Are AIs Discovering New Science?

FUTURE

Without a doubt, the most exciting trend in the past few weeks has been the realization that AIs are finally starting to discover new science in areas such as materials science and medicine, and more recently, physics and mathematics, including offering the first real progress on a Millennium Prize problem in more than a century.

If true, it would make good on one of our main predictions in our 2025 list at the beginning of the year, in which I predicted AI would have its first real discovery. And what do we know, that prediction might come true next week.

But is this what we think it is? What has changed?

Today, you will learn how AI is changing the world in magical ways, and more importantly, what evidence and research back the realization that, this time, it’s for real, including a brand-new research paper that has proven one of my long-lasting beliefs to be wrong.

Let’s dive in.

AI’s Greatest Feature?

Whenever we think about AI, we usually think first of productivity.

That is, AI’s most significant benefit to civilization is the capacity to make our world more productive, allowing tasks, businesses, and processes that yield economic value to be enhanced, doing more with less, or even automating them altogether.

But is productivity the best AI can give us?

I believe not, as more than a year ago, I was championing the idea that AI’s greatest superpower would be its ability to answer humanity’s most burdening questions.

And we might finally start to see this at scale. But first, let’s do a quick rundown of AI-led discoveries in the last few years.

AI has been leading the way for a while

We can begin with the clearest case of discovery: materials. Almost two years ago, DeepMind’s GNoME used AI to predict the stability of inorganic crystals at an unprecedented scale.

The headline result is 2.2 million new crystal candidates, with roughly 380,000 predicted to be stable; hundreds have already been independently realized, and an autonomous “A-Lab” synthesized dozens guided by those predictions.

The claim here isn’t that AI mixed chemicals on its own, but that it drastically expanded the viable search space and provided experimentally useful “recipes,” turning months of trial-and-error into a ranking problem that labs can actually prosecute.

The scale of material discovery that GNoME unlocks

These days, companies like recently-unveiled Periodic Labs want to take AI’s pattern-matching superpowers to professionalize what DeepMind showed us with a fully-fledged AI scientist. The key is that while AIs can propose new materials and other stuff we’ll see below, they lack the capacity to test those potential discoveries.

Thus, after raising $300 million, the idea of this startup is to create autonomous laboratories where AI can not only propose new ideas, but also test them.

The goal? Create new knowledge.

Besides material engineering, another area that holds great promise is none other than healthcare, particularly in the field of antibiotics.

MIT’s team trained deep models on ~39,000 compounds against…

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