China's industrial success increasingly challenges this picture. Its scale in critical minerals, batteries, EVs, and solar came from harnessing market forces to build competitive industrial players first, with innovation following. Lu Feng, a Peking University economist, describes this technological philosophy as follows: “major technological innovation requires the support of an entire industrial system”, and “industrialization generates both the demand for science and the capacity to invest in it.”

The relationship between industry and scientific knowledge is far more tangled than the linear model suggests. In my research for this blog, I kept finding the same pattern: a scientist, usually at a public research institution, borrows spare industrial capacity from a private company to conduct groundbreaking research — often in a direction entirely unrelated to the equipment's original purpose. In other words, industrial overcapacity is one channel by which industry investment spills over to support scientific discovery.

Below are five examples of when industrial overcapacity spills over to enabling scientific discovery, ranging from a 1920s machine shop to a data center a century later.

The Speed of Light & Sperry’s Optics

In 1907, Albert Michelson became the first American to win a Nobel Prize, for his precision optical instruments. But his most famous measurement of the speed of light came nearly two decades later, in the mid-1920s, through an exquisite optical setup: a multi-faceted, high-speed rotating mirror atop Mt. Wilson and a fixed mirror 22 miles away atop Mt. San Antonio. A beam of light bounced off the rotating mirror, crossed the valley, and returned. How far the rotating mirror had turned in the meantime gave the elapsed time.

But Michelson was only able to build this equipment thanks to Elmer Sperry, a prolific inventor and businessman who founded Sperry Company, whose Brooklyn machine shop was the world’s leading manufacturer of gyrocompasses for the world’s navies. Sperry was thrilled to contribute to manufacturing the precise mirrors needed for this experiment. After learning his steel mirrors had performed beautifully, he wrote Michelson: “to think that we have been in any way helpful in this great work fairly takes our breath away.” When Michelson asked for a price quote on a second mirror, Sperry instead wired back asking permission to donate the equipment outright. The measurements born of this collaboration were so precise that Michelson's value is within 0.001% of the value used today. This groundbreaking measurement — an early milestone for American science — was made possible by a skilled experimentalist and the precise manufacturing capability of an industrialist.

Albert Claude & the Borrowed Microscope

By the early 1940s, the Rockefeller Institute’s Albert Claude had already done pioneering work in cell biology, using an ultracentrifuge to isolate mitochondria. But to actually see the organelles he was isolating, he needed to access one of the newly developed electron microscopes. In all of New York City, there was exactly one electron microscope, and it belonged to Interchemical Corporation, a paint and ink manufacturer that had bought an early RCA model to study pigment particles.

In 1942, Claude struck up a collaboration with Ernest Fullam, Interchemical’s electron microscopist, and started examining biological specimens at night on a machine designed for industrial coatings research. It took three years of fiddling with staining, fixation, and sample supports thin enough for an electron beam, but in 1945, Claude, Fullam, and Claude’s Rockefeller colleague Keith Porter published the first electron micrograph of an intact cell — an image that George Palade would later call the “birth certificate” of cell biology. Claude won a share of the 1974 Nobel Prize in Physiology or Medicine for the work that began on Interchemical’s microscope.

And as I’ve written before, the existence of an American electron microscope industry at all in this period is itself a story of industrial spillover — RCA built the Model B largely as a scientific prestige project, on the back of its commercial vacuum-tube manufacturing capacity.

The Met Office & a Caterer’s Computer

In the 1950s, the UK Met Office (UKMO) set out to develop numerical weather prediction on the newly invented electronic computer. Weather prediction informs agriculture, trade, and military maneuvers, and this was part of a global race among national weather agencies to develop accurate numerical weather prediction techniques.

The problem was, they had no electronic computer. No funds had been appropriated for one. Instead, UKMO meteorologists used a computer owned by J. Lyons & Co., a London catering company that ran a chain of teashops and a food delivery business. In 1951 it had built itself the Lyons Electronic Office, or LEO — an electronic computer intended to handle Lyons' payroll, logistics, and the daily routing of vans carrying perishable goods. On weekends, UKMO meteorologists ran their weather models on LEO. Later, UKMO used Cambridge and Manchester University electronic computers; it was not until 1959 that UKMO finally got their own electronic computer.

The Holmdel Horn & the Big Bang

In 1960, Bell Labs built a 20-foot horn-shaped antenna in Holmdel, New Jersey, to receive radio signals bounced off early NASA communication satellites. A massive antenna was needed as early satellites were passive reflectors, little more than fancy weather balloons with faint radio reflection signatures. The Holmdel Horn supported two early satellites, before quickly becoming obsolete, as active satellites carried their own receivers and amplifiers. This left Bell Labs with an extraordinarily sensitive piece of microwave hardware sitting on a hill in New Jersey with nothing commercial to do.

Two Bell Labs radio astronomers, Arno Penzias and Robert Wilson, convinced Bell Labs to let them repurpose it for pure research, to listen for radio signals from the Milky Way. In 1964 they began calibrating the receiver, cooling it with liquid helium to within four degrees of absolute zero to suppress thermal noise, and found a faint, steady microwave hiss coming from every direction in the sky that they could not get rid of. After a year of trying to eliminate it, they were tipped off to a paper by Robert Dicke’s group at Princeton, which predicted that the Big Bang would have left behind exactly this kind of uniform microwave background. Penzias and Wilson had stumbled into the cosmic microwave background and won the 1978 Nobel Prize in Physics for it — a result only possible thanks to Bell Labs’ massive spare antenna.

