Air Street Press

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.

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.

Over the past few weeks, it’s been hard to keep up with AI in biology. Profluent signed a $2.25B partnership with Eli Lilly on AI-designed gene editors, Verve put out striking base-editing data, CZ Biohub published new scaling results on protein models, and Isomorphic Labs pulled in another large raise.I couldn’t think of anyone better to discuss this with than Ali Madani, the founder and CEO of Profluent. Profluent is an AI lab building frontier models to design proteins, with the goal of taking medicine from discovering molecules nature already made to designing the ones it didn’t. I first read Ali’s ProGen paper back in 2021, DMed him on then Twitter, and wrote the largest first check from Air Street Capital into the company at inception. Last month, Profluent announced a $2.25B deal with Eli Lilly, one of the largest to date between a frontier AI biology lab and big pharma.We discuss the shift from discovery to design, why Profluent bet sequence-first while others went structure-first, the Lilly deal and large-scale DNA editing, fine-scale base editing, whether LLM-style scaling laws hold for proteins, and much more. You can either watch the interview in full here or on YouTube or read the transcript below.Timestamp timeline0:00 – Teaser: AI-designed molecules & the $2.25B Lilly deal0:22 – Intros: Nathan Benaich (Air Street Capital) & Ali Madani (Profluent)2:10 – What is Profluent, and why AI matters5:45 – The landscape: readers vs. writers7:45 – Profluent’s edge: 100B+ sequences and a wet lab9:20 – OpenCRISPR and the exponential curve12:55 – Why sequence beats structure14:50 – The Eli Lilly deal and large gene insertion16:20 – Fine-scale vs. large-scale editing18:00 – Why it’s hard: the pre-AI era and the activity/specificity trade-off20:45 – The Verve news, and scaling beyond one-offs23:45 – Rare vs. common disease26:10 – “What do you know that no one else does?”27:40 – bio × AI is an undersaturated field32:40 – When will a top-10 pharma be AI-first?34:30 – Every molecule will be designed with AITimestamp

Sereact's Cortex 2.0 marks a shift in robotics from reactive control to predictive planning. In this episode, we examine how Sereact’s world-model architecture generates and scores imagined futures before acting, improving success rates and eliminating human intervention across complex warehouse tasks. We break down the benchmark results, the planning budget trade-off, and what it means to deploy world models in real industrial environments rather than simulation.

In this episode of the State of AI, we break down the growing disconnect between rapid AI capability gains and collapsing software valuations, with nearly $300B wiped from public markets in weeks. We cover the agent shock triggered by Anthropic and OpenAI’s latest releases, why investors are repricing long-term SaaS revenues, and how AI sovereignty is fracturing across U.S. policy, state-level infrastructure pushback, and China’s accelerating model and talent pipeline. We also look at the security risks of computer-use agents, the infrastructure arms race spanning GPUs, memory, power, and data centers, and the latest research breakthroughs in autonomy, medicine, and reinforcement learning. Plus, a full rundown of the month’s largest AI financings, IPOs, and acquisitions.

In this episode, Air Street Capital shares its 2025 year in review. We cover what changed as AI moved into large-scale deployment, from the emergence of reasoning models and agents in production to the economics of frontier AI, energy constraints, and geopolitics.We reflect on the year across our investment portfolio, angel investments, Air Street Press, the State of AI Report, and our global community, and look ahead to what it will take to deploy AI reliably and at scale in the years to come.

As 2025 came to a close, conversations about AI swung between excitement and anxiety. Markets debated bubbles, capital cycles, and constraints, while researchers quietly shipped systems that worked.In this audio essay, Nathan Benaich takes stock of what AI actually delivered in 2025 — drawing on recent writing by Tim Dettmers, Dan Fu, and Andrej Karpathy, alongside conversations with Sebastian Borgeaud at Google DeepMind.Rather than speculating about distant futures, this episode focuses on what changed in practice: why AI crossed a usability threshold, how constraints reshaped progress rather than stopping it, and why the shift from models to systems matters more than any single benchmark.The result is a grounded look at AI progress as it enters 2026 — not as hype or prediction, but as an evolving system that continues to compound.Read the full essay at press.airstreet.com