State of AI with Nathan Benaich

Latest Episodes:

Turning compute into intelligence Jul 09, 2026

Show notes

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

Accelerating science and medicine with collaborative agents Jul 07, 2026

Show notes

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-scientist
1:41 Origins: Med-PaLM and the leap to hypothesis generation
5:10 System 1 versus System 2 thinking
6:34 Borrowing from AlphaGo: self-play and search
8:02 Generate, debate, evolve, and tournaments
11:47 Testing in real labs: Imperial College and antimicrobial resistance
13:29 Ten years in two days: Penadés reacts
15:44 More breakthroughs: leukemia, liver fibrosis and vorinostat
18:44 Plant immunity and protein design
20:09 Democratizing medicine: from Med-PaLM benchmarks
21:28 AMIE and the value of experience
23:12 Diagnosis, empathy and augmenting doctors
25:19 Real patients: the Beth Israel feasibility study
27:21 The co-clinician and the new triad of care
28:31 Audience Q&A

Beyond hill climbing: the path to superhuman scientific discovery Jul 02, 2026

Show notes

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

Compute scarcity is an engineering problem Jun 30, 2026

Show notes

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

State of AI: Compute Index 2026 Jun 29, 2026

Show notes

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 cluster

Links:

Full index and charts: https://www.stateof.ai/compute
State of AI Report: https://www.stateof.ai
Air Street Press: https://press.airstreet.com

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STARK: Europe's next defense prime Jun 26, 2026

Show notes

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

How Revolut runs AI at scale Jun 25, 2026

Show notes

Nikolay Donets, Head of Machine Learning Engineering at Revolut, on what it takes to run AI across more than 70 million customers, 200+ products, and 40+ countries - and why the hard part is no longer the model but the control plane around it: one gateway, a use-case-based governance layer, fallback chains, cost controls, and mandatory human oversight. Recorded at RAAIS 2026.

Chapters:

0:00 Intro - Revolut's AI at scale

1:24 The problem: classical ML and three libraries

2:54 The 2022 shift to API-served models

4:25 Four internal groups, four sets of needs

9:39 The decision: govern the use case, not the model

10:54 One central gateway vs. distributed libraries

14:10 Performance monitoring and drift detection

17:33 Lesson: fallback chains and the silently-dead model

20:02 Lesson: frontier vs. non-frontier cost (up to 8x)

20:48 Lesson: the platform is the org chart

22:59 Case study: from Rita to AIR

26:40 Voice support at scale

28:21 AIR, the in-app assistant

30:25 Q&A: human oversight, hallucinations, AI as judge

Odyssey raises $310M Series B for world models Jun 23, 2026

Show notes

Odyssey just raised a $310M Series B at a $1.45B valuation to build world models. We wrote the first check into the seed back in July 2024, so in this episode we walk through what the team has actually built, and why it is more interesting than "AI video."

The short version: the scarce input for world models is experience. We get into how Odyssey is attacking that on three fronts. Starchild-1 gives world models sound, generating audio and video together in real time. Agora-1 is a learned game engine that drops four players into the same generated world, frame by frame. And PROWL lets a model hunt down its own failures and train on them.

Along the way we cover why a world model is not a video generator, what self-driving taught Oliver Cameron and Jeff Hawke, and where this goes next for robotics, agents, and simulation.

From Air Street Press. Read the full piece at press.airstreet.com.

Macrodata: robots need a data refinery Jun 22, 2026

Show notes

Macrodata just raised a $4M pre-seed, led by Air Street, to build the data layer for robotics. The team behind FineWeb - Guilherme Penedo and Hynek Kydlíček - is bringing the discipline that made open LLMs work to messy physical-world robot data, through their open-source framework Refiner. We cover why physical AI is the next scaling paradigm, what Refiner does, and why we wrote the first check. From Air Street Press. Read the full piece at press.airstreet.com.

Europe cannot rent its way to AI sovereignty Jun 21, 2026

Show notes

Last week the US government ordered Anthropic to switch off its most capable model for every foreign national on earth - four days after it launched. Nathan Benaich (founder of Air Street Capital, co-author of the State of AI Report) argues this exposed the AI risk almost no one is naming: not that the machines go rogue, but that everyone outside the US and China rents their intelligence from a landlord who can cut them off at will.

Adapted from remarks given at a private dinner this week, this is the case for why Europe can't regulate its way to sovereignty - and what government, industry, civil society and academia each need to do to build it instead.

Read the full essay and subscribe at press.airstreet.com.

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