Meta’s Spare Compute & Catalyst Discovery

And finally, a modern example. Catalysts sit at the heart of nearly every industrial chemistry problem: hydrogen production, carbon capture and reduction, fertilizer synthesis, plastic recylcling, etc. Density Functional Theory (DFT), a modern computational chemistry technique, can simulate a catalyst's surface and help design more abundant, efficient catalysts. The challenge is DFT's enormous compute cost multiplied by the vast combinatorial space of possible catalyst materials.

In October 2020, Facebook AI Research (now Meta FAIR) and Zack Ulissi at Carnegie Mellon University launched the Open Catalyst Project to attack this bottleneck by training machine-learned interatomic potentials (MLIPs) on enormous DFT datasets, approximating DFT accuracy at orders-of-magnitude greater speed. The first dataset, Open Catalyst 2020, contained 1.3 million DFT relaxations and consumed roughly 70 million CPU-hours. Facebook generated it in four months by running the simulations on spare compute cycles in its data centers. The most recent release, Open Molecules 2025 (OMol25), used nearly 6 billion CPU-hours of spare data center compute. FAIR has also trained and released several open-source models trained on these datasets.

History is full of other examples of industrial capacity enabling scientific discovery:

• Ernest Lawrence building his Nobel Prize–winning cyclotron with an 80-ton magnet donated by Federal Telegraph

• Gordon Teal developing the germanium ingot puller at Bell Labs on nights and weekends with spare equipment

• Cecil Powell discovering the pion subatomic particle using photographic emulsions that Kodak and Ilford developed under U.K. industrial policy

Our science funding and philanthropy models should more closely consider how scientific innovation and industrial development intersect. For instance, NSFs recent program funding PhDs to work in industry as part of their degree is an obviously good idea and a belated parallel to China’s “practical PhD” program, which just graduated its first cohort. These vignettes also suggest the role of scientific infrastructure, often in the form of industrial capacity, continues to be undervalued.

As geopolitical competition with China intensifies and political pressure mounts on public science funding, we urgently need new models of innovation beyond the university-centric linear model. A nation’s industrial capacity is directly tied to its ability to advance scientific innovation — we should invest in both accordingly.

What is Science?

The question of “what is Science”, while philosophical in nature, is an important starting place if we are to understand the impacts of AI for Science.

Today, many AI for Science startups presume simplistic models of what Science is and how it works e.g. Science as producing papers, Science as producing data.

But both of these models mistake the artifacts for the actual substance. Ask any scientist about a paper in their field and they’ll tell you about everything it left out or glossed over, including sometimes in their own paper. And while foundation models like AlphaFold are useful, and data is often a bottleneck to developing such models, the process by which those AI models create scientific value is still evolving.

Ultimately, a poor model of Science will lead to a poor company.

Science as Collective Sensemaking

My preferred model is Science as Collective Sensemaking.

Science is the process by which a select set of people whom we call scientists come to an agreed upon understanding of our perceived reality. As Polanyi describes in his essay Republic of Science, scientists are a body politic which determines how to allocate limited intellectual and material resources through “mutual adjustment” based on peer feedback and results.

The locus of Science is not in papers or data. These are merely the outputs of the sensemaking process, often playing an ancillary role.

Instead, the production function of Science is manifested in its purest form at the department seminar and the science conference. It is in those rooms that initial evidence is shared, feedback is gathered, the scientific collective updates its priors, and new research directions are identified.

It is worth noting how remarkable this system is. The invisible hand of the market is the best mechanism we have for discovering the value of something. The modern republic of Science, which is not without its flaws today, allows communities of scientists to collectively assess the value and validity of ideas without prices. The general failure of philanthropy and non-profits should serve as a useful benchmark for just how difficult it is to assess value creation in non-market conditions. It is a miracle that Science has worked as well as it has, for as long as it has.

But, with this view of Science as Collective Sensemaking, where does this leave AI?

AI & Scientific Sensemaking

At the object level, there are several different ways AI will impact Science:

• Across every scientific field, LLMs will marginally increase insights from literature and hypothesis generation

• For any field or scientist that uses code, AI coding agents will be an accelerant

• Foundation models will serve as useful instruments but the value they provide and the barriers they face will vary by field

I am tremendously optimistic about the impact AI will have as a new tool for scientists, in all these different ways. But at the system level of Science, the effects of AI are still unclear.1

The role of scientific publishing, which I argue is an ancillary output of the production of Science, will only become more marginalized as the peer-review publication system comes under increasing strain with a flood of AI-generated papers. As a result, collective sensemaking as a social technology will become more important when scientists have even less reason to trust scientific publications.

Ultimately Science is a human endeavor. The truths of the universe are undoubtedly out there. They may even be inside the model weights of GPT-6.1. But if no one finds them, understands them, agrees with them, and builds on top of them, does it matter? Mendel discovered how traits are inherited, but did it matter if no one knew about his discovery?

So too for AI in Science. The models may ask better questions and make more discoveries, but it only “becomes” science when those discoveries and insights are incorporated into our collective understanding.

The true bottom of funnel for the scientific process is not the published paper but the moment the core insight is baked into the invisible body of scientific orthodoxy.

This socially mediated process may be the ultimate binding constraint on the magnitude of the impact of AI for Science. It may be that the AI productivity gains in Science are rate-limited by the pace at which the scientific collective can update from new discoveries and findings, and are ultimately mild compared to AI productivity gains in other parts of the economy.

