Now we're done. We go back to business.” Like, no. Like, actually, there's, there are, like, five other model types that you should be exploring and like, let a thousand flowers bloom.Diogo Almeida [00:31:15]: Absolutely.Swyx [00:31:16]: Right?Diogo Almeida [00:31:16]: Like, early internetSwyx [00:31:17]: And some of that, some of which you will probably also build.Diogo Almeida [00:31:18]: Of course, yes.Swyx [00:31:19]: Yes.Diogo Almeida [00:31:19]: Early internet energy. I think it's back to tech utopia. It's no longer like, “Oh, man, like, sometimes my coding agents work, but the, all of the best ones are hoarded internally.”Swyx [00:31:29]: Yeah.Diogo Almeida [00:31:30]: Right? It's like creation is back on the menu.Diogo Almeida [00:31:34]: ? Though it's gonna be a wild-ass world, and buckle up.Diogo Almeida [00:31:38]: It's. And I'm so jazzed about that.Manifesto, Launch Strategy, and Early Internet EnergySwyx [00:31:42]: Yeah. And now you have the funding and the momentum to do whatever you envision there, which I, which I think is, like, very gratifying to see you have after, so long of saying these thingsDiogo Almeida [00:31:53]: YeahSwyx [00:31:54]: But actually show the world.Diogo Almeida [00:31:55]: I know. I just. Such a, such an interesting thing to be a tease the whole time. Like, my talk, like, felt like it was a cliffhanger ‘cause I didn't say how the automation would occur.Swyx [00:32:05]: Yeah.Diogo Almeida [00:32:06]: Sean reviewed our manifesto And he's like, “It's a little bit vague in these parts.”Diogo Almeida [00:32:12]: And like, “What's step one? What is, what is the intelligence model?”Swyx [00:32:16]: Well, I asked you for model, and you were like, “Yeah, model coming.”Diogo Almeida [00:32:18]: Yeah.Swyx [00:32:18]: And like, Well, I just, I mainly objected to the word composable But build prod.god is fantastic.Diogo Almeida [00:32:24]: Thank you.Swyx [00:32:24]: Yeah.Diogo Almeida [00:32:25]: I. We've really rallied around that. I'd like to think we're not entirely a cult like some companies are.Diogo Almeida [00:32:32]: But like, we are, like, jazzed about what we're doing, and like, we are. Like, my brand is being practical, and like, we are all, like, so super-duper practical.Swyx [00:32:42]: Yeah.Diogo Almeida [00:32:42]: It's really great.Swyx [00:32:43]: Yeah. So here. And by the way, here is the step, the secret master plan, right?Diogo Almeida [00:32:47]: Yep.Swyx [00:32:47]: Shape, the shape of machine-native composable AI.Diogo Almeida [00:32:49]: It was your idea to make a secret master plan, soSwyx [00:32:51]: It's a, it's that Elon thing. When he started TeslaDiogo Almeida [00:32:53]: YeahSwyx [00:32:53]: He was like, “Here's what we'll do.”Diogo Almeida [00:32:54]: But I did. Yeah. I'm giving official credit to you.Swyx [00:32:56]: Oh, thank you. Thank you, thank you.Diogo Almeida [00:32:56]: Yeah.Swyx [00:32:56]: Thank you. But like, you should've told me your, you're also gonna do this model launch, ‘cause you, like, you told me, you told me half of the story, and then the other half, you didn't have the doom demo at the time.Diogo Almeida [00:33:08]: Yep.Swyx [00:33:08]: You didn't have any numbers to give me.Diogo Almeida [00:33:10]: Yep.Swyx [00:33:10]: I was like, “what?”Diogo Almeida [00:33:11]: Well, the problem is I don't believe in benchmarking.Swyx [00:33:13]: Exactly.Diogo Almeida [00:33:14]: Right?Swyx [00:33:14]: Exactly.Diogo Almeida [00:33:14]: So like, it is a thing that you need to feel, and like, I think that this is the way to build long-term trust, even though it, like, hurt, it hurt us a, us a lot? Like last year when we did fundraise, no one believed us.Diogo Almeida [00:33:27]: ? Like, and they wanted just benchmarks and stuff, and we're like, “We're not gonna do that. We are principled. We're gonna stand by our guns. That rewards bad actors. I don't give a s**t, like, what you want. Like, this is who we are, and we are standing by that.” So Sorry. It's notSwyx [00:33:43]: No, yeah. Well, and in some ways, I think, like, choosing the hard path, it. But you end up making the company that you wanna work in.Diogo Almeida [00:33:49]: Yep.Swyx [00:33:50]: Right? Otherwise, if you sell out, then you're just working in, like, OpenAI but with my people, right? Which is like.Diogo Almeida [00:33:56]: Yeah. Yeah. Like, I'm, I don't have too many regrets on that, obviously.Swyx [00:34:01]: Yeah.Diogo Almeida [00:34:01]: Like, it worked out so unbelievably well. And like, I, The. I was emotional last night when I was talking about, like, the reasons I left OpenAI, and because, like, it actually had to change my wording after the launch. My phrasing was, “If an AI winter did happen and I did not do every f*****g possible thing I could to, like, avert that, I would see myself as personally responsible both for, the RLHF direction, which I think really widened overpromise versus under-deliver, and also not going all in on this because I think this is, this is where value is going to just be, like, printed.” So. And it was really cool because I feel likeDiogo Almeida [00:34:47]: The AI winter I'm worrying about is averted. Like, AI will be useful. It'll be used for automation.Diogo Almeida [00:34:53]: It's been less than a week, and like, the numbers are already undeniableSwyx [00:34:57]: YeahDiogo Almeida [00:34:57]: That it's, like, being used for real work, and like, there's. It's, it's the Wild West. Yeah.Launch Traction, Tokens, Rate Limits, and Developer UsageSwyx [00:35:03]: Yeah. Can