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:

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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).

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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.

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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We are delighted to announce Raia Hadsell as a returning speaker - she first spoke at RAAIS in 2017, when she was a Senior Research Scientist at DeepMind.

Raia is now VP of Research at Google DeepMind, where she co-leads the Frontier AI unit. She joined DeepMind in 2014, when it was still a 50-person startup freshly acquired by Google, and her work has since spanned some of the field’s hardest open problems: continual and transfer learning, deep reinforcement learning for robotics and navigation, and the models that power today’s frontier systems.

The arc of a career

What makes Raia’s research career unusual is the consistency of its through-line. She earned her PhD under Yann LeCun at NYU, where her dissertation on long-range vision for off-road robots received the Outstanding Dissertation award. That work helped shape metric learning and Siamese neural networks - architectures now so standard they underpin most modern contrastive learning. Her most highly cited papers include Dimensionality Reduction by Learning an Invariant Mapping and Learning a Similarity Metric Discriminatively, with Application to Face Verification, foundational contributions to representation learning that have collectively gathered tens of thousands of citations.

After a postdoc at CMU’s Robotics Institute with Drew Bagnell and Martial Hebert, and a stint at SRI International’s Vision and Robotics group, she joined DeepMind and turned her attention to a problem that had been nagging the field for decades: catastrophic forgetting.

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Why continual learning matters

Neural networks are powerful learners but terrible rememberers. Train a model on task B and it forgets task A. This is catastrophic forgetting, and it has been one of the deepest obstacles to building AI systems that improve over time rather than being retrained from scratch. Raia’s 2017 paper Overcoming Catastrophic Forgetting in Neural Networks proposed elastic weight consolidation, a method for protecting important learned parameters while still acquiring new knowledge. Alongside Progressive Neural Networks and Distral: Robust Multitask Reinforcement Learning, this body of work laid much of the groundwork for how the field thinks about lifelong and multitask learning today.

It’s also the thread that connects her navigation research - including a landmark Nature paper demonstrating that artificial agents trained to navigate develop grid-like neural representations resembling those found in rodent brains - to her more recent work on generalist robotic agents like RoboCat and bipedal robot locomotion published in Science Robotics.

From research to frontier systems

Raia’s selected publications tell a story about where frontier AI is actually heading. Her recent work includes contributions to Gemini 2.5, Gemma 2, and RecurrentGemma, alongside RoboCat - a self-improving foundation agent for robotic manipulation that can pick up new tasks from as few as 100 demonstrations - and research on teaching bipedal robots to play agile soccer using deep reinforcement learning.

This range is what makes her unusually well-placed to speak at RAAIS. She sits at the intersection of frontier language models, embodied intelligence, and the kind of continual adaptation that will determine whether AI systems can operate reliably outside the data centre - in factories, hospitals, homes, and the physical world.

Beyond the lab

Raia’s influence extends well beyond her own research. She founded and serves as Editor-in-Chief of Transactions on Machine Learning Research (TMLR), launched in 2021 as an alternative venue for rigorous ML publication. She sits on the executive boards of CoRL (Conference on Robot Learning) and WiML (Women in Machine Learning), is a Fellow of ELLIS, and is a founding organiser of NAISys (Neuroscience for AI Systems).

In November 2025, she was appointed as an AI Ambassador for the UK government’s Department for Science, Innovation and Technology, where she chairs peer review panels for national AI research initiatives - a role that puts her at the centre of UK AI policy at a pivotal moment.

She holds a PhD from NYU, and - in a detail that says something about the breadth of her thinking - an undergraduate degree from Reed College in religion and philosophy.

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

Philip is co-founder and CEO of Starcloud, a company building data centers in space to address one of AI’s most pressing constraints: energy. As model size and compute demand continue to grow, terrestrial data centers are running into hard limits in grid capacity, cooling, land use, and permitting timelines. Starcloud’s thesis is that space offers a different path: solar power, radiative cooling, and a route to scaling compute beyond what is practical on Earth.

From proof of concept to orbital compute

In November 2025, Starcloud launched Starcloud-1, a 60 kilogram satellite carrying the first NVIDIA H100 GPU ever operated in space, delivering roughly 100x more powerful GPU compute than had previously been deployed in orbit. Within weeks, the company achieved two notable firsts: training a GPT-style language model in orbit using NanoGPT, and running Google’s Gemma model in space on a high-powered GPU.

Processing AI workloads in orbit, close to the satellites generating the data, can cut latency from hours to minutes. Synthetic aperture radar satellites, for example, can produce huge volumes of data that are costly and slow to downlink. Analyzing that data in orbit could materially change both speed and cost.

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Why space could matter for AI infrastructure

Starcloud argues that orbital data centers could deliver major reductions in both energy cost and emissions over their lifetime, even after accounting for launch. The appeal is straightforward: space-based systems are not constrained by terrestrial grids, and space offers a naturally favorable environment for radiative cooling without the land and water footprint of conventional data centers.

The company’s long-term ambition is a fully solar-powered orbital data center with 5 gigawatts of capacity, large enough to rival major power plants on Earth without requiring land, transmission infrastructure, or connection to a terrestrial grid.

