The case for intelligence beyond language

AGI in 2027, scientific superintelligence, world models, and putting AI to work.In this audio edition of my Air Street Press essay, I reflect on my conversation with Matt Turck on the MAD Podcast. I share how agents are changing my investment research, why scientific judgment is the next frontier, and how world models could help train robots and test autonomous systems.I also discuss AI for cyber defense and an idea I would love someone to build: a Genius Bar for AI that helps people put the technology to practical use.Watch the full interviewExplore the State of AI Report 2026Subscribe to Air Street Press

What changed in artificial intelligence in 2026, and what comes next? From AI agents helping build better AI to advances in robotics, the State of AI Report 2026 examines the breakthroughs, businesses, and risks shaping the field.I’m Nathan Benaich, founder of Air Street Capital and author of the State of AI Report. In this 34-minute walkthrough, I explain the findings that matter most across research, industry, politics, and safety, then share our predictions for the next 12 months.In this video:• The competition between OpenAI, Anthropic, and Google, and the rise of Chinese open models• AI agents automating research and taking on longer tasks• Robotics, world models, and autonomous driving• AI adoption, company growth, and the impact on jobs• NVIDIA, GPU demand, and the data center buildout• Sovereign AI, government policy, and control over frontier models• AI safety, cybersecurity, and the risks of agents acting in real systems• Our predictions for the year ahead, including AGI in 2027Read the full report, including charts and sources: https://www.stateof.ai/Get ongoing AI analysis through the State of AI newsletter on Air Street Press: https://press.airstreet.com/Learn more about Air Street Capital: https://www.airstreet.com/Which prediction do you disagree with most? Tell me in the comments.

Jack & Jill has raised a $40 million Series A led by Air Street Capital to build AI agents for careers and recruiting. In this episode of State of AI, Nathan Benaich explains why he backed founders Matt Wilson and Saaras Mehan, and the opportunity to make personal talent representation available to everyone.Jack gets to know your skills, passions, and career ambitions. Jill learns what companies need from their next hire. Together, they have arranged 25,000 interviews without candidates submitting applications. Nathan explores how ongoing conversations can lead to better introductions, why both talent and employers need a brand they trust, and how your next opportunity could extend beyond a conventional job search.Explore Jack & Jill.From Air Street Press. Subscribe to State of AI with Nathan Benaich, read the State of AI Report, and leave a rating - it helps the show.0:00 Why we invested in Jack & Jill1:21 A candidate is much more than their resume3:38 Meet Jill, your company’s recruiting agent4:58 Your next move might surprise you

Europe is promising to rearm, but its defense startups often cannot access the realistic test ranges needed to win contracts. Drawing on conversations with a dozen founders and operators, Nathan Benaich examines the scarce slots, fragmented permits, unsuitable facilities and missing procurement follow-through slowing new systems. He argues for mission-ready ranges, clear paths from successful tests to orders and a cross-border campaign passport for reusable paperwork.

Scientific papers preserve the happy path and lose the abandoned experiments and arguments that produced it. Starting with Alasdair Russell's genome editing group at the CRUK Cambridge Institute, Nathan Benaich examines whether scientific search histories could teach AI research taste. The essay explains why logs are not automatically training data, how selective labels hide the outcomes of rejected experiments, and what autonomous laboratories and Registered Reports can contribute. From Air Street Press. Subscribe at press.airstreet.com, read the State of AI Report at stateof.ai, and leave a rating - it helps the show.

Poolside trained Laguna S 2.1 on 4,096 NVIDIA H200s and published the open weights sixty days later. The 118-billion-parameter mixture-of-experts model runs multi-hour autonomous coding sessions, handles up to a million tokens of context, and fits quantized on a single desktop-class machine.Nathan Benaich of Air Street Capital walks through the Model Factory that made that schedule possible, the system co-founders Jason Warner, GitHub’s former CTO, and Eiso Kant built to take a research idea to a production run in under a week. The episode covers the 409,000 training environments behind the model, the 50-minute session in which it built a browser rendering engine with no human intervention and no ability to see its own output, why the licenses attached to open weights differ so sharply between Poolside, Tencent and Moonshot, and what it means for a company to run a capable coding model inside its own security boundary rather than renting one through an API.Read the full piece: press.airstreet.com Poolside’s launch post: poolside.ai/blog/introducing-laguna-s-2-1 Evaluation trajectories: trajectories.poolside.ai RAAIS 2025 - Eiso Kant, Inside poolside’s path to AGI: press.airstreet.com/p/eiso-kant-poolside-ai-raais-2025From Air Street Press. Subscribe at press.airstreet.com, read the State of AI Report at stateof.ai, and leave a rating - it helps the show.