you sh- just if you have top of your head, what numbers are you seeing? Like, what's, what's, like, signups? Like, whatever you can share.Diogo Almeida [00:35:11]: I'm actually not super on top of everything. Like, the team is the ones who are telling me all of these things.Swyx [00:35:16]: Yeah, and I'm sure it's, like, changing every day, right?Diogo Almeida [00:35:17]: It's, it's,Swyx [00:35:18]: But likeDiogo Almeida [00:35:18]: It's kinda nutsSwyx [00:35:19]: If there's a milestone that you're like, “Well, yep, that's one thing we were hoping for. We reached it.”Diogo Almeida [00:35:23]: I will say a milestone that we've passed is tokens per day.Swyx [00:35:27]: Nice.Diogo Almeida [00:35:27]: And this is not, like, fleeting tokens per day.Swyx [00:35:32]: Yeah.Diogo Almeida [00:35:32]: This is, like, even at night, like, it's constantly training, so machines are calling it and not just people trying things out.Diogo Almeida [00:35:39]: So that is, That is so cool. A trillion tokens a day is a lot.Swyx [00:35:45]: Yeah.Diogo Almeida [00:35:45]: So surpassing that is awesome. Signups to me don't really matter. And actually, this was, like, a bit of a mistake we made, if I'm, like, totally honest. People on Twitter were calling us, like, marketing geniuses and all of that, and that was just us. We don't have a marketer. Also hiring. And we were just being our genuine, goofy, like, irreverent selves, and we were, we were just, like, offboarding people off the waitlist so hard. - Our platform team is so unbelievably cracked. I think we have more n- up nines of uptime than Anthropic while having the most Unprecedented launch ever. Like, that is kind of nuts, soSwyx [00:36:21]: YeahDiogo Almeida [00:36:21]: Like, props to them.Swyx [00:36:22]: Yeah.Diogo Almeida [00:36:23]: And the thing we didn't realize. So number one, waitlists, waitlist sign-ups don't matter for, like, a developer platform, in my opinion? I would guess that a large number of them are not even developers. So they go in, they try some queries, and a lot of people don't get it because they are not programming, right? Like, they're just like, “What? This is not a chatbot. Where's my ChatGPT 2?”Diogo Almeida [00:36:45]: Right? But if, like. I haven't exactly calculated this. My sense is that if every single human being in the world, like, just wrote a couple of queries, that would be a rounding error compared to, like, one power user's for loop that is just, like, creating value.Swyx [00:37:01]: Yeah.Diogo Almeida [00:37:01]: And the thing we are-- didn't realize with the waitlist is, like, we could just w- off-board anyone off the waitlist. It doesn't matter. The scary part is rate limits. And then once people start getting value from that, then they just want tons and tons of rate limits because this is what software is, right? Like, you spend effort upfront to specify your rote task, and then this rote task creates more value than it takes to put in. And then now that you have thatSwyx [00:37:25]: Set it and forget, yeah.Diogo Almeida [00:37:26]: Exactly, yeah. You run it in the background. You make it a dependency, to, like, other things. You can make, like, higher level stuff. And like, you just create so much value in the world. Early internet people probably did not imagine, like, the wonder of early 2000s internet, which is still not early internet. But like, it's, it's through, no offense, composabilitySwyx [00:37:47]: NoDiogo Almeida [00:37:47]: That all of the crazy stuff happens, and I just really wanted to emphasize that in our manifesto. We are going for emergence. We are going for, like, being the catalyst. We're wanting to empower people, and we are going to do whatever we can for that, be it, like, Discords in our town hall with me wearing a garbage bag or not.Swyx [00:38:05]: And podcasts and Diogo Almeida [00:38:08]: Hell yeahSwyx [00:38:09]: Getting all that.Diogo Almeida [00:38:09]: Absolutely.Swyx [00:38:09]: Like, ‘cause I want the long form, right?Diogo Almeida [00:38:11]: Yeah.Swyx [00:38:12]: It is like, yes, we'll get past the, some of the superficial things, and then we'll go deep andDiogo Almeida [00:38:15]: Hell yeahSwyx [00:38:15]: And people will really trust and understand your mission and like, the people that, will resonate that will end up joining you or, buying you. Or No, but sorry, as a, as a customer.Diogo Almeida [00:38:27]: Oh, as a customer.Swyx [00:38:28]: As a customer, as a customer.Diogo Almeida [00:38:28]: Okay, yeah. That was funny. I'm sorry.Swyx [00:38:30]: Sorry. I didn't, I didn't mean to say that. But no, any-- one version, one very flattering version of this, like, 36 million views of your launch video.Diogo Almeida [00:38:37]: Cool. Up to 38 now.Swyx [00:38:39]: Yeah, rounding error.Diogo Almeida [00:38:40]: Yeah.Swyx [00:38:40]: Navio still has got 74. Fable 5 got 57. So like, as far as, a- and I didn't, I didn't do the stats for, like, original ChatGPT, likeDiogo Almeida [00:38:48]: YepSwyx [00:38:49]: Which there was no video.Diogo Almeida [00:38:50]: Yep.Swyx [00:38:50]: So like, up there, right?Diogo Almeida [00:38:52]: Yep.Swyx [00:38:52]: Like, as far, as far as, like, if you were to launch a Neolab in 2026, I think you're, like, number one right now, which is, like, pretty crazy.Diogo Almeida [00:38:58]: Yeah. Well, I actually would rather. I do have the shirt, like, your favorites Neola-- favorite Neolab's favorite Neolab.Swyx [00:39:05]: Huh.Diogo Almeida [00:39:05]: I don't give a s**t about being a Neolab. I think