What's Europe's Unique Wedge to Win With Physical AI? — with Vasileios Balntas & Mala Valroy
The easy answer treats "Physical AI" as one problem with one obvious path: build a VLA, feed it a massive sim-to-real pipeline, scale. But that bundles three separate bets into one: Sim-to-real is a data/training methodology: sim is just one way to get data, teleop and on-robot RL are others. VLA is an architecture hypothesis about mapping perception and language to action: you can do sim-to-real without a VLA, and train a VLA on nothing but real data. Physical AI is the outcome, agnostic to both. The unspoken equation is usually: Physical AI = big VLA + massive sim-to-real pipeline. That's a current research bet, not a definition. And, it's precisely the strategy that rewards whoever has the most compute and the most deployed robots.
This roundtable will pull the bundle apart: where sim-to-real actually breaks (perception, dynamics, or contact), whether VLAs are learning physics or just pattern-matching until they aren't, and what's left to bet on once you stop assuming compute and fleet size are the only paths to Physical AI. Finally, it discusses whether there is a sub-vertical only Europe could win — and what would €1B of European capital need to stop funding to go after it?
Speakers:
Vasileios Balntas — Leadership Team, Staer. Former Head of Research at Scape (acquired by Meta) and Senior Research Science Manager at Meta Reality Labs, where he built the SceneScript environment model; led Staer's open synthetic warehouse dataset release for physical AI.
Mala Valroy — Founder & General Partner, Thursday VC. Backs early-stage deep tech and climate tech; Thursday VC positions itself as Europe's first pure-play physical AI fund.