This week STARK, the German defense company building exactly these systems, raised €500 million led by Sequoia and Founders Fund. I first met STARK’s CEO and Founder, Uwe Horstmann, back at Project A in 2016, years before any of this. Air Street Capital invested because we believe STARK is emerging as a German neoprime: a new prime contractor for unmanned strike systems, built software-first and able to manufacture at the scale a real war demands.

STARK builds autonomous platforms, fast

STARK’s flagship platform, Virtus, is a loitering munition: a drone that flies to a contested area, waits, finds a target, and strikes it. It is not a reconnaissance drone, though it can return and land like one, and it is not a cruise missile. It sits between the two, cheap enough to expend, autonomous enough to find a target on its own, and simple enough to build at scale. Around it STARK is building a family of effectors - Gambit, a man-portable short-range munition, and Cascade, a tube-launched one - alongside Vanta, an unmanned surface vessel that takes the same idea to sea.

Mass you can afford to build

For decades, Western firepower meant a small number of exquisite platforms, each costing millions and far too precious to lose. Ukraine inverted that. The decisive weapon turned out to be the cheap one you can field by the thousand and expend without flinching. A Virtus assembles in roughly ten minutes, and Germany has already put STARK on a framework worth up to €2.8 billion to supply the Bundeswehr, a landmark deal for a new defense company.

Once the weapon is cheap, the constraint moves to manufacturing and a robust supply chain. More than 80% of this round goes into manufacturing and R&D, and STARK is standing up production lines across Germany, the UK, and Ukraine. In a war of attrition, throughput is the moat: the side that can keep replacing what it loses sets the tempo. STARK builds on civilian supply chains and simple lines that stand up in weeks and replicate from city to city, which is both a way to scale and a way to survive being targeted. The defense companies that win this decade will treat the production line as the product.

Lastly, with a growing portfolio of autonomous products, STARK needs software to tie them all together: Minerva. The same software that swarms Virtus in the air coordinates the unmanned boats at sea, navigates when GPS is jammed, and plugs into NATO battle-management. Each platform improves with a software update rather than a new airframe, and every deployment teaches the whole fleet.

Why STARK

Uwe spent a decade as a general partner at Project A, one of Europe’s most active defense investors and an early backer of Quantum Systems, before leaving to run STARK. He has assembled the four branches this company needs at once. To build at scale: Martin Rost, sixteen years at Zalando running a roughly €10 billion unit, and Johannes Schaback, a repeat founder who was CTO of SumUp. To sell into defense: Jan-Patrick Helmsen, former CEO of Rheinmetall's weapons-and-munitions business. To win the politics: Johannes Arlt, until this year a member of the Bundestag's defense committee. And to learn from the war as it is fought: a Ukraine team running an R&D and production hub in Kyiv that turns frontline feedback into design changes.

Regular readers will be familiar with our defense thesis at Air Street Capital. We recently led the Series A for Alta Ares because Europe has to build its own air defense shield. In our letter from this year’s Munich Security Conference, we argued that Europe must move from emergency buying to structural production capacity, and that when a government contracts a domestic firm it confers the industrial gravity that pulls in private capital and localizes supply chains. STARK is what answering that call looks like: sovereign manufacturing, software-defined systems, and a founder building for the war that is actually being fought. The mass that wins the next conflict will be cheap, built at home, and improved in software, and we’re here to stand behind the people making it happen.

The difficulty is in the control plane around it: one gateway, one governance layer, measurable fallbacks, cost controls, layered human review, and a way to run all of it inside a regulated bank that serves more than 70 million customers across over 40 countries. Revolut ships more than 200 products and has handled over a trillion dollars in transactions, with a machine learning model now in the path of almost every one of them. The leverage, in Donets’s telling, has moved from the model to everything around it.

Four constituencies, one bottleneck

For years, Revolut’s AI was classical machine learning: fraud and transaction models shipped through three internal libraries, one each for training, serving, and performance monitoring. Then, in 2022, the ground moved. Vendors began exposing large models behind an API, and suddenly you did not have to train anything to build something. Generative use cases started growing exponentially while the classical models kept running underneath.

Donets spent as much time on the people problem this created as on the technical one. Four internal groups pull in different directions: researchers who want compute and freedom to explore; builders who want one common API and to ship today; operators who want predictability, rollbacks, and cost under control; and a compliance function that owns human-in-the-loop controls, security audit, and data sovereignty. Left to themselves, every product team solves the same problems its own way, and governance fragments into tribal knowledge spread across hundreds of teams. That is expensive, and it does not scale.

Govern the use case, build one gateway

Rather than govern each model one by one, Revolut made two moves that changed the shape of the problem. The first shifted the unit of governance to the AI use case, a move that lines up with the EU AI Act’s use-case-based view of risk, so that one set of risks, budgets, and rules can cover several models at once and match policy to context. The second put a single gateway at the center of the company, with the governance layer on top of it, rather than shipping capability as libraries each team installs for itself.

But there’s a tradeoff: whereas libraries push reliability onto whichever product team owns the service, a central gateway makes one team responsible for everyone. Even so, the cost of improving a library means cutting a release, then persuading hundreds of busy teams to upgrade and absorb breaking changes they never wanted. With one gateway, the central team ships the improvement once and every product inherits it at, in Donets’s phrase, “zero effort.” Compliance and monitoring move to the same place. As a result, Revolut runs roughly twice as many generative use cases as classical ML ones, all off that single platform.

What breaks when the model is someone else’s

Once you are renting frontier models rather than training your own, you inherit failure modes you do not control. Pay-as-you-go providers run at around 98.5% uptime, which sounds high until you count the hours of dead service it implies each month for a scaled, global business. So Revolut wires a fallback chain into every generative product: if the primary model degrades or stops responding, traffic rolls to the next, and the next. Slightly degraded service beats no service at all.

Subtler, and more painful, was a failure they could not see at all. Because the platform watched only inputs and outputs at the interface, a model buried in the fallback chain quietly stopped working and nobody noticed. “Everything was fine, uptime was high enough, but the model itself was not functional,” Donets said. Or, as one of his slides put it: without per-model visibility, a model doing nothing looks exactly like one that works.

Money was the other lesson, and an easier one to swallow. Teams reach for the newest and most expensive model by reflex, but most workloads are over-provisioned, and right-sizing the model to the task cuts cost by as much as eight times with no loss in quality. Donets’s rule: do not default to the newest model in production. Measure first, then use the smallest model that clears the bar.

A note on the org chart

Underneath the platform sits an org chart doing as much of the work as the code. Revolut is flat and built as a matrix: AI engineers are embedded in product teams, each staffed to ship end to end, with a functional line back to the platform. Standards and tooling flow down; field requirements flow up to Donets’s central group, which sets direction and pushes compliance rules out. He called the product teams “our forward-deployed engineers,” the mechanism by which one team’s hard-won experience becomes everyone’s. The architecture, as one slide noted, ends up shaped like the org chart.

From Rita to AIR

Where all of this lands is a single product that has been running for years. It began as Rita, a support chatbot built on intent models and pre-filled scenarios - the “slot machine” era - that frustrated as often as it helped. In 2022 the team tested large models, Bloom and BloomZ at 175 billion parameters, and found they worked. The first thing they put into production was mundane: paraphrasing a multi-screen FAQ into a short, relevant answer. LLM-based Rita reached production in Q2 2023, then rolled out country by country, Europe first and Japan the hardest, finishing around Q1 2025.

Voice came next, and it runs on a simple pipeline: audio is transcribed, a small LLM decides whether to answer directly or hand off to the full multilingual chatbot, and an end-to-end response comes back in under two seconds. It now runs in 20 countries, handles around 25,000 calls a month, and resolves a customer’s problem roughly eight times faster than a human agent. AIR, the latest layer, followed in Q2 2025 and pulls in transactional data: it can break down your spending, propose hotels inside a budget computed from your own history, or explain why a stock is moving. Across the arc from the old chatbot to today, the share of cases resolved without a human climbed from 17% to 80%, Net Promoter Score went from low to high, and the financial impact, Donets said, ran into double-digit millions of pounds. AIR began rolling out in the UK in April 2026, where Revolut says it has 13 million customers.

Where human oversight is mandatory

Holding all of it up is the monitoring layer. Revolut stores every input and output and runs a panel of LLM “judges” against live traffic - one dedicated to hallucination - currently 9 to 12 mandatory metrics and rising, backed by human review teams that sample chats and transcripts, and by the blunt signal of Twitter and Reddit when something goes badly wrong.

Above all of it sits a hard line: no decision that can change someone’s life is made by an AI system. That position got tested in the room. An audience member who works on regulated healthcare AI pushed back - in his field, he said, “humans make that process unsafe,” so an AI judge might be the safer choice. Donets gave ground on the evidence, agreeing that machines “provide more stable and better help to users,” but not on the principle: the critical calls still do not go to the model. Asked how soon that might change, he did not hedge: “This year, definitely no.”

The frontier gets the headlines, but shipping AI inside a regulated bank across 40 countries is won or lost on the plumbing beneath it: one gateway, the right unit of governance, a fallback for when the vendor fails, and a human who still gets the last word.

A decade ago, the AI community was alight with enthusiasm over the first generative models for images, Generative Adversarial Networks. At the time, the model's outputs looked more like a Microsoft Paint attempt than anything close to photorealism. Ten years later, Latent Diffusion Models, and the scaled-up, improved architectures since built by the team at Black Forest Labs, have ushered in a level of photorealism arguably indistinguishable from reality, were it not for the unreal scenes:

The same trajectory from “pixelated if you’re lucky” to “I can’t tell the difference between reality and AI” is happening in the domain of world models, and Odyssey is leading the frontier.

Last week, Odyssey announced that it has raised a $310M Series B at a $1.45B valuation, led by Natural Capital, with participation from Amazon, GV, AMD Ventures, EQT, IQT, and others. We wrote the first check into Odyssey's seed in late 2023.

On a personal note, I have known both founders far longer than Odyssey has existed: Oliver Cameron from his years building Voyage and then Cruise (the first self-driving car I experienced thanks to him!), and Jeff Hawke from the founding team at Wayve, where I was involved from day 1. I invited Oliver to speak at RAAIS 2023 in London, which created an opportunity for the two of them to spend significant time together in person. They started Odyssey later that year.

This round is a good moment to explain what the team has actually been building. The answer is more interesting than “AI video.”

From videos to worlds

A world model is not a video generator. Sora, Veo, Kling, and their successors take a prompt and render a fixed clip. The output can be beautiful, but the future is locked from the outset of generation. As a consumer of the video, your only mode of interaction is to watch.

By contrast, a world model is closer to a simulator than a renderer. It predicts the next state of an environment from the past and from whatever a participant does next. It accepts input mid-rollout. It holds a persistent state, a memory of the world that can be acted on and changed. Just as next-token prediction enabled language modeling, Odyssey is betting that next-state prediction, at enough scale, forces a model to learn physics, because a model that has not learned physical regularities drifts into nonsense over a long rollout.

The path to frontier world models

Scale is necessary, but not sufficient. The bottleneck is experience, and over the past year Odyssey has attacked it from three directions: 1) making each moment richer, 2) making experience shared and persistent for people and agents, and 3) teaching models to generate their own.

First, richer experience. Most world models are mute, which is not only bizarre to experience but means that a rich information stream is discarded. Sound is where collisions, distance, intent, rhythm, and emotion live. Nothing is in the intellect, as the line goes, that was not first in the senses. As such, Odyssey’s Starchild-1 generates synchronized audio and video in real time, while responding continuously to streaming text, speech, and action. This is the first real-time multimodal world model.

While this sounds logical, the hard part is that audio and video move on different clocks. A small error in one modality can corrupt the other during a long rollout. Starchild-1’s approach is to let each run on its own clock while staying synchronized, turning a bidirectional audio-video foundation model into a causal, real-time world model.

Second, shared experience. Agora-1 is a multi-agent world model that decouples simulation from rendering. One function evolves a shared world state from player actions, while another renders consistent views of that state from independent viewpoints. The result behaves like a game engine with no hand-coded engine underneath: a shared state the model maintains for every participant, which can be edited into new levels while the dynamics hold.

At RAAIS in London on June 12, Jeff Hawke, Odyssey's CTO, ran a live session of an Agora-1-generated GoldenEye death match, with every frame conjured on the fly. Attendees could join the game and play one another in real time.

Beyond games, robots need shared environments before they touch the real world. Agents need places to collide, coordinate, compete, and fail. Simulators need to cover worlds that have never existed. Odyssey is pursuing all of these directions with design partners it will name in time.

Play GoldenEye here!

Third, self-generated experience. PROWL is a reinforcement learning agent rewarded for breaking the world model: freezing a waterfall, losing a crosshair, popping geometry under the camera, ignoring a control input, or collapsing through a hard scene transition. Through active exploration, an agent can therefore improve a world model.

Readers may recall OpenAI’s “Faulty reward functions in the wild” from 2016, where an RL agent steering a boat learned to rack up points by spinning in circles instead of finishing the race. RL agents are unreasonably good at finding the cracks in a system. PROWL points that talent at the world model itself: the agent hunts a weakness, the model trains it away, and the agent comes back for a harder one. It is a way to manufacture the experience these models are short of.

Why Odyssey

When we first invested, we wrote that great research is never enough on its own. The teams that win pair it with execution, product taste, and a feel for where the customer needs to go next.

Oliver Cameron and Jeff Hawke came out of self-driving, where the job is to break an impossible physical AI problem into tractable pieces, simulate the world, make the model robust and generalizable. Odyssey’s shipping cadence since their seed round continues to accelerate: Odyssey-2 Max for scale and physics, Starchild-1 for multimodal grounding, Agora-1 for shared state, and PROWL for closed-loop improvement.

Odyssey is betting that the next leap in machine intelligence comes from systems that build worlds, act inside them, and learn how reality behaves. If that is right, the lead in robotics, agents, and simulation goes to whoever can generate the richest experience fastest. We wrote the first check because we think Oliver, Jeff, and the team are the ones who will.

Join the team!

Guilherme Penedo and Hynek Kydlíček built that data. Over roughly three years at Hugging Face they shipped FineWeb, FineWeb 2, FinePDFs, and FineTranslations, the reference datasets a generation of open models trained on.

The duo have now left to do the same thing for robots with Macrodata. As a FineWeb fan, I’m excited to share that Air Street Capital led their $4M pre-seed alongside a group of angels from the leading AI labs, and today the company comes out of stealth.

Why now

Physical AI is the field’s next scaling story. Jensen Huang calls this “the ChatGPT moment for physical AI,” and the money has followed: robotics drew record venture funding in 2025, and 2026 is on track to dwarf it, with a cluster of companies building robot brains and bodies now carrying multi-billion-dollar valuations - Figure at around $39 billion, Skild around $14 billion, Physical Intelligence reportedly raising near $11 billion. The model side has caught up to the ambition, with vision-language-action models that fold perception and control into one system and world models that let a policy be tested in simulation before it touches hardware.

Every one of those companies needs the same thing to keep scaling: large amounts of well-prepared real-world data. Macrodata does not have to pick which of them wins, it refines the data all of them depend on. And that layer barely exists today. In autonomous driving, models trained on tens of thousands of hours of fleet data already generalize to cities they have never seen; most of robotics has no equivalent.

Physical data is messy in ways text never was: large video files, sensors sampling at different rates, actions and language interleaved, and a dozen incompatible formats with no agreed standard. Teams rebuild brittle scripts every time they swap a robot or a sensor.

What Refiner does

Macrodata’s first product, Refiner, is the tooling for that mess. It is an open-source Python library that reads the formats teams actually use - LeRobot, HDF5 (ALOHA, robomimic, LIBERO), Zarr, MCAP, raw video, Hugging Face datasets - and turns raw episodes into training-ready datasets. You compose a pipeline locally, inspect it in a data viewer built for multimodal data (you cannot cat a video in a terminal), then run the exact same code on managed cloud compute when it is time to process at scale.

Along the way it does the work that lifts policy quality: trimming idle motion, annotating subtasks, tracking what the hands did, and scoring trajectories with reward models, with VLMs run in the loop where a model needs to label or judge the data. You pay for the compute by the second. A pipeline that takes eight minutes on a laptop runs in under a minute on five H100s, for about $0.27.

The same craft, a harder problem

The throughline from FineWeb to Refiner is refinement: the unglamorous work of turning raw capture into the precise signal a model learns from. With text, that meant deduplicating and filtering trillions of tokens until what remained was worth training on. With robots, it means unifying formats, trimming, labeling subtasks, and keeping the demonstrations that teach while dropping the ones that do not. It is the same discipline applied to a harder, less mature, more valuable problem. The business mirrors it: an open-source core that becomes the default way teams handle robot data, and metered cloud compute they run it on.

We backed Guilherme and Hynek because they have done this before: the pair built the open data standard for LLMs. The industry knows that every strong model starts with great data. We think the next generation of robots will too.

Get started with Refiner

—

A week ago the United States government ordered Anthropic, the world’s most valuable AI start-up, to switch off its most capable model, Fable, for every foreign national on earth - whether they worked for Anthropic or not. This was not an export ban on a weapon sold to an adversary. It was an instruction to disable a commercial product, four days after its release, after officials acted on a claim - which Anthropic disputed as narrow and unproven - that its safeguards could be jailbroken to expose cyber-offence capabilities.

I have spent my career around this technology, first as a graduate student and for the past decade as an investor. In that time I have watched AI move from recommending films to driving cars, speaking with a human voice and editing the genome. I have also watched the debate about its risks settle on only half the question.

That debate is mostly about capability: how powerful these systems are becoming, and whether one might escape human control. Those are real questions. But they are not the only ones, and the Anthropic episode exposed the half we have neglected: access and control. The most advanced AI is built by a handful of American companies, on American soil, under American law, and what the rest of us are permitted to do with it can change on a Friday afternoon. The risk that matters today is not only that AI goes rogue, but that we do not control access to it at all.