What comes next

Starcloud-2, currently planned for October 2026, is set to be the company’s first commercial mission. It will carry several NVIDIA H100 GPUs alongside NVIDIA Blackwell hardware, with persistent storage and continuous customer access to orbital compute. The mission will also include a cloud platform from Crusoe, making it possible for customers to deploy and operate AI workloads directly from orbit.

Starcloud is backed by Y Combinator, NVIDIA through its Inception program, and investors including NFX and In-Q-Tel.

Philip’s background

Philip is a second-time founder. He previously co-founded Opontia and earlier worked at McKinsey & Company on satellite projects for national space agencies, giving him firsthand exposure to both the potential and the constraints of space infrastructure.

He holds an MPA in National Security and Technology from Harvard University, an MBA from Wharton, and an MA in Applied Mathematics and Theoretical Physics from Columbia University. He is also a CFA charterholder.

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

Jeff is co-founder and CTO of Odyssey, a frontier AI lab developing general-purpose world models.

Jeff is working on one of the most ambitious problems in AI: building systems that can understand, predict, and simulate the real world. Odyssey’s work sits at the intersection of generative modeling, embodied intelligence, and large-scale learning, with the potential to unlock major advances across robotics, autonomy, and interactive digital environments.

Before co-founding Odyssey, Jeff spent 15 years building AI for autonomous driving. As VP Technology at Wayve, he helped pioneer visual policy learning and contributed to a new wave of end-to-end learning approaches for autonomy, pushing beyond hand-engineered systems toward models that learn directly from real-world experience.

His work has consistently focused on taking frontier machine learning research and applying it to complex, real-world problems where robustness, generalisation, and deployment matter most.

Jeff holds degrees in engineering and computer science from the University of Auckland and Georgia Tech, and completed his doctorate at the University of Oxford.

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Air Street AI meetups bring together ~150 researchers, founders, engineers, and operators who are actually building and deploying AI systems. These are deliberately small, curated evenings designed for people doing the work, not talking about the work. The goal is simple: share hard-won lessons, surface emerging technical frontiers, and help exceptional builders find one another.

Over the next few weeks, we’re hosting Air Street AI meetups in Munich, Zurich, and Paris featuring speakers from Black Forest Labs, Odyssey, Google DeepMind, Sereact, and Polar Mist. I’ll be presenting updates from our State of AI Report, the most widely read and trusted analysis of key developments in AI.

Air Street Munich AI, 17 Feb 2026

Against the backdrop of the world’s most important defense and national security gathering, the Munich Security Conference, this evening focuses on AI systems that reason about, perceive, and act in the physical world.

Fabian Gura, Member of Technical Staff, Odyssey
Odyssey is building frontier world models that simulate how visual and physical environments evolve over time and under intervention. With Odyssey-2 Pro, the team describes a “GPT-2 moment for world models,” where interactive, real-time simulations become viable and begin scaling rapidly in capability. Odyssey’s work pushes world models beyond static video toward systems that can be explored and acted upon.

Gustaf von Grothusen, CEO, Polar Mist
Polar Mist is building autonomous maritime defense systems for operation in GPS-denied and contested environments. Its Semper platform combines an unmanned surface vessel that can carry a variety of payloads with Polar Mist’s vision-based navigation and positioning system, enabling persistent autonomy without navigational drift.

Air Street Zurich AI, 19 Feb 2026

This evening centers on how AI systems learn to see, model, and manipulate the physical world, from pixels to policies.

Robin Rombach, CEO, Black Forest Labs

Black Forest Labs is a frontier AI research lab developing state-of-the-art visual intelligence, including its latest FLUX models for controllable image generation and editing. Founded by the original authors of latent diffusion, the company recently raised $300M at a multi-billion-dollar valuation, cementing its position as core infrastructure for next-generation visual AI.

Marc Tuscher, CTO, Sereact

Sereact is a frontier robotics research and deployment company, starting with picking and handling in warehouses and industrial environments. The company’s models enable robots to perceive, reason, and act in highly variable, real-world settings without brittle rule-based pipelines. Sereact’s work targets one of robotics’ hardest problems: robust generalization from vision to action in production. The systems are deployed across hundreds of robots for large enterprises in Europe and the US.

Air Street Paris AI, 11 March 2026

An evening focused on frontier visual intelligence and open source models.

Cyril Diagne - Black Forest Labs

Cyril works at the intersection of visual intelligence, generative models, and creative tooling. His work explores how modern vision models can move beyond recognition toward interpretation, controllability, and human collaboration. At Black Forest Labs, he focuses on pushing visual foundation models into new expressive and interactive regimes.

Edouard Yvinec - Google DeepMind

Edouard is a research scientist at Google DeepMind and a core contributor to DeepMind’s Gemma family of open-weight large language models, including Gemma 3, which was designed to be among the most capable models that can run on a single GPU.

We look forward to meeting you on the road!

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London AI brings together 100 of London’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 London AI, we’ll cover the following topics:

• Enabling next-gen AI capabilities - Ross Taylor, CEO of General Reasoning

• AI-first video - Youssef Alami Mejjati, Head of Research at Synthesia

• State of AI Report 2025 - Nathan Benaich, Air Street Capital

We’ll follow the talks with plenty of time to meet people over drinks and nibbles.