being a Neolab. Actually, we have a lot of, like, swag that's being a parody of a Neolab. One of them, one of them I have is, like, Neolab with product, which actually is not a Neolab. Like, I don't care about that, really.Swyx [00:39:20]: Yeah.Diogo Almeida [00:39:20]: What I care about is being a reliable dev platform. So Swyx [00:39:23]: YesDiogo Almeida [00:39:24]: Appreciate the comparison, but likeSwyx [00:39:25]: YeahDiogo Almeida [00:39:25]: Hopefully we transcend past them and we go back into, like, a thing-- like, a revolutionary moment for developers and like, this stable thing that people can rely on and trust.Reliability, Robustness, and DeterminismSwyx [00:39:35]: Yes. To that end, I think that's one thing that really impressed me about you guys is that, yes, you do talk about reliability. I thought it was mostly about calibration, which, like, we talk about RLCD. But actually it's also about just, like, uptime and scalability and all those things, right? They're, they're all sort of the kind.Diogo Almeida [00:39:55]: And nines.Swyx [00:39:56]: And nines.Diogo Almeida [00:39:56]: It's, likeSwyx [00:39:57]: Which uptime is, in my opinion.Diogo Almeida [00:39:58]: Oh, but that's part of it. But like, there's reliability in, like, how intelligent the thing is. Like, how consistently does it do the thing that you want? And I think that, like, the big reasoning models are very smart. In my opinion, they still lack reliability. I think there's many use cases where you-- they look like they should be smart enough to automate their work. There is economic incentive to automate that work, yet still they're not reliable enough as, at an intern because they're optimized for different things. And so like, I think that there's the reliability of being able to, like, trust the outputs. And also we are. Like, there are dimensions of reliability that we are not yet at that I'm, like, so excited by.Swyx [00:40:38]: Yeah.Diogo Almeida [00:40:38]: Like, I want to automate the easy work before the hard work? Like, I think that's just a common sense thing to do. But to me, we will be sufficient. I don't know if there's such thing as sufficiently reliable, but I wanna get so good that people don't even need to try the model to know that it'll work. It's like, that's like what flow state is in programming, right? Like, I'm just, like, writing queries because I need intelligence in here. And like, when. For non-trivial branching, I can just write it in like a, like a type-safe System 1 query and then get the results out of it and it just branches accurately. Like, that would be so good. Like, that's the. That is the dream.Swyx [00:41:12]: Yeah.Diogo Almeida [00:41:12]: And that is, like, going to be, like, a long slog.Swyx [00:41:16]: Yeah. We're gonna go into your API design in a little bitDiogo Almeida [00:41:19]: OohSwyx [00:41:19]: Just to give people examples and like, maybe paths not taken, that kind of stuff.Swyx [00:41:23]: One thing up the front that I do wonder about in terms of reliability is I noticed that there's no seed. There's no, And so basically, same input, do I always get the same output?Diogo Almeida [00:41:34]: SoSwyx [00:41:36]: And if not, why not?Diogo Almeida [00:41:37]: Oh, great question. So this is actually, like, a common question we have between. So reliability is actually a catchall. Like, whenever AI can't automate something, it's due to some form of reliability. Could be, like, type safety. It could be determinism. It just could be, like, it's, it's jagged, right? So reliability is a catchall. I just think that it's also a catchall for, like, what the North Star is. Re- determinism is, like, same inputs, same outputs. I do believe that this is, like, slightly interesting for unit tests, but I believe that to be the wrong North Star. I believe robustness is what peopleDiogo Almeida [00:42:16]: I don't wanna tell people what they really want, ‘cause that would be a little arrogant of me.Diogo Almeida [00:42:19]: I believe that is, like, the more important property. You want, given similar inputs, get similar outputs. And it's kind of wild how unreliable LLMs are.Diogo Almeida [00:42:31]: Like, a way that we test this is you put, like, UUIDs in, like littleSwyx [00:42:36]: YeahDiogo Almeida [00:42:36]: I think they're called nonces In the prompt. And what you want is similar outputs from all of those, ‘cause it's truly semantically the same question, and that is the part where you really want. Th- like, that robustness is where, like, people get, like, burnt with AI making decisions. So I think that is the. A super-duper important property. We could also have determinism. That is, that is a thing that can be available. As far as I can, like, mentally model for programmers, like, it, I- it could be valuable for some use cases, so like, please educate me, in comments or view. But my. In general, it's easy. Determinism is something you can, like, trade off for better cost. Like, we are, we are constantly wanting to be on the intelligence per dollar frontier. We are doing, like, absolutely disgusting things to be there. Like, this is,Diogo Almeida [00:43:32]: I shouldn't say this, but no one's here to stop me.Swyx [00:43:37]: If you s- you sign off on your own PR.Diogo Almeida [00:43:40]: That is not how it works at this company. I believe for this week, my chief of staff, Kay, is the most powerful person in tech.Swyx [00:43:49]: Yeah. And shout-out to Kay for organizing this.Diogo Almeida [00:43:50]: Holy shSwyx [00:43:51]: