Why I want to talk to you about AI In scIence

Breakthroughs, anyone?

It’s almost 2026. AI hasn’t fully cured cancer or solved climate change yet. It would probably be ideal to have that before the hilarious or super professional videos AI can create, also the awful ones.

We need big scientific breakthroughs, and we needed them yesterday. What’s more contested is whether AI can actually help deliver them, and if so, how.

The US announced the Genesis Mission as their big bet to make America the leader in AI-driven science. The launch is surely political to some degree, for example it could unite the people of the US around “solving humanity’s greatest challenges” in case the Democrats try their chance with a strong anti-AI message in the next election cycle.

What I believe though is way simpler: unlike AI’s promised gains in other areas (such as an AI CEO in the Fortune 500 or a robot picking up your kid from daycare), AI’s benefits in science are already demonstrable. AlphaFold won the Nobel Prize. AI-assisted research is on the rise and is proven to be beneficial in different ways. For the evidence-driven policy circles, the gap between the AI promise and the actual seems smaller in science than anywhere else. Morevoer, research & development drives substantial economic returns already. So it all makes sense to go big on “AI in science” doesn’t it?

What’s actually in the way?

So back to it: cancer, climate change, AI?

The US Government announced a request for information tied to the launch of the Genesis Mission. “AI in science” is only a part of it. It is all about how to redesign the scientific infrastructure: processes, systems, habits, institutions, talent… 

This makes a lot of sense, because AI is not a band aid to all problems in science. You are a PhD researcher at one of the best academic institutions in your field (probably in Europe for this story). You spent many months unable to take the most important measurements for your thesis, because an expensive machine had been broken, and repairs were slow. And you see all this “AI, AI, AI…” talk. But AI can’t solve everything! There are real-life problems real scientists deal with.

It’s true that many problems in science are about resources, workflows, coordination, bureaucracy… AlphaFold can’t fix a broken spectrometer. But AI is an invitation to ask: do we need to change some things fundemantlly in scientific research? If the measurement analysis will be way faster using AI, how much more expensive is the downtime for measurement? Going nitty-gritty on this particular example: What was the reason for failure? How common is this across different labs in that institution, or even bigger, in a country? How much research time gets sacrificed due to machine downtime? If this is a systemic problem, is there AI that can predict the machine failure for that machine in particular, or does it worth investments (public or private) to make that happen? So the question becomes bigger “when can we get this machine fixed, if ever?” to “can we make machine downtime a thing in the past”?

So why does this matter now?

These aren’t new questions. Call it metascience or not, people have been offering insights into how to solve the bottlenecks in scientific research or whether it is marginally more difficult to come up with scientific breakthroughs. Some of these insights are landing, even if slowly. For example, the OECD published a great compilation of articles on AI in science back in 2023, and their point about “give academics abundant amounts of compute” is finally in action.

But the stakes have shifted. AI is now the center of top public debates, and AI in science is framed as a sacred mission. But let’s remember: AI is not a band-aid. These promises only materialize through systemic change in how science gets done.

This is why moments like the European Union designing FP10, its science and R&D funding strategy for the next decade, or the US Government seeking public input into its science funding strategy matter so much. None of these initatives can be an apple-to-apple comparison, but there are only a few mega-billion scale initiatives in science. So it makes sense for us, the public, to demand that policymakers get these right.

Over to you

From a distance, scientific breakthroughs seem more possible than ever. Fly closer, the scene changes: all the tangled branches, the spider webs, the messy bushes and hidden thorns…

As a citizen of the world, not a scientist, I believe:

• Science is one of the few things that can unify across ideological lines, and
• Getting it wrong would mean years of lost prosperity, health, and wellbeing…

So more and more, it’s where public expectation and pressure will land.

As a citizen of the world, not a scientist, I also wonder whether it is both exciting and overwhelming to be a scientist. Publishing becomes a numbers game, there are so many problems that nobody even has the time to raise, convenience slowly creeps up on ambition… And on top, you have to adapt to but also be accountable for new ways of doing things?

I stumbled upon a recent survey, a small but targeted sample, in which scientists claimed that lack of skills is a barrier to AI adoption at an increasing rate from 2024 to 2025. More than half asked for guidance on how to use AI tools. I’d bet science policymakers would want the same: more guidance on what scientists need or want.

The information flow between the scientists, policymakers is probably more important now than it’s been in a long time. And there are so many questions: can AI revolutionize science such that we answer harder questions, not just publish faster, reliably? How intentional are you or your institution about improving the process, making what works work even better?

Whether you’re in academia, a startup, an FRO, a corporation, philanthropy, government, civil society, whatever altitude you’re at, I’m curious what questions you’re sitting with. Happy to chat, or point me to someone who I should talk to.