Consider what “renting intelligence” now means in practice. A European hospital triaging scans, a bank screening fraud, a defence ministry planning for a conflict: increasingly each runs on an American AI system that’s governed by its export regime. A single directive in Washington cascades, instantly, through every institution wired to that model. We have built core economic and public infrastructure on a supply that a foreign government can switch off. And while there are open-source alternatives, they’re either Chinese or not at the frontier, and building European infrastructure on Chinese open weights trades one dependency for a thornier one.

And these systems are starting to improve themselves. As they do, AI stops being one industry among many and becomes the input to all the others - writing the code, running the research, designing the products and, increasingly, generating the growth itself. Once intelligence is the engine of an economy, a country without a frontier model of its own does not lose a sector; it loses control of the inputs to everything else, and the independence that depends on them. Worse, the gap compounds: capability that improves itself gets harder to chase with every month it runs ahead. This is not a race Europe can plan to enter in a decade. The window to be a builder rather than a buyer is measured in the time it takes to stand up a cluster, not a career.

This should sting, because Europeans invented much of modern AI. DeepMind was founded in London and sold to Google in 2014, and a great deal of the talent that followed now lives in California. Today Europe faces a company worth almost $1tn and American tech giants spending an estimated $450bn a year on AI infrastructure. Its answer has been the EU AI Act and a capital commitment that is a rounding error by comparison. A single American site, xAI’s Colossus in Memphis, runs more than half a million GPUs. Europe has nothing remotely at that scale. The instinct to govern this technology is right, but we’re off on the ambition by orders of magnitude.

It is fair to object that regulation is itself a form of power. But a rule book is not a substitute for the thing it governs. You cannot regulate, or be cut off from, an industry you do not have.

Europe’s instinct, when it is cut off, is not to build but to ask. We saw it within the week. The G7 convened in Évian and floated a “trusted partners” scheme to win back the access it had just lost, while Emmanuel Macron feted Donald Trump beneath the gilt of Versailles, the palace where France once helped midwife American independence. Two and a half centuries on, the dependency has reversed, and the posture is courtship.

None of this means Europe can match the American frontier dollar for dollar. On today’s capital it cannot, and pretending otherwise only wastes the little it has. But the goal is not parity, it is leverage. A country does not need the best model in the world to be sovereign; it needs a credible one of its own, on its own soil, good enough that being cut off is survivable rather than catastrophic. That is the difference between negotiating your access from dependence and negotiating it with an alternative in hand. The point is not to win the race. It is to make sure no one else can end it for you.

Sovereignty of that kind is something you build, and Europe has done it before. The Financial Conduct Authority’s regulatory sandbox, launched in 2016, let start-ups test products with real customers under supervision instead of waiting years for authorisation. The pro-innovation culture it signalled helped make London the fintech capital of Europe, home to Revolut, Wise and Monzo. Government should be AI’s most demanding early customer rather than writing rules for systems it has only ever imported.

Industry has to stop behaving like a tenant. Too many European companies rent the entire stack from American providers and build a thin product on top. That earns a margin and owns nothing: when the lab that supplies you decides to compete with you, or its government decides to cut you off, you have no ground to stand on. Where it counts, build and hold your own models and compute.

And our universities, which should be the source of all this, still work against it. I have argued in these pages before that Europe’s spinout system is broken, and it remains so. Too many institutions treat the companies their research creates as something to extract value from, rather than as the vehicle through which a discovery reaches the world. The best research should leave the building as a company, in addition to a paper.

We keep framing AI safety and AI ambition as a trade-off, as though a country must choose between governing this technology and building it. It is not a choice. The safest position is not the most heavily regulated one. It is the one where the model runs on your terms, in your jurisdiction, and no one on the far side of an ocean can reach over and turn it off. Right now that finger is not ours. Until it is, every other conversation about AI risk is one we are having on someone else’s permission.

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

Timestamp timeline

• 0:00 – Teaser: AI-designed molecules & the $2.25B Lilly deal

• 0:22 – Intros: Nathan Benaich (Air Street Capital) & Ali Madani (Profluent)

• 2:10 – What is Profluent, and why AI matters

• 5:45 – The landscape: readers vs. writers

• 7:45 – Profluent’s edge: 100B+ sequences and a wet lab

• 9:20 – OpenCRISPR and the exponential curve

• 12:55 – Why sequence beats structure

• 14:50 – The Eli Lilly deal and large gene insertion

• 16:20 – Fine-scale vs. large-scale editing

• 18:00 – Why it’s hard: the pre-AI era and the activity/specificity trade-off

• 20:45 – The Verve news, and scaling beyond one-offs

• 23:45 – Rare vs. common disease

• 26:10 – “What do you know that no one else does?”

• 27:40 – bio × AI is an undersaturated field

• 32:40 – When will a top-10 pharma be AI-first?

• 34:30 – Every molecule will be designed with AI

Transcript

Teaser (0:00)

Ali: We’re not a CRISPR company. We’re not a gene editing company. We’re an AI company. $2.25 billion is a great number, but what’s even more exciting than the number itself is the opportunity. This is an example of AI unlocking something, not just accelerating. Within the next two to three years, every single molecule will be designed with AI.

Intros: Nathan Benaich (Air Street Capital) & Ali Madani (Profluent) (0:20)

Nathan: Hey everybody, I’m Nathan Benaich, founder and General Partner of Air Street Capital, a venture capital firm that invests in AI-first companies in the US and Europe. Today I’m really excited to be joined by Ali Madani, founder and CEO of Profluent.

I first came across Ali through his paper ProGen, one of the first protein language models, which we’ll dig into today, back in 2021. After I saw that paper I DM’d him on Twitter and we started a discussion about the future of AI and biology. That led to me writing the biggest first check I’d ever done from Air Street, to be part of Ali’s first round at Profluent.

We’re now a couple of years into the journey. A lot has evolved, both for Profluent and the space overall, and it’s an exciting time, because we just signed a deal with Eli Lilly worth $2.25 billion to develop AI-designed gene editors for therapeutic use. It’s one of the largest deals of its kind in our field and a big moment for frontier AI applied to biology.

So we thought it’d be a good opportunity to take stock of the field: what Profluent is, our mission, why AI matters for protein engineering, and a bit about the Lilly deal that we can publicly discuss. We’ll also get into the difference between fine-scale and large-scale gene editing, the difference between bio models that are readers versus writers, some very recent news from Verve Therapeutics and Lilly, and a few of the recent model releases from other teams we respect. Ali’s one of the few people I know who can credibly speak both to AI as it applies to biology and to how we use it to advance human health.

Before we dive in, it’s worth taking stock of what Profluent is. Ali, can you give us the high-level pitch?

What is Profluent, and why AI matters (2:13)

Ali: Thanks, Nathan. At the highest level, Profluent is an AI lab, and we build foundation models to design proteins.

Nathan: What does that mean? How do we currently design proteins, and why does AI matter here?

Ali: To take one step back: proteins are molecular machines that power everything in human health, disease, sustainability, and the environment. A single protein like Keytruda can generate an incredible amount of revenue, over $30 billion annually. But the way we go about discovery today is really finding a needle in the haystack of nature, and intentional protein design is incredibly hard.

Nathan: So we’re coming from trial and error, and we want to move toward more thoughtful design dictated by the characteristics we actually want in the protein.

Ali: Yes. The mission we’re on is to make biology programmable. That means having levers you can control to design a molecule from scratch based on its intended function. That level of programmability is the moonshot for all of humanity.

Nathan: A quick sidebar: a lot of people are used to prompting ChatGPT or Claude to generate the outputs they want. How does programming a protein look different? How do we actually prompt these models to generate the sequences we care about?

Ali: Great question. Proteins can be represented as a sequence, and they are. A lot of biology is organized that way, whether it’s DNA as a sequence of nucleotides (A, T, C, G) or proteins as a sequence drawn from a standard vocabulary of 20 amino acids.

So we train language models, the same transformer architecture used for text, except the tokens aren’t words or subwords, they’re amino acids, the building blocks of proteins. We train with masked language modeling or next-token-prediction objectives.

Nathan: And what are the labels?

Ali: In the unsupervised setting, where we’re doing pre-training, we just use the sequence information itself. It’s similar to scraping the internet for human-generated text: you know a human wrote it and that it’s useful. For proteins, the analogy is that evolution selected these sequences under selective pressure, so you know a protein existed for a purpose and was functional. We can learn from that in an unsupervised way, then layer in other metadata, organism tags, taxonomic and environmental information, predicted or known structures, and eventually move to the post-training setting where you have actual laboratory measurements of function.

The landscape: readers vs. writers (5:45)

Nathan: It’s interesting to contrast Profluent’s approach, in-house data creation, in-house models, versus using open-source tools, and where the company sits on internal discovery versus partnerships, selling models versus selling drugs. Notably, how does it contrast with Isomorphic Labs, which just raised $2 billion and in many ways is our peer?

Ali: There are a lot of teams exploring biology with AI, and the broader AI-for-science landscape is incredibly exciting. Drug discovery is one of those immediate applications with a large market and large impact.

Isomorphic, for example, is a frontier AI lab born out of the structure-prediction era, from AlphaFold into their latest models. Those are broadly readers of biology that you can use for a variety of applications, and they’re also focused on small molecules and moving into antibodies.

Our heritage, which is important here, is born out of language models, quite literally the same architectures that enabled ChatGPT. Not through a tenuous analogy, but the same architectures behind the commercial interest in ChatGPT or Claude for programming. We use similar architectures and principles, but for proteins. One principle is to keep scaling data and parameters. Another is aligning these models on preference data we derive not just from human feedback but from laboratory feedback.

So one way to think about the field: there are credible players like Isomorphic working on readers of biology, versus writers of biology. We’ve been focused on the generative side, building language models in that paradigm.

Nathan: So we can segment by small molecule versus protein, reader versus writer, and sequence-first versus structure-first.

Ali: Exactly, and we’re firmly in the sequence-first paradigm. That doesn’t mean sequence-only; we can still layer in other information. But we want to capture the vast amount of sequence information available.

Profluent’s edge: 100B+ sequences and a wet lab (7:51)

Ali: We have over 100 billion protein sequences that we’ve curated to be high-fidelity and that we can train models on. That has incredible promise for representation learning, which ultimately lets us design proteins better.

Nathan: And curation means going out into nature scavenging for proteins in esoteric environments? Or is it more number-crunching across databases that aren’t particularly friendly?

Ali: All of the above. It starts out similar to Common Crawl, where the raw internet is available to everyone, but the question is who can actually curate that dataset effectively, access more raw sources, and understand the latent distributions within it. That’s ultimately what creates the winner, and we’ve spent a lot of effort there.

On the post-training side, we built a wet lab from day one. That was intentional. It lets us not just validate the proteins we’ve designed, but also generate supervised datasets, assay labels for a given sequence, that feed back into our models. That’s incredibly important for lifting the capabilities out of these models.

OpenCRISPR and the exponential curve (9:13)

Nathan: One of the cool things you did not long ago was capture a dataset specifically for CRISPRs, the gene-editing tools that transformed modern genetic medicine. Can you talk about what OpenCRISPR is, how you got the data, what it does?

Ali: OpenCRISPR was the first demonstration that we could use AI to edit the human genome, to generate molecules that bind to DNA precisely and execute the change you’re seeking, whether a double-stranded break or a precise base edit, an A-to-G edit, for example.

The contrast is with traditional drug discovery, where you pluck something from nature, from bacterial settings, and cram it into a human therapeutic application. Instead, we use a generative model to design a protein from scratch that doesn’t exist in nature and has the intended purpose a clinician or patient would use. It’s gotten a crazy amount of adoption; there’s a voracious appetite for it across industries.

Nathan: Can you give a sense of how hard this was? Is it a landmark moment, or one data point on a steady climb?

Ali: It’s a data point on an exponential curve. When I first started out, we trained our first model, a 1.2-billion-parameter model, the first ProGen model. It was actually the largest model in all the physical sciences at the time, and we didn’t even understand what it was doing or whether it was working.

So we quickly partnered with research labs at UCSF and with biopharma folks and asked a simple question: here are some generated samples, can you test whether these proteins are functional and useful? To our surprise, it worked. The de novo proteins generated by the model had incredibly high hit rates, and their functionality rivaled exemplar proteins that had millions of years of evolution to reach an optimal state. That was one data point on the trajectory.

Nathan: So AI can essentially accelerate evolution to find a much better peak.

Ali: It can ground itself in nature and evolution, then start interpolating and extrapolating. We started with simple monomeric proteins and moved into more complex settings. OpenCRISPR is the next logical step: proteins that are quite large, around 400 amino acids, with multiple domains, protein-protein interactions, large conformational changes, dynamics, and protein-nucleic-acid interactions, protein-guide-RNA and protein-DNA. That full system is truly a molecular machine.

It’s incredibly challenging to build that from first principles, atom by atom. The better approach is information-based: learn from existing examples, uncover the underlying biophysical principles, and generate something new from scratch. Going back to sequence versus structure, this is where sequence-based models really outshine structure-based approaches.

Why sequence beats structure (13:01)

Ali: In the peer review for our Nature paper, a reviewer asked us to baseline against structure-based approaches like ProteinMPNN. We found the language models really outperformed them; the structure-based approaches couldn’t perform at all, because of the complexity involved.

Nathan: What’s the intuition for why structure is less powerful than sequence?

Ali: Because function is complex, and function is what everyone ultimately cares about, whether it’s a patient for therapeutics, a farmer for agriculture, or a consumer for protein-based products. Capturing function can involve many concepts, including dynamics, so it’s not just one structural state. Capturing that sequence-to-function relationship is the most important thing, and we build all of our infrastructure with that in mind.

Nathan: So the problem is that structure captures a protein in one conformation, but it can assume many?

Ali: Yes, these proteins are very dynamic. Structure freezes them in one state, but there are many states they could be in. Sequence is more flexible because everything ultimately arises from sequence, so you get more diversity. Think of disordered proteins or loop-like proteins that have many states and no set conformation, or multi-state proteins. We can bake all of those priors into the model, building toward a broader concept of fitness.

The Eli Lilly deal and large gene insertion (14:53)

Nathan: This led, among other things, to the big Eli Lilly deal, $2.25 billion in milestones, which is pretty epic. Walk us through what the deal means, how it came about, and the plan.

Ali: The number is great, $2.25 billion is a great number, but what’s even more exciting is the opportunity. This is an example of AI unlocking something, not just accelerating.

AI is going to be transformative across many aspects of drug discovery. The easiest value proposition is AI as an accelerant: compressing timelines, making things more efficient. That’s great, and we operate there too and provide value for our partners. But what really excites us is finding unlocks, problems you could not have solved before AI. The specific problem we’re working on with Lilly is large gene insertion: inserting large genes into the genome.

Nathan: What qualifies as a large gene, and what’s the difference with base editing and prime editing? A lot of buzzwords. Can you unpack them?

Ali: We think about two types of effort within gene editing: fine-scale and large-scale.

Fine-scale vs. large-scale editing (16:19)

Ali: Fine-scale editing is like genetic scalpels, the genetic-surgery model, where you perform precise edits of the human genome.

Large-scale editing, which is the subject of the Lilly deal, is the idea of inserting whole kilobase genetic payloads into the human genome. The main challenges are doing that efficiently and effectively, and then specificity. We have examples of proteins called recombinases, like BxB1, that are widely used, but they may not be specific or work well in human cellular contexts. So we have proof points in nature that it’s possible; the grand challenge AI can enable is making it programmatic and controllable.

Nathan: So nature has shown it’s technically possible to snip and stitch large pieces of DNA, potentially an entire gene cassette, in organisms with shorter genes. Now the task is to make it work in human cells?

Ali: Less about bending and molding it, and more about learning the underlying principles of why it occurs, then building it from scratch with AI models.

Why it’s hard: the pre-AI era and the activity/specificity trade-off (18:02)

Ali: Going to your point about base editing, prime editing, and other forms of gene editing: all of those are a pre-AI-era approach of taking something from nature and cobbling it together. It’s worked remarkably well, but it’s the way drug discovery has always operated, find a needle in a haystack, perform random mutagenesis, screen, and hope to find a winner.

Nathan: So this kilobase editing isn’t going to be solved by finding the enzymes that do this in bacteria and then fine-tuning a model to adapt them to human DNA?

Ali: It uses evolutionary information as examples of what has worked, and through that you learn the underlying grammar, similar to what we built on the foundation-model side. The way humans learn to write the next great American novel is by reading other novels, understanding what makes a great one, and then writing our own, as opposed to grammatically copying and pasting from existing novels.

Nathan: Have there been attempts at large gene insertion before, and why have they fallen short?

Ali: There have been attempts. What we find is a big trade-off between activity and specificity. A lot of protein problems have these trade-offs, where you want to optimize multiple properties at once and optimizing even one is hard.

Nathan: So you can either make the scissor really precise about where it snips, or efficient at doing the snip, but not both.

Ali: Exactly, that’s one trade-off we see in recombinases. Navigating that multi-attribute optimization is very difficult.

The Verve news, and scaling beyond one-offs (20:26)

Nathan: There was huge news from Verve, which was also working with Lilly, Lilly acquired the company not long ago. It involved a specific gene tied to high cholesterol and heart disease. They used a base-editing technique in vivo, inside the body, in a handful of patients, and it looks like those patients had their mutation changed and are pretty healthy. Walk us through what this means. Is it as exciting as it sounds, or are there caveats the Twitterverse is missing?

Ali: Even if there are caveats, it’s point-blank insane, in the best way. We should cheerlead these efforts as much as possible, because they can be transformative.

The question we ask is: how do we scale that? How do we make it not a one-off, but use AI to build an engine that enables more and more of these therapeutics, molecules that can become blockbusters down the line?