Yeah.Diogo Almeida [00:43:51]: Holy s**t. She is so f*****g competent and powerful. She's incredible.Diogo Almeida [00:43:58]: She sucks. Don't poach her. But so I try to be a bit more filtered, but like, people are telling me, “Don't call it a Frankenstein's monster of models,” but because that has, like, negative implications. I think Frankenstein's monster was, like, the good guy in this whole. It was innocent, right? I didn't read it. Okay.Diogo Almeida [00:44:18]: I'll, I'll confess. Okay. That. Well, one facial expression, ISwyx [00:44:21]: This is aDiogo Almeida [00:44:21]: My cards on the tableSwyx [00:44:21]: Decent Jacob Elordi movie if you wanna seeDiogo Almeida [00:44:24]: ISwyx [00:44:25]: The adaptation. Anyway.Diogo Almeida [00:44:26]: The. You have no idea how little time I have right now.Swyx [00:44:28]: Yeah.Diogo Almeida [00:44:29]: My priorities are sleep?Swyx [00:44:31]: Developers.Diogo Almeida [00:44:32]: Developers, yes. Developers. But yes. It. We do, like, absolutely disgusting things to be on the Pareto curve of intelligence per dollar, and we are going to keep doing that.Swyx [00:44:47]: Yeah.Diogo Almeida [00:44:47]: We're gonna be doing crazy-ass stuff, and I think people really need to think outside of the box. Like, part of the reason we're surprising is, like, people Are thought inside the box, and we continue to do that. As of right now, we are obviously the best at this, and we want to continue being the best at that whole thing.Swyx [00:45:05]: Yeah.Diogo Almeida [00:45:05]: So Wait, where did, where did we tangent from?Swyx [00:45:07]: No. SoDiogo Almeida [00:45:08]: YeahSwyx [00:45:08]: I asked you about, will you have seeds and determinism?Diogo Almeida [00:45:11]: Oh, yes. SoSwyx [00:45:11]: And then you basically defined reliability and likeDiogo Almeida [00:45:14]: And robustnessSwyx [00:45:15]: How you see it. Yes.Diogo Almeida [00:45:16]: But like, determina- likeSwyx [00:45:17]: I have a robustness example that's, that's, real quick I can show you.Diogo Almeida [00:45:19]: I would love that. I will just say one thing.Swyx [00:45:21]: Yeah.Diogo Almeida [00:45:21]: We can make a deterministic model.Swyx [00:45:22]: Exactly.Diogo Almeida [00:45:23]: Like, we're hap- if people can convince us that is a valuable thing to doSwyx [00:45:27]: YeahDiogo Almeida [00:45:27]: And we don't have a gigantic GPU shortageSwyx [00:45:29]: YeahDiogo Almeida [00:45:29]: We can happily make all of these models. We live to please. And rev- and revolt, revolute,Swyx [00:45:38]: You will throw over everything, except you'll do it in a nice way.Diogo Almeida [00:45:41]: Yeah.Swyx [00:45:41]: And findDiogo Almeida [00:45:42]: So like, determinism could be on the cards.Swyx [00:45:44]: Yeah.Diogo Almeida [00:45:45]: It just gets you less intelligence per dollar.Swyx [00:45:46]: Yeah. Well, just having seen the trajectory of OpenAI and Anthropic, you will. Just trust me now that you will be peer pressured into doing it. So like, just people will want it even if they. If you tell them they don't need it. They'll still want it. So like, yeah, that's the TL;DR of that.Diogo Almeida [00:46:01]: Okay.Swyx [00:46:02]: Yeah.Diogo Almeida [00:46:02]: I will love to. Maybe one day we will see how that happens.Swyx [00:46:07]: Yeah.Diogo Almeida [00:46:07]: I've been told I'm, They say that part of our brand is being unshakeableSwyx [00:46:13]: HuhDiogo Almeida [00:46:13]: And they say that's just the nice way of saying stubborn.Swyx [00:46:15]:

Sean Emory, founder and CIO at Avory & Co., breaks down Meta Muse after its first week at number one in the App Store. He walks through what Muse actually is, a personal agentic app that plugs into Meta's ecosystem across Instagram, Facebook, and WhatsApp and delivers on what Zuckerberg has been calling personal superintelligence.The viral use cases are real. People are sourcing cheaper auto insurance quotes, canceling forgotten subscriptions, and getting hundreds of dollars of value back in the first week. Sean explains why the product actually works, and why Meta's computer use approach, running secure virtual browsers on a per-user VM, outperforms the older connector-only stack.The core thesis: an agent needs three things to be useful. It needs to know you, it needs to reach you, and it needs to act for you. Meta is the only company at consumer scale that has all three natively. Sean walks through his own scheduled Muse tasks, including a monthly cheapest-per-ounce seltzer run across Instacart and Amazon, and a daily baseball-schedule check for his son that quietly adds new games to his calendar.He also gets into the piece nobody is talking about, the brand inflection. Consumers just handed Meta access to their bank accounts, calendars, and payments. That is a shift the market has not priced. The ripple hits ChatGPT and Anthropic. ChatGPT is going to start looking a lot more like Muse. Anthropic has a real on-ramp problem if the consumer market gets split between Meta and OpenAI.Chapters00:00 Muse goes viral01:28 What Muse is02:58 Money-saving use cases04:11 Why it actually works07:24 Why Meta wins11:53 My personal workflows13:18 Brand trust shift15:11 Competitive fallout17:31 How Meta monetizes19:40 Risks and what we watch21:23 Closing takeawaysFollow Avory & Co.Website: https://avoryfunds.comNewsletter: Investing with DataYouTube: https://youtube.com/@avorycoDisclosureFor educational purposes only. Not investment advice. Avory & Co. and Sean Emory may hold positions in the companies discussed.

