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Arca head of AI Aman Singh joins Latent Space head of editorial Richard MacManus at AI Engineer New York, to discuss how Arca is building an AI-native wealth management firm. Singh explains Arca's architecture, which includes a custom harness and persi...Full show notes and links
för 16 tim sedan latent.space
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Watch the full show on YouTube: https://www.youtube.com/watch?v=v-d1WTqIULsFull show notes and links
31 okt. youtube.com
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Katelyn Lesse and Angela Jiang join Latent Space to explain the engineering behind the Claude Platform: launching Opus 5.5, separating the model’s “brain” from the agent’s “hands,” scaling APIs, and building reliable managed agents. A new introduction ...Full show notes and links
27 okt. lspace.beehiiv.com
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AI agents are creating more software—and that changes what a backend needs to do. Supabase co-founders Paul Copplestone and Ant Wilson join swyx to discuss running long-lived workloads alongside the database, making schemas easier for agents to underst...Full show notes and links
26 okt. latent.space
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Watch the full show on YouTube: https://www.youtube.com/watch?v=vhPCo-8K78UFull show notes and links
26 okt. youtube.com
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Warm welcome to Basil Chatha, our newest cohost from the Forward Deployed side of the AI Engineering world! We’ll be ramping up a new show with him and couldn’t pass up an interview with Modal’s CEO Eric on the occasion of their monster Series D!Two years ago, we spoke with Erik Bernhardsson about his vision for truly serverless AI infrastructure: developers should be able to run code without provisioning clusters, predicting load, or paying for idle resources. CTO Akshat then joined us to talk about Modal’s infra primitives and stack to offer those primitives and agent experience.A lot has changed in the past 5 months. Since their $5B series C, Modal now reports it crossed $1 billion in annualized run rate, raised a $750 million Series D at a $16B valuation, and has spun up nearly three billion sandboxes. In this conversation, Erik joins Vibhu to explain the infrastructure and economics behind that growth—and why demand for compute rarely looks like the contracts used to buy it.Watch the full conversation on YouTube →GPU demand isn’t rectangularA typical capacity contract gives a customer a fixed number of GPUs for a fixed period.
22 okt. latent.space
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Anekha Sokhal, CEO of Moshi, joins swyx to discuss building a recommendation engine for commodity traders, turning market data into useful signals, and designing AI that fits users’ workflows.Watch: YouTube · Moshi · Anekha0:00 AI Engineer New York and “you are the team”0:54 Meet Anekha2:09 From mathematics to energy derivatives3:46 Fulbright and becoming a founder5:45 What Moshi does6:10 Market data9:03 Bloomberg and market workflows10:03 Bloomberg, Claude and Netflix12:03 From dashboards to workflows15:03 Trusted sources and signal17:13 Models and document extraction18:20 AI Engineer New York20:32 Customer meetings23:54 Hiring and customers25:55 swyx’s path into AI This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
22 okt. latent.space
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Alex Lieberman and CJ Hess join swyx at AI Engineer New York to discuss Tenex’s approach to AI-native engineering, paying for outcomes, MetaHarness, enterprise AI adoption and rebuilding SaaS.Show notes:Tenex: https://www.tenex.co/Building an AI-native SDLC: https://www.tenex.co/blog/building-an-ai-native-sdlcEarlier Tenex episode: https://www.latent.space/p/10x-ai-engineers-with-1m-salariesKill My SaaS: https://www.youtube.com/watch?v=zlD-CkLVKtUFull video: https://www.youtube.com/watch?v=Vki6cklFqxo0:00 Cold open0:26 Introductions0:51 What Tenex does1:32 Why CJ joined Tenex3:17 Paying engineers for outcomes7:20 Inside MetaHarness10:25 Models and long-running tasks15:11 Token spending and ROI19:29 The enterprise AI adoption gap23:22 Why agent deployment is hard24:42 Vertical AI and enterprise software30:32 Kill My SaaS35:23 AI and jobs38:18 New York’s AI ecosystem42:28 Hiring at Tenex This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
21 okt. latent.space
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This was recorded on the sidelines of the new Compute Trading track at AIE NYC. Catch us next at AIE CODE, our last flagship for the top agentic engineers in the world.In the early days of cloud, former guest Martin Casado made something so disruptive that Cisco was threatened enough to pay $600m for it, and VMware outbid for $1.3B:That concept was software-defined networking, which virtualized the routing network by separating the control plane from the data plane. That revolution is now coming for GPUs - where previously running your own supercompute cluster required $50-100m upfront to get commitments and do the entire stack from hardware to firmware to kernels, SF Compute is now enabling you to not only buy or sell GPU hours now and in the future, but also to programmatically control everything from your network to memory.We last spoke with Evan about commoditizing compute. At AI Engineer New York, we went deeper: what does it take to physically deliver a GPU contract, and what has to be true before a market in compute can become liquid?From selling a room to running the hotelEvan describes SF Compute’s expansion with a hotel analogy.