Recent meetups have included people from DeepMind, OpenAI, Anthropic, Google, Meta, Palantir, top UK labs at Oxford, Cambridge, Imperial, UCL, and startups including ElevenLabs, Synthesia, Revolut, Monzo, Helsing, Granola, Delian Alliance Industries, and many others.

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

Request a spot here

See you in London.

Join us in New York City for the launch of the 2025 State of AI Report!

We’re hosting a community meetup on Thursday, October 16, bringing together founders, operators, and researchers working across AI. The evening will feature:

• Nathan Benaich, General Partner of Air Street Capital, on the State of AI 2025

• James Cadwallader, CEO of Profound, on search and agents

• Victor Riparbelli, CEO of Synthesia, on AI video

• Eiso Kant, co-CEO of poolside, on AGI

We’ll wrap up with happy hour drinks and plenty of time to connect with others in the AI community.

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The audacity of “solving disease”

Isomorphic Labs, the biotech spinout from DeepMind, has declared a radical mission: solving disease. It's not a metaphor. It’s a systems-level wager that the same kinds of models that cracked Go can crack biology. Not only by digitizing wet labs, but by encoding the dynamics of biomolecules into machine-learnable substrates. If AlphaGo marked the start of AI systems inventing strategies never before seen in human play, Isomorphic wants AI to invent medicines we’d never stumble across in the lab.

This is a moonshot, a bet that biology is information science, and machine learning is our best bet at learning its language.

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Inspired by games

Jaderberg’s own roots go deep into reinforcement learning (RL). Back at DeepMind, RL was the magic behind AlphaGo’s “move 37", an unexpected yet genius move that shocked the Go world. It wasn’t brute force. It was the AI’s emergent creativity inside a massive search and feedback loop. For Max, it was a formative proof that neural networks could invent, not just interpolate.

Unlike games, however, biology doesn’t offer perfect simulators. The world is slow, stochastic, hard to manipulate, and expensive to probe. If Go was the proving ground, biology is the ultimate generalization test. In both domains, the goal is the same: build a world model that supports reasoning and invention. But in biology, the board is invisible.

Reversing Eroom’s Law

Drug development is stuck in a cruel inverse of Moore’s Law called Eroom’s Law, which describes how R&D spend grows while output shrinks. It’s not for lack of effort. Biology is just too complicated. A single protein’s function emerges from its structure, which emerges from its atomic sequence, which interacts with other molecules in a soup of dynamics we can’t write equations for.

Meanwhile, compute power is still compounding. And AI models, especially transformers, have shown that with enough data and clever architecture, you can reverse the entropy of complexity. Isomorphic Labs is staking its future on this arbitrage: replace trial-and-error with in silico reasoning, and unlock order from the mess.

From AlphaFold to AlphaFold 3: simulating the interactome

Everyone in biotech has heard of AlphaFold 2, DeepMind’s 2021 breakthrough that predicted protein structures from sequences with near-experimental accuracy. But proteins don’t act in isolation. They bind to DNA, RNA, and small molecules. These interactions define function and dysfunction.

Enter AlphaFold 3, Isomorphic’s most important release to date. Jaderberg revealed how the team extended AlphaFold’s neural architecture to simulate multi-molecular interactions. The core innovation is the “PairFormer”: a network that, unlike language transformers which process sequences, reasons over 2D interaction grids between molecules.

This means AlphaFold 3 doesn’t just predict what a protein looks like, it simulates how it behaves when confronted with other actors, like a drug molecule or a strand of DNA.

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What makes Isomorphic special

For founders and investors, Jaderberg’s framing of Isomorphic Labs wasn’t just about the models. It was also a study in company design. Unlike many AI-for-biology companies, Isomorphic isn't bolting AI onto biotech. It’s re-architecting the stack from first principles, with model development, inference infrastructure, and biological interpretability all co-designed.

In Jaderberg’s words, AI models are not “tools” for scientists. They are the scientists, reasoning agents embedded in compute. Isomorphic’s goal is to make these agents fluent in biology.

What’s next

With over $600 million raised in its first external funding round, Isomorphic Labs is gearing up for a new chapter. The company is scaling its AI-native drug design platform with clinical ambition: real-world drug trials are on the near horizon. It has inked billion-dollar partnerships with Eli Lilly and Novartis to pursue multi-target discovery across oncology, cardiovascular disease, and neurodegeneration. And with the appointment of Dr. Ben Wolf as Chief Medical Officer and the launch of a U.S. outpost in Cambridge, MA, Isomorphic is assembling the pieces of a full-stack AI-first biotech.

The broader field isn’t standing still either. The next frontier may not be protein folding or drug binding alone, it’s full biological systems modeling. Here, companies like Profluent are taking cues from frontier model scaling and applying them directly to building generative models that output functional proteins from scratch. The race is on not just to simulate biology, but to write it.

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The timing couldn’t be more acute. The war in Ukraine has shown how low-cost drones and software can upend conventional military doctrine. More recently, the escalation of hostilities with Iran has further underscored the volatility of global security and the growing relevance of digital warfare and drone-enabled asymmetric tactics. In the U.S., a second Trump administration has accelerated European defense policy, with Germany, Poland, France, and others pushing military spending to levels unseen since the Cold War. The European Commission plans to deploy €800 billion toward defense by 2029.

