A breakthrough, in my view, would be evidence of a different capability trajectory.

Today's frontier AI capacity is overwhelmingly concentrated in a few US and Chinese labs, and Europe mostly consumes what others build. More fundamentally, the entire field has over indexed on a single architectural bet: transformer-based scaling. Nearly every frontier lab is now running variations of the same recipe: bigger transformers, more data, more compute. We're hitting the economic and energy limits of that paradigm, and training runs in the hundreds of millions or billions of dollars, with diminishing returns per added parameter, are not a game Europe can or should try to win on incumbents' terms. The transformer monoculture has crowded out serious exploration of alternative architectures: state-space models, neurosymbolic approaches, energy-based models, and paradigms not yet named, even though there is no principled reason to believe attention-plus-scale is the endpoint of AI research. Europe's opportunity is precisely here: to build labs that are born on the next S-curve rather than chasing the tail of this one, more efficient, auditable, and sovereign by design. If we get this right, in five to seven years we'll be shaping how frontier AI works and what it's optimized for.

First of all, every team can get up to €26.5M, and the most exciting part about Next Frontier AI is the permission structure it creates. For 24 months, teams can behave like a frontier lab, in contrast to a grant project: run real experiments, build production‑grade MLOps, discover technical secrets, and iterate on architecture without having to prove quarterly revenue. They also gain access to an expert jury, SPRIND’s network, and a clear path to roughly €1B in follow‑on capital per winner, enough to actually train and deploy frontier‑scale systems, in addition to writing great papers.

A breakthrough, in my view, would be evidence of a different capability trajectory. That could be a system that matches today’s leading models on core tasks with 10–100x less compute, or an architecture that brings verifiable reasoning, long‑horizon planning, or sim‑to‑real robotics into the mainstream of enterprise use. Just as important is whether a lab can convert a prototype frontier system into a durable institution: with proven scaling laws, secure infrastructure, industrial pilots, and an investment-grade plan to reach full frontier scale.

When you build foundation models, you are effectively defining the default behavior of thousands of downstream systems, so the responsibility is systemic. In an enterprise context, that means traceable data flows, robust evaluation, safety and compliance baked in (not bolted on), and governance that can stand an audit. It also means respecting data sovereignty, contributing lawful datasets and benchmarks, and being transparent enough that regulators, customers, and citizens can trust the infrastructure we’re creating.

We see world‑class research across Europe every day, so the biggest challenge isn’t talent or ideas. In my opinion, it’s the absence of enough institutions willing and able to take frontier‑level risk at frontier‑level scale. Historically, Europe funded many interesting projects but very few labs with the mandate, capital, and governance to go toe‑to‑toe with leading US or Chinese labs. If we don’t fix that, Europe will excel at policy papers and pilots while others own the core models, the data network effects and, ultimately, the value capture.