State of AI with Nathan Benaich

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-2025
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.

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 Lab
0:40 What Black Forest Labs and mimic released
1:20 Early access and open weights
1:50 The preference-test results
2:40 What a model must represent to predict video
3:30 Reading actions off the video path
4:20 The frozen-backbone ablation
5:00 101 milliseconds, and the Audi tasks
5:50 What we see in robotics deal flow
6:30 The road to physical intelligence
Links: 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.
Timestamps
0:00 Introduction (Nathan Benaich)
0:35 From philosophy to DeepMind: the frontiers of intelligence
2:37 The twenty-year lesson: one recipe for complex systems
4:34 DiffusionGemma and the Gemma 4 open models
5:32 How text diffusion works
7:34 Speed, self-correction, and the sudoku test
10:51 World models: better agents need better worlds
12:56 Genie 1 to Genie 3
15:01 Genie 3 demos: typing a world into being
18:20 World models for education
19:38 Grounding Genie in Street View
20:41 Robotics: a brain and a spine
23:14 Gemini Robotics-ER 1.6 and Boston Dynamics' Spot
24:42 The vision-language-action model
25:45 The data bottleneck and closing the loop
27:24 Beyond language: the domains still to crack