Bryce Keane

Uwe Horstmann and I started out as venture investors in Europe around the same time. He has a military background, and he was investing in defence years before it was obvious – honestly, before most of us would have taken the meeting. Then he stopped investing in it and went to build it. We were the first to back him at inception and have tripled down every round since. STARK is what he and @jschaback built. A multi-domain neo-prime for European defence: inexpensive, AI-driven systems – from drones to unmanned boats and beyond across air, land and sea – designed from the first prototype to be mass-produced. Within 18 months of founding they had landed contracts worth billions, which @gradypb notes is unheard of in this industry. They went from zero to 700 people in two years, most of them engineers. Uwe’s view is that the real lesson of Ukraine isn’t drones. It’s the innovation ecosystem – soldiers giving feedback, talent willing to work in defence, capital, a robust supply chain, procurement that moves in months instead of years. Combat ready technology, from the real world. The other thing is how close they stay to the user. The buyer is a general. The customer is a staff sergeant. STARK has a large team in Ukraine and shows up at the training grounds with a new version every couple of days. This feedback is critical. They take it, go back, and come back with something a little better. That is how you win trust with soldiers. Johannes is an engineer’s engineer. He tells the story of a public test that didn’t go to plan, a frozen shooting range in November, three shifts around the clock, and the moment two weeks later when “from there on, we’ve always hit.” A few things we cover: – The four things STARK did right: innovate next to the battlefield, fund the R&D themselves, design for certification and mass deployment from day one, and make the factory part of the weapon. “Production is deterrence” – Soldier-centricity: why soldiers wanted a wood box they could burn vs plastic, and what that taught the team about the lab vs. the field – Europe’s mandate to step up on defence, and why its changes in procurement are structural, not a moment. Europe doesn’t change because of a charismatic leader; it changes when reality kicks in – “As complex as a washing machine”: the edge a two-year-old company has over a 70-year-old prime – Startups inside a startup: autonomous programs for maritime, aerial and strike systems, plus capability teams for GNSS-denied navigation, target re-identification and comms – Sub-meter precision on a target moving at 60 km/h, and why it’s neither a hardware problem nor a software problem but both, tuned together down to the compute budget – Born out of fire: the Virtus test that failed in public, and the four weeks that followed – A single strike from launch to target: the operator sets the intent, the system plans around no-fly zones, the swarm shares what it sees, and the human stays in the loop where the customer wants them – Where AI shows up: perception on the device, Minerva for mission planning, and NATO’s responsible-use principles as hard-coded safeguards – The next hard problem isn’t a product. It’s contract design – building surge capacity into the contract the way you’d buy cloud, because that is where real deterrence comes from 0:00 – Introduction 1:03 – Billions in contracts in 18 months: Stark's four-part playbook 2:28 – The staff sergeant matters as much as the general, and wood beats plastic 7:42 – Why Europe is ready for a neo prime, and why procurement reform will last 11:45 – Startup DNA, venture-style programs, and "as complex as a washing machine" 16:27 – Born out of fire: the failed trial and the round-the-clock comeback 21:13 – Where Stark finds talent, and why defense is no longer off-limits 23:48 – Anatomy of a strike, from the wooden crate to the kill zone 27:22 – Vertical integration, multiple domains, and shifting left on manufacturing 32:53 – After the Hugging Face hack: guardrails on autonomous weapons 35:56 – Matching the primes on R&D and scaling to 700 people in two years 39:24 – Deterrence by contract, and Europe's next chapter with the US

Announcing No Limit: a bot building tournament hosted by @Sequoia, @citsecurities and @OpenAI. Teams of 1 or 2 get 60 minutes to build a pokerbot from scratch. Agents play thousands of games across six scored rounds. Open to university students and recent grads (‘25 or ‘26). Regionals in Boston, Pittsburgh and the Bay Area in early November. Top five teams from each fly to the Finals. $100K grand prize.

