"The window matters now because those bets haven't been locked in yet."

Markus Wulfmeier has spent his career pushing artificial intelligence beyond the digital realm and into the physical world. As Chief Scientist at Nomagic and previously a Staff Research Scientist at Google DeepMind, his work sits at the intersection of robotics, reinforcement learning, and real-world deployment. At a time when much of the AI industry is converging on the same approaches, Wulfmeier believes the most important breakthroughs will come from challenging underlying assumptions – and building systems that can interact with and learn from the world around them.

Markus Wulfmeier is the Chief Scientist of Nomagic and a leading researcher in robotics, reinforcement learning, and physical AI. Previously a Staff Research Scientist at Google DeepMind, he worked on helping bridge advances in machine learning with real-world deployment. He received his Ph.D. from the University of Oxford, where he later conducted postdoctoral research, and has held visiting roles at UC Berkeley, MIT, and ETH Zurich. Dr. Wulfmeier has authored more than 50 publications, holds patents in machine learning and robotics, and his award-winning research has been featured in Wired, BBC, and 60 Minutes.

Why must this initiative exist - now?

The field has concentrated its attention and compute on a small set of recipes, and the underlying assumptions are starting to strain. That leaves entire directions (new architectures, training paradigms, and forms of intelligence that act in the world rather than just describe it) genuinely open. The window matters now because those bets haven't been locked in yet, and Europe has the research depth to make them.

Beyond money: what's the real 'operating space' teams get here?

A protected time horizon. Hard problems need many experiments and room to iterate on fundamentals, which doesn't survive quarterly pressure. The runway, the compute, and credibility with the researchers you need to recruit matter as much as the capital.

"Speed and care aren't opposed."

What would a real breakthrough look like?

Evidence of a genuinely different trajectory. This does not mean a marginal gain on a saturated benchmark, but a result that forces leading labs to update their assumptions. That could be an order-of-magnitude jump in sample or compute efficiency, a training paradigm that doesn't depend on ever-larger data, or verifiable reasoning becoming reliable rather than incidental. The harder test is institutional: turning that result into a durable lab with proven scaling behaviour.

What responsibility comes with building foundation models?

Once a model becomes the backbone for many downstream systems, responsibility becomes an engineering discipline, not a disclosure exercise. It means external evaluation, interpretability and failure analysis that scale with capability, and honesty about a system's limits.

That rigour is exactly what makes a system trustworthy to build on: speed and care aren't opposed.

What's the biggest challenge for Frontier AI in Europe right now?

Europe trains some of the best people in the field, who then often build elsewhere. The gap is institutions willing to take frontier-scale risk over a frontier-scale horizon, and the conviction to back a genuinely different bet.

For embodied AI there's a second gap: the path from excellent robotics research to systems deployed and improved at scale is still too thin.

Who should apply - and who shouldn’t?

Teams with a sharp, defensible thesis about where the next frontier lies and early evidence they can bend the curve - especially those willing to take on the physical world, where the problems are hard and the differentiation is real.

You have to run a research lab and a company at once. Not for anyone whose plan is a bigger transformer, a fine-tuned wrapper, or simply a European version of something.

Contact person

Markus Wulfmeier

Chief Scientist, Nomagic