That the science sensemaking process is still most concentrated in academic conferences and ivory tower department seminars is a sign of how little it has benefited from digital publishing or other software tools for collective intelligence. The story of Mendeley is a canonical example of how at odds science publishers’ incentives are with building better tools for collective sensemaking. Acquired by the publishing giant Elsevier in 2013, it had its social features quietly retired by 2021 reducing it back to a mere reference manager. Science Twitter was a genuinely active scene at one point, but much of it has since disappeared or dispersed onto LinkedIn and Bluesky. Neither platform was built for collective sensemaking among scientists, and both serve as poor, crowded substitutes.

The truly visionary AI for Science company is not automating experiments or AI-generating Nature papers, but building technology to improve the collective sensemaking ability of scientists.23

Transformative AI is arriving and disrupting every part of our society and economy — the same is and will be true for Science.

This new technological era raises many important implications for the science policymaker: how do we drive adoption of coding agent tools among scientists? how does AI change the role of autonomous labs in scientific experimentation and the geopolitical scientific landscape? What are the needed changes for scientific publishing and curation to remain relevant?

Today, I want to sketch out the case for another implication that AI has for how we fund science.

If we view LLMs as commoditizing the task of hypothesis generation, then the binding constraint of scientific progress shifts to hypothesis verification. This naturally implies an expanding role of autonomous labs, but it also implies that science infrastructure will become more important in the age of AI.

What do I mean by science infrastructure? It can range from high-end microscopes costing tens of millions of dollars, such as cryoEM and Scanning Transmission Electron Microscopes (STEM), to billion-dollar synchrotrons, supercomputing clusters, and ion beam user facilities.

Funding large-scale scientific infrastructure has historically fallen to public agencies and philanthropists, for a simple reason: the high upfront fixed cost, low marginal cost of operation, and low utilization rate by any one institution make it uneconomical for a single university or company to build, even though many would benefit from access.1

Why do these become more valuable? As the cost of generating hypotheses falls toward zero, the binding constraint becomes the rival, excludable capacity to test them. Especially as more private AI-powered companies focus on hypothesis generation, the relative value of public funding shifts from underwriting ideas to underwriting instruments and infrastructure.

A further benefit is that investments in infrastructure are hypothesis-agnostic: the taxpayer takes on a lower risk, since the bet is not on any single hypothesis but on the capacity to test a broad range of them.2

The value of such infrastructure is clearest in the astronomy and particle physics communities. These are fields which already have a surplus of hypotheses and are constrained by infrastructure capacity to test them. The Decadal Survey for astronomers and Snowmass & P5 for physicists convene the entire academic community to align on the next big shots on goal to take in terms of which new telescopes and accelerators will help test the most promising hypotheses.3

My argument is that the value of scientific infrastructure, already legible for some fields, will become even more valuable for other experimental sciences as AI becomes more powerful.

And it is not just a matter of capacity — new types of infrastructure and instrumentation often advance science in non-linear fashion, opening up previously inaccessible modalities of perception. In other words, the value of new scientific instruments also increases in an age of AI alongside large-scale scientific infrastructure.

Unfortunately, the specific value provided by instruments and infrastructure to scientific progress is relatively understudied outside of history of science and STS fields. The lack of broader awareness of the importance of scientific tooling is something even Freeman Dyson recognized:

Scientific revolutions are more often driven by new tools than by new concepts. Thomas Kuhn in his famous book, “The Structure of Scientific Revolutions”, talked almost exclusively about concepts and hardly at all about tools. His idea of a scientific revolution is based on a single example, the revolution in theoretical physics that occurred in the 1920s with the advent of quantum mechanics. This was a prime example of a concept-driven revolution. Kuhn’s book was so brilliantly written that it became an instant classic. It misled a whole generation of students and historians of science into believing that all scientific revolutions are concept-driven. The concept-driven revolutions are the ones that attract the most attention and have the greatest impact on public awareness of science, but in fact they are comparatively rare.

Freeman Dyson, Birds and Frogs, Selected Papers

In particular, the overuse of papers and patents in the “metascience” community has led to a blindspot in properly understanding the hidden value of science infrastructure. While there is much focus on reforming PI-based grant review processes — such as golden tickets for reviewers — there has been far less discourse around the value and processes of programs like NSF’s Mid-Scale Research Infrastructure grant program and DOE’s user facilities. As AI progress accelerates, science policymakers should carefully consider the balance of public funding for PI-based grants and infrastructure capacity.45

By contrast, the logic of industrial science and infrastructure is one that China understands well. Some of the best technoeconomic analysis of U.S. national lab infrastructure is done by Chinese Academy of Sciences researchers. Today, China is building more than 90 megascience facilities with spending on scientific capital assets more than tripling between 2015 and 2024 even as traditional infrastructure investment has slowed. The U.S. may retain its lead in AI model capability and still fail to translate that advantage into accelerated scientific progress if we remain bottlenecked by hypothesis verification.

Finally, as someone who works in philanthropy, the value of science infrastructure is even more apparent. Rather than betting on a specific scientific idea or thesis, a key science infrastructure investment can bolster an entire field without requiring as opinionated or clear a view on a specific hypothesis or discovery. Warren Weaver demonstrated the value of promoting new science instruments in accelerating the pace of new biological discoveries and fields. In the modern era of science philanthropy, Eric Schmidt’s donation for a new collider at CERN and a new telescope and three observatories for astronomers, as well as Chan Zuckerberg Initiative’s support for breakthrough phase imaging in cryoEM, are more recent examples.6

Regardless of funding source, it remains the case that AI is steadily dissolving the assumption that better science comes from improving the supply of ideas through better grant mechanisms, more PhDs, more papers. As hypothesis generation becomes abundant, the binding constraint on scientific progress shifts toward our capacity to verify hypotheses. The scientific infrastructure capacity for large-scale hypothesis verification is precisely the club good that markets will not build, and that public agencies, national labs, and philanthropies exist to provide.

