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Latent Space

11 referat i flödet. Sammanfattningarna är skrivna av Vibekollen — innehållet tillhör Latent Space.

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Academia is for Ambition — Alex Zhang, MIT

Last call for regular tickets for AI Engineer NYC! As an exclusive for Latent Space subscribers, the first 30 of you can take a 30% off code if it helps - for new tickets only, no refunds! See you in 2 weeks!While we tend to cover industry on the pod, every so often we celebrate a clearly emerging superstar PhD. In 2024 we featured Shunyu Yao, who went on to build Operator at OpenAI and is now Chief AI Scientist of Tencent. In 2025 we featured Jack Morris, who went on to cofound Engram at $600m and is now a leading voice on continual learning. This year we are proud to feature the work of Alex Zhang of MIT.From GPU kernels and KernelBench to Recursive Language Models, Mismanaged Geniuses, and massive multi-agent swarms, Alex Zhang is exploring how much capability we’re leaving on the table by wrapping increasingly powerful models in primitive systems.

2 okt. latent.space

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Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week

Three months ago Dwarkesh, who has been posting incredible blogs and episodes about RL, posted a framing question for his video essay on RLVR which upset a lot of Computer Use folks:We are no strangers to learning in public and are no strangers to the stress of getting things wrong when you have a big platform. However, we were at Anthropic for the Computer Use launch, there for Claude Cowork with the first big podcast on it, organized the first Computer Use track at AIE presenting the state of the art, and were close to the OpenAI-Sky Software acquisition that now powers the complete domination of computer use that Codex enjoys today.

1 okt. latent.space

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Claude Code’s Next Era — Thariq Shihipar, Anthropic

We are excited to have Anthropic share their latest AI x Finance work at AI Engineer New York, coming up in 2 weeks!In case you’ve been under a rock, here’s a non-exhaustive list of what Anthropic has been shipping since closing the largest fundraise of all time in May at $47B ARR:* June: Launched Claude Tag and Sonnet 5 and Fable 5* July: Opus 5, /checkup. crossed $65B ARR* Last month: Fable/Mythos 5.1, and EFS (upcoming pod)* IPO target $2T, end 2026 ARR estimated $100B* Cowork/chat merged before did* Claude Mods* Dario endorses the same Pacing the Frontier message cosigned by all labs* Last week: Opus 5.5, Plugins portal, Cloud Sessions/Claude Projects* Today: Sonnet 5.5!Today’s episode should catch you up, with Thariq Shihipar, the explainer-king of Anthropic, who we last caught up on Fable launch day with The Field Guide to Fable:The Future of Mutable SoftwarePay special attention to Claude Mods (especially the cheatsheet):In general this is also the inverse of the other viral tweet from Thariq:Cloud Brain, Local HandsAnd give a try to Claude Projects:The “hands” terminology is not just an analogy for the local/cloud paradigm that is being built up at frontier coding agent com…

29 sep. latent.space

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OpenRouter: from Seed to Stripe — with OpenRouter’s Alex Atallah & AMP’s Anjney Midha

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.

26 sep. latent.space

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Runway’s WorldPrompt and the Engineering of Real-Time Worlds

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.

25 sep. latent.space

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🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)

The OpenAI → Hugging Face attack has people asking “what else do we need to worry about?” and Anthropic’s filters flag two things: cyber-security and biology. The natural question is: what about bio-security, then? Clem Delangue argues that cyber-warfare defensive capabilities need to be open and to keep pace with frontier models’ attack capabilitiesRadical Numerics co-founder Eric Nguyen sat down with us and explained why the same models that increase biological capability can also keep defense from falling behind.Building a virus from scratchWhile he was at Stanford, Eric couldn’t get traction on Genomic Language Models (GLMs) for a long time. Biologists didn’t believe it would work, didn’t think they could verify the output, and didn’t see important applications beyond what they could already do. He kept pushing, eventually helping lead the development of Evo and contributing to Evo 2 at Arc Institute.

23 sep. latent.space

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🔬 An Oscar, Two Asteroids, and the Algorithm in Your sklearn: John Platt on AI for Science

How often do you get to talk to a guest who has both an Academy Award and who invented textbook machine learning algorithms? John Platt has an Oscar, two textbook algorithms, two named asteroids, and an Erdos-Bacon number of 6. This was easily the most fun bio of all the guests we’ve read to date. And the result was an epic and fun chat covering Google’s Empirical Research Assistance (ERA), how AI can help battle climate change, and tons of great stories about the co-evolution of science and AI.John’s colleague Dave Bacon likes to tease John that his career has been defined by being twenty years early to the next big thing. This may be convolutional neural networks (some credit him with coining the term), fusion research, quantum computing. John and Google have been working on solving some of humanity’s hardest problems with AI and computation for well over a decade now. Recently John and his team set their sights on using AI to solve any scientific problem that can be written down as a score.Google’s Empirical Research Assistance (ERA)John’s team has taken on many hard scientific problems over the years.

22 sep. latent.space

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Jev: System One models for Prod, not God — with Diogo Almeida, CEO, TypeSafe AI

Tickets for AIE NYC and applications for the invite-only AIE CODE now open. 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 our favorite AI influencer/educator has probably already done one.

22 sep. latent.space

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Underwriting Superintelligence: Backing Agents you can Sue — Rune Kvist, AIUC

AIUC, grundat av Rune Kvist från Anthropic, tillkännagav en 40 miljoner dollar finansieringsrunda för att lösa ett växande problem: vem är ansvarig när AI-agenter gör fel? Företaget utvecklar AIUC-1, en standard för att testa och försäkra AI-system mot hackningar, hallucinationer och datalakning. Kvist argumenterar att tillit — inte AI-förmåga — kommer bli den största begränsningen för att få AI i faktisk användning. Företag som Cursor, Harvey och ElevenLabs möter allt svårare frågor om ansvar när autonoma system skadar, och AIUC bygger tester, standarder och försäkringsinfrastruktur för att göra detta möjligt.

16 sep. latent.space

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Humanity’s Last Invention — Richard Socher of Recursive

Richard Socher, grundare av Recursive, tror att nästa stora steg för AI är självreferensierande förbättring — system som kan automatisera och påskynda sin egen utveckling. Hans vision är en "Eureka-maskin" som kan accelerera både AI-forskning och vetenskaplig upptäckt på bred front. Recursive har samlat forskare på självförbättrande agenter och säker in 4,65 miljarder dollar. Redan tidigt har de visat system som löst optimeringsproblem snabbare än människor och upptäckt förbättringar i NVIDIA GPU-kod utan behov av specialister. Socher menar att AI-forskning som idag tar tusentals personer och år kunde komprimeras till veckor.

14 sep. latent.space

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🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

Anima Anandkumar, professor vid Caltech, har utvecklat AI-modeller för att simulera fysikaliska system som väder, fusion och vätskeflöde — områden som traditionellt krävt massiva superkomputrar och decennier av utveckling. Hennes teknik, Neural Operators, låter AI lära sig från både data och fysikaliska lagar samtidigt, vilket gör att modeller kan arbeta med mycket mindre datamängd än vanliga språkmodeller kräver. Hon lyckades skapa FourCastNet, som kan förutsäga väder lika bra som klassiska fysik-baserade simuleringar men kör på vanliga grafikkort. Nyckeln är att bygga in den struktur som den fysikaliska världen redan har — till exempel genom att använda rätt matematiska grund för ett problem — istället för att försöka träna på omöjligt stora datamängder.

26 aug. latent.space

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