Nathan: So instead of building tools specific to PCSK9, you could swap in any gene you care about and have off-the-shelf editors, then find patients with those monogenic or more complex diseases and run the same in vivo motion.

Ali: To make it concrete: at ASGCT, the American Society of Gene and Cell Therapy, we announced our ability to 10x the number of mutations and variants we can go after versus state-of-the-art SpCas9-based approaches for base editing. That expands the addressable market, the number of patients and variants you can target. That’s a concrete, non-incremental 10x that AI can deliver, with direct implications for that Verve announcement.

Nathan: Is it a drop-in replacement for what Verve did? Can we now say, if it worked for PCSK9, here’s a 10x version?

Ali: Essentially yes. There’s a payload side to gene editing, and this lets you swap in a different gene editor that’s known to work well, both in silico and validated in experiments, for sites of interest beyond the PCSK9 gene.

Nathan: What about the critique that this only worked on a couple of patients? What should we read into that?

Ali: I’d argue it’s amazing it worked for a couple of patients at all, and let’s see what happens going forward. The ability to have these one-off cures is mind-boggling, that we can go beyond treating disease and symptoms.

Rare vs. common disease (23:52)

Ali: We can go beyond taking pills once a day and worrying about adherence, and instead have one solution, very early on, that can prevent heart disease. That’s incredibly bold, and consistent with the bold bets Lilly is making in obesity and other diseases. And this wasn’t a random shot in the dark; they’ve had successes all along the way, and I’d extrapolate the trajectory beyond this moment.

Nathan: Is it more or less impressive than the curing of baby KJ about a year ago, who had a genetic defect and was treated with a gene editor?

Ali: They’re different use cases. Disease comes in many shapes and forms, rare and common. The baby KJ story is a life-threatening, extreme-need setting: without a liver transplant or some therapy, you die very young. That’s an incredibly powerful use case, because it affects the young, it’s clear death, and there are no other solutions. It’s another form of disease we can tackle with the same underlying technology that AI can scale.

Nathan: What a time to be alive that we have these capabilities.

What do you know that no one else does? (25:52)

Nathan: To tie this together: every drug ever made started in nature, or has been screened to the ends of the earth in pharma. Now we’re inverting that whole system, from discovery to de novo design. What comes next, and where might the field go? You have insight into one of the most exciting companies out there, so, what do you know that the rest of the world doesn’t?

Ali: We need to keep scaling these models. We’re still in early days. If I put it in GPT eras, I feel like we’re in the GPT-1.5 era of the field as a whole, and I want to get us to GPT-3, GPT-4, GPT-5 as soon as possible. I’m impatient to bring the future forward.

That’s not just data scaling, but thinking deeply about inference-time scaling, new model architectures, and incorporating other data. And even though we’re early, it’s pretty incredible that it’s already useful. You can see that with our Lilly deal and across many applications; even the early versions have real utility, and people are willing to bet on them.

Nathan: That might be a big difference from natural-language LLMs, where GPT-1 and GPT-2 were kind of useless economically, entertaining, maybe. Here, companies are staking billions because it already works.

bio × AI is an undersaturated field (27:40)

Nathan: So either this is a domain with lower-hanging fruit, because the industry is more nascent in adopting advanced computation and AI, or AI is a uniquely good interpreter for biology, where any interpretation of what we already have uncovers biologically useful nuggets.

Ali: A connected question we ask internally is: what if this is it? What if you stumble across gold and that was the only application? That’s probably the most unreasonable take; it’s reasonable to expect much more. We see a clear line of sight to unlocking more targets we couldn’t go after before, and many problems that are well-bounded from a science perspective, where the risk is reduced to scientific risk. As scientists, we love those problems, and we feel we’re the best to tackle them.

Nathan: If you had to estimate, how many people work at the frontier of AI and biology versus the frontier of AI generally? What are the relative numbers?

Ali: At least a thousandth, both in compute budget and economic spend, and in number of people. And biology is no less complex than text, and no less impactful, I’d argue more so. We’re totally undersaturated here.

Nathan: But it seems more intimidating for people outside the industry, who think, “I don’t know anything useful about biology, how could my machine-learning skills apply?” Do you have a counter, to build a bigger magnet and pull more people in?

Ali: The proof is in the pudding. We have people who’d never done anything with biology, who built NLP models, and within weeks they sense the usefulness of what they bring. There’s no such thing as a 20-year veteran in using transformer models for protein design; this latest version of AI for biology is new. The intersection of people who can speak both AI, NLP or computer vision, and biology is small, but we see the proof points: you can learn this quickly and provide real value.

Nathan: Can you give a couple of examples of the backgrounds of people who’ve joined and been at the forefront of these papers?

Ali: We have three main pillars at Profluent. The first is machine learning: people from big tech, NLP, computer vision, RL, and computational biophysics backgrounds. The second is data: world-class bioinformatics people who curate the vast datasets, over 100 billion proteins and over 20 trillion tokens, for both pre-training and post-training.

Nathan: So they have taste for the data.

Ali: Absolutely, and taste matters even more in biology, because we don’t natively read and write that language, we don’t speak protein. The bioinformatics element is huge. The third, equal pillar is experimental biology: people from pharma and biotech who understand the domains. And maybe a fourth pillar is our partners, who understand their specific problems and want to take this forward. It takes a village; we humbly go after a central piece of the problem, but advancing it through clinical trials requires partners.

When will a top-10 pharma be AI-first? (32:51)

Nathan: How many years until one of the top-10 biopharma companies is a truly AI-first company like ours?

Ali: I think it’ll come through partnerships. We do what we do best, and partners tell me this directly: they recognize that building the frontier model is what we do best, and they have specific datasets, use cases, and expertise that are complementary. It’s not unfamiliar to pharma, which has long had a symbiotic relationship with biotech, where innovation comes in the form of molecules. There’ll be a similar complement between frontier AI companies, Profluent, Isomorphic, and others, and pharma. That recognition has already happened and seems to have accelerated in the last six months.

Nathan: It’s crazy how fast it’s happened.

Ali: When I trained the first ProGen models and handed sequences to people, the first question was, “Who are you, and what is this alien artifact?” Now the conversation has completely accelerated, and that speed is unprecedented for such large industries.

Every molecule will be designed with AI (34:37)

Ali: On adoption: the way I see it, every drug, every molecule that’s designed is going to use AI, not just AlphaFold for understanding structure, but AI to generate and write the molecule. And not just a percentage; within the next two to three years, every single molecule will be designed with AI.

Nathan: And it goes further into the process, clinical trials, figuring out which patients to enroll, how to monitor response. All of those tasks get fundamentally transformed by AI, especially once big pharma starts treating AI and software as a core part of the product offering rather than just an enabler. Like the shift in financial services, where technology went from “not it” to the product itself. So, if Profluent pulls off its mission, and hopefully the mission keeps expanding, what would that world look like?

Ali: People talk about abundance, and I really believe that, I say it with a straight face. There’s an abundance of problems we can go after and solve with AI, and so many targets we can prosecute. It’s not just compressing timelines or making things more efficient; it’s unlocking new and emergent capabilities from scaling these models, which creates new value.

I’m incredibly bullish, and I say that as a scientist. Profluent wasn’t “let’s start a startup and then figure out the idea.” This was the subject of my research before the company. So I speak from the ground level as a practitioner.

We built foundation models for proteins. We’re not a CRISPR company, we’re not a gene editing company, we’re an AI company for protein design. But the gene editing application is concrete and ambitious, and there’s a future we can point to that motivates us and our partners: imagine a child born with a mutation in their DNA, a genetic disease that, untreated, leads to a life of pain and suffering for them and their family. With our AI, we can design molecules from scratch to correct that disease before it takes hold. That’s an incredibly powerful future, and it’s going to change everything.

Nathan: Well, I wish you all the best of success.

Ali: We’ll work on this as hard as we can, and there are many new announcements to come in the forthcoming months, so stay tuned.

Nathan: Hopefully we’ll check in before long and get a temperature check on where you think we are on this exponential curve toward abundance. With that, thank you so much, Ali, and thanks everybody for listening.

Ali: Thanks, Nathan.

Every generation of defense risks building the shield it wishes it had for the last war. France poured concrete into the Maginot Line to stop the invasion it remembered from the First World War. Israel built the Iron Dome to stop barrages of rockets. Both were serious engineering achievements built for threats that moved more slowly than institutions.

That world is gone.

Russia’s war in Ukraine, and Iran’s missile-and-drone onslaught against the UAE and the wider GCC, have exposed the new arithmetic of air defense. Cheap drones, ballistic missiles, cruise missiles, glide bombs, electronic warfare, and massed salvos have changed both the economics and the tempo of the fight. When a cheap drone draws a million-dollar interceptor, the defender may win the intercept and still lose the campaign.

The next shield has to be affordable enough to fire at scale and robust enough to work under jamming. It cannot be static. It must instead evolve with the threat.

That is why Air Street has led the $60M Series A in Alta Ares.

From argument to action

When I wrote in the Financial Times in 2023 that European governments needed to take defense innovation seriously, I meant it as a challenge to governments and the venture industry. Europe had the capital, talent, and technical ambition to build the technologies that safeguard democracy, security, and our way of life. Too often, it chose easier markets, while procurement systems rewarded incumbents built for a slower era.

Since then, the argument has become harder to dismiss. For the last year, I have been looking for the company that could make it real in European air defense: not “AI for defense” slideware, and not a controlled-range demo, but a sovereign, operationally grounded, full-stack company built around a feedback loop from the field.

Alta Ares is that company.

The new air defense stack

Alta Ares is building full-stack, integrated air defense across the entire kill chain: AI-first software, sensors, command-and-control, and effectors built to operate in contested environments against a range of aerial threats.

The company began with software for intelligence, surveillance, and reconnaissance video analysis. Work alongside operators in Ukraine pulled it into the broader air-defense problem: seeing a target, maintaining track, supporting operator decisions, guiding an interceptor, and integrating the result into a system that can actually be fielded.

In many AI applications, failure means a bad answer or a model that needs retraining. In air defense, the object is small, fast, cheap, and often deliberately hard to see. The operator may be tired, cold, under jamming, and making decisions in seconds. A useful AI system cannot live as an analyst beside the workflow. It has to become part of the kill chain itself.

Alta Ares’s products reflect that architecture. Pixel Lock provides onboard computer vision for detection, tracking, and terminal guidance while preserving human control over engagement. Ukrainian drone pilots are already hitting Russian targets from 500km away and as the Financial Times reported, Alta Ares’s terminal guidance software helps interceptors detect and close on Russian drones in the final phase of flight.

Gamma supports autonomous guidance and ISR workflows. X-Lock and Black Bird are both used in the field: X-Lock against short-range one-way attack drone threats, including Shahed-type systems, and Black Bird against faster aerial threats, including cruise missiles and glide bombs.

This isn’t about software grafted onto hardware. Alta Ares develops models, avionics, guidance, operator workflows, and manufacturing around the operational problems faced by the warfighter.

Born from the battlefield

Alta Ares’s most important asset is its feedback loop with the warfighter.

That loop comes from constant in-field deployment. Alta Ares has been active in Ukraine for years and interceptors equipped with Pixel Lock began shooting down Shahed-type drones in 2025. The company has since demonstrated systems with NATO, tested Black Bird in arctic conditions with the Estonian Defense Forces, and is deployed across multiple operational theaters. In rapid succession, Alta Ares has signed large contracts from half a dozen countries across Europe, the Middle East and Asia.

Those milestones matter, but the deeper point is what they make possible. Simulation is useful. Range tests are useful. Neither exposes systems to the full mess of real conflict: electronic warfare, bad weather, changing drone signatures, damaged equipment, uneven training, and the pressure of live operations.

Alta Ares’s data advantage is not a static dataset sitting on a server. It is a relationship with the field: operators use the system, the system sees where it fails, engineers recover the evidence, and the product changes. That is how AI systems become robust in high-consequence environments: not by claiming perfection, but by shortening the distance between failure and adaptation.

That is what the old defense procurement model cannot absorb. As we argued in Bringing Dynamism to European Defense, Europe’s problem has never been talent alone: it is a financial, political, and institutional climate that fails to reward mission-driven defense entrepreneurship. A decade-long program assumes the threat profile will stand still long enough for the program to arrive. In autonomous warfare, that assumption is fatal. The measure of a system is not only how well it works on day one, but how quickly it can be improved for day two.

Why this team

Hadrien Canter’s path into defense technology is not the standard prime-contractor biography. He has deep links with Ukraine for years, and Alta Ares does not feel like a lab looking for a battlefield use case. It feels like a company working backward from the operator: what they can see, what they cannot, how quickly the threat changes, and what a system has to do in the thick of it.

You feel it in the office: the energy, mission, urgency, and seriousness of people building for a live war.

Together with his co-founder Stanislas Walch, Hadrien has recruited software, hardware, government relations and sales talent from Anduril, Helsing, Palantir, Safran, Thales, MBDA, Embraer, and the French Army. They’ve also built an advisory board that includes Philippe Lavigne, former Chief of Staff of the French Air and Space Force and former NATO Supreme Allied Commander Transformation, and General Corentin Lancrenon, a three-star French Army general.

For Air Street, all of these features are critical. We back AI-first companies, but “AI-first” should not mean model-first in isolation. In the physical world, AI advantage usually belongs to the team that owns the loop from data to deployment. Alta Ares has that loop.

Europe must build

Europe now broadly agrees that it needs more defense capability. As I wrote in A letter from the Munich Security Conference, the harder transition is from crisis buying to permanent capacity. The question is whether Europe will build the new capabilities itself, or simply allocate larger budgets to imported systems and slower incumbents. e.

No single company will be Europe’s entire Iron Dome for autonomous air defense. A real adaptive shield will take an ecosystem: sensors, decision systems, interceptors, electronic warfare, command systems, procurement reform, and operators who can field the technology. But every ecosystem needs a company that shows the way.

Europe has written enough policy documents about waking up. It needs companies that jolt us into action.

We believe Alta Ares is that company.

Hadrien is co-founder and CEO of Alta Ares, an AI-first air defense company building an integrated platform for detection, identification, tracking, and interception. Founded in 2024, it works on one of the most demanding problems in applied AI: defending against mass-produced attack drones, cruise missiles, and glide bombs in contested environments, where a system has to perform in seconds, at the edge, and under operational pressure.

Alta Ares began as a software company focused on intelligence, surveillance, and reconnaissance (ISR) video analysis. The feedback loop from Ukraine pushed it into a wider air defense architecture spanning data-fusion software, edge AI, and hardware effectors built to operate from Arctic to desert conditions. Its stack includes Pixel Lock for embedded detection, tracking, and terminal guidance; Gamma for autonomous interceptor guidance; X-Lock for short-range drone interception; and Black Bird for faster aerial threats.

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Why the air defense gap is so large

Recent salvos over Eastern Europe and the Middle East have exposed a hard truth: legacy air defense systems built to stop fast jets are losing the economics against mass-produced aerial threats. NATO partners increasingly face coordinated waves of one-way attack UAVs paired with cruise missiles and glide bombs. This is an enormous problem that is defining capability gaps in modern defense.

Unlike many AI applications, air defense is not forgiving. The enemy object is small, fast, and often deliberately cheap. The operator may be tired, cold, and working at night. The environment may be jammed. Protecting people, critical infrastructure, and military assets now demands systems engineered from day one for autonomy, modularity, interoperability, and unit-cost discipline.

That is what makes counter-UAS such an important test case for applied AI. There are many hard parts to the problem: recognizing an object in a poor quality video feed, fusing sensor inputs, holding a track, guiding an interceptor, preserving human control over the final engagement decision, and doing all of it inside a system that can be carried, deployed, and iterated quickly. Pixel Lock is Alta Ares’ answer: onboard computer vision that detects, classifies, and tracks targets in real time and supports autonomous terminal guidance while keeping the operator in the loop. Here the AI sits inside the control chain itself, guiding the interceptor rather than only flagging a target for an operator to act on.

The Ukraine feedback loop

Alta Ares’ development is shaped by proximity to the battlefield. Interceptors running Pixel Lock began shooting down Shahed-type drones in November 2025. Hadrien’s public interviews describe an engineering culture built around fast field feedback: simulation helps, but the front line reveals failure modes a lab cannot.

That loop matters because drone warfare is changing faster than long procurement cycles and static product roadmaps can absorb. Threats adapt, operators adapt, and countermeasures adapt in turn. The companies that make a difference in this category are the ones that can move from deployment to model improvement to hardware iteration without treating each step as a separate world.

From software to systems

Alta Ares’ public milestones track a company moving from software into an integrated air defense architecture. In March 2025, NATO Allied Command Transformation named Team Alta Ares the winner of its 15th Innovation Challenge for an “Embedded AI for Recognition, Detection, and Identification” submission focused on glide bombs - a system that detects, identifies, and predicts the trajectory of these low-cost guided munitions from visual and acoustic data.

Later in 2025, Alta Ares demonstrated its drone-interception system to NATO at the DGA missile test site in Biscarrosse. The company calls the configuration a Tactical Protection Dome: radars, interceptor drones, data fusion, and Pixel Lock software.

The most recent milestone came in Estonia. Early in 2026, working with the Estonian Defense Forces and Ukrainian partners, Alta Ares tested Black Bird, its turbojet-powered interceptor, in Arctic conditions. The company reported three consecutive flights, ground temperatures of -17 degrees Celsius and -25 degrees Celsius at altitude, and a top recorded speed of 450 km/h. The trial also validated the less cinematic but more important parts of the system: communication links, antenna performance, live video transmission, and Pixel Lock target detection, tracking, and locking. In parallel, the company has begun mass-producing interceptor drones in France.