So: where does that money go? For once, not just to the legacy primes. There’s an opening for new entrants, namely startups building fast, iterating with operational focus, and pushing beyond the traditional defense hardware playbook.

From Silicon Valley to the battlefield

Dimitrios’s story is instructive. He left the relative safety of Cupertino’s R&D labs to build from scratch in Athens and London, grounded in robotics and autonomy. Why the shift?

“Because the genie is out of the bottle,” he said. “Revisionist powers are deploying lethal autonomous systems at scale with no moral reservations. Trying to argue about the ethics of putting a missile on a drone while your adversary is mass-producing them is a dead-end debate.”

He saw what many in the West ignored: how new technologies obliterated conventional forces in the Armenia-Azerbaijan war, particularly during the 2020 Nagorno-Karabakh conflict. The lesson? Defense is no longer just about steel and soldier count. It’s about software, autonomy, rapid manufacturing and robust supply chains. And unless liberal democracies catch up, we risk strategic irrelevance.

What changed for me

Like many in tech, I grew up in the era of “technology must not be be evil.” Defense was taboo: socially radioactive, professionally off-limits. But that's intellectually lazy.

Living in Europe and the US showed me just how uneven societies are in valuing their armed forces. In the U.S., defense is part of civic life. In Europe, it’s often invisible. That disconnect is dangerous. The freedoms we enjoy, such our ability to build companies, argue about politics, live openly, are underwritten by national defense.

And in a world where AI is being absorbed into geopolitical competition, the stakes are only rising. If I can’t build these systems myself, I can at least help the people who can.

Autonomy: the battlefield’s next interface

As Dimitrios explained, two trends dominate the AI-defense nexus:

• Sensor fusion at scale: Modern battlefields are saturated with pixels. Drones, smartphones, cameras. The challenge is connecting them all, i.e. “any sensor to any effector”, through a central brain.

• Operating in denied environments: Communications will fail. Autonomy is the only answer. Systems must navigate, patrol, identify targets, and ask for permission to engage all without persistent links.

We’re still in the early innings. Teleoperation dominates today, much like lane-assist dominated early self-driving. But the direction is clear: militaries want Level 5 autonomy. The tech curve is steep, and the next decade will define winners.

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The opportunity and challenge for new entrants

For all the urgency, startups face brutal friction. Defense procurement remains built for peace: bureaucratic, risk-averse, captured by incumbents. European tenders often require three years of prior revenue with the ministry, which is a catch-22 for any new entrant.

And unlike SaaS, defense AI isn’t just about code. “You need hardware, ops, training, and integration,” Dimitrios said. “Software alone is irrelevant if it’s not actionable in the field.”

That’s where investors come in. Historically, it’s been hard to make money in defense. The post-war peace dividend led to consolidation, leaving only a few primes. But that’s changing. Private equity is moving in. Governments are becoming more startup-friendly. If software is eating the battlefield, the first forks are in.

My bet is that we’ll see a biotech-style model emerge: small, fast-moving defense companies take on early risk; primes acquire them to scale. Some startups, Delian among them, have the chance to grow into new proto-primes. The capital and technical stack required is daunting, but the prize is a defensible, strategically critical foothold in the future of geopolitics.

What we need today

So what do liberal democracies need to prioritize?

• GPS-denied autonomy: Spoofing is real. Eastern flanks are already in blackout. We need computer vision systems that can localize without satellite access, think self-driving tech for war zones.

• Maritime autonomy: Aerial autonomy has advanced rapidly, but oceans remain under-resourced. Electromagnetic silence makes navigation tricky. We need breakthroughs here.

• Resilient supply chains: Defense startups must manufacture. But supply chains designed for peace are brittle. Who’s your parts supplier when your neighbor becomes your adversary?

And beyond tech, we need better policy: open procurement, mandated non-prime spending, clearer export rules, and startup-friendly funding mechanisms.

A call to action

To founders: if you're building frontier AI, this is your Manhattan Project moment.

To investors: the risk is real, but so is the reward, and not just financially. You're backing freedom.

To policymakers: align incentives. Streamline buying. Fund moonshots. Defense is a public good, treat it like one.

To skeptics: pacifism isn’t a strategy when adversaries build kill chains. The most unethical path is inaction.

The future of warfare won’t wait. The West must move fast, or others will move faster.

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Andreas is no stranger to the field. He was among the original researchers behind Stable Diffusion and later worked with Stability AI before striking out on his own to build the model he always wanted: a unified, fast, and open infrastructure for pixel generation, whether from text, images, or combinations of the two.

From the Black Forest to the frontier of pixels

Founded less than a year ago, Black Forest Labs is headquartered in Freiburg, Germany, with a growing team of 30. Its mission is focused: build frontier models for text-to-image and text-to-video generation. Rather than pursuing a consumer product, Black Forest Labs offers its models through APIs, designed with B2B use cases in mind.

In a notable departure from closed commercial offerings, the company is deeply committed to open source. They release model weights for non-commercial use, and licensing them for commercial applications.

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FLUX.1 Kontext

The centerpiece of Andreas’ talk was FLUX.1 Kontext, the lab’s latest frontier model. At a high level, it fuses text-to-image generation and image editing into a single unified system. This differs from traditional approaches that required multiple models, custom pipelines, and fine-tuning for each new task.

