State of AI with Nathan Benaich

Nathan Benaich (Air Street Capital)

The State of AI podcast from Nathan Benaich - investor at Air Street Capital and author of the State of AI Report, the most widely read analysis of AI, published every year since 2018. Two things, every week. Short, opinionated breakdowns of what's actually moving in AI: frontier research, the geopolitics, and the capital. And long-form interviews and playbooks with the founders and scientists building the field - the people behind companies like ElevenLabs, Profluent, Synthesia, Wayve, Black Forest Labs and Anthropic. Subscribe to the newsletter at press.airstreet.com and www.stateof.ai

  1. 5h ago

    Europe’s air defense gap

    At RAAIS 2026, Nathan Benaich sits down with Hadrien Canter, co-founder and CEO of Alta Ares, the air-defense company building AI-guided interceptors to shoot down cheap drones and cruise missiles. They get into why Europe has lost air superiority for the first time in modern history, what the data loop looks like when you run it on a freezing front line instead of a laptop, why "quantity is the quality" in the industrialization race, and how the talent pool is shifting toward European defense. Recorded live at RAAIS 2026 in London. Chapters 00:00 — Introducing Hadrien Canter and Alta Ares 00:47 — 2022 in Ukraine, and how Europe lost air superiority 03:23 — The Series A and the Airbus partnership 05:13 — The data loop, edge AI, and the three phases of a mission 08:22 — What no simulation can reproduce 10:09 — Hiring for the mission; defense as the precondition for peace 12:36 — Open research questions and the human in the loop 14:22 — How the adversary uses AI: evasive Shaheds, drone mesh, China 17:27 — Two interceptors, and why quantity is the quality 20:50 — Iron Dome math, budgets, and peace-time vs war-time 23:41 — Q&A: keeping pace with a fast-changing front 26:31 — Q&A: talent and the shift toward European defense 29:30 — Q&A: re-arming without permanent war 32:56 — Freedom doesn't come for free

  2. 2d ago

    From driving the world to dreaming it

    World models are the bet that AI should learn the world by watching it and acting in it, not just by reading about it. At RAAIS 2026, Odyssey co-founder and CTO Jeff Hawke makes the case: a world model is a neural simulator - an interactive stream of pixels that runs in real time, models physics, and answers back. He walks through Odyssey's four research fronts - streaming interactive pixels (Odyssey-2), joint audio and video (Starchild-1), shared multiplayer worlds (Agora-1, demoed live as a fully generated game of GoldenEye), and PROWL, which sends a reinforcement-learning agent to find and fix a world model's own failures - and argues the field is at its GPT-2 moment: promising, but pre-ChatGPT, with the GPT-3-style commercial unlock still ahead. Recorded at the 10th Research and Applied AI Summit (RAAIS), London, June 2026. Timestamps 00:00 Intro: Nathan on Odyssey and world models 01:05 Jeff Hawke: from self-driving to world models 01:40 The bet — a missing form of intelligence 02:40 Why world models suddenly matter (the late-2025 flip) 03:16 What a world model actually is (and isn't) 04:45 The neural simulator 06:34 Two principles: end-to-end learning and generality 07:19 The "GPT-3 of world models" and four research themes 08:46 Odyssey-2: streaming, interactive pixels 10:33 Starchild-1: generating audio and video together 13:03 Agora-1: multiplayer world models 13:57 Live demo: the room plays GoldenEye 16:20 PROWL: improving the model by breaking it 18:39 Where Odyssey goes next 19:55 Still the GPT-2 era 21:30 Q&A: physics limits, safety, compute cost, merging with LLMs

  3. Jul 9

    Turning compute into intelligence

    Ted Moskovitz leads the Science of Scaling team at Anthropic, the group that works out how to turn compute into smarter models. In this RAAIS 2026 fireside with Air Street Capital's Nathan Benaich, he argues that frontier scaling has become an empirical science - a discipline for cutting uncertainty before spending the compute, not just buying more of it. They get into the honest measure of AI acceleration (it's the counterfactual, not the benchmark), why a bigger model can be cheaper than splitting a task across small ones, whether a model can have research taste, and why safety and capability turn out to be the same axis. Plus the highest-leverage AI work to do in 2026, and why Anthropic's London office no longer feels like a satellite. Recorded live at RAAIS 2026 in London. Timestamp: 00:00 - Meet Ted Moskovitz and the Science of Scaling team 00:45 - What "the science of scaling" actually means 01:18 - Why scaling is a science, not an art 02:55 - Big labs vs the new "neo labs" 04:47 - How a research finding reaches the product 06:44 - What neuroscience carries over to AI (and what doesn't) 09:12 - "When AI builds itself" and the real measure of acceleration 10:33 - Trust, bypass mode, and the latest model jumps 11:42 - One big model vs many small ones 13:13 - Can a model have research taste? 15:39 - How safety research makes products better 17:36 - Emergent misalignment and the alignment race 19:14 - The highest-leverage AI work in 2026 20:21 - Inside Anthropic's London office 21:34 - Audience Q&A

  4. Jul 7

    Accelerating science and medicine with collaborative agents

    Vivek Natarajan, Research Lead for AI, science and medicine at Google DeepMind, on porting the self-play and search recipe behind AlphaGo into scientific and clinical reasoning. He walks through the AI co-scientist, which generates and debates hypotheses (one matched a decade of lab work in two days), and AMIE, a diagnostic dialogue system trained in simulation. Recorded at RAAIS 2026. Chapters:0:00 Welcome and introducing the AI co-scientist1:41 Origins: Med-PaLM and the leap to hypothesis generation5:10 System 1 versus System 2 thinking6:34 Borrowing from AlphaGo: self-play and search8:02 Generate, debate, evolve, and tournaments11:47 Testing in real labs: Imperial College and antimicrobial resistance13:29 Ten years in two days: Penadés reacts15:44 More breakthroughs: leukemia, liver fibrosis and vorinostat18:44 Plant immunity and protein design20:09 Democratizing medicine: from Med-PaLM benchmarks21:28 AMIE and the value of experience23:12 Diagnosis, empathy and augmenting doctors25:19 Real patients: the Beth Israel feasibility study27:21 The co-clinician and the new triad of care28:31 Audience Q&A