Description:Black Forest Labs has launched FLUX 3, a multimodal foundation model that learns jointly from images, video and audio within a single architecture, and mimic robotics has released FLUX-mimic, a video-action model built on that backbone and being tested on real assembly work in Audi's Production Lab. Nathan Benaich of Air Street Capital, an investor in Black Forest Labs, reads his Air Street Press essay on why a model trained to predict how scenes evolve turns out to be a usable robot controller. Covers the FLUX 3 preference results against Runway Gen-4.5, Grok Imagine Video, Kling v3 Pro, Seedance 2.0 and Gemini Omni Flash; how a lightweight action decoder reads the video prediction path without ever generating video; the frozen-backbone ablation against π0.5; the 101-millisecond system reaction time on a single RTX 5090; and what Air Street's own robotics deal flow says about where the constraint really sits.Chapters (estimated at ~150 wpm, slide proportionally against final audio):0:00 A robot arm in Audi's Production Lab0:40 What Black Forest Labs and mimic released1:20 Early access and open weights1:50 The preference-test results2:40 What a model must represent to predict video3:30 Reading actions off the video path4:20 The frozen-backbone ablation5:00 101 milliseconds, and the Audi tasks5:50 What we see in robotics deal flow6:30 The road to physical intelligenceLinks: FLUX 3 · FLUX-mimic (BFL) · FLUX-mimic (mimic) · Odyssey Series B · BFL Series B ·

Raia Hadsell, VP of Research at Google DeepMind, makes the case that intelligence is more than language: the same recipe that learns the patterns of text can learn the patterns of any complex system. She walks through DiffusionGemma and text diffusion, the Genie world models, and DeepMind's robotics stack, where world models now generate training data you cannot tell from the real thing. Recorded at RAAIS 2026.Timestamps0:00 Introduction (Nathan Benaich)0:35 From philosophy to DeepMind: the frontiers of intelligence2:37 The twenty-year lesson: one recipe for complex systems4:34 DiffusionGemma and the Gemma 4 open models5:32 How text diffusion works7:34 Speed, self-correction, and the sudoku test10:51 World models: better agents need better worlds12:56 Genie 1 to Genie 315:01 Genie 3 demos: typing a world into being18:20 World models for education19:38 Grounding Genie in Street View20:41 Robotics: a brain and a spine23:14 Gemini Robotics-ER 1.6 and Boston Dynamics' Spot24:42 The vision-language-action model25:45 The data bottleneck and closing the loop27:24 Beyond language: the domains still to crack

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.Chapters00:00 — Introducing Hadrien Canter and Alta Ares00:47 — 2022 in Ukraine, and how Europe lost air superiority03:23 — The Series A and the Airbus partnership05:13 — The data loop, edge AI, and the three phases of a mission08:22 — What no simulation can reproduce10:09 — Hiring for the mission; defense as the precondition for peace12:36 — Open research questions and the human in the loop14:22 — How the adversary uses AI: evasive Shaheds, drone mesh, China17:27 — Two interceptors, and why quantity is the quality20:50 — Iron Dome math, budgets, and peace-time vs war-time23:41 — Q&A: keeping pace with a fast-changing front26:31 — Q&A: talent and the shift toward European defense29:30 — Q&A: re-arming without permanent war32:56 — Freedom doesn't come for free

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.Timestamps00:00 Intro: Nathan on Odyssey and world models01:05 Jeff Hawke: from self-driving to world models01:40 The bet — a missing form of intelligence02:40 Why world models suddenly matter (the late-2025 flip)03:16 What a world model actually is (and isn't)04:45 The neural simulator06:34 Two principles: end-to-end learning and generality07:19 The "GPT-3 of world models" and four research themes08:46 Odyssey-2: streaming, interactive pixels10:33 Starchild-1: generating audio and video together13:03 Agora-1: multiplayer world models13:57 Live demo: the room plays GoldenEye16:20 PROWL: improving the model by breaking it18:39 Where Odyssey goes next19:55 Still the GPT-2 era21:30 Q&A: physics limits, safety, compute cost, merging with LLMs