Why does this matter? Today’s image generation models have mastered complex spatial composition, photorealism, and even basic text rendering. But when it comes to editing, especially iterative edits, character preservation, style transfer, or complex instruction following, most systems fall short. They’re slow, brittle, and require fine-tuned workflows for every use case.

FLUX.1 Kontext introduces a zero-shot, multi-modal interface that lets users:

• Generate images from text (the classic "text-to-image" path),

• Edit existing images using instruction prompts (e.g. “remove the star from her face”, “make this into a Lego scene”, “extract the skirt and put it on a white background”), and

• Perform style transfer, object removal, character consistency, and typography editing all without retraining or switching models.

Fast, general, and iterative

Speed is a key differentiator. FLUX.1 Kontext generates one-megapixel images in just 2.6 seconds, over 20x faster than GPT-4 Image, with competitive output quality. For image-to-image tasks (like editing), it’s 10x faster than comparable models. That speed unlocks real-time, iterative workflows: editing product images, tweaking branding assets, or generating personalized content for marketing, all in seconds.

It’s also available to try. Black Forest Labs has launched a public playground accessible at playground.bfl.ai where anyone can interact with FLUX.1 Kontext in a no-code interface.

A foundation for the visual internet

Andreas’ final call was pragmatic and pointed: “Everyone needs marketing images, everyone needs visual content.” In a world where pixels are becoming programmable, tools like FLUX.1 Kontext are infrastructure, not novelty.

With a performant, open, and composable foundation, Black Forest Labs is poised to be the go-to stack for image and video generation in B2B workflows from e-commerce to entertainment.

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Today, ElevenLabs is one of the fastest-moving companies in the agentic voice space. Their platform powers narration for authors, dubbing for studios, real-time agents for enterprises, and everything in between. At the 9th Research and Applied AI Summit in London, Mati and I recounted the story of how they got here, and where they're going next, and lessons learned for any founder building in AI.

Why Voice?

Mati and his co-founder are Polish, and they grew up on a peculiar audiovisual experience: every dubbed film in Poland is narrated by the same monotone male voice, regardless of gender or character. “We just accepted it,” Mati said on stage at RAAIS. “But once you realize how bad it is, you can't unsee it.”

Audio had been left behind. While vision and text saw wave after wave of model innovation, voice was stuck in the uncanny valley—too robotic, too stiff, too limited in language and emotion. And yet, the dream of speaking fluently to machines remained as alive as ever.

ElevenLabs chose voice not because it was trendy, but because it was neglected. They saw a technical opportunity and a cultural one. “We wanted to make audio human-level,” Mati explained. That meant starting not with chat agents, but with narration and voiceovers, the use cases where poor quality was most obvious and impactful.

From prototype to product

In 2022, voice AI wasn’t hot. Crypto and the metaverse were still dominating headlines. ElevenLabs raised a $2 million pre-seed, which Mati now calls “life-changing.” It let them acquire some GPU capacity, hire fast, and bet on pushing the research frontier.

But quality is slippery. “You never fully know if you’ve cracked it,” Mati said. They tested by emailing over a thousand YouTubers with demo samples. The response? Crickets. But among the five or so who were interested, a signal emerged: creators didn’t want full dubbing, they wanted better voiceovers, cleaner narration, simpler fixes.

So ElevenLabs refocused. They let users play. An early author copy-pasted his entire book into their text box, generated audio section by section, stitched it into an audiobook and fooled a human moderation team that banned AI voices. He got glowing reviews. That’s when Mati thought: “Oh damn, we might be onto something.”

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One product, many paths

ElevenLabs didn’t customize their product for different customer segments. Whether you’re a novelist narrating your own work or an enterprise integrating voice into agent workflows, you’re using the same product.

This product-led strategy compresses the sales cycle: prospective enterprise users can try it themselves, build confidence in quality, and prototype real use cases before anyone hops on a call. “We serve a wide range,” Mati said. “Media, creators, enterprises such as Twilio, Cisco, and others. But the experience is unified. That’s powerful.”

It’s a smart move in a category often cluttered by opaque demos, bespoke integrations, and slow-moving pilots. In voice AI, where quality is subjective and use cases are diverse, letting users self-qualify is a distribution edge.

The importance of the product layer

Today, ElevenLabs’ models support 70+ languages and control tone, accents, whispering, shouting like a film director. But Mati is clear: “Without a great product, the model has limited impact.”

Even now, they resist patching problems with product hacks. Pronunciation, for example, was bad early on. But rather than build a custom interface, they waited for the model to improve, confident it would. They only added a pronunciation editor later, for niche use cases like audiobook greetings.

This speaks to a broader philosophy. ElevenLabs straddles the model-product boundary, but leans toward product as the long-term moat. “Models will commoditize,” Mati said. “The time is now, we need to build the ecosystem before that happens.”

What’s next for audio AI?

We’re still not at the voice Turing test, Mati believes, but we’re close. “In the next 6-12 months, we’ll get there,” he predicted. The key? Context preservation, ultra-low latency, and moving from unimodal to multimodal systems.

That’s where ElevenLabs V3 comes in, the company’s most powerful model yet. Trained to support over 70 languages, V3 brings human-level nuance to generated speech: tone control, accents, whispering, shouting, speed variation. It lets users direct voice like a filmmaker, orchestrating delivery across emotion and pacing with fine-grained precision.