20 okt. latent.space
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A few years ago I stumbled upon Leash Bio’s Substack, and was hooked. Be it their Kaggle competition that showed that no, we really haven’t solved protein-ligand binding, or their crazy discovery that knowing a molecule’s author is perhaps all you need to know to predict how good it is. Their substack — or at least Owl Posting’s deep dive on them — should be required reading for any scientist trying to do chemistry with AI.So I was super excited to finally be able to sit down with Ian Quigley from Leash Bio, and pick his brain. Leash specializes in DNA Encoded Libraries (aka “DELs”), a way of collecting ridiculously high-throughput interaction data between proteins and small molecules. Before I discovered Leash, I had heard nothing but horror stories from DEL users. And yet, Ian and friends somehow found a way to tame the beast, extracting clean and reproducible data that is just perfect for large scale modeling. Without teasing too much, the answer comes down to epistemic rigor, and an incredible conviction that you just have to Do The Thing.It gets even better!
14 okt. latent.space
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Watch along AIE NYC and join us at AIE CODE in SF next month!This episode includes a live demo of the Taku robot that is best enjoyed on YouTube.Last month, Dyna made waves by announcing Dyna-2, a world-action model pre-trained on one million hours of human video, claiming several new scaling laws on human data across four orders of magnitude. They have now followed up the model with a physical robot agent, Dyna-2.1, that has to be seen to be believed:In his talk at AIE this year, Jason talked about how the real challenge for robotics in the real world is reliable, long-horizon autonomy, and how Dyna got its models to a 99.4% success rate folding restaurant napkins for 24 hours straight, including recovering after it pulled over the whole stack.From training a world-action model on over one million hours of human video to deploying robots that fold thousands of napkins daily at Din Tai Fung, Dyna Robotics is pushing physical AI beyond impressive demos and into real-world production.
14 okt. latent.space
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From the Bitter Lesson of AI scaling to the unsolved mysteries of protein folding, Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido are rethinking what it takes to build AI that truly understands biology. In this special panel moderated by Brandon Anderson, they explore why AlphaFold’s breakthrough was only the beginning, why scaling compute and data alone won’t solve biology, and how the next generation of AI models could transform our understanding of proteins, cells, and human disease.We go deep on the future of AI-driven biology: finding scaling laws in biological data, the tradeoffs between scientific intuition and general-purpose architectures, why protein structure prediction is far from solved, and what it would take to build predictive models of living systems. Pushmeet reflects on the lessons behind AlphaFold, the limits of human interpretability, and whether future frontier models could understand other AI systems better than we can.
10 okt. latent.space
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It’s hard to believe that Periodic was only launched last September:One year later, it is considered one of the pre-eminent AI scientist labs, with dizzying talent density and astonishing progress in the autonomous lab buildout:Most people are familiar with the standard credentials of Liam and Dogus, but we found an incredible “talent slope” while learning more about Periodic, where each successive employee seems more impressive than the last:From building AI systems that reason over noisy physical experiments to creating laboratories where every instrument can become intelligent, Periodic Labs is betting that the next frontier of AI won’t come from simply training on more internet data, it will come from letting models experiment with the real world.
8 okt. latent.space
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Watch the full show on YouTube: https://www.youtube.com/watch?v=MWX36ZYnsm0
2 okt. youtube.com
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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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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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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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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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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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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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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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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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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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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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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