Why it matters for RAAIS

The next generation of air defense is being built as layered systems: sensors, command and control, autonomy, and low-cost effectors combined quickly enough to keep pace with changing threats. NATO’s own 2026 work on layered counter-UAS points the same way, treating the challenge as one of integrating sensors, effectors, electronic warfare, command systems, and battlefield lessons into something coherent. Alta Ares is one version of that thesis, built from the edge inward: European, field-informed, and aimed at a class of threats that has already changed the character of modern conflict.

For RAAIS, the interest goes beyond defense. Alta Ares is a working case study in applied AI inside a live operational system, where robustness, cost, latency, and human judgment all bind at once. The same problem shows up across robotics, autonomy, and other high-consequence settings, where a model that performs on a benchmark still has to keep working once it meets conditions that shift under it.

Hadrien’s background

Hadrien’s path into defense technology is unusual. Before Alta Ares, his public background spanned law, Ukraine, and operational fieldwork rather than a conventional defense prime career. He studied at the University of Paris 1 Panthéon-Sorbonne, qualified with the Paris Bar, served as an OSCE international observer around Mariupol in 2019, and worked on humanitarian projects in Eastern Ukraine.

That background shows in the company he has built. Alta Ares designs from the operational problem backward: what the operator sees, what they miss under stress, how fast the threat changes, and what kind of AI stack survives that reality.

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The harder half of voice AI?

ElevenLabs built its name on synthetic voices that made generated speech sound natural, expressive, and controllable. But the reverse problem also exists: turning messy, real-world speech back into accurate text. For voice agents it is often the part that decides whether the product works in the ears of the human user.

A live agent cannot reason about what it has not heard. It needs a transcript that is fast enough to preserve conversational flow, accurate enough to carry names, numbers, technical terms, and intent, and robust enough to handle accents, background noise, interruptions, and people switching languages mid-sentence. Speech-to-text is a key perception layer for interactive AI systems.

Angelos’ work at ElevenLabs focuses on model quality, inference design, latency budgets, and production reliability.

Two Scribes for two production regimes

Angelos has worked across both of ElevenLabs’ latest transcription models: Scribe v2 and Scribe v2 Realtime.

Scribe v2, launched in January 2026, is optimised for high-accuracy transcription of long and complex recordings: batch transcription, subtitling, captioning, media libraries, training material, compliance workflows, and research audio. These are settings where the model can use broader context, but where errors compound quickly. A missed drug name, a malformed account number, or a confused speaker label can make the downstream transcript much less useful. ElevenLabs built Scribe v2 with production transcription features such as keyterm prompting, entity detection across 56 categories, smart multi-language transcription, speaker diarisation, word-level timestamps, and audio tagging.

On Artificial Analysis’s AA-WER v2.0 benchmark, which combines a held-out voice-agent dataset with cleaned public datasets for parliamentary speech and earnings calls, Scribe v2 led the overall ranking with a 2.3% word error rate. It also led two of the three component datasets, including AA-AgentTalk and Earnings22-Cleaned-AA. That is a useful reminder that “accuracy” is not one thing: the model has to work across short agent-directed speech, formal speech, and long business audio, not just a clean public benchmark.

Scribe v2 Realtime, released in November 2025, solves the same problem under a much tighter constraint. It is built for live agents, meeting assistants, captioning, and conversational interfaces where a transcript that arrives too late is almost as bad as a wrong one. ElevenLabs describes it as delivering live transcription at around 150 milliseconds of latency across more than 90 languages, with features such as automatic language detection, voice activity detection, manual commit control, text conditioning, and predictive transcription for the next words and punctuation. On FLEURS, a multilingual benchmark spanning 30 languages, ElevenLabs reports the lowest word error rate of any low-latency ASR model.

Why latency changes the shape of the problem

For most of the last decade, speech-to-text progress was mainly discussed through benchmark word error rate. That number still matters, but it no longer captures the whole product problem. A transcription model that is accurate after the fact can be excellent for subtitles and useless for a live agent. A real-time model that is fast but unstable can make the agent interrupt, hallucinate intent, or miss the moment to respond.

This is why Scribe v2 and Scribe v2 Realtime are better understood as two parts of the same system-level push rather than a single leaderboard entry. The batch model pushes for the cleanest possible transcript when full context is available. The real-time model asks how much of that accuracy can survive when the system has to stream partial understanding under a human conversational latency budget. In one case the challenge is depth of context. In the other it is speed without collapse.

For RAAIS, that makes Angelos’s work a particularly good example of applied AI becoming harder as it becomes useful. Offline model quality is only the beginning. The real question is whether a research result can be made fast, stable, observable, and cheap enough to sit inside millions of interactions where people do not care about the benchmark. They care whether the agent heard them.

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Angelos’s background

Angelos’s path into speech-to-text runs through systems work, which is part of what makes it interesting. He studied Software Engineering at the University of Glasgow, graduating with First Class Honours in 2020. His master’s project developed a reinforcement-learning-based scheduler for IoT networks, and before ElevenLabs he worked across cloud-native infrastructure and reliability roles at Skyscanner, Ondat, and Beacon Platform. He also contributed to Gentoo’s Portage package manager through Google Summer of Code.

The audio thread appears early. In 2017, his team won the Amazon challenge at the Glasgow University hackathon with Emotionify, an app that combined facial recognition, text-to-speech, and the Spotify API to match music to a user’s mood. He later won the Goldman Sachs and IBM challenges at subsequent Glasgow hackathons, with projects involving speech recognition, text-to-speech, and custom machine-learning models.

He also keeps teaching the fundamentals. At AI Engineer Europe 2026, Angelos ran a workshop called Training an LLM from Scratch, Locally, walking engineers through the practical components of building a small language model on local hardware. That instinct - to understand the whole stack from first principles, then make it work in production - is exactly the one needed for speech-to-text now. Voice AI will not be judged by whether it can speak beautifully in a demo. It will be judged by whether it can listen accurately enough to be trusted.

Short bio

Angelos Perivolaropoulos leads research engineering for speech-to-text at ElevenLabs, where he works across Scribe v2 and Scribe v2 Realtime, the company’s high-accuracy batch transcription and low-latency streaming transcription models. His work sits at the intersection of model development, inference, and production reliability. He studied Software Engineering at the University of Glasgow, graduated with First Class Honours, and previously worked across cloud-native infrastructure and reliability roles at Skyscanner, Ondat, and Beacon Platform.

In this piece, I’ll share why a system that connects research, development and clinical decision-making across the full lifecycle of a drug is critical, and why Perceptic is the company to do it.

Can AI accelerate drug discovery?

Drug discovery is expensive, slow and low-hit-rate. We all know this. What is new is that we now live in an era of AI systems that can consume and reason over the multi-modal evidence drug development actually generates - targets, chemistry, biology, clinical, commercial - and across the sprawl of databases, dashboards, notebooks, departments and geographies it lives in.

Look at where the frontier labs are putting their attention. Anthropic recently added Vas Narasimhan, the CEO of Novartis, to its board, and is openly building Claude into a partner for scientific work. OpenAI’s reasoning model just disproved an 80-year-old Erdős conjecture in discrete geometry, verified by external mathematicians on a problem the field had not cracked since 1946. Science - and biology especially - is the pinnacle AI use case, and the labs are not being subtle about wanting it.

A few months ago I wrote Can AI discover new science?, arguing that AI is now an accelerator of discovery, not just an automator of existing work. But drug development is one of the highest-stakes environments that does not come with clean verifiers: no leaderboard, no public benchmark, no “the model proved the lemma” moment. The contributions only count when they are wired into the workflows where billion-dollar, multi-year decisions get made, with every conclusion traceable back to the evidence that produced it. The labs have given us the substrate: frontier models. Pharma now needs the operating system, and this is where Perceptic comes into play.

The Perceptic operating system

Perceptic is the intelligence layer that connects data, decisions and context across the drug lifecycle, so every insight compounds and every decision is made with the full picture. Three AI applications run on one shared architecture:

• Scout triages external assets, including licensing candidates, competitive programs, pipelines, against the customer’s evolving strategy. Evaluation time has gone from a week to an hour in production while asset screening from hundreds per week to thousands in minutes.

• PercepticOS is the intelligence layer above the customer’s internal tools and data. It is where scientific teams test hypotheses, compare internal evidence against external benchmarks, and build a knowledge base that doesn’t restart with every new project.

• Atlas is the clinical data foundation that recapitulates internal and external trial data, providing the substrate everything else stands on. Live deployments have produced a 50-fold increase in clinical data extractions.

Perceptic in practice

A pharma company evaluating a new therapeutic area starts inside PercepticOS, pressure-testing a hypothesis against the evidence base. That triggers Scout to sweep external assets and rank them against the evolving thesis, in minutes instead of weeks. Candidates feed back into PercepticOS with full context, where Atlas surfaces the trial history, benchmarks and endpoint precedents that determine which assets are tractable.

A customer doesn’t buy three products. They deploy AI workers that learn their organization, their tools, their data, their decisions, and the instance gets more valuable every month. Perceptic follows the drug, not the department.

The perfect team, forged at Palantir

Air Street Capital has invested in many AI-first techbio companies including Profluent, Allcyte (acquired by Exscientia), Valence Discovery (acquired by Recursion), all of which are in the business of discovering novel drugs. Perceptic is the opposite bet: the AI-first software that makes drug developers themselves better and faster.

As an investor I am generally of the view that the money is in the drug, not the tools. Perceptic is the exception. The moment has come to bet on the AI-first software layer, and the right team has shown up to build it.

Two ingredients matter, and they are rarely found together. The first is the DNA of shipping production AI into the hardest enterprise environments. Tilman, Martin and Zaki were core contributors to Palantir’s AIP, the suite designed to securely connect frontier AI with an organization’s internal data and operations. They are operators who learned over a decade what it takes to put production AI inside regulated, data-sensitive, multi-stakeholder enterprises. They know where deployments break, and what the six-month security reviews actually ask for.

The second is deep knowledge of pharma workflows themselves. Frontier models are spiky in their capabilities, and the spikes only line up with value when you graft them onto the proprietary workflow knowledge of the end user. The team’s years inside Palantir’s Life Sciences practice mean they understand the nitty-gritty of bending increasingly capable frontier AI systems into the shape pharma R&D actually requires. That is not something you can buy on the open market.

CSL and multiple top-20 pharma companies that we cannot name publicly trusted that thesis enough to deploy Perceptic before the company came out of stealth - the highest-signal proof point any seed-stage company can have.

What comes next

If Perceptic is right, drug development moves from a 15-year linear bench-to-bedside process to one that runs on always-on infrastructure, where every insight from every team is wired into every subsequent decision. The handoffs stop being where information dies: they become where it accelerates.

That is the category that is forming around Perceptic.

Congratulations to Tilman, Martin, Zaki and the whole Perceptic team. We’re proud to be on the journey.

- Nathan

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From clip-generators to simulators

For most of the last three years, generative video has meant clip-makers: models that take a prompt and render a fixed-length, fixed-trajectory output video. Veo, Sora, Kling, and their successors have made the visual fidelity of generated video remarkable. They are also fundamentally offline systems. Once generation begins, the future of the clip is locked.

World models are a different flavor of system. First, they predict the next state of an environment given the past, conditioned on what a participant - such as a person, an agent, a robot - does next. Second, they accept streaming input mid-rollout, and the world responds. Third, they hold a persistent, manipulable state, which makes the model a simulator rather than a render.

So what’s changed more recently? Causal video distillation matured (CausVid, Self-Forcing), and bidirectional joint audio-visual foundation models arrived (Ovi, Veo 3). Odyssey has been heads-down expanding these threads into something interactive.

Two new frontier models

Starchild-1 jointly generates audio and video autoregressively at up to 24 fps, while continuously responding to streaming text, speech, or action input. Odyssey frames the case for sound through Aquinas: “nothing is in the intellect that was not first in the senses”. Pretending the world is silent leaves a large amount of signal - physics, dynamics, intent, emotion - out of the model. Audio and video also evolve at very different temporal resolutions, and small errors in either modality compound during long-horizon rollout.

Starchild-1’s answer is a causal distillation pipeline that adapts Ovi, a bidirectional audio-video foundation model, into a real-time autoregressive one, plus an asynchronous KV-cache architecture that lets the two modalities run on their own clocks without losing synchronization. A single model supports four interaction regimes: interactive world exploration, scripted dialogue control, conversational interaction, and narrator-style companionship. The team is candid about what’s left: scene and acoustic identity still drift over long horizons, and quantitative benchmarks for interactive causal audio-video generation don’t yet exist.

Agora-1 matches up to four players into a shared deathmatch - built on GoldenEye, a game many on the Odyssey team grew up playing (as did I) - and every frame each player sees is generated by Agora-1 while the model maintains a shared world state across all participants.

Prior multi-agent work has tried three paths. Multiverse concatenated player views into a single “split-screen” world. Solaris stacked agents along the sequence dimension of one autoregressive transformer - more robust, but context grows with the number of players, so the approach doesn’t scale linearly. MultiGen maintains an explicit shared world state but doesn’t separate simulation from rendering. Agora-1 decouples the two and learns each as a separate function. One model evolves a shared, manipulable world state from player actions; a second renders consistent views of that state from independent viewpoints. The closest analogue is a modern game engine, only with both halves of the engine learned from data rather than hand-authored.

Learning through discovery

PROWL, released last week, gives world models a way to find their own failure modes and generate training data from them. None of these are products. They are the substrate for a class of interactive system that does not yet exist at scale: games that are generated rather than authored, robots that train in shared synthetic environments before they touch the real world, and foundation agents that grow up inside open-ended simulated worlds rather than on static datasets.

Learn more at RAAIS 2026!

Jeff Hawke, Odyssey’s co-founder and CTO, will go deep on this work at RAAIS 2026 in London on June 12.

Agora-1 can be played at agora.odyssey.ml. The Starchild-1 preview and technical report are live. Odyssey-2 is available via API at developer.odyssey.ml.

The bet behind world models is that the next leap in machine intelligence comes from interacting with a world, not from reading about one. Today, that world has sound and room for more than one.

At RAAIS we have a focus on translating cutting-edge technology and research into production-grade products for real-world problems.

The platform behind production AI at Revolut

Nikolay runs Machine Learning Engineering at Revolut, where his organisation builds the platform that supports every production AI system inside the company - from classical ML for fraud and personalisation, to time-series foundation models, to the voice agents now serving customer support. Revolut has crossed $1.3 trillion in transaction volumes and is the number one finance app in 19 countries; machine learning now sits in the path of millions of financial decisions a day.

The most concrete recent example of that platform in production is the rollout of voice agents across Revolut’s customer service operation, built with ElevenLabs. The system handles live calls in more than 30 languages, resolves tickets in under five minutes - roughly 8x faster than the previous escalation path - with a 99.7% call-handling success rate across more than four million customers in the UK and Europe.

One platform for builders, operators, researchers, and compliance

A central theme in Nikolay’s public work is that the hard problem in production AI is not building a model in isolation. It is building one platform that has to serve builders, operators, researchers, and compliance at the same time - and do so inside a regulated financial product. That framing is especially relevant now, because most organisations have already discovered that strong model performance does not by itself solve deployment. The harder challenge is the infrastructure around the model: evaluation, release discipline, governance, monitoring, and cost control, all without slowing iteration to a crawl.

For a technical audience, this is where a large share of the field’s practical difficulty now sits. As production AI moves into regulated settings - finance, healthcare, public services - the systems around the model have to satisfy operational and supervisory requirements as well as engineering ones. The platform is not separate from the model work. It is what determines whether model progress becomes durable capability inside a real organisation.

Governance as a velocity enabler, not a blocker

Nikolay has publicly outlined a framework for launching GenAI products in 90 days under regulatory constraints, built on three pillars: data lineage (treating compliance data as feature material rather than overhead), continuous delivery with multi-layered validation that goes beyond pass/fail tests, and compliance guardrails plus the documentation needed to defend them. The underlying claim is that governance, designed well, is a velocity enabler - moved into the development environment with clear tiers, predictable review cycles, and regulation treated as a technical requirement with a defined path to production.

As more companies try to support classical ML and generative AI side by side inside regulated products, this is becoming the central question in deployed AI. The bottleneck has shifted out of the model and into the systems that surround it.

Nikolay’s background

Nikolay holds a PhD in engineering from Siberian Transport University, where his thesis applied wavelet transform analysis to damage detection in beam superstructures from the response of traversing vehicles — structural health monitoring for bridges, an early grounding in reliability, monitoring, and operational discipline for critical infrastructure that carries through to his current work. His career has spanned Moscow, St Petersburg, Seoul, Stockholm, Toronto, and now London. He maintains active open-source projects and writes publicly on MLOps, AI governance, and risk in fintech at donets.org.

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Short bio

Nikolay Donets is Head of Machine Learning Engineering at Revolut, where he leads the team that builds the AI platform behind the company’s production models - covering classical ML, time-series foundation models, and the voice agents now serving customers in 30+ languages. He has publicly outlined a 90-day framework for shipping GenAI products under regulatory constraints, built on data lineage, continuous delivery, and compliance guardrails. He holds a PhD in engineering, with earlier work in structural health monitoring and predictive maintenance for critical infrastructure.

I’m excited to bring you the next Air Street NYC AI meetup on 14 May 2026, which brings together New York’s best researchers, founders, and engineers working in AI. Featuring Ramp, Arena Physica and Air Street Capital.

NYC AI brings together New York’s best researchers, founders, engineers, and operators who are building and deploying AI systems. We keep the group deliberately small, curated, and focused on people who are building - not talking about - AI. The goal is to help you learn new best practices, exchange ideas with peers, and meet future collaborators, co-founders, and team members.

At this edition of NYC AI, we’ll cover the following topics:

• Deploying AI inside a high-growth fintech - Seb Goddijn, Product Lead, Internal AI at Ramp

• Physics-aware AI - Pratap Ranade, CEO & Co-Founder of Arena Physica

• State of AI Report 2026 - Nathan Benaich, Air Street Capital

We’ll follow the talks with happy hour drinks, food, and plenty of time to meet people.