V3 isn’t just better; it’s more adaptable. The model understands context, reduces pronunciation errors, and offers the kind of expressive control necessary for long-form content, virtual agents, and real-time interaction. Mati even uses his own voice as a test case, ensuring accents and cadence land exactly right.

Voice AI is getting bigger, too. Audio models used to be lightweight, relying more on architectural tricks than raw data. That’s changing as use cases and expectations grow. Speed matters. Mati wishes he’d bought more GPUs earlier.

He’s also excited about emerging uses ElevenLabs never planned for. At one hackathon, a team built a camera-narration system for blind users. “We never imagined that use case,” Mati admitted. “But it was perfect.”

That’s why the platform stays open. You can build for the known use cases—but leave the door open for the rest.

Final word

At the end of our fireside chat, I asked Mati a final question: if ElevenLabs could give one person their voice back, who would it be?

He paused, then smiled. “Stephen Hawking.”

If ElevenLabs has its way, the next generation of voices, restored, created, or imagined, won’t just sound human. They’ll sound like us.

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In his closing talk at RAAIS this year, Hughes argued that we’re entering a new phase in the evolution of artificial intelligence: one where open-endedness becomes the central organizing principle. Not just solving problems, but defining them. Not just predicting the next token, but surfacing previously unknown unknowns. If he’s right, the next generation of AI systems won’t just be tools, they’ll be participants in the scientific process itself.

Open-endedness as the engine of innovation

The conceptual heart of Ed’s talk is a deceptively simple idea: to build AI that does science, you need to build AI that innovates. And to automate innovation, you need open-endedness: models that continuously generate novelty and ensure that the novelty is learnable.

This distinction matters. Static randomness (like staring at a noisy TV) is novel but useless. AlphaGo’s Move 37, by contrast, was novel and altered the theory of Go. It was innovation in the full sense: a step forward in the knowledge matrix from unknown unknown to known known.

Ed formalizes this through a recursive cycle: conjecture, experiment, analysis, theory-building that mirrors the scientific method. If that sounds familiar, it should: it’s AlphaGo’s architecture, abstracted.

But AlphaGo is bound by the constraints of its game. The real prize lies in domain-general innovation. Ed’s vision is to take the open-ended loop of AlphaGo and scale it to the messy, multidimensional complexity of reality.

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Simulators as scientific petri dishes

To get there, DeepMind turned to simulation. In XLand 2.0, a procedurally generated Unity world with a task space of 10⁴⁰ variations, the team trained an Adaptive Agent agent called AdA. This wasn’t just another reinforcement learner. AdA was trained to meta-learn, which means it forms hypotheses, runs experiments, analyzes outcomes, and refines theories in real time, across an ever-shifting task space.

In one task, AdA has to produce a black cube by manipulating various objects under hidden rules. After stumbling through its first attempt, it starts to identify causal rules and eventually solves the task with efficiency and grace. In other words, it discovers, adapts, and improves like a scientist. At 500M parameters, AdA was once among the largest RL agents ever built. Today, that number feels quaint, but the behavior remains profound.

Still, simulation has limits. Even XLand’s enormous variety doesn’t approach the richness of the real world. So DeepMind introduced Genie, a generative world model trained on 200,000 hours of internet gameplay video. Genie can render playable environments from text prompts or images, letting kids play games based on their own drawings or run scientific-style experiments in 2D and 3D simulations.

As in Genie 1, in Genie 2 these worlds evolve further: reflections emerge, parallax motion appears, and consistency across time steps hints at deeper physical priors. With agents like SIMA layered on top, these world models become testbeds for behavior, turning the internet’s passive data into an active substrate for scientific discovery.

From language models to Popperian AI

So far, the story tracks the standard DeepMind arc: large-scale, multimodal systems in custom environments. But Hughes pivots into more controversial terrain with his proposal for AI²: AI systems that can perform AI research.

This is where language models come in. Hughes argues that LLMs, thanks to their training on massive corpora of human discourse, are already latent models of human notions of “interestingness.” They don’t just complete prompts, they filter ideas through a socially tuned aesthetic lens.

In experiments like the Donor Game, LLMs mutate and evolve strategies through gameplay. Claude 3.5 Sonnet forms cooperative cultures. GPT-4o becomes risk-averse. These behaviors, Hughes suggests, can be repurposed: LLMs as mutation operators, selection filters, and even code-generating experimenters.

Enter Popper. Hughes proposes a “Popperian AI” that uses LLMs to propose falsifiable theories, write experiments to test them, and recursively improve itself. This isn’t hypothetical: Sakana’s AI Scientist has already produced auto-generated workshop papers; AlphaEvolve discovered a more efficient matrix multiplication algorithm than any known since 1969.

The most recent instantiation is the Darwin-Gödel Machine, a recursive system that interleaves self-modification with downstream evaluations. It achieves state-of-the-art results on software engineering benchmarks on an academic compute budget.

Safety, culture, and the edge of the unknown

There’s a looming question behind all this: is recursive self-improvement safe? Hughes takes a cultural evolutionary stance. Human societies already implement safe recursive intelligence: through education, institutions, norms. We’re not afraid of raising children, he notes, because we’ve wrapped superintelligence (read: our kids) in culture.