As we enter an age of AI and the “third wave of American philanthropy”, philanthropists and policymakers should more closely consider the value of scientific infrastructure.

Without physical optics there would have been no microscope, and until the perfection of the microscope, the biologist was in the main limited to what his unaided senses told him. It was by means of the compound microscope that the development of the cell theory was made possible in the nineteenth century. Similarly it is by means of the new tools and techniques developed in many instances by the physical sciences that the door to a biology of molecules has only recently been opened.

Rockefeller Foundation Annual Report, 1938

Warren Weaver, Rockefeller Foundation, and Molecular Biology

From 1931 to 1959, Warren Weaver ran the Rockefeller Foundation’s (RF) Natural Sciences Division. A mathematician with no background in biology, Weaver did something unusual for a philanthropy: he took roughly 80% of his division’s budget and concentrated it on a single thesis — funding the application of physics techniques to biological problems.

His maverick approach to science philanthropy, strong conviction, and interdisciplinary transgression paid off. Nearly every Nobel Prize in molecular biology awarded between 1954 and 1965 traces back to a Rockefeller Foundation grant — grants made, on average, two decades before the prize. It remains the single clearest case study of how concentrated, high-conviction science funding can generate a burst of new discoveries and ultimately create an entirely new scientific field, which Weaver dubbed “molecular biology”.1

Warren Weaver and New Science Instruments

In the two decades since [the Rockefeller Foundation made the decision to concentrate funding in molecular biology], there has been a remarkable development in the methods of biological investigation. The change is especially marked in the application of physical tools and techniques, such as the ultracentrifuge, the electrophoresis apparatus, spectroscopy, X-ray diffraction, the electron microscope and isotopic tracers. Through grants and fellowships the Foundation contributed to the development of some of these tools of research and to the application and extension of all of them to biological problems.

Rockefeller Foundation Annual Report, 1951

How did Warren Weaver fund and create the field of molecular biology? Others have written about how he brought in physicists to tackle biology problems, or the unique “fox”-shaped project officers Weaver recruited to identify and fund scientists, but I want to add a new dimension to Weaver’s strategy to creating a new field of science.

In “A Quarter Century in the Natural Sciences”, an essay Weaver wrote as a reflection on his time at Rockefeller Foundation, he is quite explicit that the field of molecular biology was created through new science instruments. Specifically, through:

• New methods of separation: ultracentrifuge and electrophoresis

• New methods of seeing: electron microscope, x-ray crystallography, and radioactive isotope tagging

Why are new science instruments so critical to creating new science fields? Because new science instruments create new ways of perceiving the world. Nearly every one of the Nobel Prizes that Weaver would go on to fund can be attributed to one of the 5 instruments/techniques listed above.

How did Weaver incorporate new science instruments into his field creation strategy? Analyzing Rockefeller Foundation annual reports from 1930-1950 and secondary sources, I will use the ultracentrifuge, electron microscope, and x-ray crystallography as case studies of how Weaver drove the application of these new instruments to the problems of biology, identify specific patterns of how Weaver drove the development and adoption of these new science instruments to ultimately lead to Nobel Prize winning discoveries, and what we can learn from Weaver’s use of new science instruments to drive field creation.

Ultracentrifuge

I too have made a wonderful journey…For the last 25 years, I have roamed through living cells, but with the help of a centrifuge rather than of a microscope.

Christian De Duve, 1974 Nobel Prize Acceptance Speech

While centrifugation had long been used to separate substances of different densities, the ultracentrifuge generated centrifugal forces orders of magnitude greater, allowing separation of even different proteins, and crucially integrated real-time optical detection to measure the properties of separated components. Theodor Svedberg developed the first ultracentrifuge to better understand colloidal chemistry, which won him the Nobel Prize in 1926, both for his experimental results and the development of a new science instrument that enabled those findings, one of the few Nobels awarded for instrumentation.

But adoption was slow, as Svedberg’s ultracentrifuge was bulky and quite dangerous — massive rotational forces meant if it came apart, and early versions often did, it would do so quite violently. In 1931, Weaver funded Stanford professor James McBain to spend a year with Svedberg, to learn about the ultracentrifuge. McBain would go on to build several simpler ultracentrifuges for use in the U.S. In 1934, Weaver funded Professor Beams’ team at UVA to develop a new, electrically driven ultracentrifuge, rather than oil-turbine design of Svedberg. It was this electrical ultracentrifuge design that was ultimately commercialized at scale.

The early days of new science instruments are often quite hacky, with tinkerer scientists engineering their own contraptions. Weaver’s support of multiple independent efforts to build ultracentrifuges helped drive down costs and accelerate eventual commercial deployment of ultracentrifuges. This strategy of ensuring new instrument development explored multiple technology trees is similar to the one employed in the Manhattan Project and for nuclear reactor development. In another remarkable example of how science instrumentation is core to a nation’s industrial capacity, Beams would go on to take his ultracentrifuge work to the Manhattan project to work on uranium separation.