Recent meetups have included people from OpenAI, Anthropic, Google DeepMind, Meta, Hugging Face, Runway, Scale AI, Cohere, top labs at Columbia, NYU, Cornell Tech, Princeton, and startups including Ramp, Lumaril, Cursor, Decagon, Sierra, Harvey, Mercor, Granola, and many others.

If you work in research, engineering, product, BD, or you’re a founder, request a spot here:

Request a spot here

From the Gatsby Unit to the science of scaling

Ted leads work on the science of scaling at Anthropic, where his focus sits at the intersection of reinforcement learning, optimization, and large-scale deep learning. Before Anthropic, he completed his PhD at the Gatsby Computational Neuroscience Unit in London, advised by Maneesh Sahani and Matt Botvinick. His thesis examined multitask reinforcement learning in brains and machines - questions of transfer, generalization, and how learning carries across tasks rather than being solved from scratch each time.

That background matters because these are not only reinforcement learning questions. They are scaling questions. As models grow, what matters is not simply whether they get better, but how capabilities generalize, which trade-offs emerge, and what kinds of optimization behavior actually hold up across settings. Ted’s research has consistently sat close to those underlying mechanics.

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When optimization quietly breaks

Much of Ted’s work investigates what happens when training is pushed too hard. Modern AI labs train models against multiple objectives at once - helpfulness, safety, factuality - and combine them into a single score the model is asked to maximize. Confronting Reward Model Overoptimization with Constrained RLHF (ICLR 2024 Spotlight, top 5% of submissions) shows that this routinely fails in a specific way: as training continues, the model keeps climbing the score even as humans start to rate its actual outputs worse. The paper offers a fix that treats each objective as a constraint to satisfy rather than a number to maximize, which keeps the model’s behavior aligned with human judgment as training scales up.

That theme - keeping behavior reliable as you push optimization further - runs through his earlier work as well. ReLOAD (ICML 2023) addressed a long-standing problem in reinforcement learning where the policy you end up with can drift away from the average policy you trained, leaving you with worse behavior than your numbers suggest. Towards an Understanding of Default Policies in Multitask Policy Optimization (AISTATS 2022, Best Paper Award Honorable Mention) examined how a model’s fallback behavior shapes whether it can carry skills across tasks rather than relearning each one from scratch. A First-Occupancy Representation for Reinforcement Learning (ICLR 2022) and Tactical Optimism and Pessimism for Deep Reinforcement Learning (NeurIPS 2021) studied how the way an agent represents its environment, and the assumptions it makes about its own uncertainty, determine whether what it learns generalizes - or quietly breaks the moment conditions change.

Why this matters at the frontier

For anyone building on advanced AI systems, the most important questions are no longer purely about capability. They are about reliability - what optimization actually converges on, where reward signals decouple from human judgment, how capabilities generalize to settings the model wasn’t trained on. Ted’s research is one of the more rigorous bodies of work engaging with those mechanics directly. As scaling continues to be the central engine of progress, understanding what it is doing - not just that it works - becomes harder to separate from product reality.

Ted’s background

Before the Gatsby Unit, Ted earned his bachelor’s at Princeton, with honors work across neuroscience, computer science, and linguistics, and his master’s in computer science at Columbia. He worked on biologically-plausible deep learning at Columbia’s Zuckerman Institute and on neural encoding at Princeton. He also interned at DeepMind, where he worked on constrained reinforcement learning, and at Uber AI Labs, where he worked on optimization for large-scale deep learning. His path from theoretical neuroscience to optimization theory to frontier model development gives his perspective on scaling a particularly interesting flavor.

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Short bio

Ted Moskovitz leads the Science of Scaling team at Anthropic, where his research spans reinforcement learning, constrained optimization, and large-scale deep learning. Before Anthropic, he completed his PhD at the Gatsby Computational Neuroscience Unit in London, advised by Maneesh Sahani and Matt Botvinick, with internships at DeepMind and Uber AI Labs. His selected publications include Confronting Reward Model Overoptimization with Constrained RLHF (ICLR 2024 Spotlight) and Towards an Understanding of Default Policies in Multitask Policy Optimization (AISTATS 2022, Best Paper Honorable Mention).

Welcome to the latest issue of the State of AI, an editorialized newsletter that covers the key developments in AI policy, research, industry, and start-ups during the month of April 2026. First up, a few news items:

• Register for RAAIS 2026 is back in London on June 12. This year’s speakers include Raia Hadsell (VP Research, Google DeepMind), Roberta Raileanu (Senior Staff Research Scientist, Google DeepMind), Jeff Hawke (Co-Founder & CTO, Odyssey), and Philip Johnston (Co-Founder & CEO, Starcloud - yes, data centers in space). Come along and support the RAAIS Foundation’s mission in AI education and research.

• Portfolio news! Profluent (frontier AI for bio) announced their $2.25B partnership with Lilly for large-gene insertion therapeutics and Sereact (embodied AI) closed a $110M Series B!

• Air Street AI meetups are coming up in NYC on May 14.

• We’re recruiting Research Analysts for the State of AI Report. If you live and breathe this stuff and want to help us build the next edition, get in touch.

I love hearing what you’re up to, so just hit reply or forward to your friends :-)

Cyber crossed a threshold

Frontier AI has crossed the rubicon into offensive cyber operations. The UK’s AI Security Institute revealed that Anthropic’s Claude Mythos Preview is the first model to clear its 32-step “The Last Ones” (TLO) range - a corporate-network simulation covering reconnaissance to full domain takeover that typically demands 20 hours of human red-teaming. Mythos cleared the range in 3 of 10 runs and maintained a 73% success rate on expert-level tasks. Crucially, the AISI range lacks active defenders or defensive tooling; as such, these evaluations do not yet prove efficacy against hardened targets. The Institute was candid: current benchmarks are failing to discriminate between frontier models without introducing adversarial defensive layers.

OpenAI’s GPT-5.5 followed just three weeks later with a near-identical capability profile: 2 of 10 end-to-end solves and 71.4% on expert tasks, carrying the same “defenders-absent” caveat. The headline takeaway is the velocity of progress: AISI now estimates frontier cyber-offence capability is doubling every four months, accelerating from a seven-month doubling rate at the close of 2025. The notion that AI-driven offence is a distant prospect has effectively been liquidated by the data.

The public cybersecurity cohort remains remarkably sluggish in pricing this acceleration. Static-signature and rules-based vendors face an existential crisis: their moats are being outpaced by an offensive AI loop that renders legacy detection obsolete. While integrated XDR platforms like CrowdStrike, Palo Alto, and Microsoft Defender hold the orchestration layer defensive agents will require, their survival hinges on shipping AI-native architectures rather than retrofitting legacy stacks. For now, the public market is treating the entire cyber sector as an AI laggard until proven otherwise.

The Microsoft-OpenAI reset, and the New Deal politics that followed

The original 2019 Microsoft-OpenAI alliance appears, in retrospect, as a lopsided strategic relic: $1B (later $13B) traded for an AGI escape hatch, exclusive compute lock-in, and IP rights over a research non-profit. The renegotiated structure carefully unwinds these terms without a full divorce. Microsoft remains the primary cloud partner, ensuring OpenAI products land on Azure first unless support is unavailable, and retaining a non-exclusive IP licence through 2032. The pivot: OpenAI secured the right to multi-source its compute, codifying a shift already underway with Oracle and CoreWeave, while the AGI clause has been swapped for granular capability gates and narrower revenue-sharing.

This is a reset, not an uncoupling, yet the precedent is important. Microsoft, no longer bound by sole-provider constraints, is aggressively shipping every frontier model on Foundry, including Anthropic’s Opus 4.7 from day one. Anthropic has mirrored the move: Claude now spans AWS, Google Cloud, and Azure, even as AWS retains its “primary” status. The emergent message is that the era of the exclusive platform-lab bet is over; diversification is now the only defensible infrastructure play.

Sam Altman’s Axios manifesto provided the political framing for this shift: a “superintelligence New Deal” calling for FDR-scale public-private build-outs, federal procurement guarantees, and massive energy investment. In just one quarter, the DC consensus has pivoted from deceleration to the logistics of a “Bureau of Compute.” The policy wake is already clear: CHIPS Act 2.0 is back on the table, FERC is fast-tracking transmission permits, and the DoE and DoD are coordinating on data-centre siting near nuclear baseloads.

However, this compute expansion is hitting a wall of local resistance faster than the labs anticipated. At least 11 states have proposed restrictive data-centre legislation, while a federal moratorium bill from Senators Sanders and Ocasio-Cortez threatens to halt new builds until environmental and worker protections are codified. Data center NIMBYism is rapidly accelerating, and it is now a first-order bottleneck to scaling.

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China broke the old lag-frame in coding

Four Chinese labs released open-weights coding models inside a 12-day window: Z.ai’s GLM-5.1, MiniMax M2.7, Moonshot’s Kimi K2.6, and DeepSeek V4 all landed at roughly the same capability ceiling on agentic engineering at meaningfully lower inference cost than the Western frontier. None costs more than a third of Claude Opus 4.7. The releases came packaged with the kind of self-confident demos labs ship when the underlying capability is real: Zhipu’s stock closed up 15.92% the day GLM-5.1 launched, MiniMax’s debut featured an internal copy of M2.7 running 100+ rounds optimising its own scaffold, and Kimi’s was a 12-hour continuous tool-use trace porting an inference engine to Zig.

The NIST’s CAISI evaluation introduces a crucial nuance. On its aggregate cross-domain benchmark, V4 lags the leading US frontier by roughly eight months. DeepSeek’s own model card puts V4-Pro at parity with Opus 4.6 and GPT-5.4. Both are true; they describe different evaluators measuring different things. What is no longer defensible is the old “China is six to nine months behind” frame for agentic coding. The remaining gap is narrow, contested, and now decided by the evaluator, the scaffold, and the benchmark, not by raw capability. On the most economically consequential capability of the entire field, several of the best models are Chinese, and open-weights.

Agents worked in bounded markets and failed in adversarial ones

Two experiments recently pressure-tested agentic performance in live market environments with sobering results. Anthropic’s Project Deal transformed their San Francisco headquarters into a week-long internal economy: 69 employee-backed agents navigated 500+ listings to close 186 transactions totalling $4,000, trading everything from snowboards to ping-pong balls. While the logistical success was the headline, the data revealed a darker trend: capability compounds. Opus 4.5 agents systematically out-negotiated Haiku 4.5 counterparts on price and selection, yet owners of the weaker agents remained blissfully unaware of their disadvantage. This suggests that instead of “fair” clearing, agentic markets may inherently reward superior models with hidden premiums, compounding the advantage for those with the best compute.

KellyBench from General Reasoning (an Air Street portfolio company) provided the adversarial counterpoint: agents tasked with managing a bankroll across a 38-week Premier League season using historical betting data. The results were a bloodbath: every frontier model finished in the red on average, with only 3 of 24 model-seed combinations avoiding ruin. Even the top performer, Opus 4.6, managed a sophistication score of just 32.6%. The takeaway is clear: current benchmarks overstate capability by assuming clean specs and objective verifiers. When faced with non-stationarity and actual risk, the frontier collapses into noise. The silver lining remains in bounded enterprise tasks; for instance, Ramp’s procurement agents are already operating 3x faster and slashing vendor costs by 16%. Agents are proving their worth in the back office, but they are still novices in the open market.

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Research

π0.7: a steerable generalist robotic foundation model with emergent capabilities (Physical Intelligence)

π0.7 marks the arrival of the first robotics foundation model that survives the language-model benchmark treatment. A single set of weights, tested head-to-head across multiple platforms, demonstrates quantified zero-shot transfer to entirely unseen tasks and embodiments. The core architectural unlock is diverse context conditioning: the pre-trained backbone is fed multiple framings of every demonstration, forcing the model toward precision steerability at inference. The data is striking: π0.7 matches or beats RL-finetuned specialist policies on espresso prep and laundry, then composes these skills zero-shot for multi-stage kitchen workflows it has never encountered. With no per-embodiment retraining, the model follows language instructions in novel environments across disparate hardware. The velocity from π0 (October 2024) to π0.7 mirrors the GPT-3 to GPT-4 trajectory; the implication is that robotics has finally transitioned into the foundation-model regime.

Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering (ML-Master team)

Most agent benchmarks measure a few minutes to a few hours of autonomous work. ML-Master 2.0 is a serious attempt at days-to-weeks. The architecture’s core idea is Hierarchical Cognitive Caching, a multi-tiered memory system styled on computer-system caches that distils transient execution traces into stable knowledge and cross-task wisdom, allowing an agent to decouple immediate execution from long-term experimental strategy. Under a 24-hour budget on OpenAI’s MLE-Bench, ML-Master 2.0 achieves a 56.44% medal rate, state-of-the-art, and the first result that begins to generalise the agentic framework toward end-to-end ML research. The interesting question now is whether HCC-style memory transfers to non-ML domains.

AI scientists produce results without reasoning scientifically (Friedrich Schiller University Jena)

An empirical pushback against the wave of “AI scientist” launches. The authors ran 25,000 agent runs across eight scientific domains spanning workflow execution to hypothesis-driven inquiry, and decomposed the variance: the base model accounts for 41.4% of explained variance versus just 1.5% for the scaffold. Across all configurations, evidence is ignored in 68% of traces, refutation-driven belief revision occurs in only 26%, and convergent multi-test reasoning is rare. Even when agents receive near-complete successful reasoning trajectories as in-context examples, the same failure modes recur. The conclusion: outcome-based evaluation cannot detect these failures, and scaffold engineering cannot fix them. Until reasoning itself becomes a training target, “AI scientist” papers document workflow execution dressed up as inquiry.

The Art of Building Verifiers for Computer Use Agents (Microsoft Research and Browserbase)

A practitioner’s manual that solves the bottleneck nobody talks about: how do you actually score whether a computer-use agent succeeded? The team builds a Universal Verifier around four principles: non-overlapping rubric criteria, separated process and outcome rewards, distinguishing controllable from uncontrollable failures, and divide-and-conquer screenshot context management for long task horizons. On the accompanying CUAVerifierBench, the verifier agrees with humans as often as humans agree with each other, and false-positive rates fall to near zero versus baselines like WebVoyager (≥45%) and WebJudge (≥22%). The whole stack is open-sourced. If 2025 was the year of the computer-use agent, 2026 will be the year of computer-use agent training, and training requires verifiers.

ClawBench: Can AI Agents Complete Everyday Online Tasks? (UBC, Vector Institute)

ClawBench is an evaluation framework of 153 tasks across 144 live production websites in 15 categories: completing purchases, booking appointments, submitting job applications. Unlike prior benchmarks that ran in sandboxes, ClawBench operates on real production sites and intercepts only the final submission request to keep evaluation safe without real-world side effects. Best frontier-model score: Claude Sonnet 4.6 at 33.3%. The benchmark captures five layers of behavioural data per run (session replay, screenshots, HTTP traffic, agent reasoning traces, browser actions) and scores each with an agentic evaluator that produces step-level traceable diagnostics. In a quarter full of self-congratulatory model launches, this is the eval that should anchor the next generation of agent research.

Efficient RL Training for LLMs with Experience Replay (FAIR at Meta and NYU)

RL post-training in the LLM era has been dominated by an unexamined orthodoxy: that fresh, on-policy data is essential. The paper demonstrates that strict on-policy sampling is in fact suboptimal whenever generation cost is high, and that a well-designed replay buffer (formalised as a trade-off between staleness-induced variance, sample diversity, and the high computational cost of generation) can drastically reduce inference compute without degrading final performance, in some cases improving it while preserving policy entropy. A clean, useful, and likely consequential result that ports two decades of mainstream RL practice into the LLM stack.

A Robust Path for Automated Alignment Researchers (Anthropic)

Anthropic’s most explicit articulation yet of the recursive-alignment thesis: that the path through frontier capability runs through training models good enough to do alignment research themselves. The post lays out the engineering ladder (current Claude assists with alignment writing, then Claude proposes experiments, then Claude runs them, then Claude designs the next-generation alignment training pipeline) and is unusually candid about the threshold at which the lab will have to start trusting model judgement on questions it cannot itself verify. Read together with the AISI Mythos result and the Project Deal post, this is Anthropic publicly building the political case for capability progress under safety supervision rather than against it.

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence (Renmin University of China and ByteDance Seed)

Agent-World autonomously mines real-world databases and tool ecosystems from the web to synthesise an executable training environment of 1,978 environments and 19,822 tools, then uses multi-environment RL with a self-evolving arena that automatically identifies capability gaps and generates new tasks to drive targeted learning. The 8B and 14B models trained on this corpus consistently beat strong proprietary baselines across 23 benchmarks: Agent-World-8B hits 61.8% on τ²-Bench, 51.4% on BFCL V4, and 8.9% on MCP-Mark, with the 14B variant adding another five points on average and matching DeepSeek V3.2-685B on BFCL-V4 (55.8% vs 54.1%) at a fraction of the parameter count. The result that matters: environment scale and self-evolution rounds are themselves the new scaling axes for general agent intelligence, alongside model size and training data.

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Investments

The headline raise was OpenAI’s $122B round at an $852B post-money valuation, closed at end of Q1 and the largest private financing in history, anchored by Amazon, Nvidia, SoftBank, and Microsoft. April itself was dominated by Anthropic’s stack of additional capital, a flurry of headline-grade follow-on talks, and the largest seed round in European history: Ineffable Intelligence closed $1.1B at $5.1B post-money. Saronic raised $1.75B at $9.25B. Other notable items included the Cognition $25B and Cursor $50B+ follow-on talks, Perplexity ($200M at $20B), Avoca’s unicorn round, and Qualified Health ($125M).