By this logic, open-ended AI systems embedded in human cultural processes may be safer than brittle tools isolated in labs. But that requires visibility, control, and aligned incentives, none of which are guaranteed today.

In a world where benchmarks are saturating and scientific discovery is slowing, Hughes offers a reframing: perhaps the bottleneck is no longer in answering questions, but in asking the right ones. This is the domain of open-endedness. And in 2025, it might just be the most important problem in AI.

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From bystanders to power players

For years, politicians saw technology as a “geeky side issue,” said Benedict. He and Chris, while at the Tony Blair Institute, used to pitch tech as a tool to modernize the state only to be met with polite indifference. Not anymore. ChatGPT broke the dam. Suddenly, AI wasn’t just a productivity story; it was a political one.

Now governments are scrambling to catch up. But the institutional muscle memory is slow. “Governments are incredibly bad at any form of exponential growth,” Benedict said. The risk? That policy is either reactive, fueled by fear, or prescriptive in all the wrong places.

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AI regulation: the EU’s cautionary tale

Europe’s AI Act became a lightning rod in the discussion. Chris, who leads Meta’s policy work in Europe, described the growing disconnect between the Act’s original intent and its eventual scope. “It started as about regulating high-risk applications,” he said, “but now it’s about regulating the base technology itself.”

This shift from use-case oversight to upstream control has spooked more than just American giants. European companies are now worried the Act will make them slower, less agile, and unable to compete globally. What a surprise! We explored these tensions in detail in our earlier essay "Is the EU AI Act actually useful?” on Air Street Press, where we argued that without careful recalibration, the Act risks prioritizing risk aversion over innovation.

Benedict was blunter: “Brussels has long been the regulator of first resort and the innovator of last.” Even as the EU tries to walk back some of the Act’s more onerous clauses, the message to founders is clear: the regulatory mood music in Europe still leans toward caution, not ambition.

A geopolitical wake-up call

What’s changing that mood, if anything, is geopolitics. “There’s now a recognition,” Lionel observed, “that AI is a national power lever, not just a productivity tool.”

Benedict agreed and laid it out in stark terms. China and the U.S. are in a full-stack arms race, building not just models but vertically integrated systems across defense, biotech, clean tech, and manufacturing. India is rapidly deploying digital public infrastructure. The Gulf states are bankrolling massive AI compute capacity. As I wrote in my recent essay for Fortune, this race for AI sovereignty is as much about political narrative and national leverage as it is about technology. Countries that fail to define their position in the AI stack risk being left behind.

And Europe? It's still debating the instruction manual.

Benedict offered an analogy: just as countries buy aircraft from Boeing or Airbus but still plant their flags on national carriers, so too might they need to be realistic about the AI stack. Full sovereignty is a myth for most, especially when foundational model development is a trillion-dollar sport. And it's worth noting that both Europe and the U.S. might feel differently about this analogy if each didn’t happen to have an aircraft manufacturer of their own on their continents.

But sovereignty does matter selectively. Control over data, over healthcare and defense applications, over the regulatory posture that shapes diffusion—these are the battlegrounds. “You don’t need to own the entire stack,” Benedict said. “But you do need to know where your strategic advantages lie.”

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Diffusion Is destiny

One of the most compelling themes was that diffusion - not just invention - will determine AI’s long-term impact. “We’re just at the beginning,” Chris said, pointing to Meta’s AI assistant rollout and consumer-grade smart glasses that translate speech or narrate scenes for the visually impaired.

This is where policy can go “maximally right”: by lowering the friction for public-sector adoption, by treating compute and health data as national infrastructure, and by backing the clinician-turned-data scientist building applications on top of open models.

But diffusion also demands restraint. The U.S. is debating how freely models should flow; China is already setting standards; and Europe, if it over-indexes on control, risks falling behind on impact.

A better compact between tech and state

The adversarial tone that defined the last tech cycle - move fast, break things, ignore regulators - won’t work this time. Nor will bureaucratic hand-wringing. A new compact is emerging, one built on mutual recognition that AI isn’t optional. It’s foundational.

Governments are no longer technophobic. They’re hungry for solutions. “They’ll be on the hunt for companies building real capabilities,” Benedict said. And not just another SaaS layer for accounting margins, but applied AI in education, energy, security, and science.

It’s a historic opening. But only for those builders and policymakers ready to take the long view.

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Kant’s message was clear: if you want to compete at the frontier of model capabilities, the secret isn’t just scale, it’s iteration speed. And to iterate fast, you need infrastructure that matches the ambition of your research. This is the thesis behind poolside’s “model factory,” a production-grade system for turning ideas into results at industrial speed.

Reinforcement learning as the next frontier

Back in 2023, poolside made a contrarian bet. While the AI zeitgeist was still intoxicated by the notion that bigger models and more data would eventually yield AGI, poolside went the other way. “We didn’t believe you could just fine-tune your way to general intelligence,” Eiso told the audience. “Our view was that reinforcement learning, not parameter count, was the key scaling axis.”

That bet is paying off. Eiso predicts that within 36 months, frontier models will reach human-level capabilities across most knowledge work done behind a screen. What will power this transition? A shift from human-in-the-loop assistance to agents-as-contributors—software entities that learn, act, and improve themselves in semi-autonomous environments.