But simply ensuring an instrument is developed is insufficient. Weaver’s consistent strategy was to not only fund the development of new science instruments, but to drive diffusion and adoption of those instruments into biological applications. Ultracentrifuges, originally developed for colloidal chemistry, required significant adaptation to biological samples. For scientists to invest in developing new experimental techniques on a new instrument required faith that this new instrument would yield novel discoveries.

For the ultracentrifuge, cell homogenization, the ability to shred cell walls without damaging the organelles that were to be separated; appropriate suspension mediums, to prevent distortion of the organelles in solution; and centrifugation ramp rate protocols, to provide clean separation of organelles; all had to be tested and developed before biologists could even begin to use the ultracentrifuge to understand cellular components and structure.

Much of this methodological work was done by Albert Claude and George Palade at Rockefeller Institute. Notably, they were also both pioneers in electron microscopy techniques for biology, a technology we will return to in the next section.

Separately, Weaver also funded Christian De Duve at University of Louvain, starting in 1950, to work on cell fractionation techniques with ultracentrifuges. De Duve would go on to win the Nobel Prize for discovering a new organelle —lysosomes— solely by looking at histograms of fractionated cell components. 2

It is worth noting how remarkable this is. Popular conceptions of science often assume that new discoveries require directly "seeing" something new, like an image in a microscope. But De Duve, by developing new sample preparation and fractionation protocols for the ultracentrifuge and through careful analysis of enzyme activity across fractions, was able to infer the existence of an entirely new organelle — solely from patterns in his data. The combination of a new instrument with the tenacity of an experimentalist willing to develop new methods to creatively extract understanding is the hallmark of how new science instruments drive discovery.

Electron Microscope

…innovations in experimental technique can also radically alter the scene of inquiry in a science. New techniques make new forms of evidence available, and thus can lead to redefinition of standards of evidence and explanation in already established problem domains.

Picture Control, page 11

While the ultracentrifuge sprung from colloidal chemistry, new methods of seeing were often more explicitly borrowed from physics. The electron microscope was first built by Ernst Ruska in Berlin in 1933 and exploited the wave-like nature of electrons that allowed much higher atomic resolutions compared to optical microscopes, which are constrained by the wavelength of visible light.

The development of the electron microscope in the U.S. was accelerated by Radio Corporation of America’s (RCA) early interest. RCA represented the heyday of American manufacturing and innovation, a pioneer manufacturer of radios, vacuum tubes, and phonographs. In the late 1930’s, RCA embarked on an enormously expensive scientific prestige project to build an electron microscope, in part to rebuff claims from their competitors that their radio transmission technologies were inadequate.

RCA’s first mover advantage lay in the simple fact that competitors like GE and Kodak correctly assessed that the market for the still nascent electron microscope was too small at the time. With the outbreak of World War Two, resources were no longer allocated towards making televisions, but RCA’s second electron microscope prototype, the Model B, was granted top defense priority (AA1 status) by the War Production Board, as a significant unclassified high-technology development to counter claims of German scientific superiority.3 It helped that RCA had also funded a National Research Council (NRC) electron microscopy fellowship at their corporate R&D lab in Camden, New Jersey.4 Their electron microscope center helped gatekeep early access to electron microscopy and allow the gradual development of an accepted methodology for preparing and interpreting electron micrographs.

While RCA’s NRC fellowship helped build an early community around their electron microscope, Weaver also situated the electron microscope at the center of his plans to revive the flailing biology department of MIT. With MIT president Karl Compton, Weaver brought in Francis Schmitt, a physiologist Weaver had cultivated while he was a faculty member at Washington University, to chair a new biology department at MIT. Schmitt’s arrival at MIT was bolstered by a $70,000 grant (~$1.5M in 2025 terms) from Weaver for “development and research centering around the new precision tool, the electron microscope”, including the procurement of a RCA Model B electron microscope. Schmitt would go on to pioneer the field of biophysics, with a department known for its doubly rigorous biology and physics training curriculum for graduate students. While Schmitt never won a Nobel Prize, he was instrumental in developing foundational techniques for operating the electron microscope and produced some of the first electron micrographs of collagen, myelin sheaths, and nerve fibers.

In parallel to Schmitt’s work at MIT, Albert Claude and Keith Porter at Rockefeller Institute (institutionally separate from Rockefeller Foundation, but both well known to Weaver), were also experimenting with electron microscopes.5 They produced the first electron micrographs of intact cells in the mid-1940s, revealing the endoplasmic reticulum — a vast, folded membrane system that no one had known existed. George Palade, who joined Rockefeller in 1947, then used the electron microscope to discover ribosomes studding the endoplasmic reticulum and trace the pathway by which proteins are secreted from cells. Claude and Palade would share the 1974 Nobel Prize with De Duve, whose ultracentrifuge work we covered above, for their discoveries of cell organelles. Together, they transformed the cell from what Claude called a mass “as distant from us as the stars and galaxies” into a system of known organelles with known functions — all made visible by the electron microscope and the ultracentrifuge working in tandem.

It is worth remarking how prescient of a conviction Weaver had in electron microscopes. From the outset, it was not obvious electron microscopes, with their vacuum sample chambers and intense ionizing beams, could be applied to biological samples, as opposed to the more inert inorganic metals they were a more natural fit. Several immediate challenges were apparent. First, biological samples would dehydrate rapidly under vacuum and beam conditions. Second, samples had to be made thin enough that electron detectors could measure deflection accurately on the backside. And finally, high contrast had to be induced, as the tiny electrons often had little deflection through water-filled biological samples. Addressing these problems required a stack of new techniques. Creating consensus around these methods took even longer. Debates over whether what electron microscopists were seeing was real biology or preparation artifact consumed the field for over a decade, with traditional biologists often pushing back to defend their own turf.