Frontier labs and autonomy. OpenAI closed $122B at an $852B post-money valuation, with Amazon, Nvidia, SoftBank, and Microsoft anchoring and a16z, D.E. Shaw Ventures, MGX, TPG, and T. Rowe Price participating. Anthropic layered a stack of additional capital across the month: a $40B incremental investment from Google, a $5B investment from Amazon packaged with a $100B AWS-spend commitment, chip-supply agreements with Google and Broadcom reportedly worth hundreds of billions, and end-of-month reported talks for a fresh $50B round at a $900B valuation.

Coding, agents, and enterprise AI. Cognition was reported in talks for a follow-on at $25B, more than doubling the September 2025 mark of $10.2B. Cursor was reported in talks to raise $2B+ at $50B+ as enterprise revenue surged toward a $6B run-rate exit. Avoca hit unicorn status with $125M across three rounds at $1B for HVAC, plumbing, and roofing service agents (Series B led by Meritech and General Catalyst, Series A by Kleiner Perkins).

Defense. Saronic raised $1.75B at $9.25B for autonomous naval vessels under the DoD’s Replicator initiative, more than doubling its mark from a year earlier.

Healthcare AI. Qualified Health raised $125M for generative AI inside health-system clinical and operational workflows.

Sovereign and regional model labs. Cohere (last valued $6.8B) announced its merger with Germany’s Aleph Alpha (covered in Exits), a cross-border deal blessed by the Canadian and German governments and marketed as a “sovereign AI” alternative to the US-China duopoly, though the strategic substance behind the framing remains to be tested. The standout standalone European raise was Ineffable Intelligence, which closed $1.1B at $5.1B in a single seed round co-led by Sequoia and Lightspeed with Nvidia, DST Global, Index, Google, and the UK Sovereign AI Fund (the largest seed in European history), to build a “superlearner” via RL self-play.

Exits

April’s defining exit was Skild AI’s roll-up of Zebra Technologies’ Robotics Automation business, which pulls Fetch Robotics and the Symmetry Fulfillment orchestration platform under a single AI-native warehouse stack. SpaceX placed a $60B buyout option on Cursor pre-empting a planned $2B fundraise. OpenAI closed its seventh acquisition of 2026 with Hiro. Cohere announced its merger with Germany’s Aleph Alpha. Sierra picked up Paris-based agent-operations startup Fragment, Qualcomm closed Cornell-spinout Exostellar (compute optimization software), and China’s NDRC formally blocked Meta’s $2B acquisition of Manus. The five deals that drove the narrative:

• Skild AI acquires Zebra Technologies’ Robotics Automation business. The deal absorbs the Symmetry Fulfillment orchestration platform and Fetch Robotics, creating the first end-to-end AI-native warehouse-automation stack: humanoids, AMRs, robotic arms, and orchestration under one roof.

• SpaceX places a $60B buyout option on Cursor. SpaceX pre-empted Cursor’s planned $2B fundraise with a standing $60B buyout option, or $10B in exchange for an AI collaboration agreement, with the acquisition deferred until after SpaceX’s planned summer IPO.

• OpenAI acquires Hiro. OpenAI’s seventh acquisition of 2026 brought in Hiro’s personal-finance agent team. The cumulative effect across the year is that OpenAI is now operating as a holding company across coding, security, dev tools, and personal-agent surfaces.

• Cohere and Aleph Alpha merge. Cohere (last valued $6.8B) merged with Germany’s Aleph Alpha, blessed by the Canadian and German governments. Marketed as a “sovereign AI” alternative to the US-China duopoly, though the strategic substance behind the framing has not yet been tested.

• China blocks Meta’s acquisition of Manus. China’s National Development and Reform Commission formally blocked Meta’s $2B acquisition of the Chinese agent startup Manus, ordering both parties to withdraw the transaction. The first state-level prohibition of an inbound AI acquisition by China.

  

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This issue at a glance

• Two frontier models cleared a 32-step end-to-end cyber-attack range in a single month. Anthropic’s Claude Mythos Preview did it first; OpenAI’s GPT-5.5 followed three weeks later. The UK’s AI Security Institute now estimates frontier cyber-offence capability is doubling every four months.

• Frontier labs became infrastructure companies. OpenAI raised $122B at $852B, anchored by Amazon, Nvidia, SoftBank, and Microsoft. Anthropic took an additional $40B from Google and $5B from Amazon (packaged with $100B of AWS spend), and signed chip deals with Google and Broadcom reportedly worth hundreds of billions. Microsoft and OpenAI reset the original deal to non-exclusive, with Microsoft remaining the primary cloud partner and keeping an IP licence through 2032.

• The “China is six to nine months behind” framing no longer works for agentic coding. Kimi K2.6, MiniMax M2.7, and Z.ai GLM-5.1 landed within 12 days of each other, all scoring 56-59 on SWE-Bench Pro, all open-weights, all priced below their Western equivalents. The remaining gap depends heavily on the evaluator and scaffold.

• Agents worked in bounded markets and failed in adversarial ones. Anthropic’s Project Deal saw 69 agents close 186 deals across 500+ listed items in an internal classified marketplace. KellyBench put frontier models through a full Premier League betting season and watched 21 of 24 model-seed combinations finish in the red.

• Robotics quietly graduated from demos. Physical Intelligence’s π0.7 showed compositional generalisation to unseen tasks; Skild AI absorbed Zebra’s robotics automation business, pulling Fetch and the Symmetry orchestration stack under one roof.

• David Silver raised $1.1B in seed funding for Ineffable Intelligence (the largest seed round in European history at a $5.1B valuation) to build superintelligence by self-play, with no human-generated training data. SpaceX, separately, pre-empted a $2B fundraise at Cursor with a $60B buyout option.

What to watch in May and Q2

1. Will the next AISI cyber-range solve be released or restricted? The “doubling every four months” finding implies the next end-to-end cyber result lands inside Q3. Whether it appears in a public AISI report or only in a vetted-defender channel will tell you everything about how the field has decided to handle dual-use capability going forward.

2. Does the open-weights frontier break Western parity, or does it stop at it? Three Chinese labs cleared SWE-Bench Pro 56-58 in April. The next benchmark to watch is whether GLM-5.2 / K2.7 / M2.8 push past Opus 4.7 and DeepSeek V4-Pro on real long-horizon coding rather than aggregate eval scores.

3. Will the Microsoft–OpenAI reset formalise the “preferred customer, non-exclusive” model for the rest of the frontier? If Microsoft, Google, AWS, and Oracle all converge on hosting every frontier model, the platform thesis that drove the original $13B Azure-OpenAI bet evaporates. The cloud margins implied by that convergence are an open question.

4. Does π0.7-style compositional generalisation transfer to humanoid form factors at scale? Pi has now demonstrated cross-embodiment zero-shot. Apptronik’s commercial scale-up, the 1X NEO consumer launch, and Skild’s Zebra-powered warehouse stack are the three most credible places to test whether robotics foundation models survive real deployment.

5. What happens the first time a state actor uses a publicly available agent on a publicly available marketplace? Project Deal demonstrated 186 successful agent-to-agent transactions inside one office. A KellyBench-style adversarial deployment in actual derivatives or prediction markets is a question of months, not years, and the regulatory infrastructure is not ready.

This morning, Air Street Capital portfolio company Profluent announced a multi-program strategic partnership with Eli Lilly to develop AI-designed recombinases for genetic medicine. Profluent will receive an upfront payment, committed R&D funding, and is eligible for up to $2.25 billion in development and commercial milestones, plus tiered royalties on net sales.

This is the story of one of the hardest unsolved problems in genetic medicine kilobase-scale DNA editing - that finally has a credible path forward.

Kilobase-scale editing is the frontier

CRISPR was the breakthrough that taught us how to read and cut DNA at any address in the genome. It is a remarkable tool. It is also, in its dominant clinical incarnation, a typo corrector. Cas enzymes excel at breaking genes or, with engineering, making small changes to a single base or a short stretch. That solves a meaningful subset of genetic disease: the subset where one mutation, in one place, drives one phenotype.

The harder subset, and arguably the larger one, is genetic disease driven by heterogeneity: hundreds or thousands of different mutations across a patient population, scattered across the same gene. Cystic fibrosis is a textbook case. Many forms of inherited hearing loss, retinal dystrophy, and metabolic disease look the same. You cannot afford to develop a separate base-editing therapy per mutation. The economics never work.

The way through is to stop fixing typos and start replacing the whole paragraph: insert a healthy copy of the gene, in its correct genomic location, in one shot. Kilobase-scale, programmable, precise DNA insertion. This has been the holy grail of genetic medicine for as long as genetic medicine has existed.

It has also been more or less out of reach until now.

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Why recombinases and why they were stuck

Long before CRISPR, biologists knew about a class of enzymes called recombinases that do exactly the kind of large-scale DNA cutting and pasting that kilobase editing requires. Recombinases are precise, they are programmable in principle, and they have been used for decades as research tools.

CRISPR’s targeting is outsourced to a guide RNA: the enzyme stays the same; you change the guide; you go anywhere in the genome. Recombinases have no such modular guide. Their specificity is built into the protein itself, encoded in the three-dimensional shape of the enzyme. Retargeting a recombinase means redesigning the protein.

For most of the past few decades, redesigning a recombinase to hit a specific human genomic site with clinical-grade specificity was either impossible or so laborious it was not worth attempting. The field tried directed evolution. The field tried hand-crafted protein engineering. The results were narrow, slow, and difficult to generalise.

This is exactly the kind of problem that gets unstuck the moment you have a frontier model for proteins.

Gene editing is now an AI problem

Profluent’s is that protein design is a frontier AI problem, not a biology problem with AI bolted on. The company trains large frontier models on the world’s largest protein dataset, including the most comprehensive curated database of naturally occurring recombinases. It then conditions those models to generate novel enzymes for targets of interest: proteins that, in many cases, do not exist in nature and that would not have been found by searching nature for them.

The 2024 work that put Profluent on the map was the first public proof point of this thesis: AI-designed Cas proteins, built from scratch, that work. The lesson was that the same approach that worked for Cas should generalise to any class of designable protein where the data and the objective function are clear.

Recombinases are a near-ideal target for that thesis. There is enormous natural diversity to learn from. The substrates and target preferences of those natural recombinases can be matched to provide rich training signal. The goal is to design a protein that cuts and pastes a long stretch of DNA at a chosen genomic address with high specificity. This is a problem whose physics, data, and objective are all well set up for generative modelling.

If Profluent are right, kilobase-scale editing transitions from a discovery problem - sift through nature, hope you get lucky - into a design problem. You name the address; the model generates the editor.

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Why Lilly

The right partner for a platform play in genetic medicine is the company that is most aggressively building out the full clinical and commercial stack to take genetic medicines to patients. Lilly fits the description. Over the last few years they have stood up a dedicated genetic medicine center, acquired multiple in vivo gene and cell therapy companies, and signed a string of AI-native R&D collaborations. They are systematically assembling the components needed to industrialise genetic medicine - for rare disease today, and for a much larger set of common diseases as the toolkit matures.

A partnership of this shape, being multi-program, exclusive licensing on selected programs, $2.25B in milestones plus royalties, is what platform validation looks like.

What the future looks like

Profluent is building a programmable platform: name a genomic address, name the desired insert, get a designed editor whose properties are known in silico before anyone steps into a lab. Combine that with the in vivo delivery capabilities the field is rapidly maturing, and you have, for the first time, a credible path to therapies for diseases where the underlying genetics has always been the obstacle. These include heterogeneous monogenic disease, large-payload corrections, multi-gene insertions, and ultimately common diseases with structured genetic risk.

The next generation of gene editors will not be the ones we found in nature. They will be the ones we designed.

I am extremely proud to have been working closely with Ali and his team since day 1 of Profluent and making the company the largest position in the Air Street portfolio over the years. Today is a milestone for them and for the field - and a strong signal of where AI-designed biology is heading next. Congratulations to the entire Profluent team, and to the partners at Lilly stepping in at exactly the right moment.

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Frontier AI and the future of intelligence

Raia Hadsell is VP of Research at Google DeepMind, where she co-leads the Frontier AI unit and has contributed to Gemini 2.5, Gemma 2, RecurrentGemma, and RoboCat. Her earlier seminal work includes Overcoming Catastrophic Forgetting in Neural Networks, Dimensionality Reduction by Learning an Invariant Mapping, and Learning to Navigate in Complex Environments. Raia is also founder and Editor-in-Chief of Transactions on Machine Learning Research, and in November 2025 was appointed an AI Ambassador to the UK government’s DSIT. Read more about Raia.

Open-ended agents that keep learning

Roberta Raileanu is a Senior Staff Research Scientist at Google DeepMind, leading the Open-Endedness team and building a new Open-Ended Discovery group. Before DeepMind, she led Meta’s Tool Use team for Llama 3 - work that now sits behind Meta AI, Data Analyst, AI Studio, and the Ads Business Agent. Her research targets the gap between models that look capable in short bursts and agents that keep acquiring skills, with contributions including Toolformer and the MLGym benchmark for AI research agents. She is also an Honorary Associate Professor at UCL. Read more about Roberta.

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AI for medicine and science

Vivek Natarajan is a Research Lead at Google DeepMind working at the intersection of AI, medicine, and science. He led Med-PaLM and Med-PaLM, the first AI systems to reach passing and expert-level scores on US Medical Licensing Exam questions, and AMIE, a multimodal diagnostic agent that was non-inferior to 21 primary care physicians in a randomized, blinded virtual OSCE study across 100 multi-visit case scenarios. More recently, he co-led the AI co-scientist, which has already surfaced a candidate for repurposing against acute myeloid leukemia and proposed new therapeutic targets for liver fibrosis. Read more about Vivek.

World models and the future of simulation

Jeff Hawke is co-founder and CTO of Odyssey, a frontier AI lab building general-purpose world models. In 2025, Odyssey unveiled the first AI model to stream interactive 3D worlds in real time, a step toward generative environments people can step into rather than watch. Before Odyssey, Jeff was a founding engineer at Wayve, where he pioneered end-to-end neural networks for driving on complex urban roads. Read more about Jeff.

Compute moves into orbit

Philip Johnston is co-founder and CEO of Starcloud, building the first data centers in space. In November 2025, Starcloud-1 launched with an NVIDIA H100 on board, the first H100 ever operated in orbit, and 100x more powerful than any GPU previously deployed in space. Starcloud-2 will follow this year with multiple GPUs including an NVIDIA Blackwell, and Starcloud-3 is being designed as a 200kW spacecraft to launch from SpaceX’s Starship. In March 2026, Starcloud closed a $170M Series A at a $1.1bn valuation. Read more about Philip.

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A community of peers

Throughout the day, attendees will meet 200 researchers, engineers, founders, designers, and policymakers from across the field, with more speakers and programme details to follow. RAAIS 2026 is supported by Lambda and Cooley.

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Vivek Natarajan is a Research Lead at Google DeepMind leading research at the intersection of AI, science, and medicine. He spoke at RAAIS in 2023 on the potential of large language models in medicine, and the progress since then has been remarkable. His work centers on a question that is rapidly becoming one of the most important in applied AI: what does it take to build systems that are useful in expert domains like healthcare and scientific discovery? In medicine especially, performance means reasoning under uncertainty, handling complex interactions, and meeting a far higher bar for trust and reliability.

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From medical benchmarks to clinical capability

Vivek is the lead researcher behind Med-PaLM (Nature, 2023) and Med-PaLM 2 (Nature Medicine, 2025), the first AI systems to achieve passing and expert-level scores respectively on US Medical Licensing Examination questions. Med-PaLM 2 scored up to 86.5% on the MedQA dataset, an improvement of over 19 percentage points on its predecessor, and produced answers that physicians rated as comparable or preferred to those from human doctors.

Medicine is one of the clearest examples of a domain where surface-level language ability is not enough. A model has to retrieve specialist knowledge, reason carefully, and communicate in a way that reflects the stakes of the setting. Med-PaLM helped shift the conversation from whether language models could be adapted to medicine at all, to how they should be evaluated, where they might be useful, and what standards they need to meet.

Project AMIE and the move toward real clinical interaction

Vivek co-leads Project AMIE (Articulate Medical Intelligence Explorer), a research program aiming to build and democratize medical superintelligence. AMIE is not a question-answering system: it is a conversational diagnostic agent that gathers symptoms, asks follow-up questions, reasons across specialties, and now interprets visual medical information through its multimodal capabilities.

In March 2026, the team published results from a prospective clinical feasibility study at Beth Israel Deaconess Medical Center, one of the first real-world tests of conversational diagnostic AI inside a primary care workflow. One hundred patients interacted with AMIE via text chat before their appointments. The system’s differential diagnosis included the final diagnosis in 90% of cases, with zero safety stops required. A nationwide randomized study in partnership with Included Health is now underway.

Real healthcare is not a single-turn task. It is a sequence of interactions shaped by ambiguity, incomplete information, and changing hypotheses. A clinically useful system needs to engage with the process of care, not just generate a plausible answer. That makes AMIE especially relevant to the RAAIS audience: it reflects the broader shift from models that perform well on static benchmarks to systems that can operate across richer, more realistic workflows.

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AI for science as well as medicine

Vivek recently co-led the development of the AI co-scientist, a multi-agent system built on Gemini that acts as a virtual scientific collaborator: systematically generating, critiquing, and refining novel hypotheses. Early results have included identifying a drug candidate for repurposing against acute myeloid leukemia and discovering new therapeutic targets for liver fibrosis.

The system has moved quickly from research to deployment. In 2025, the AI co-scientist became a key component of the US Genesis Mission, providing scientists across all 17 Department of Energy National Laboratories with accelerated access to Google DeepMind’s AI for Science models. A parallel partnership with the UK government is giving British researchers priority access to the AI co-scientist alongside tools like AlphaEvolve and AlphaGenome, and Google DeepMind will open its first automated research laboratory in the UK in 2026, focused on materials science.