Physical AI is taking off 🚀 Listen to @joshavata at @MeckaAI and my partner @sonyatweetybird dive into where things stand today, the biggest challenges, and the exciting future ahead! 👇

David Silver is one of the all-time greats. He was the creator of AlphaGo, which changed the course of history by showing that neural nets could achieve superhuman performance for the first time. He also has a unique intuition and knack for forecasting when things will happen in AI. With computer Go, he predicted the Lee Sodol outcome 10 years before, right to the year. He got the year right for Starcraft and won a bet inside Deepmind. Same for AlphaProof. Same for the IMO. David's prediction for superintelligence: 3 years. !!! He has a strong sense that now is the time to build a superleaner, and that superlearners are now possible, thanks to the enormous progress in compute, simulators, autoresearch, and the sophistication of technology in the stack since the AlphaGo days. What is a “super learner”? A super learner discovers all knowledge from its own experience. Start from nothing (no internet pre-training), and go beyond human knowledge. Drop it into any stream of experience, simulated or physical, and the superlearner learns. He expects the progress will be rapid. When you get the recipe for a superlearner just right and remove humans from the development loop altogether, a superlearner can learn extremely quickly. Just look at AlphaGo. AlphaGo took two years to develop with humans in the loop. When David and his team at @GoogleDeepMind got the formula for AlphaZero correctly, it surpassed the human-developed algorithms in just a few hours. Human data is a shortcut, but you have to let go of the human data to shoot beyond it. In January, David left GDM to found @IneffableLabs, a research-led lab with one mission: build a "super learner" that discovers all knowledge from its own experience, and make first contact with superintelligence. He joins us with co-founder Lasse Espeholt, who built IMPALA and SEED RL, the foundations of scaled-up RL, and now runs Ineffable's compute. A few things we cover in this conversation. — What a super learner is: depth (start from nothing, go beyond human knowledge) and breadth (drop it into any stream of experience, simulated or physical). The human brain is the only existence proof — Why they're giving up human data: AlphaGo took two years with humans in the loop. AlphaZero outperformed it in a few hours. "You have to let go of the human data to shoot past the competition" — Crucibles: simulated worlds with enough emergent complexity (physics engines, millions of interacting agents) for an agent to rediscover technology, science, maths and language on its own — Simple rewards, emergent complexity: evolution optimized fitness and got all of life. Don't breadcrumb the objective, give the agent something simple to optimize in a world with a lot to learn about — Go as the existence proof: one agent is the most boring game imaginable, two agents is "an infinite onion of layers of complexity" — Why human language is probably not the endpoint for reasoning, and how training on it bakes in a flat earth and the four humors — Can superintelligence be built in simulation? If the sim is Turing complete, computation, economics, language and maths are all expressible, and it happens 10-100x faster and safer — Alignment: language-based monitoring is reaching its limits as agents learn to say one thing and do another. Shadow actions, superintelligences monitoring superintelligences, and building a real science of how they interact peacefully — Accelerate in simulation, pace in the real world: Ineffable will gate real-world deployment on evidence that it goes well — Timing as the determinant: Silver called Computer Go to the year, then StarCraft, then the IMO. He thinks this is the moment — Predictions: True superintelligence within three years. Timestamps: 0:00 – Introduction 1:09 – What held true from Go to today, and what the LLM era changed 3:35 – Why leave DeepMind to start a new lab 5:58 – Why "Ineffable" (and the secret second reason) 7:03 – Lasse's path: scaling RL at DeepMind and Google Brain 9:31 – Defining a super learner and first contact 12:35 – Super learners vs. LLMs that make discoveries 13:59 – Day to day: the RL stack, crucibles and human-journey milestones 16:13 – Don't breadcrumb the objective: simple rewards, emergent complexity 21:16 – What a crucible looks like 24:14 – Go: one agent is boring, two is infinite 25:41 – Will agents rediscover language? 27:22 – Removing human priors: AlphaGo to AlphaZero 32:05 – Keeping human bias out of the crucibles 33:35 – Confidence, timing and super forecasting 36:24 – Is compute the driver? 38:13 – The ceiling on simulation and Rich Sutton's objection 43:44 – Alignment and the science of superintelligence 47:45 – Monitoring agents that don't think in English 51:01 – Pacing the frontier 52:54 – The compute setup: Vera Rubin on GCP 55:18 – Training, inference, simulation and RL scaling laws 57:40 – IMPALA and SEED RL 1:00:58 – 20 people and the compute-per-person ratio 1:04:49 – A research-led culture 1:05:47 – Predictions

Robinhood and Revolut democratised access to financial markets. But access ≠ sophistication. AI has the potential to change that. Thrilled to share that @Sequoia has led @catalystapp's $30M seed round.