  5. Jul 2

    Beyond hill climbing: the path to superhuman scientific discovery

    At RAAIS 2026, Google DeepMind's Roberta Raileanu lays out a recipe for superhuman scientific discovery: AI systems that make groundbreaking discoveries across domains faster than people can. She walks through three ingredients - reinforcement learning to discover solutions where progress can be measured, open-ended divergent search to find new problems rather than climb known ones, and meta-learning to speed up discovery on problems no one has posed yet. The through-line: we can search for anything we can measure, but we still cannot measure what makes a discovery good. The bottleneck isn't the search. It's the signal. Chapters: 00:00 - Introduction 00:51 - Defining superhuman scientific discovery 01:40 - The state of play: real progress, real plateau 06:58 - Ingredient one: discovery as reinforcement learning (Move 37, MLGym) 12:51 - Ingredient two: open-ended search and why greatness cannot be planned 18:30 - Rainbow Teaming: quality-diversity in practice 21:07 - Ingredient three: meta-learning the process of discovery (DiscoBench) 25:06 - The recipe, and the missing signal

  6. Jun 30

    Compute scarcity is an engineering problem

    Angelos Perivolaropoulos, a research engineer at ElevenLabs, on turning GPU scarcity into an inference-engineering problem: how to serve far more users on the same hardware, from batching to frontier architecture changes. Recorded at RAAIS 2026. 00:00 Introduction: ElevenLabs and the GPU squeeze 00:38 The question: how to scale when you can't add capacity 01:11 About Angelos: Scribe, speech-to-text and text-to-speech 01:56 GPU scarcity meets exponential demand 02:44 What a token actually costs: compute vs memory bandwidth 03:38 Prefill, decode and the KV cache 05:53 Batching and continuous batching (1 → 15 users/GPU) 08:37 FP8 quantization and quantize-aware training (→ 20) 11:29 Speculative decoding and multi-token prediction (→ 28) 15:13 Compressing the KV cache: TurboQuant and distillation (→ 70) 17:27 Frontier architectures: MLA, linear attention, state-space (→ 140) 20:39 Trade-offs: nothing is free 22:03 Q&A: papers vs production, token subsidies, TTS evals

  7. Jun 29

    State of AI: Compute Index 2026

    The fifth State of AI Compute Index, in collaboration with Zeta Alpha. After a soft 2025, open AI research citations rebounded in 2026 - and NVIDIA still appears in ~91% of them. But the bigger story has moved off the page: Hopper is now the live installed base, Blackwell is mostly still pipeline, and frontier labs have started buying compute by the gigawatt. Nathan walks through what changed, what didn't, and why "GPU count" is becoming the wrong question. Read the full piece and explore the live charts: https://www.stateof.ai/compute Chapters (00:00) What's new in v5 - citations, infrastructure, and gigawatts(01:25) The breather was short: 2025 was a pause, not a rollover(03:15) NVIDIA at ~91%, and the challengers - AMD, Huawei, Apple, TPU(05:05) Inside NVIDIA: the handover from A100 to Hopper to Blackwell(06:55) Startup silicon fragments - Groq, Cerebras, and the NVIDIA deal(08:20) Hopper is the installed base: 460k deployed GPUs(09:50) Blackwell is mostly pipeline: 80% still announced(11:00) The demand side, measured in gigawatts(12:15) Looking ahead, and why a GPU order isn't a clusterLinks: Full index and charts: https://www.stateof.ai/computeState of AI Report: https://www.stateof.aiAir Street Press: https://press.airstreet.comIf you found this useful, rate State of AI with Nathan Benaich five stars and share it with someone building in AI infrastructure - it genuinely helps.

  8. Jun 26

    STARK: Europe's next defense prime

    Air Street Capital backs Stark, the German multi-domain defense company, in its €500M led by Founders Fund and Sequoia. In this episode, we discuss why cheap, software-defined unmanned systems in the air and at sea are the decisive lesson of Ukraine, and why we think co-founder and CEO Uwe Horstmann - a Project A GP and Bundeswehr reservist - is building the German neoprime Europe needs. Round led by Sequoia and Founders Fund, with the NATO Innovation Fund, Project A, and Air Street. Links: stark-defence.com · full post at press.airstreet.com · YouTube version

See All (138)

About

The State of AI podcast from Nathan Benaich - investor at Air Street Capital and author of the State of AI Report, the most widely read analysis of AI, published every year since 2018. Two things, every week. Short, opinionated breakdowns of what's actually moving in AI: frontier research, the geopolitics, and the capital. And long-form interviews and playbooks with the founders and scientists building the field - the people behind companies like ElevenLabs, Profluent, Synthesia, Wayve, Black Forest Labs and Anthropic. Subscribe to the newsletter at press.airstreet.com and www.stateof.ai

Information

  • Creator
    Nathan Benaich (Air Street Capital)
  • Years Active
    2021 - 2026
  • Episodes
    138
  • Rating
    Clean
  • Copyright
    © Nathan Benaich (Air Street Capital)
  • Show Website
    State of AI with Nathan Benaich

You Might Also Like