What would a real breakthrough look like?

I don't have a clean answer to this, and I'd be wary of anyone who does. What I'd hope to see out of these labs is a result that forces the major labs in the US and China to update their assumptions about what frontier AI has to look like. Either a more sample- or compute-efficient path to current capability, which would be a real economic and strategic shift, or substantive progress on something the field is honestly stuck on; training methods that don't depend on ever-larger data piles, alignment properties that can be verified rather than inferred, or AI for science that genuinely shifts the pace of discovery; a direction where Europe's scientific depth gives it a real chance to lead rather than chase. What I'd be less excited to see is a slightly later, slightly less capable version of work that's already been published elsewhere.

What responsibility comes with building foundation models?

Foundation models look more like infrastructure than consumer products, and the teams building them inherit obligations closer to those of a pharmaceutical company or an airline than a software vendor. That includes serious evaluation work the lab didn't itself design, red-teaming by reviewers outside the company's incentive structure, honest disclosure about training data and what the system is genuinely capable of, and an interpretability investment that scales with capability rather than being added later in response to incidents. There's also a responsibility that sits upstream of any specific model, such as preserving the conditions that make course-correction possible later, in case something does go wrong. That means protecting open scientific exchange, keeping power distributed enough that someone outside the lab can credibly object, and not letting the information environment get flooded with synthetic content past the point of recovery. The riskiest combination to ship into the world is high capability paired with low transparency, and good intentions inside the lab

Who should apply - and who shouldn’t?

The teams that should apply are the ones with a specific research thesis they can defend to skeptical reviewers: a real bet on architecture, training, data, or alignment, with a plausible path from that bet to frontier-class capability. The thesis matters, but the team matters at least as much. Building a frontier lab is as much an organizational challenge as a scientific one, and the founders who succeed at it tend to combine genuine technical depth with the ability to recruit and retain serious talent, hold a clear long-term vision when external conditions get harder, and run an organization well over a multi-year horizon. The SPRIND timeline isn't built for big pivots, so it's worth being honest with yourself about whether you and your team can commit to this work for the duration. If your differentiation is mostly that you're based in Europe, or if you're building primarily on top of someone else's foundation model, this probably isn't the right fit. And if you treat safety as a regulatory afterthought rather than a research problem, the case will be a hard one to make to this jury.