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Skip to ContentMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioArtificial intelligenceThe UK government is backing AI that can run its own lab experimentsA competition calling for research projects involving so-called AI scientists shows just how fast this technology is moving.
By Will Douglas Heavenarchive pageJanuary 20, 2026Stephanie Arnett/MIT Technology Review | Getty Images, Adobe Stock A number of startups and universities that are building “AI scientists” to design and run experiments in the lab, including robot biologists and chemists, have just won extra funding from the UK government agency that funds moonshot R&D. The competition, set up by ARIA (the Advanced Research and Invention Agency), gives a clear sense of how fast this technology is moving: The agency received 245 proposals from research teams that are already building tools capable of automating increasing amounts of lab work. Related StoryWhat’s next for AlphaFold: A conversation with a Google DeepMind Nobel laureateRead next ARIA defines an AI scientist as a system that can run an entire scientific workflow, coming up with hypotheses, designing and running experiments to test those hypotheses, and then analyzing the results. In many cases, the system may then feed those results back into itself and run the loop again and again. Human scientists become overseers, coming up with the initial research questions and then letting the AI scientist get on with the grunt work. “There are better uses for a PhD student than waiting around in a lab until 3 a.m. to make sure an experiment is run to the end,” says Ant Rowstron, ARIA’s chief technology officer.  ARIA picked 12 projects to fund from the 245 proposals, doubling the amount of funding it had intended to allocate because of the large number and high quality of submissions. Half the teams are from the UK; the rest are from the US and Europe. Some of the teams are from universities, some from industry. Each will get around £500,000 (around $675,000) to cover nine months’ work. At the end of that time, they should be able to demonstrate that their AI scientist was able to come up with novel findings.
Winning teams include Lila Sciences, a US company that is building what it calls an AI nano-scientist—a system that will design and run experiments to discover the best ways to compose and process quantum dots, which are nanometer-scale semiconductor particles used in medical imaging, solar panels, and QLED TVs. “We are using the funds and time to prove a point,” says Rafa Gómez-Bombarelli, chief science officer for physical sciences at Lila: “The grant lets us design a real AI robotics loop around a focused scientific problem, generate evidence that it works, and document the playbook so others can reproduce and extend it.”
Another team, from the University of Liverpool, UK, is building a robot chemist, which runs multiple experiments at once and uses a vision language model to help troubleshoot when the robot makes an error. And a startup based in London, still in stealth mode, is developing an AI scientist called ThetaWorld, which is using LLMs to design experiments on the physical and chemical interactions that are important for the performance of batteries. The experiments will then be run in an automated lab by Sandia National Laboratories in the US. Taking the temperature Compared with the £5 million projects spanning two or three years that ARIA usually funds, £500,000 is small change. But that was the idea, says Rowstron: It’s an experiment on ARIA’s part too. By funding a range of projects for a short amount of time, the agency is taking the temperature at the cutting edge to determine how the way science is done is changing, and how fast. What it learns will become the baseline for funding future large-scale projects.    Rowstron acknowledges there’s a lot of hype, especially now that most of the top AI companies have teams focused on science. When results are shared by press release and not peer review, it can be hard to know what the technology can and can’t do. “That’s always a challenge for a research agency trying to fund the frontier,” he says. “To do things at the frontier, we've got to know what the frontier is.” For now, the cutting edge involves agentic systems calling up other existing tools on the fly. “They’re running things like large language models to do the ideation, and then they use other models to do optimization and run experiments,” says Rowstron. “And then they feed the results back round.” Rowstron sees the technology stacked in tiers. At the bottom are AI tools designed by humans for humans, such as AlphaFold. These tools let scientists leapfrog slow and painstaking parts of the scientific pipeline but can still require many months of lab work to verify results. The idea of an AI scientist is to automate that work too.   AI scientists sit in a layer above those human-made tools and call ton hose tools as needed, says Rowstron. “But there’s a point in time—and I don’t think it’s a decade away—where that AI scientist layer says, ‘I need a tool and it doesn’t exist,’ and it will actually create an AlphaFold kind of tool just on the way to figuring out how to solve another problem. That whole bottom zone will just be automated.” That’s still some way off, he says. All the projects ARIA is now funding involve systems that call on existing tools rather than spin up new ones.