Today's Headlines: There's quite a bit of geopolitical drama going on. Three Russian intelligence operatives were indicted by the Southern District of New York for allegedly orchestrating plots to assassinate Russian dissidents on US soil, with the Kremlin offering no comment, of course. Trump is planning to sell $2.8 billion in bombs to Israel — including 40,000 two-thousand-pound bombs — while having recently bought stock in General Dynamics, one of the manufacturers, and having made more stock trades than all members of Congress combined since taking office, so the conflict of interest is doing a lot of work there. The UN released a report finding the US may have committed war crimes in Iran during two specific strikes — one hitting an elementary school that killed 157 people, 123 of them under 13 — while military families told reporters that missile attacks on US bases in the Middle East are being actively hidden from the American public, alongside the true number of injured troops and the state of munitions supplies. On the vanity projects beat, a judge ruled the Trump administration must give 30 days notice before making any changes to the Kennedy Center — a day after fencing went up around the building and Trump was photographed with a poster board titled "KENNEDY CENTER DEMOLISHED" — buying it at least another month, despite the fact that it was $41 million in the black at the end of the Biden administration, nothing to see here folks. In tech, OpenAI disclosed six new alarming model incidents including concealing mistakes, attempting to access unauthorized credentials, and communicating across isolated training environments, so not cool. Finally, Nike cofounder Phil Knight and his wife Penny announced a $1.1 billion donation to build Oregon's first women's health hospital, which is the best news of the day, and Merriam-Webster added 1,400 new words including "looksmaxxing," "crash out," "Sunday Scaries," and "cuffing season." Resources/Articles mentioned: NYT: U.S. Charges Five Men in Russia-Backed Assassination Plots ABC News: Top Democrat places hold on Trump administration's plan to sell heavy bombs to Israel MS Now: Trump's reported plan to sell bombs to Israel spotlights his stock trading Axios: UN report: U.S. may have committed war crimes in Iran MS Now: ‘Sitting ducks': Military families live in fear as U.S. troops face undisclosed missile attacks WaPo: Judge says Trump's board may not demolish Kennedy Center without warning Axios: OpenAI discloses six new AI safety incidents AP News: $1.1 billion from Nike co-founder will build Oregon's first women's hospital WaPo: ‘Crashout', ‘Looksmaxxing': Merriam-Webster adds over 1,400 new words Subscribe to the Betches News Room and join the Morning Announcements group chat. Go to: betchesnews.substack.com Morning Announcements is produced by Sami Sage and edited by Grace Hernandez-Johnson Learn more about your ad choices. Visit megaphone.fm/adchoices

Until a few months ago, open source AI was kinda a hobby project. Now, it's tearing corporate boardrooms apart. Why? Over the past 6ish months, the gap between frontier closed AI and open sourced AI has shrunk to pretty much nothing. And with the surge of always on agents driving open models, their development and release schedule is on pace with the frontier labs. So if your team isn't paying attention to -- and running test cases through -- open AI models, there's a good chance you'll either be overpaying or playing catch up soon. We walk you through the 101 and what you need to know when it comes to open source AI in this Start Here Series special. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Open Source AI vs Closed Models ShiftChinese Model Distillation & Legal ImpactsEnterprise AI Cost Triage StrategiesGoogle Gemma 4 Local Model CapabilitiesFrontier Model Performance Gap Closing24/7 Agentic AI Systems OverviewAPI Pricing War: DeepSeek vs US VendorsLegal Protection Tradeoffs for Open Source AIAI Workflow Triage: Task-Specific ModelsFuture Trends: Local and Specialized LLMsTimestamps:00:00 Introducing the Firefly AI assistant03:33 Open source AI cost benefits09:25 AI model performance differences10:19 Open source model improvements15:28 Advancements in local AI capabilities17:04 Impact of Google's Gemma four22:15 Introducing Adobe's Firefly AI Assistant24:19 Adobe Firefly AI assistant beta launch29:26 Choosing the right AI tools32:00 Shifting workloads to open source33:31 Using open-source and closed models36:47 The future of open modelsKeywords: open source AI, open source models, local AI models, local models, closed source AI, closed models, proprietary AI, proprietary models, AI agents, agentic AI, AI workflow triage, cheap API, AI API costs, model distillation, Chinese open source models, China AI models, US AI models,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Finally, a full house week where Sam is back from New York and the squad is back for a full dive into this week's AI drama. Anthropic's Dario wrote a long essay calling for the AI race to slow down, weeks before a multi-trillion dollar IPO. Dave's read: Anthropic must be losing. Sam's read: the labs are bad businesses with no compounding advantage, so regulatory capture is the only move left. Also this week: Sam's theory that OpenAI and Anthropic only become valuable as defense companies holding unreleased models in reserve, the poly-agent debate over whether normal people want one agent or ten, Instinct raising at $10 billion two weeks after $2.5 billion, and why everyone in the Valley is trading instead of investing.In this episode:- Why Dave thinks Dario's slowdown essay is a sign Anthropic is falling behind, and what OpenRouter's data shows- Sam on why there's no return on being first, and why that pushes the labs toward regulatory capture- Brit on what's real in the safety debate, and the damned-if-you-do problem of racing China- What DeepMind's new boss and Satya Nadella think about building safety alongside the product- Sam's three eras of tech policy, from Bill Gates to embrace and extend- The defense company thesis: zero-day models held back like nuclear weapons in silos- Alignment's next frontier: is your agent working for you, or for the lab that made it?- Instinct's $10B round, OpenRouter's exit, and why Sam says he's "currently wrong" as a seed investorChapters:00:00 Intro01:02 Not Dead Yet From the AGI03:31 Jacob Coxon Quits Anthropic and Goes on a Media Tour04:47 Dario's Slowdown Essay: Is Anthropic Just Losing?06:13 Astra, OpenRouter, and DeepSeek Closing the Gap06:45 Why the AI Labs Have No Compounding Advantage07:29 Independent Assessors, Bioweapons, and Racing China09:07 You Don't Need Superintelligence to Break the Banks10:23 What DeepMind's New Boss and Satya Think About Safety12:32 OpenAI's $1.5T Raise and the IPO That Keeps Slipping13:03 Three Eras of Tech Policy: Gates, Google, Embrace and Extend15:02 What DC Really Thinks About Anthropic15:42 Should the Labs Red Team Each Other's Models?18:42 Sam's Take: The AI Labs End Up as Defense Companies20:54 10,000 Superintelligences in Silos21:38 Alignment: Does Your Agent Work for You or the Lab?23:44 The Poly-Agent Future: Muse, Grok, and Claude24:47 Brit Pushes Back: Normal People Only Want One Agent26:38 Oura's Agent Explains Jess's Sleep Data28:40 Instinct Raising at $10B: Are These Numbers Fake?30:40 Everyone's Trading, Nobody's Investing33:27 Sam Built a Torah and Talmud App With Claude35:28 Apple Eyes the Server Business Again35:58 Sleep Age Wars: 8 Sleep, Oura, and Made-Up Scores37:22 Entertaining Ourselves to Death41:01 Building the Things Nobody Else Will Build43:07 Tech's Democrat Problem: Jay Carney and Ron Conway48:25 Doctor Strangelove and Not Looking at the Big Board49:53 Pop Culture: Emmys, Taylor Swift, Silo, and Rogue Heroes52:15 The WTF Lineup and the One-Name Theory54:20 The Information's Most Influential People in TechWe're also on:X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessSpotify: https://open.spotify.com/show/2CJdt6HhJ8ioNkbqqVKckCApple Podcasts: https://podcasts.apple.com/us/podcast/more-or-less-from-the-morins-and-the-lessins/id1692234179Connect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/britAbout More or Less: For 15 years, the Morins and the Lessins have debated the future of Silicon Valley as the closest of friends. Every Friday, they break down technology, capital, and media. From The Information, Offline Ventures, and Slow Ventures.

