Albert Michelson wrote in 1903 that the facts of physical science had all been discovered. Stephen Hawking predicted in the 1980s that theoretical physics might finish by the end of the century. With the arrival of artificial intelligence, that feeling is back. This time, a Nobel Prize accompanies the claim.
In 2024, Demis Hassabis and John Jumper from Google DeepMind won part of the Nobel in chemistry for AlphaFold. The neural network predicts the three-dimensional structures of proteins by learning from thousands of experimentally measured shapes. That problem had resisted systematic attacks for half a century. AlphaFold seemed to solve it once and for all. The world became fixated on the promise of the approach. Hassabis and his team called AlphaFold “the template for how AI can accelerate all of science to digital speed.” Startups building foundation models for biology, chemistry, and materials discovery raised billions of dollars, buoyed by DeepMind’s success. AlphaFold showed that the combination of AI and sufficient data could make groundbreaking discoveries, even if the underlying mechanisms were not understood. It seemed a path through the rest of science was laid out before us.
A rare template
AI will bring extraordinary changes to science, but AlphaFold and things like it may not be the best template for that metamorphosis. Though it is a profound achievement, the conditions that produced the likes of AlphaFold are rare. The time to meet those conditions in other fields will be measured in decades, not years. The acceleration of science will come about thanks to another approach: AI agents.
The primary condition for AlphaFold’s success was the existence of the Protein Data Bank. It is a data set of roughly 170,000 experimentally validated protein structures on which DeepMind’s team could train its model. The creation of the Protein Data Bank was not simple. It took 53 years of international scientific cooperation and, by a recent estimate, roughly $21 billion worth of experimental work to assemble. Efforts of that scale are infamously difficult to fund, next to impossible to coordinate, and hugely time-consuming to execute. They have often been unsuccessful as a result.
Even in fields with the requisite cohesion and resources, where the relevant data are not rendered inaccessible by commercial ownership, another barrier is too little discussed: the scientific impossibility of generating comparable data. In the case of protein structures, the key experimental technique—protein crystallography—is an unusually replicable and dependable tool. Over 25 Nobel Prizes have relied on it. But in most of experimental science, results vary more often than not. Cell lines drift. Chemicals have trace contaminants. Lab humidity changes. The creation of measured datasets that will be consistent enough, accurate enough, precise enough, and scalable enough to train a modern neural network in biology or most of chemistry would require new kinds of measurement and new standardized approaches. None of this will be ready anytime soon.
There are a handful of fields where these requirements are met: weather forecasting, much of genomics, very limited areas of chemistry. These may see AlphaFold-style breakthroughs soon, if they have not already. Government support for the production and coordination of those datasets will be critical, as the US National Security Commission on Emerging Biotechnology has argued. But for most open questions in science, a different plan is needed, at least in the short term. Something quieter and more modest has begun to show promise.
Reasoning under uncertainty
Scientists have always reasoned under uncertainty. Biologists working to identify new drug targets have never had perfect datasets. Instead, they combine docking calculations and known structures, factor in molecular dynamics, run a handful of binding assays, and use their judgment to weigh each method according to its particular strengths and points of failure. The skill of science is not in any single tool. It is synthesizing what many tools produce, and revising the results as the evidence comes in. This is how most working research actually proceeds. Until very recently, no software could do it.
Agents now can. An agent is an AI reasoning engine that has been given access to tools—digital or physical—and the capabilities to use them. Over the last few years, a fundamental architectural shift in AI has enabled the rapid proliferation of these programs. They are powered by large language models, dramatically reducing the need for scientifically specialized datasets. For science, this represents a foundational change. It has allowed us to create digital tools that can mimic the iterative, highly contingent process of actual research. Tools like AlphaFold apply a powerful approach to a limited question. Agents are inherently generalists. They do not represent a new way to do science. Instead, they digitally model the human process of discovery.
Google’s AI Co-Scientist was announced in May. Researchers gave it a one-page brief and a goal: Figure out how antibiotic resistance spreads between bacterial species, a key driver of drug-resistant infections. The system spun up sub-agents. One drafted hypotheses from the literature. Another picked them apart like a peer reviewer. A third ran tournaments to rank the strongest candidates. A fourth refined the winning hypothesis. The agent concluded that resistance genes were hitching rides on bacterial viruses, borrowing whichever virus could ferry them into a new host. The hypothesis was correct. Researchers at Imperial College London had spent a decade reaching the same conclusion through painstaking wet-lab work. Their paper, previously unseen by Co-Scientist, was still in peer review.
Agents like Co-Scientist are still novel tools. There are real challenges to overcome before they become a ubiquitous part of the scientific process. They are still liable to hallucinate. Their judgment is not consistent. They have memory and input constraints that limit the time they can run autonomously. These technical barriers will fall away. As they do, we will begin to notice the compounding effects of scientific agents on the reliability, consistency, and velocity with which science is done.
Fixing reproducibility
Agents offer a structural fix for science’s “reproducibility crisis,” the widespread problem of researchers’ inability to replicate each other’s results. For decades, the scientific community has begged researchers to share their raw data and exact code in an effort to standardize experimental processes. Researchers have long resisted this tedious administrative work, which happens after the interesting science is already done. Agents, in contrast, automatically log every move they make. They create an exact record of the method that led to their results and allow for precise replication.
A second consequence will be an amplification of scientific memory. The transfer of knowledge between researchers is a famously murky process. If it is not done over years of training and observation, graduate students are left to pore through the messy lab notebooks kept by decades of predecessors, looking for the details that will make or break their protocol. As agents become an increasingly large part of the scientific process, a lab’s entire scientific history will be recorded in a central, standardized repository of institutional knowledge.
The most important impact of agents will be speed. In any field, when testing an idea takes less time than arguing about it in a meeting, people stop debating and just run the test. An agent that can read a thousand papers in an hour, design 500 molecules, and learn from its failed tests by morning will bring down the cost of experimentation and fundamentally change the pace at which science gets done. It will also give researchers the freedom to chase bold, strange questions they never would have risked their time on before, opening scientific doors we have yet to imagine.
While the AlphaFold template will certainly be key to incredible discoveries, it alone will not bring us to the end of science. Instead, the shift toward agentic AI represents a much rarer tier of breakthrough: a tool that envelops every field of science at once. Historically, tools of such scope have arrived just a handful of times: calculus, statistical inference, spectroscopy, the computer. Each revealed a world of problems no one had thought to formulate, and those problems, in turn, defined their fields anew. With agents, another such transformation is upon us.
Who wrote this
Eric Schmidt was the CEO of Google from 2001 to 2011. In 2024, with his wife Wendy, he co-founded Schmidt Sciences, a philanthropic venture to fund unconventional areas of exploration in science and tech.
Suhas Mahesh leads AI for Science work at the AI Center of Schmidt Sciences. He is a specialist in AI for materials discovery.
Additional research was done by Maya Levin, associate and sciences lead, Office of Eric Schmidt.