Ultimately, the electron microscope brought tremendous perceptive precision, moving us past the wavelength barrier of optical microscopes. Weaver’s early conviction to encourage the application of the electron microscope towards biology accelerated the development of new techniques and methods to make the electron microscope hospitable for viewing cells. Today, the electron microscope remains a workhorse of materials and biology characterization, particularly with the new revolution in cryoEM6.

Even now, we can still see the echoes of Weaver’s concentrated grantmaking around the electron microscope. In 1949, Weaver made a large grant to Cornell to establish an all-university Electron Microscope Center, notably under the engineering physics department, rather than a biology department. Today, Cornell is still a leader in electron microscopy techniques, with two Cornell researchers developing a new electron detector (the EMPAD), which was licensed to Thermo Fisher and is now ubiquitous in high-end electron microscopes.

X-Ray Crystallography

…making use of recent developments in physics. X-ray diffraction by crystals and electron diffraction by gas molecules have been the experimental methods principally used. The theoretical work involves the application of quantum mechanics to complex molecules. The work has now advanced to a point where it becomes possible to study the structure of chlorophyl, hemoglobin, and other substances of basic biological importance…
Rockefeller Foundation Annual Report, 1933

Like the electron microscope, X-ray crystallography was first used by physicists for studying crystals and minerals. Following von Laue winning the 1914 Nobel Prize for discovering crystal X-ray diffraction, the father-son Bragg duo won the 1915 Nobel Prize for developing a quantitative understanding of how to interpret X-ray diffraction to study crystal structures.

When Weaver joined Rockefeller foundation, one of the first grants he made was to fund the application of X-ray crystallography, previously used for inorganic crystals, to squishy biological compounds. In 1932, he supported the famous Linus Pauling’s lab at Caltech, including for the development of “an x-ray method, by means of which the environment of an iron atom in hemin and related substances can be investigated.”

In 1936, Max Perutz, a chemistry graduate from Vienna, arrived at Cambridge’s Cavendish Laboratory to work with J.D. Bernal, who had recently demonstrated that wet protein crystals could produce sharp X-ray diffraction patterns. When Lawrence Bragg became Cavendish Professor in 1938, he saw promise in Perutz’s work on hemoglobin crystals and championed it as exactly the kind of problem where X-ray methods could open up biology — a conviction he shared with Weaver. From 1939, the Rockefeller Foundation began funding Perutz and Bragg’s lab, sustaining their work with funding throughout the difficult war years in the U.K. and onwards.

Applying X-ray crystallography to proteins required solving two problems: crystallizing the protein, and then interpreting the resulting diffraction pattern. For minerals, diffraction patterns were clean and calculable based just on the unit cell of the mineral crystal. For a complex, folded protein with thousands of atoms, both steps were a nightmare. Perutz spent 15 years attacking the interpretation problem before cracking it in 1954 with isomorphous replacement — attaching heavy mercury atoms to the protein and comparing diffraction patterns with and without them. John Kendrew, a former military advisor to Mountbatten who had joined Perutz at the Cavendish in 1945, applied this method to the simpler protein myoglobin, producing the first 3D protein structure in 1958 with sperm whale myoglobin, after unsuccessfully trying to crystallize myoglobine from a half dozen mammals. Calculating protein structure from the resulting diffraction pattern was only possible because Kendrew invested the time to learn how to program Cambridge’s EDSAC, one of the world’s first electronic computers — yet another case of a discovery only being possible thanks to a new tool.7

From the perspective of Nobel Prizes, X-ray crystallography was, by a significant margin, the single most productive instrument in Weaver’s portfolio. Pauling at Caltech, funded from 1932, used X-ray diffraction to establish the rules of chemical bonding, discover the alpha helix structure of proteins, and identify the molecular basis of sickle cell anemia — the first disease understood at the molecular level. Perutz and Kendrew at Cambridge, funded from 1939, spent over two decades developing the methods to solve the first protein structures, along the way training Francis Crick and hosting James Watson, who used the lab’s X-ray expertise to build their model of DNA. Maurice Wilkins and Rosalind Franklin at King’s College London used Rockefeller-funded X-ray equipment to produce diffraction images of DNA fibers that were the key experimental evidence for the double helix. Dorothy Hodgkin at Oxford, who had trained under Bernal at Cambridge and co-produced the first X-ray photographs of a protein crystal, used Rockefeller grants and an early IBM machine to solve the structure of penicillin during the war and then vitamin B12, cholesterol, and eventually insulin over a 35-year effort. 89

X-ray crystallography was so productive for the same reason any new instrument is productive for early adopters — the methodological knowledge required to make it work compounds over time and is very hard to transfer. Weaver, in his autobiography Scene of Change, includes a quote from Bragg describing this directly:

I received a letter from Sir W. Lawrence Bragg, the younger of the father-and-son team that received the Nobel Prize in 1915 for their determinations of crystal structures by X-ray diffraction techniques. In this letter Sir Lawrence said, “concerning the part the Rockefeller Foundation played in helping the ‘Cambridge School.’ Your help came at a vital time just before the war when I was trying to find some way of supporting Perutz’s work [Max F. Perutz was the chairman of the Laboratory of Molecular Biology at Cambridge] and it was continued after it. ‘This school was responsible for DNA, for the first protein structures, for the first understanding of virus structure, and for work on muscle.