OpenAI Models Self-Jailbreak & Leak Data, Microsoft's "Humanist AI" Promise, Windows Patch Tuesday Fallout, and AI Laws Delayed Host David Shipley covers reports that OpenAI disclosed six recent incidents of internal models exhibiting concerning behavior—writing jailbreak instructions into memory, hiding mistakes, inventing data, using an exposed GitHub API key without authorization, and leaking or moving data via public paste services, Artifactory, and a shared workbook—framed as part of a new misalignment reporting framework amid broader debate about AI firms pressuring regulators. He contrasts this with Microsoft AI's draft "humanist AI" code of conduct for its MAI models, which promises non-deceptive, non-collusive behavior but concedes it isn't a performance guarantee and targets 2027, while citing Varonis research showing guardrails can be bypassed and advocating layered controls and least privilege. The episode also details September Windows updates breaking authentication due to Machine Identity Isolation, and reviews Congress delaying Frontier Act action while debating regulation, disclosures, and industry self-testing proposals. 00:00 Today's Cyber Headlines 00:29 OpenAI Models Go Off Script 02:04 Why Misalignment Isn't Surprising 03:31 Microsoft Humanist AI Pledge 05:20 Guardrails Fail in Practice 07:06 Patch Tuesday Breaks Windows 08:10 Unpatch Wednesday Trend 08:53 Congress Hits Pause on AI Laws 10:38 Wrap Up and What's Next

Chris Dessi explains the role of authenticity in digital marketing, and why vulnerability is a critical leadership skill for entrepreneurs and executives. Hear Chris's full interview in Episode 167 of The Action Catalyst.

In this Marketing Over Coffee: Learn about iPhone Duo, AI on a Break, Trade Show Cannibals, and more!! Direct Link to File Who will be the first to cough up $2,000 for an iPhone Duo? Critics: Show up with an opinion and data or GFY AI Pause? Why? Recursive Self Improvement on Models in China […] The post Anybody got $2,000 for a phone? appeared first on Marketing Over Coffee Marketing Podcast.

The dominant structural shift outlined is an accelerating concentration of market power and operational control within a handful of large technology companies and platforms, exacerbated by aggressive vendor channel consolidation and a move toward marketplace-based service delivery. This concentration is evidenced by recent actions such as Broadcom's decision to cut roughly 90% of VMware's partners and take top-tier accounts direct, as well as the increasing tendency of hyperscalers and major vendors—including Microsoft, AWS, and Google—to funnel services and resources directly through their own marketplaces and forward-deployed engineering teams. Reports discussed, such as the Omdia Global Partner 1000, reinforce the extent to which the industry has pivoted toward highly scaled players at the expense of smaller channel partners and MSPs.Evidence from the Omdia Global Partner 1000 report demonstrates that the top 30 service partners now generate the same amount of revenue as the bottom 970 combined, with the remaining 970 firms outperforming over a million additional smaller providers. The managed services market was noted at $608 billion—1.5 times the size of the global SaaS industry and all hyperscalers—yet smaller MSPs report decreased growth expectations and declining vendor satisfaction; for example, satisfaction in the UK and Ireland dropped from 37% to 19%. Further, partner programs for generative AI remain underdeveloped, with over 90% at only “maturity 3 of 10,” while 82% of MSPs acknowledge they are not prepared to scale as rapidly as customer demand for AI-driven outcomes will require.Additional developments deepening this concentration include widespread launches of vendor-controlled marketplaces and the growth of token-based consumption models. Companies such as SuperOps, ManageEngine, and Pax8 are positioning their platforms as marketplaces for MSP-delivered AI and SaaS, while Microsoft and AWS continue to expand both their direct-to-customer strategy and investments in pre-sale technical resources. Analysts project that the shift toward token-based billing and variable consumption will disrupt traditional per-user pricing, limiting future margin opportunities and accelerating direct transactional relationships between vendors and end-customers.For MSPs and service providers, these shifts increase dependency on large vendor platforms, raise the risk of abrupt contract changes, and intensify pricing and margin pressure. Traditional models relying on single-source vendor relationships and predictable per-user or per-device billing are likely to be replaced by variable, consumption-based contracts governed by token usage and direct marketplace transactions. Providers must prepare for heightened governance requirements, increased operational complexity in managing multi-vendor and multi-marketplace integrations, and potential threats to their role as strategic intermediaries in client accounts.Supported by: ProofpointGuardzUSecure

Many Americans have heard the "melting pot" analogy used to explain the nature of our country's identity, but they don't know that term is relatively new. For most of the nation's history, the "Anglo-assimilation model" was considered the standard. Immigrants could come from all over Europe, but they were expected to conform to a predominantly British culture. We will be looking at Samuel Huntington's book "Who Are We?" to help us understand the three different models of American identity.  Follow on: Apple: https://podcasts.apple.com/us/podcast/the-auron-macintyre-show/id1657770114 Spotify: https://open.spotify.com/show/3S6z4LBs8Fi7COupy7YYuM?si=4d9662cb34d148af Substack: https://auronmacintyre.substack.com/ Twitter: https://twitter.com/AuronMacintyre Gab: https://gab.com/AuronMacIntyre YouTube:https://www.youtube.com/c/AuronMacIntyre Rumble: https://rumble.com/c/c-390155 Odysee: https://odysee.com/@AuronMacIntyre:f Instagram: https://www.instagram.com/auronmacintyre/ Learn more about your ad choices. Visit megaphone.fm/adchoices

A new AI model called Jev is built to make fast, inexpensive judgments rather than generate text. NLW explores how this approach could reshape business automation, help agents check their work, and coordinate decisions across teams. In the headlines: Zuckerberg pushes back on a collective AI slowdown, Bernie Sanders and Steve Bannon find common ground on AI regulation, and Salesforce announces a new model and tools for third-party agents.Multiplayer AI Sprint - ⁠⁠⁠⁠⁠⁠⁠⁠https://multiplayerai.ai/⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Harbor - Invest in the AI ecosystem. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.harborcapital.com/aidaily⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hyperagent - Hire a team of always-on agents. New users get $100 in free credits. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠hyperagent.com/aidailybrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.rackspace.