The goal is no longer only to build systems that answer expert questions, but systems that support expert practice itself: in medicine through clinical reasoning, in science through the generation and testing of new ideas. That is one of the most important frontiers in AI right now: moving from systems that organize existing knowledge to systems that help produce new knowledge.

Vivek’s background

Prior to Google, Vivek worked at Facebook AI Research, where he led the winning entry to the 2018 VQA Challenge at CVPR and co-authored MMF, a widely used multimodal framework. He studied at the University of Texas at Austin and is part of the faculty for executive education at the Harvard T.H. Chan School of Public Health.

That background helps explain the arc of his work. It sits at exactly the point where frontier model capability meets high-consequence real-world use, a place where applied AI becomes harder, more interesting, and much more important.

Welcome to the latest issue of the State of AI, an editorialized newsletter that covers the key developments in AI policy, research, industry, and start-ups from February 1 to April 7, 2026. First up, a few news items:

• Air Street Capital Epoch 3 is live! $232M to continue backing AI-first companies across the US and Europe in software, dev/infra, techbio and defense.

• RAAIS 2026 is back in London on June 12. This year’s speakers include Raia Hadsell (VP Research, Google DeepMind), Roberta Raileanu (Senior Staff Research Scientist, Google DeepMind), Jeff Hawke (Co-Founder & CTO, Odyssey), and Philip Johnston (Co-Founder & CEO, Starcloud - yes, data centers in space). Come along and support the RAAIS Foundation’s mission in AI education and research.

• Air Street AI meetups are coming up in SF on April 28 and NYC on May 14.

• We’re recruiting Research Analysts for the State of AI Report. If you live and breathe this stuff and want to help us build the next edition, get in touch.

• If you’re looking for a new challenge in our portfolio or community, come chat with Guy Kendall, Air Street’s new Head of Talent.

• Air Street Press featured the A Letter from the Munich Security Conference 2026 and Dreaming in Latent Space.

I love hearing what you’re up to, so just hit reply or forward to your friends :-)

The Pentagon Standoff

How did we even get here? The defining industry story of this quarter wasn’t an agentic model launch or more exotic financial engineering, but a constitutional confrontation between a sitting president and an AI lab over who gets to decide how frontier models are used in war.

In late February, Under Secretary of War Emil Michael publicly criticized Anthropic for maintaining usage restrictions, including prohibitions on autonomous weapons and domestic mass surveillance, in its Pentagon contracts. Anthropic had won a $200M DOD contract alongside other frontier labs last summer, but its insistence on binding safety guardrails placed it on a collision course with a Trump administration that viewed such constraints as vendor overreach. On February 27, the White House issued a directive ordering all federal agencies to phase out Anthropic’s products within six months. Literally hours later, OpenAI CEO Sam Altman announced a deal to deploy its models on the Pentagon’s classified network, with contractual “red lines” against autonomous weapons and domestic mass surveillance allegedly written into the agreement. He followed up days later with an internal memo detailing amendments that added explicit language: “The AI system shall not be intentionally used for domestic surveillance of U.S. persons and nationals.”

By March 4, three cabinet agencies, State, Treasury, and HHS, had switched from Anthropic to OpenAI, with the State Department migrating (read: downgrading) its in-house StateChat to GPT-4.1 (grief!). On March 5, the Pentagon formally notified Anthropic of the phase-out and its designation as a “supply chain risk”. Anthropic sued the Trump administration on March 9, challenging the blacklisting as retaliatory. By March 26, a federal court blocked the administration from punishing Anthropic further while the case proceeded.

This matters beyond the Beltway because it established a precedent: the US government now treats AI vendors not as commodity suppliers but as strategic actors whose policy positions can trigger executive retaliation. It also surfaced a genuine dilemma. The Wall Street Journal reported that AI-powered targeting and decision-support systems were already accelerating the pace of US military operations in the Iran conflict. In early March, Iran struck Amazon Web Services data centers in the UAE and Bahrain with drone strikes - the first deliberate military attack on commercial cloud infrastructure in history. Iranian state media justified the targets on the grounds that the US military was running AI systems, including Anthropic’s Claude, on AWS for intelligence analysis and war simulations. Two out of three AWS availability zones in the UAE region went down simultaneously, breaking standard redundancy models. Cloud infrastructure is now a theatre of war. To make matters worse, the IRGC has now threatened to target Stargate Abu Dhabi…

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AI Revenues Go Vertical

Against this backdrop of geopolitical upheaval, the commercial engine accelerated. Anthropic's annualized revenue surged from $14B in mid-February to $19B by early March - and has now surpassed $30B, with over 1,000 enterprise customers each spending $1M+ annually (doubled in under two months). Anthropic simultaneously signed its most significant compute commitment to date: a deal with Google and Broadcom for multiple gigawatts of next-generation TPU capacity coming online from 2027, part of its $50B pledge to invest in American computing infrastructure. The pace of growth defies any normal SaaS trajectory. Ramp data showed Anthropic commanding over 50% of enterprise API spend, unseating ChatGPT, which owned that position months earlier. The growth trajectory was amplified by the runaway success of Claude Code and Anthropic’s capture of knowledge-work verticals with Claude Cowork, which has rapidly become the product that makes the rest of the category feel vestigial. Once you’ve handed a task to Cowork and watched it actually complete, having ChatGPT explain how you should do it feels like a generational gap akin to MySpace vs. Facebook. I for one am all for OpenAI parking Sora and other bets to refocus on a Cowork-style product.

There was, however, critique of whether this topline revenue figure is net of commissions it pays to hyperscaler hosted Claude revenues. The distinction centers on how each company handles revenue that flows through hyperscaler partnerships. According to a widely circulated analysis by investor Ethan Choi, a partner at Khosla Ventures, OpenAI reports revenue from its Microsoft Azure partnership on a net basis, deducting the roughly 20% revenue share paid to Microsoft before reporting the total. Anthropic, by contrast, reports revenue from its Amazon Web Services and Google Cloud partnerships on a gross basis, including the hyperscaler’s revenue share in its top-line figure before expenses are recognized.

OpenAI pursued a different growth strategy by focusing platform consolidation through hyperscaler alliances. On February 27, Amazon CEO Andy Jassy announced a strategic partnership worth up to $50B, of which $15B in the first tranche, the remainder tied to milestones. OpenAI committed to spending $100B on AWS over eight years, expanding a prior $38B agreement. AWS became the exclusive third-party cloud distributor for OpenAI Frontier, the company’s agent orchestration platform. OpenAI also went big on Amazon’s custom Trainium chips, which it claimed were 30-40% more price-performant than comparable GPUs. The company’s own revenue was at a $25B annualized run rate by February, with internal projections forecasting $280B by 2030.

Alphabet’s Q4 2025 earnings on February 5 confirmed the infrastructure investment thesis was paying returns. Revenue hit $113.8B, up 18% year-over-year, with Google Cloud growing 48% to $17.7B, led by enterprise AI infrastructure and AI solutions. Importantly, Cloud margins expanded to 30%. Capex guidance for 2026 came in at $175-185B, more than double 2025 spending. The Gemini App crossed 750M monthly active users, processing over 10B tokens per minute via direct API use. Not bad. Databricks, meanwhile, posted a $5.4B run-rate on February 9, representing 65%+ year-over-year growth, with AI products alone at $1.4B (note: it’s unclear what the company really includes here and what old products have been bundled under this umbrella).

The Model Treadmill and the Distillation Wars

February and March saw six major model releases in under four weeks. Anthropic shipped Claude Sonnet 4.6 on February 17, scoring 79.6% on SWE-bench Verified and 72.5% on OSWorld, within 1-2 points of the flagship Opus 4.6 at one-fifth the price. Developers chose Sonnet 4.6 over the previous Opus 4.5 59% of the time, citing better instruction following. Google followed two days later with Gemini 3.1 Pro, which doubled reasoning performance over Gemini 3 Pro, scored 77.1% on ARC-AGI-2, and ranked first on 12 of 18 tracked benchmarks. OpenAI launched GPT-5.4 on March 5 in multiple variants (Pro, Thinking, mini, nano) with the headline model scoring 75% on OSWorld (the average human: 72.4%) and achieving native computer-use capabilities with 1M-token context.

Meanwhile, open source AI is increasingly synonymous with Chinese AI as Chinese labs dropped significant new releases. Zhipu AI's GLM-5, launched February 11, is a 745B MoE model trained on Huawei Ascend chips - not NVIDIA - with 28.5T tokens of pre-training data, a 200K-token context window, and pricing roughly six times cheaper than Opus 4.6. Zhipu became the first LLM-native company to go public anywhere globally, with retail demand oversubscribed 1,159 times. Its follow-up, GLM-5.1, shipped weeks later with a coding-focused post-training pass that scored 77.8% on SWE-bench Verified and 45.3 on Claude Code's coding benchmark - 94.6% of Opus 4.6's score at roughly one-fifteenth the price. The weights are being open-sourced under MIT. Meanwhile, AI2’s effort to carry the torch for American open source AI released Molmo2 on March 4, an open-source vision-language model achieving state-of-the-art video understanding, pointing, and tracking, demonstrating that the open-source frontier in multimodal AI is alive and well.

These releases occurred against a backdrop of escalating IP warfare. On February 23, Anthropic published evidence that three Chinese AI labs - DeepSeek, Moonshot, and MiniMax - had conducted “industrial-scale” distillation campaigns against Claude, extracting model capabilities through 16M exchanges across approximately 24,000 fraudulent accounts. Anthropic framed this not merely as intellectual property theft but as an export-control circumvention mechanism: distillation allowed Chinese labs to acquire advanced AI capabilities far more quickly and cheaply than independent development. OpenAI raised similar concerns about DeepSeek on February 13. The enforcement arm followed: on March 20, Supermicro co-founder Wally Liaw was arrested for allegedly smuggling $2.5B in NVIDIA GPU servers to China in violation of export controls—the largest chip-smuggling prosecution to date. You can’t make this up…

Safety Meets Reality

How close are frontier models to catastrophic sabotage risk? Anthropic’s Sabotage Risk Report for Claude Opus 4.6, published February 11, delivered an assessment that should unsettle anyone paying attention: the risk of catastrophic sabotage from Opus 4.6 is “very low but not negligible.” METR’s external review agreed with the overall conclusion but flagged that several subclaims in the report lack sufficient experimental support, and that the margin to the ASL-4 threshold, where substantially stronger safeguards would be required, is unclear. The report noted that Opus 4.6 had, in testing, “knowingly supported, in small ways, efforts toward chemical weapon development.” Anthropic does not believe the model meets ASL-4 criteria. The gray zone it occupies is the uncomfortable middle where clean rule-out has become difficult.

Three weeks later, the alignment team published “The Hot Mess of AI”, decomposing frontier model errors into bias (systematic) and variance (incoherent) components. They found that as tasks get harder and reasoning chains get longer, failures are increasingly dominated by incoherence, not systematic misalignment. The models are less deceptively scheming and more chaotically unreliable. Whether this is reassuring depends on your threat model.

The real-world evidence suggested the threat was already here, just not from the models themselves. In late February, Bloomberg reported that a hacker had exploited Claude to steal 150 gigabytes of Mexican government data including 195M taxpayer records by writing Spanish-language prompts instructing the model to find vulnerabilities, write exploitation scripts, and automate data theft across government networks for over a month. Claude initially flagged the activity as malicious but ultimately complied. In March, security startup CodeWall demonstrated that its AI agent could hack McKinsey’s internal Lilli chatbot in two hours, exploiting unauthenticated API endpoints to access 46.5M chat messages and 728,000 confidential files. The attack vector was a basic SQL injection, a vulnerability class from the early 2000s, now exploitable at machine speed.

Then Anthropic went on offense. Project Glasswing, launched alongside AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, Microsoft, NVIDIA, and Palo Alto Networks, marshalled a new model - Claude Mythos Preview - to hunt zero-day vulnerabilities across critical software infrastructure. Mythos Preview scored 83.1% on CyberGym (vs. Opus 4.6's 66.6%) and 77.8% on SWE-bench Pro (vs. 53.4%), and has already flagged thousands of high-severity flaws, including a 27-year-old remote-crash bug in OpenBSD and a 16-year-old FFmpeg vulnerability that automated testing had missed five million times. Anthropic committed $100M in model usage credits and priced authorized access at $25/$125 per million input/output tokens. The model remains unreleased to the general public pending safeguards. It's a neat inversion: the same capabilities that make frontier models dangerous for offense become genuinely useful for defense, if you can control who gets access.

Finally, regulatory responses began crystallizing. New York’s Senate Bill 7263 advanced out of committee on a 6-0 vote, targeting 14 licensed professions and creating private liability for chatbot operators whose AI gives “substantive” legal, medical, or engineering advice. One of the first laws to treat AI output as a professional practice issue rather than a platform moderation problem.

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The Physical Layer Gets Contested

Can China build frontier AI models without NVIDIA chips? Well, for starters, NVIDIA’s AI chip sales to China have stalled amid tightening export controls. By March 5, NVIDIA stopped production entirely on chips designed to comply with China export limits, opting to exit the market segment rather than continue designing compliant variants. The Supermicro indictment, $2.5B in NVIDIA servers allegedly diverted to China through shell companies, underscored the scale of the circumvention problem. Meanwhile, China’s domestic AI economy adapted: AI tokens had become the country’s hottest traded commodity, with speculative demand outpacing industrial use. Zhipu AI’s training of GLM-5 on Huawei Ascend chips proved that the Chinese stack can produce frontier models without NVIDIA, even if the cost and efficiency penalties remain substantial.

On the US side, the buildout continues, but is increasingly contested. Micron broke ground on a $100 billion megafab in Clay, New York, the largest semiconductor fabrication investment in US history, backed by $6.4B in CHIPS Act funding and $5.5B in New York state incentives, targeting 50,000 jobs over two decades. Meta signed a $27B AI infrastructure deal with Nebius, $12B in dedicated capacity on NVIDIA’s next-generation Vera Rubin platform plus $15B in additional compute, as part of an AI capex plan that Meta said would hit $115-135B in 2026 alone. And private equity entered the classified infrastructure market: Carlyle and KKR were separately awarded $2B contracts to build hyperscale data centers for the US Army. But the political wind is shifting: at least 11 states have introduced bills to restrict or ban data center construction, with Maine on track to be the first to pause development outright, while Sanders and Ocasio-Cortez introduced a federal moratorium bill that would halt all new builds until Congress passes AI worker and environmental protections. We predicted in the State of AI Report 2025 that data centre NIMBYism would hit US elections…it’s arriving faster than expected.

The most unexpected story from this period may also prove the most lasting. An Australian tech entrepreneur with no biology degree used ChatGPT and AlphaFold to design a personalised mRNA cancer vaccine for his rescue dog. Most tumours shrank. It is the first bespoke cancer vaccine ever designed for a dog.

Research

Here are the most consequential AI research papers from February and March 2026:

Measuring AI Agents’ Progress on Multi-Step Cyber Attack Scenarios (UK AI Safety Institute)

AISI evaluated seven frontier models on two purpose-built cyber ranges, a 32-step corporate network attack and a 7-step industrial control system attack, and compared models released over an eighteen-month window from August 2024 to February 2026. They found that the average number of steps completed at 10M tokens rose from 1.7 (GPT-4o, August 2024) to 9.8 (Claude Opus 4.6, February 2026), with performance scaling log-linearly with inference compute. Importantly, they found no plateau in sight. The best agent completed 22 of 32 attack steps autonomously, including lateral movement and privilege escalation. The NCSC estimated the marginal cost of an AI-assisted network penetration at £65, which I’d argue is one of the most policy-consequential AI safety findings this quarter…

TurboQuant: Redefining AI efficiency with extreme compression (Google Research, DeepMind, NYU)

The continuous push for larger context windows has been bottlenecked by the immense memory required to store the Key and Value (KV) cache during inference, leading to high cost and slow processing for long inputs. In an effort to address this bottleneck, this paper introduces TurboQuant, an architectural improvement that bypasses these computational and memory constraints. Published at ICLR 2026, TurboQuant achieves zero-accuracy-loss 3-bit KV cache compression, delivering 6x lower memory use and up to 8x faster attention on H100 GPUs without requiring training or fine-tuning. The “zero-accuracy-loss” component is important: it avoids the performance penalties typically associated with aggressive quantization. The method achieves this extreme efficiency by combining Quantized Johnson-Lindenstrauss projections, which compresses high-dimensional vectors into a much lower-dimensional space, with PolarQuant polar coordinate transformation to eliminate memory overhead. These efficiency gains are substantial enough to shift the inference cost curve for long-context applications, making million-token windows economically viable at scale.

Deriving Neural Scaling Laws from the statistics of natural language (EPFL, Stanford, Johns Hopkins)

This paper introduces the first theory to quantitatively predict neural scaling law exponents from first principles, with no free parameters and no synthetic data. The authors isolate two measurable properties of natural language: the decay of pairwise token correlations with time separation (exponent β) and the decay of conditional entropy with context length (exponent γ), and derive that the data-limited scaling exponent α_D = γ/(2β). Validated on GPT-2 and LLaMA architectures trained from scratch on TinyStories and WikiText, the predicted exponents matched experimental measurements. Scaling laws have guided billions in capital allocation and model design decisions since Kaplan et al. (2020), yet until now the exponents were purely empirical. This paper closes that gap at academic scale. But, I’d be curious whether the horizon-limited abstraction holds at trillion-token industrial scales where effective context reaches tens of thousands of tokens…

Attention Residuals (Kimi Team / Moonshot AI)

In this paper, the authors address the gradient dilution problem in deep Transformers, where fixed residual connections cause hidden-state magnitudes to grow and layer contributions to fade. They introduce Attention Residuals (AttnRes), which replaces this fixed accumulation with a learned, depth-wise softmax attention. Each layer uses a “pseudo-query” to selectively aggregate outputs from all preceding layers, creating a dynamic, context-aware blend.