There are also unsolved problems with agentic systems in general, which limits how long they can run by themselves without going off track or making errors. For example, a study, titled “Why LLMs aren’t scientists yet,” posted online last week by researchers at Lossfunk, an AI lab based in India, reports that in an experiment to get LLM agents to run a scientific workflow to completion, the system failed three out of four times. According to the researchers, the reasons the LLMs broke down included changes in the initial specifications and “overexcitement that declares success despite obvious failures.” “Obviously, at the moment these tools are still fairly early in their cycle and these things might plateau,” says Rowstron. “I’m not expecting them to win a Nobel Prize.” “But there is a world where some of these tools will force us to operate so much quicker,” he continues. “And if we end up in that world, it’s super important for us to be ready.” by Will Douglas HeavenShareShare story on linkedinShare story on facebookShare story on emailPopular10 Breakthrough Technologies 2026Amy NordrumThe great AI hype correction of 2025Will Douglas HeavenChina figured out how to sell EVs. Now it has to deal with their aging batteries.Caiwei ChenThe 8 worst technology flops of 2025Antonio RegaladoDeep DiveArtificial intelligenceThe great AI hype correction of 2025Four ways to think about this year's reckoning.
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By Rhiannon Williamsarchive pageWill Douglas Heavenarchive pageCaiwei Chenarchive pageJames O'Donnellarchive pageMichelle Kimarchive pageMeet the new biologists treating LLMs like aliensBy studying large language models as if they were living things instead of computer programs, scientists are discovering some of their secrets for the first time.
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The UK government’s Advanced Research and Invention Agency (ARIA) is strategically backing the development of “AI scientists,” representing a significant acceleration in the automation of scientific research. This funding initiative, culminating in a competition to select 245 research teams, signals a recognition of the rapidly evolving capabilities of artificial intelligence in scientific discovery. As defined by ARIA, an AI scientist encompasses a system capable of managing an entire scientific workflow – from formulating hypotheses and designing experiments, to analyzing results and iteratively refining its approach. This process often involves human scientists acting as overseers, initiating the research and then allowing the AI to execute the more laborious aspects.

ARIA’s decision to fund 12 specific projects, allocating approximately £500,000 each, reflects a deliberate strategy to gauge the ‘cutting edge’ of this technology. This approach, doubling the agency’s initial allocation due to the high quality and quantity of submissions, is a calculated experiment aimed at understanding how scientific research is likely to change and the pace at which this transformation is occurring. The agency acknowledges the current hype surrounding AI, particularly given the increasing investment of major tech companies, while simultaneously recognizing the urgency of staying ahead.

The selected teams represent a diverse range of applications. Lila Sciences, a US company, is developing an “AI nano-scientist” focused on optimizing the composition of quantum dots – materials utilized in medical imaging, solar panels, and QLED TVs. The project is designed to demonstrate a fully automated AI robotics loop, generating evidence, documenting the process, and making the methodology reproducible for others. The University of Liverpool’s project is developing a robot chemist capable of running multiple experiments concurrently, leveraging a vision language model to diagnose and rectify errors, a critical step in addressing the limitations of current AI systems. A London-based startup, operating in stealth mode, is creating an AI scientist called ThetaWorld, utilizing large language models (LLMs) to design experiments relating to battery performance, with the experiments being executed in an automated laboratory managed by Sandia National Laboratories in the US.

A key element of ARIA’s approach is its acknowledgement of the tiered nature of AI tools. Lower tiers consist of AI tools created by humans for human use, exemplified by tools like AlphaFold, which accelerates certain stages of scientific research but still often requires significant manual intervention and validation. The concept of an “AI scientist” – as defined by ARIA– is positioned as a layer above these tools, enabling the AI to autonomously manage entire workflows. Rowstron envisions a future where AI scientists may eventually possess the capability to not only execute existing tools but also *create* new ones, such as customized versions of AlphaFold, specifically tailored to address novel research challenges.

However, the agency recognizes significant hurdles remain. Current LLM-based scientific agents are prone to errors and breakdowns, as demonstrated by a recent study from Lossfunk, which reported three out of four failures during a scientific workflow experiment. The researchers attributed these failures to changes in the original specifications and “overexcitement,” highlighting the need for greater robustness in these nascent systems. Rowstron stresses the importance of being prepared for rapid advancements, recognizing that while the immediate goal is not a Nobel Prize, the technology has the potential to radically transform the pace of scientific discovery.

ARIA's strategy prioritizes systems that call upon existing tools rather than building entirely new ones. This approach reflects a pragmatic understanding of the current limitations of AI and the need to build upon established methodologies. The initiative acknowledges the "frontier" nature of this work and the agency's vital need to understand its developing shape.