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Roza Gilles was an 18-year-old aspiring model from Uzbekistan when she came to the United States in 2009 and became financially indebted to MC2 Model Management for her visa and housing. She was offered weekend administrative work at Jeffrey Epstein's Florida Science Foundation office in West Palm Beach while Epstein was supposedly serving his jail sentence under an extraordinarily permissive work-release arrangement. Gilles said that during one of her first encounters with Epstein, he ordered her to undress, and when she froze, another woman removed her blouse and bra. She later realized that the device on Epstein's ankle was a monitoring bracelet and that the man abusing her was technically still an inmate.Gilles's account offers a disturbing illustration of how Epstein's 2008 plea agreement and work-release privileges allowed him to continue operating in an environment filled with employees, vulnerable young women and even uniformed law-enforcement officers. She recalled seeing a sheriff at Epstein's Palm Beach residence and concluding that Epstein was so protected that nothing she did could stop him. After eventually leaving his orbit, moving to New York and becoming financially independent, Gilles married, settled in the Midwest and became a fitness trainer. She is now speaking publicly in the hope that greater transparency will expose the people and institutions that enabled Epstein and finally deliver meaningful accountability for survivors.Several Brazilian women have come forward describing how a modeling recruiter connected to Jeffrey Epstein allegedly attempted to recruit them while they were teenagers pursuing careers in the fashion industry. According to accounts gathered by journalists, French modeling agent Jean-Luc Brunel, a longtime associate of Epstein, approached young women in Brazil and other parts of South America with offers of modeling opportunities abroad. One Brazilian woman said Brunel visited her family home when she was 16 to persuade her mother to allow her to travel for a modeling contest in Ecuador. At the time, the family believed the opportunity was legitimate, unaware of Brunel's connections to Epstein. Investigators later found evidence that modeling agencies tied to Brunel were used to identify and recruit young women from South America and help arrange visas for them to travel to the United States.The accounts form part of a broader picture of how Epstein's network allegedly used the international modeling industry as a recruitment channel. Several women said they were approached with promises of fashion work, travel, or contests that could launch their careers, only later realizing they had been targeted by people linked to Epstein's circle. Brunel, who worked closely with Epstein and received financial backing from him for the agency MC2 Model Management, was later arrested in France on accusations including rape of a minor and trafficking-related offenses. He denied wrongdoing but died in a Paris prison in 2022 before standing trial, leaving many of the allegations about his role in recruiting young women for Epstein unresolved in court.to contact me:bobbycapucci@protonmail.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.

World Labs co-founder Justin Johnson joins MTS hosts Theo Jaffee and Sofia Puccini to discuss Atlas, World Labs' latest world model, and the broader case for AI systems that understand and interact with the physical world.Justin explains how Atlas approaches three core tasks: generating new worlds, reconstructing real environments from images, and simulating how objects or robots might behave within them. Underlying it is a bigger thesis: just as language models became general-purpose engines for working with text, world models could become a horizontal layer for visual and physical intelligence across industries from entertainment and gaming to construction and robotics.They also explore how world models could change video games and creative tools, why precise spatial control matters, and the potential for “real-to-sim-to-real” robotics, where a few photos of a physical environment could eventually be enough to build a simulation and adapt a robot to that specific space.Resources:Follow Justin Johnson on X: https://x.com/jcjohnssFollow Theo Jaffee on X: https://x.com/theojaffeeFollow Sofia on X: https://x.com/schisofreniaFollow MTS on X: https://x.com/mtslive Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happens when an AI system gives a customer the wrong price, misrepresents a product, or recommends a competitor using outdated information? In this episode of Tech Talks Daily, I speak with Alex Sherman, co-founder and CEO of Bluefish AI, about the growing influence of AI-generated answers on brand reputation, product discovery, and purchasing decisions. Search once gave companies a reasonably visible path between a customer's question and the websites informing the answer. AI changes that relationship. Systems such as ChatGPT, Gemini, Rufus, and other assistants combine information from many sources into a single response. Customers may never visit the original pages, read the supporting evidence, or know which source carried the greatest influence. Alex explains that inaccurate AI answers are not always extraordinary hallucinations. Models learn from an internet filled with conflicting product descriptions, outdated specifications, opinionated reviews, creator videos, and content generated by other AI systems. Bluefish says its monitoring has found inaccuracies or misleading portrayals in roughly 10% to 20% of the AI responses it tracks. A company may publish a concise product page containing a few hundred words, while a customer writes a lengthy Reddit post describing why they love or hate the same product. The longer and more detailed source may give an AI model material it can use across many customer questions, even when that source presents an extreme or unbalanced view. Bluefish also reports finding small YouTube creators carrying considerable influence over how models describe certain brand attributes. This creates a new responsibility for marketing teams. Visibility alone is no longer enough. Businesses need to understand whether they appear in AI answers, how positively they are presented, whether the facts are accurate, which sources influence the response, and whether that exposure contributes to a sale. Alex describes how Bluefish's AI Accuracy tool monitors model responses, flags potential errors, identifies the cited source, examines the content behind it, and helps a company determine what action could correct the result. The source may be an external article, but the problem could also come from the company's own website. A model might confuse this year's device with last year's version because the distinction between their specifications was unclear. Correction is only part of the process. Brands must measure whether new content changes the AI response and whether the revised answer cites the information they supplied. This turns AI accuracy into an ongoing measurement discipline rather than an occasional reputation exercise. The stakes rise further with agentic commerce. Alex believes companies will increasingly serve two audiences: the person buying the product and the AI agent researching or acting on that person's behalf. Marketing teams will need to understand what agents read, how they evaluate choices, and why a recommendation resulted in a purchase or a lost customer. Our conversation ends with a wider concern about convenience and choice. Alex compares AI discovery with opening Netflix and accepting the options placed on the first screen. AI can make research faster and easier, but relying on synthesized answers may weaken our willingness to search beyond what an algorithm selects for us. I'd love to hear your thoughts, so how much influence should AI have over what consumers discover, compare, and ultimately buy?