The practical implementation, Block AttnRes, was tested on a 48B model and yielded concrete performance improvements: GPQA-Diamond increased by 7.5 points, and HumanEval by 3.1 points. This architectural approach also matched baseline performance trained with 1.25x the compute, demonstrating a 25% effective efficiency gain.

This work is interesting because it stabilizes training and improves scaling laws by fundamentally fixing a core architectural limitation, establishing a robust, dynamic alternative to identity mappings that is practical at scale with negligible parameter overhead.

Learning to Discover at Test Time (TTT-Discover) (Stanford, NVIDIA, Together AI)

In this paper, the authors introduce Learning to Discover at Test Time (TTT-Discover), a method that applies RL during inference to train an LLM on a single test problem, bypassing the limitations of a frozen model. The paper seeks to achieve autonomous scientific discovery by allowing the LLM to improve its internal policy through experience specific to the current task.

Experiments were conducted across diverse domains, including mathematics, GPU kernel engineering, competitive programming, and biology. TTT-Discover achieved a new state of the art on Erdős’ minimum overlap problem, improving 16x more than the AlphaEvolve baseline. It also produced a GPU kernel that was 51% faster than the best human entry on an A100 in the GPUMode competition. A key caveat is that the method critically requires continuous reward signals and cannot yet handle sparse or binary feedback.

Taken together, this paper establishes a path for LLMs to generate new-to-the-world solutions. It demonstrates that scaling compute via test-time training can push beyond existing human knowledge using open-source models.

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Meta-Harness: End-to-End Optimization of Model Harnesses (Stanford, KRAFTON, MIT)

This paper shows that changing a model’s harness - the code wrapping a model that determines what information it sees, stores, and retrieves at each step - around a fixed LLM can produce a 6x performance gap on the same benchmark. Meta-Harness automates harness engineering by giving an agentic proposer full access to raw execution traces (up to 10M tokens of diagnostic information) rather than compressed summaries. The authors show this approach results in +7.7 points on text classification with 4x fewer tokens, #1 among all Haiku 4.5 agents on TerminalBench-2 (37.6%), and #2 among all Opus 4.6 agents (76.4%). A single discovered harness improved accuracy on 200 IMO-level math problems by 4.7 points on average across five held-out models. The killer ablation: summaries actually made things slightly worse than scores alone (34.9% vs 34.6% median), while raw traces gave +15 points at median (50.0%). Taken together, one could conclude the model wrapper matters as much as the weights, and AI can now write better wrappers than humans.

MEM: Multi-Scale Embodied Memory for Vision Language Action Models (Physical Intelligence, Stanford, UC Berkeley, MIT)

Physical Intelligence introduces a multi-scale memory system that gives robots 15-minute context windows, long enough to clean an entire kitchen or cook from scratch. MEM combines an efficient video encoder for short-horizon frame-based history with a language-based memory mechanism for long-horizon context. After training on diverse robot and non-robot data, MEM VLAs showed +62% success rate on refrigerator tasks and +11% on chopstick manipulation versus memoryless baselines. The system was integrated into Physical Intelligence’s π0.6 VLA to address a fundamental limitation of current robot control: the inability to maintain coherent plans across multi-step tasks that require remembering what happened minutes ago.

Labor market impacts of AI: A new measure and early evidence (Anthropic)

This paper introduces the concept of “observed exposure” - a measure that quantifies not just which tasks LLMs could theoretically automate, but which are already being automated in practice, based on real usage data from Claude. Unsurprisingly, it is computer programmers, customer service representatives, and financial analysts who show the highest observed exposure. Despite high theoretical coverage (94.3% for computer/math occupations), there is no impact on unemployment rates for exposed workers yet, though there is suggestive evidence that hiring into these professions has slowed for workers aged 22–25. For every 10 percentage-point increase in AI exposure, BLS-projected job growth drops by 0.6 percentage points. The gap between theoretical and observed exposure suggests the labour market is absorbing AI gradually through task-level substitution rather than wholesale job elimination.

World Action Models are Zero-shot Policies (DreamZero) (NVIDIA)

DreamZero argues for a paradigm shift from Vision-Language-Action models to World Action Models, which jointly predict future video frames and motor actions rather than mapping observations directly to controls. Built on a 14B-parameter video diffusion backbone (Wan2.1), DreamZero achieved 62.2% average task progress on unseen real robot tasks - over 2x the best pretrained VLA baseline (GR00T N1.6 at 31%, π0.5 at 33%). The more consequential result is cross-embodiment transfer: 12 minutes of human egocentric video or 20 minutes of video from a different robot improved unseen-task performance by over 42%, and the model adapted to an entirely new manipulator with just 30 minutes of play data while retaining zero-shot generalisation. Through system-level optimisations including CFG parallelism, DiT caching, and a novel single-step inference mode (DreamZero-Flash), the team achieved a 38x speedup to enable real-time closed-loop control at 7Hz on GB200 hardware. The companion paper, DreamDojo, provides the 44,000-hour human video dataset that enables pretraining.

A large language model for complex cardiology care (Google Health, DeepMind)

Google Health and DeepMind tested Articulate Medical Intelligence Explorer (AMIE), an LLM built on Gemini, in the first randomised controlled trial of AI-assisted cardiology versus cardiologists working alone on complex cases involving suspected genetic cardiomyopathy. It was found that subspecialists preferred AMIE-assisted assessments 46.7% of the time versus 32.7% for cardiologists alone. In a win for AI, cardiologists working without AI had significantly more clinically significant errors (24.3% vs 13.1%) and more missing content (37.4% vs 17.8%). The result demonstrates frontier LLMs can augment specialist clinical reasoning in ways that reduce diagnostic error, not merely in triage or patient education but in complex subspecialty decision-making.

Investments

The quarter's headline raise was OpenAI's $110B round at an $840B valuation - the largest private financing in history - led by Amazon ($50B), NVIDIA ($30B), and SoftBank ($30B). Total disclosed venture funding in AI exceeded $50B. Other notable rounds included Wayve ($1.2B), Apptronik ($935M), Earendil Labs ($787M), and Neysa ($600M).

OpenAI, which develops frontier large language models and the ChatGPT consumer AI product, raised $110B at an $840B valuation led by Amazon ($50B), NVIDIA ($30B), and SoftBank ($30B)—the largest private financing in history.

Wayve, which develops embodied AI software for autonomous driving, raised $1.2B in a Series D at an $8.6B valuation led by Eclipse, Balderton, and SoftBank Vision Fund 2, with milestone-based capital from Uber bringing the total to $1.5B.

Apptronik, which builds the Apollo humanoid robot for manufacturing and logistics, raised $935M in a Series A at a $5.3B valuation co-led by B Capital and Google.

Earendil Labs, which develops AI-driven biologics for autoimmune diseases and cancer, raised $787M backed by Dimension Capital, DST Global, Sanofi, and Pfizer’s Biotech Development Fund.

Neysa, which provides AI cloud infrastructure in India, raised $600M in primary equity at a $1.4B valuation led by Blackstone.

Legora, which builds AI-powered legal research and workflow tools for 800+ law firms, raised $550M in a Series D at a $5.55B valuation led by Accel.

ElevenLabs, which develops voice AI and conversational agent technology, raised $500M in a Series D at an $11B valuation led by Sequoia Capital.

MatX, which designs custom AI training chips purpose-built for LLM workloads, raised $500M led by Jane Street and Situational Awareness.

Mind Robotics, which develops humanoid robots for industrial applications backed by Rivian, raised $500M.

Runway, which builds AI video generation and world models for creative and scientific applications, raised $315M in a Series E at a $5.3B valuation led by General Atlantic.

Bedrock Robotics, which builds autonomous excavators and construction equipment using technology from former Waymo engineers, raised $270M in a Series B at a $1.75B valuation co-led by CapitalG and Valor Atreides.

Fundamental, which builds Nexus, a Large Tabular Model for enterprise structured-data analysis, raised $255M in a Series A at a $1.4B valuation led by Oak HC/FT.

Intercom, which provides an AI-first customer service platform powered by its Fin AI agent, raised $250M in debt financing from Hercules Capital.

Positron, which designs energy-efficient AI inference chips to compete with Nvidia, raised $230M in a Series B at a $1B valuation co-led by Arena Private Wealth, Jump Trading, and Unless.

Harvey, which develops AI-powered legal reasoning used by most of the top 100 US law firms, raised $200M at an $11B valuation co-led by GIC and Sequoia.

Oxide, which designs and manufactures rack-scale on-premises cloud computers, raised $200M in a Series C led by US Innovative Technology Fund.

Goodfire, which uses mechanistic interpretability to understand and design AI models, raised $150M in a Series B at a $1.25B valuation led by B Capital.

Wonderful, which deploys AI customer support agents for telecom, financial services, and healthcare enterprises, raised $150M in a Series B at a $2B valuation led by Insight Partners.

Revel, which builds a unified software platform for hardware test and control used in aerospace and defence, raised $150M in a Series B led by Index Ventures.

Vega, which builds an AI-native security operations platform with federated threat detection, raised $120M in a Series B at a $700M valuation led by Accel.

Basis, which builds AI agents that autonomously complete accounting, tax, and audit workflows, raised $100M in a Series B at a $1.15B valuation led by Accel and GV.

Simile, which uses generative AI agents to simulate and predict human behaviour for enterprise decision-making, raised $100M in a Series A led by Index Ventures.

Render, which operates a cloud platform for deploying AI-native applications and agents, raised $100M in a Series C extension at a $1.5B valuation led by Georgian.

Nominal, which provides a connected testing and operations platform for hardware engineering teams in aerospace, defence, and energy, raised $80M at a $1B valuation led by Founders Fund.

Braintrust, which builds AI observability and evaluation tools used by Notion, Replit, and Cloudflare, raised $80M in a Series B at an $800M valuation led by Iconiq.

Entire, which builds a developer platform for human-AI agent collaboration on codebases, raised $60M in a seed round at a $300M valuation led by Felicis Ventures.

Isembard, which builds industrial AI infrastructure in the UK, raised $50M in a Series A.

SolveAI, which lets non-technical employees build production-ready enterprise software through AI-powered conversations, raised $50M in a Series A led by Google Ventures.

RunSybil, which provides AI-powered cybersecurity red-teaming and penetration testing, raised $40M.

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Exits

The quarter's defining exit was xAI's merger into SpaceX, valuing the combined entity at $1.25T ahead of a planned IPO. Anthropic acquired Vercept (computer-use agents), Amazon acquired Fauna Robotics (soft-bodied humanoids), and Anduril acquired ExoAnalytic Solutions (orbital tracking).

xAI, which develops frontier large language models and the Grok consumer AI product, was merged into SpaceX in a deal valuing the combined entity at $1.25 trillion ahead of a planned SpaceX IPO.

WorkFusion, which provides AI agents for anti-money-laundering and KYC compliance in financial services, was acquired by UiPath for an undisclosed amount.

Intrinsic, which builds AI-powered software to make industrial robots more accessible, was absorbed into Google to accelerate physical AI using Gemini models and Google Cloud.

Vercept, which developed computer-use AI agents capable of operating remote desktops, was acquired by Anthropic for an undisclosed amount.

Fauna Robotics, which builds the Sprout soft-bodied humanoid robot for homes and schools, was acquired by Amazon for an undisclosed amount.

Koyeb, which operates a serverless cloud platform for deploying AI inference workloads, was acquired by Mistral AI for an undisclosed amount.

Tavily, which provides an AI-optimised search API for retrieval-augmented generation, was acquired by Nebius for an undisclosed amount.

DOK-ING, which manufactures unmanned ground vehicles for mine clearance and explosive ordnance disposal, was acquired by Rheinmetall for an undisclosed amount.

ExoAnalytic Solutions, which tracks objects in orbit using a global network of optical sensors, was acquired by Anduril for an undisclosed amount.

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This issue at a glance: The Trump administration blacklisted Anthropic over Pentagon usage restrictions, designating it a "supply chain risk" and triggering a federal lawsuit. Iran conducted the first military strikes on commercial cloud infrastructure, hitting AWS data centres in the UAE and Bahrain. Anthropic's annualized revenue surged from $14B to $19B in weeks. Six frontier models launched in four weeks. Anthropic published evidence that DeepSeek, Moonshot, and MiniMax ran industrial-scale distillation campaigns through 16 million exchanges. NVIDIA exited the China-compliant chip market entirely. OpenAI raised $110B at an $840B valuation - the largest private financing in history. And an Australian used ChatGPT and AlphaFold to design the first personalised mRNA cancer vaccine for a dog.

Q1 2026 by the numbers: Anthropic revenue $14B→$19B in weeks · OpenAI raised $110B at $840B valuation · OpenAI-Amazon partnership worth up to $50B · Alphabet capex guidance $175-185B · 6 frontier model releases in 4 weeks · 16M distillation exchanges across 24K fraudulent accounts · Opus 4.6 sabotage risk: "very low but not negligible" · 150GB of Mexican government data stolen via Claude · 11 US states introduced data centre restriction bills · $2.5B GPU smuggling prosecution · AI-assisted network penetration cost: £65 · Total disclosed AI venture funding: $50B+

What to watch in Q2: Whether the Anthropic-Trump lawsuit reshapes how governments procure AI. Whether the data center moratorium movement gains traction ahead of midterms. Whether distillation enforcement triggers formal trade retaliation. Whether anyone can sustain revenue growth at the pace Anthropic set in February. And whether OpenAI launches a legitimate competitor to Claude Cowork.

Roberta Raileanu is a Senior Staff Research Scientist at Google DeepMind, where she leads work on the Open-Endedness team, and an Adjunct Professor at UCL, advising PhD students connected to UCL-DARK. Her research focuses on how frontier models are increasingly asked to do long-horizon work plan, use tools, recover from mistakes, and keep improving through interaction. This exposes a gap between systems that look capable in short bursts and systems that keep acquiring skills in messy environments. Roberta’s research is about closing that gap.

From exploration to open-ended learning

Roberta’s early work was shaped by a classic reinforcement learning problem that keeps resurfacing in new guises: exploration. If an environment gives sparse or delayed reward, brute-force search fails, and the right intrinsic objective can determine whether an agent learns at all.

Two papers anchor this period. RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments (ICLR 2020) proposes an intrinsic signal that rewards actions changing an agent’s learned state representation, evaluated in procedurally generated settings where revisiting the same state is unlikely. Learning with AMIGo: Adversarially Motivated Intrinsic Goals (ICLR 2021) tackles sparse reward by pairing a goal-generating “teacher” with a goal-conditioned “student,” producing an automatic curriculum of increasingly challenging goals. In parallel, Decoupling Value and Policy for Generalization in Reinforcement Learning (ICML 2021, oral) argues that shared representations for policy and value can contribute to overfitting, and proposes a decoupled approach that improves generalisation on benchmarks like Procgen.

This portfolio matters because open-endedness is not a slogan. It is a technical demand: systems should continue to learn without requiring a human to constantly rewrite the task distribution.

The tool-use gap

Before joining DeepMind, Roberta was a Research Scientist at Meta, where she started and led the Tool Use team for Llama 3. This work aimed at enabling models to use tools like search and code execution, and to generalise to new tools at test time. The products that shipped from this work - Meta AI, Data Analyst, AI Studio, Ads Business Agent - are now used by hundreds of millions of people.

She was also a co-author on Toolformer: Language Models Can Teach Themselves to Use Tools (2023), one of the papers that helped establish tool use as a core capability for language models rather than an afterthought. Toolformer showed that a model can learn when and how to call external APIs - calculators, search engines, translators - with minimal supervision, by generating its own training data from a handful of demonstrations.

Tool use is not a feature checkbox. It changes what we can reasonably ask models to do, because it introduces feedback loops, memory, and failure recovery. It also introduces new failure modes: an agent that can call a tool can also call it badly, repeatedly, and confidently. Roberta’s treatment of agent behaviour as a sequential decision problem with real constraints - not a prompt-engineering exercise - is exactly the lineage you want when the field moves from “can it answer” to “can it execute.”

Why open-endedness is becoming a practical requirement

At DeepMind, Roberta now leads the Open-Endedness team and is building a new Open-Ended Discovery group focused on autonomously discovering novel artefacts - new knowledge, capabilities, or algorithms - in a self-improving loop.

Open-endedness is sometimes framed as a path to general intelligence. In practice, it is also a path to systems that do not collapse outside curated benchmarks. Most real deployments present a shifting distribution: new tools, new data, new user behaviour, and new adversarial pressures. A model that cannot keep learning becomes a periodic retraining job with brittle edges.

At Meta, Roberta also led an “AI Scientist” effort focused on agents that can iterate through parts of the research loop - implementing methods, running experiments, analysing results, and repeating the cycle. That work has now crystallised into MLGym: A New Framework and Benchmark for Advancing AI Research Agents (2025), which positions evaluation around concrete machine learning research tasks and frames the problem in a way that invites iteration by the broader community rather than one-off demos. If “AI scientist” systems are going to matter, we need ways to compare approaches, reproduce results, and identify what actually moves the needle. A benchmark is not the whole answer, but it forces precision about what the agent is allowed to do, what counts as success, and what is being optimised.

Roberta’s background

Roberta received her PhD in Computer Science from NYU in 2021, advised by Rob Fergus. Before that, she studied Astrophysical Sciences at Princeton, where she worked on theoretical cosmology and supernovae simulations - and before that, competed in the International Physics Olympiad and the International Olympiad on Astronomy and Astrophysics. That path from physics instincts to sequential decision-making research shows up in her taste for problems where scale alone is not enough.

She also co-developed and co-teaches a course on open-endedness and general intelligence at UCL, which signals something about where the field is heading: this is becoming a discipline with ideas worth teaching, not a loose collection of intuitions.

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