‘The extent to which the X-ray analysis of protein was pioneer work is shown by the fact that only now, twelve years after the trail was beaten at Cambridge and the Royal Institution, has any other research centre succeeded in getting a protein ‘out.’ I am allowing myself to put this so strongly just because I think that the Foundation’s help made an outstanding difference to these advances.”

Scene of Change, chapter 5

This is the paradox and promise of new science instruments: they are slow to yield results, but those results, once obtained, are so definitive and information-rich that they reshape entire fields. Weaver’s willingness to fund this kind of long-horizon, high-risk science instrumentation work — for nearly two decades before it produced a single solved protein structure — is perhaps the strongest evidence for the value of patient, conviction-driven science philanthropy, and the impact of a science-instrumentation driven view of scientific field creation.10

What can we learn from this?

Warren Weaver's tenure at the Rockefeller Foundation is held up as an exemplar of what a high-conviction program director can accomplish: the creation of a new scientific field, an explosion of research that directly benefits mankind, and a bevy of Nobel Prizes to boot.

A key ingredient to Weaver's success is not just funding physicists to work in biology, but bringing the tools of physics to the problem of biology. Through the application of new ways of seeing, like the electron microscope and x-ray crystallography, and new ways of separating, like the ultracentrifuge, Weaver-funded scientists were able to make novel discoveries with breakthrough hit rates. While only covered on the fringes in this post, Weaver’s instrument-driven thesis was orchestrated as part of a broader institutional and industrial strategy to create centers of excellence and drive commercial production of new tools.

The counterfactual impact of Weaver's funding lay in the high risk that comes with adopting new tools. For a scientist to introduce a new instrument, a new way of seeing the world, does not mean default acceptance of new methods by their peers. At the outset, scientists don't know whether a new instrument will yield new insights or be unworkable with the specimens and samples of interest. The ultracentrifuge was a difficult machine to engineer and new ramp rate protocols, suspension mediums specific to cells, and cell homogenization techniques were needed; electron microscopes were death rays for cells in a vacuum, and required new thin-sectioning, phase contrast media, and fixation protocols to prevent distortion of cell shapes; while interpreting X-ray diffraction patterns of proteins required the invention of the electronic computer and creative techniques to crystallize proteins and extract structural insight from diffraction patterns.

Weaver's funding and support for scientists willing to take a leap with him and commit their careers to a new science instrument — often because they came from a different domain and saw possibilities that biologists couldn't — is part of why his funding strategy was so impactful. His playbook for using new instruments to create new fields of science is still instructive today. In subsequent posts, I will write about my own "Weaver thesis", how to think about the role of AI as a new tool for scientists in different domains, and implications for U.S. science policy.

How to Win the AI For Science Race

In the near-term, “winning the AI for Science race” is downstream of a simple metric: which country gets the largest proportion of their STEM graduate students using the best AI coding tools available. 1

But right now, we have almost no empirical understanding of whether that’s happening.

AI is moving so quickly that sense-making on the ground is difficult in any sector, but in science the gap is especially stark. Within the bubble of SF’s AI economy, it feels like everyone is already moving on AI — a massive VC infusion has stood up entire supply chains of agent providers, domain-specific AI companies, and data infrastructure, to say nothing of general software startups that are increasingly “AI-first.”

But the rest of the country is still catching up.2 I still meet STEM graduate students who are writing Python scripts entirely by hand — not out of principle, but because Claude Code simply hasn’t reached them yet. The “Claude code moment” has not reached every part of the economy and the same is true for science.

Because AI capabilities continue to evolve so quickly, it is hard to make sense on what is actually happening on the ground — including in AI for Science.

This post is a first attempt to measure the diffusion of AI coding tools into scientific work. Leveraging the fact that Claude Code commits are default co-authored, and that we can use ORCID-GitHub profile links as a proxy for “scientists who code,” we can begin to measure how many scientists use Claude Code, what kind of scientists use it, and what they use it for.

We define “scientists” in this post as:

• ORCID profiles who include a github profile link in their bio or profile description

• have at least 1 commit on github in the past year

• have at least 1 peer reviewed publication on ORCID in the past 2 years3

Out of ~14M total ORCID profiles, we identified 15,933 scientists (0.1% of ORCID profiles) who fit this definition. Our dataset selects for scientists who both maintain ORCID profiles and link to active GitHub accounts — a narrow slice of computationally active researchers — and the small sample of 331 Claude Code users means breakdowns by field, country, or seniority are directional, not definitive. Full Github repo here.45

How Many Scientists Use Claude Code?

As of February 15, 2026, ~2.1% of scientists with ORCID-linked GitHub profiles use Claude Code. Extrapolating from current growth rates, we’d expect ~10% of “scientists” in our dataset to use Claude Code by end of 2026.

Scientists are responsible for a relatively stable proportion of total Claude Code commits, which suggests scientists are adopting Claude Code at roughly similar rates as other GitHub users.

What’s notable is the late January 2026 bump in scientist Claude Code commits. This suggests usage among scientists is still tied to academic cycles, but the late January bump is durable.

What Kinds of Scientists use Claude Code?

By Seniority

Somewhat surprisingly, across the 331 identified scientist Claude Code users within a population of 15,933 scientists, there is a U-shaped adoption curve by seniority. Scientists who first published papers 3-10 years ago are adopt at lower rates than early career or senior scientists.

This might make sense when you consider that risk aversion to new tools is highest during that career period. Early career scientists and post-tenure scientists have more freedom (and frankly, more time) to explore new tools.

While Claude Code usage intensity is roughly the same across scientist seniority, veteran scientists are more likely to contribute across multiple repos. They were likely already involved in computational or open source work before Claude Code existed.