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The rise of the model factory

Much of the talk focused on the internal engine driving poolside’s progress: the “Model Factory.” This isn’t a metaphor. It’s a fully versioned, directed acyclic graph (DAG) of modular components, from data ingestion and preprocessing to model training and evaluation. Every experiment, every change, is tracked immutably. Researchers don’t start from scratch, they fork, tweak, and run. And if one asset in the pipeline changes, the whole model lineage downstream can be rebuilt automatically.

The results are important. One researcher pushed a new RL optimization idea to production results in under a week. Another ran 500 architecture sweeps over a weekend with a simple for loop. “When your factory is built well,” Eiso said, “the big training run is the easy part.”

Synthetic everything

poolside also stands out for its approach to training data. “The dirty secret is that 99% of foundation model data comes from the web,” Eiso noted. But instead of throwing more human labelers at the problem, poolside embraced synthetic data. Today, 20% of their training corpus is model-generated and refined through RL, guided by a small layer of high-quality human feedback. In the coming years, Eiso predicts that it will be closer to 98%.

Even post-training is synthetic-only. “We don’t believe in spending billions on human-labeled data to polish models,” said Eiso. “We think the future is: reward models trained on human judgment, used to guide synthetic data pipelines.”

In fact, Eiso and I dove into this topic in a video podcast we record earlier this year amidst the noise that “deep learning is hitting a wall”…

From agents to self-play

poolside’s RL work centers on software development agents. Initially, these were traditional models fine-tuned on code tasks. But increasingly, the model is the agent, operating inside a reinforcement loop that uses real-world codebases and synthetic programming tasks as training environments. “The agent product is now the binary that calls the model,” said Eiso. “And we’re training that loop end-to-end.”

This approach is blurring the line between model, agent, and product. In the software domain, where the environment is rich, structured, and fully digital, poolside sees a clear path to autonomy. “First, they assist. Then, they contribute. Eventually, they collaborate.”

The AGI arc: generalization through focus

Despite their narrow beachhead in software engineering, poolside is building toward general intelligence. “You can’t separate reasoning capabilities from general intelligence,” said Eiso. “Even if we’re not training a poet or a therapist, the ability to plan, understand, and reason is universal.”

The final slide hinted at a broader philosophical view. poolside isn’t trying to mimic humans with eyes, ears, and limbs. It’s trying to build agents that can think deeply “to do long, complex reasoning in digital environments.” That, Eiso argued, is the real foundation of AGI.

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Natively agentic and multi-modal

At the center of her talk was Gemini 2.5 Pro, Google’s latest flagship multimodal model. The system doesn’t just understand text, images, video, and audio. It reasons across them, writes and executes code, grounds outputs in real-time search, steers tone, and self-corrects based on error traces.

Paige’s live demos were less about tools and more about nascent AI collaborators performing tasks that, until recently, defined entire job roles:

• The AI Data Scientist: Prompted with a raw CSV file, an agent in Google Colab autonomously conceived and executed a full exploratory data analysis plan—from data cleaning and visualization to feature engineering and training a Random Forest model.

• The AI Code Reviewer: A Gemini-powered bot in GitHub not only summarized pull requests but provided actionable, line-by-line feedback, catching everything from potential model misconfigurations to minor documentation inconsistencies.

• The AI Co-Scientist: In the most profound example, a system of agents compressed a research timeline from a potential ten years to just two days, recapitulating and even discovering novel insights without prior human knowledge.

This rapid capability expansion inevitably raises critical questions about the future of work and the skills that will be most valued in an AI-native world. It shifts the focus from manual task execution to creative direction, critical thinking, and strategic oversight, essentially validating the AI's plan, questioning its assumptions, and interpreting the final output.

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The cost of narrative is collapsing

Midway through her talk, Paige paused the demos and cued up a raccoon. Specifically: a raccoon in a black jacket, dancing in slow motion in front of the pyramids. In 2023, that prompt would’ve yielded a jittery GIF. In 2024, thanks to Google’s new Veo 3, it’s rendered as a cinematic sequence complete with smooth camera pans, ambient soundtrack, and emotionally matched voiceover.

Veo 3 is essentially close to a fully-automated media stack: scriptwriting, audio, direction, and edit, all orchestrated from a single prompt. Paige showed how it can spin up video ads, devrel explainers, and even documentation walkthroughs that auto-update with each SDK release. Prompt writing becomes the new scripting.

A powerful stack for the solo entrepreneur

Paige closed her talk with a striking message for founders: you can do far more, with less. Using tools like Gemini, Veo, and the growing constellation of AI-native infrastructure, the solo entrepreneur is a practical reality.

Generative models can now write and review code, conduct research, generate media, and keep documentation fresh. Paige referenced data from Carta showing a sharp rise in solo-founder startups over the past decade. It’s no coincidence, she claimed. The modern founder is scaling compute in favor of headcount.

The distinction between developer, researcher, and marketer is beginning to blur. One person, equipped with the right stack, can span all three. The implication for founders is clear: with fewer constraints and more agency, the bottleneck is no longer the team you can hire, but how quickly a single individual can now bring a world-changing idea to life. It’s now all too tempting to invoke an Elon-ism: “what did you get done last week?”.

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