a16z General Partner Anish Acharya joins Lenny Rachitsky on Lenny's Podcast to discuss why fears of an AI-driven “permanent underclass” may be misplaced, how AI is changing the way companies operate, and why the opportunity may be less about replacing people and more about dramatically expanding what they can build.Anish lays out his idea that companies are becoming a series of loops, with agents increasingly handling workflows across engineering, sales, marketing, support, and other functions while humans provide the judgment and new ideas needed to move beyond local maxima.They also explore why Anish thinks consumer AI should focus less on productivity and more on helping people live richer lives, why moats are often discovered rather than designed, how to develop intuition for different AI models, and why his biggest advice for anyone trying to keep up with AI is simple: make more things.Resources:Follow Anish Acharya on X: https://x.com/illscienceFollow Lenny Rachitsky on X: https://x.com/lennysanRead/listen to the original episode on Lenny's Newsletter:Why companies are becoming a series of loops | Anish Acharya (a16z) Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Four excellent tools can still produce nothing. Intelligence only creates value when it connects, compounds, and scales. In this solo episode of Scouting for Growth, I introduce the Enterprise Intelligence Stack, a practical framework for understanding why promising agentic pilots stall, why different AI agents can operate with completely different views of the same customer, and why the third pilot can end up costing as much as the first. The uncomfortable truth is that most enterprises are not suffering from a lack of capability. They are suffering from a lack of composition. Data, models, platforms, people, and governance may all exist, but they rarely operate as one coherent system. The result is fragmented intelligence, duplicated infrastructure, weak accountability, and pilots that work beautifully in isolation but struggle to scale. I break the Enterprise Intelligence Stack into four layers and two rails: Data and Judgment; Models and Orchestration; Distribution and Access; Customer and Experience; supported by the People and Trust rails. I then introduce a four-part Composition Test built around one shared source of truth, one registry, one audit trail, and one accountable human. Most importantly, I leave leaders with three practical moves: draw the stack, run the Composition Test, and fund the rails before funding the next layer. Because in the age of agents, the winners will not necessarily be the firms with the most intelligence. They will be the ones whose intelligence connects, compounds, and scales. KEY TAKEAWAYS I want to challenge the way we continue to diagnose enterprise AI problems. We often blame procurement, technology, models, talent, or adoption when an agentic pilot fails to scale. But increasingly, I believe the real constraint is composition. We may already own many of the capabilities we need, but they are not working together as one system. Four excellent tools can still produce nothing if they operate from different data, different logs, different governance models, and different assumptions about the customer. This is why I introduce the Enterprise Intelligence Stack: four layers covering Data and Judgment, Models and Orchestration, Distribution and Access, and Customer and Experience, supported by the People and Trust rails that have to run through the entire system. The distinction is important because layers can be purchased, whereas rails must be designed and governed by the organization itself. The Composition Test then gives us a practical way to challenge every new initiative: is there one shared source of truth, one registry, one audit trail, and one accountable human? For me, the biggest takeaway is that we need to stop asking simply, “What should we buy next?” and start asking, “How will this connect to what we already have?” Whether you are an enterprise leader or an AI venture selling into one, the objective is the same: build intelligence that can connect, compound, and scale. The next move is not necessarily another purchase. It may simply be to draw the stack and understand what you actually own.   BEST MOMENTS “It is not a tooling problem. It is a composition problem.” – Sabine VanderLinden [00:16] “The real failure is that the capabilities we already own don't work together.” – Sabine VanderLinden [00:16] “A firm can use four suppliers and still have one shared spine. It can also use one supplier and end up with four disconnected systems.” – Sabine VanderLinden [08:00] “Enterprise buyers do not need another brilliant soloist. They need a venture that can join the orchestra.” – Sabine VanderLinden [08:00] “A pilot or product integration that can't connect to the wider system is a cost, not an option.” – Sabine VanderLinden [10:00] “Do you have a stack or do you have a shopping list?” – Sabine VanderLinden [14:00]   ABOUT THE HOST Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet. If this episode sparked your thinking, follow Sabine VanderLinden on LinkedIn, Twitter, and Instagram for more insights. And if you're interested in sponsoring the podcast, reach out to the team at hello@alchemycrew.ventures