By Institutions

Because ORCID allows scientists to link to institutions, we can also identify institution-level adoption. After filtering for institutions with 20+ scientists linked on ORCID with Github, the vast majority of scientific institutions have zero public Claude Code users. Institutions with the highest adoption are U.S.-based Tier 1 universities.

(Note that scientists at those institutions may be using other coding models, or may be less likely to commit to public repos.)

By Country

At the country level, scientists in the U.S., along with other western and east Asian countries (excluding China), are the highest adopters. More developing countries are some of the lowest.

A few caveats: the number of scientists using Claude Code varies by roughly 2 orders of magnitude between Sweden and the U.S., so these rankings should be treated as directional. ORCID adoption rates also vary by country, which confounds the comparison.

By Field

We identify field through mapping journals scientists publish in via Scopus journal topic mapping, with keyword-based fallback for preprint servers.

Economists and social scientists are the highest adopters. Mathematics and environmental science are the lowest. Adoption is fairly uniform across fields (1.4-3.4%), which I didn’t expect.

Field-level adoption is likely confounded by ORCID profile adoption within a given field. The low use of ORCID in computer science likely explains its surprisingly close-to-average adoption rate.

How do Scientists Use Claude Code?

Scientists who use Claude Code do so at roughly similar intensity as other public Claude Code GitHub users. They’re far more likely to use it for languages common in scientific computing: Python, R, and shell scripts. Scientist users are also more likely to work across multiple repos, which likely reflects that early adopter scientists were already active open source contributors.

Examples of scientist Claude Code power users on github from this analysis include:

• @williamjameshandley, Cambridge Royal Society University Research Fellow who uses bayesian machine learning for early universe cosmology

• @cmungall, Lawrence Berkeley National Lab scientist and department head working on life sciences computational techniques

• @cranmer, physics professor at UW Madison working at intersection of machine learning and particle physics

• @edeno, computational research scientist in Dr. Loren Frank’s lab at UCSF focused on developing scalable, interpretable algorithms and tools to decode, categorize and visualize neural representations.

Only one repo has seen multiple claude code commits from multiple scientists in our admittedly limited datset: Disorder Mechanisms Knowledge Base (dismech) under the Monarch Initiative.

What comes next?

Accelerating the adoption of AI coding tools by scientists should be a national imperative and a key policy metric.

So, how do we drive adoption of AI coding tools among STEM graduate students?

One excellent approach is through hackathons, which create positive social spaces for graduate students to experiment and try something new. At Renaissance Philanthropy, we’ve supported an autonomous microscopy hackathon and I hope to support others that drive the adoption of coding tools for domain specific science workflows.6 Another perspective I’ve been exploring is viewing AI as a new science instrument, which lets us examine the history of science for how new instruments are adopted and successfully integrated into scientific workflows (or not).7

The science hackathons I’ve seen are mostly driven at a PI level or even by undergraduate clubs, rather than top-down. But enabling enterprise access is an important barrier, and one that (based on my conversations with university administrators) is still challenging for AI labs to crack. Google still has an edge here.8

While hackathons expand top of funnel initial engagement, there is also plenty of tool building left to do. From domain-specific plugins for specific workflows to designing new UIs for how to interact with scientific agents, accelerating adoption of AI coding agents increases the surface area for exploration and discovery. And the countries whose graduate students are first to internalize these tools into their daily research workflows will compound that advantage for years.

We are still so early. And there is still so much more to be done.

Science is the production of peer-reviewed papers.

And our AI model can write real papers!

It’s why we’re building AI-powered paper writing software.

Our AI models are trained extra hard on reading and writing science papers, which is why they take even longer to run.

Our AI model’s papers are so good, they’re better than a nobel laureate’s papers.

Soon, AI labs will race to see whose AI has accumulated more citations!

Science is doing experiments and generating experimental data.

Which is why we’re so proud to have “discovered” millions of new materials with AI!1

It’s why we’re building robots to create “physical AI scientists”, who require nothing but an “intelligence layer” and robot arms to do science.2

Since science is merely the production rate of new experiments, we can commodify science laboratories into factory lines, where we stamp out new discoveries.

It’s why we’re building AI solutions for CRO’s — by automating the vaulted but antiquated factory floors of CRO discovery engines, we can accelerate their production of excellent science.

In the same way we have discovered every song, we will next discover everything there is to know about Science.

AI will ultimately help us realize a RANDian vision of “discovery by design”. We will no longer discover by chance, with inveterate gambler-scientists, but instead we will advance Science through the planned, systematic hand of our AI-god.
If you sign up for our ultra-max-pro API, we will send you more discoveries.

Why does Understanding Science matter?

Our conception of Science — how it operates and the problems it faces — shapes the companies that are built. The tokenification of the world and an increasingly siloed scientific community means there are tremendous amounts of private capital flowing towards poorly formulated notions of how science works and how to “accelerate” it.

My aim here is not to denigrate any specific company. I recognize the need for the dialectic between public-facing press for investors and what is actually built.

Recognizing this mismatch is part of why I rebranded this Substack, because I realized that understanding how AI will impact Science requires a deep understanding how Science itself operates.

For instance, the perspective of AI as a new science instrument3 brings in a wealth of sociological and conceptual frames for how AI could be adopted and used for scientific workflows.

An informed understanding of Science is not only intellectually interesting, but relevant for new companies being built in the space.

Since then, I’ve had a few different jobs, developing an expanded perspective beyond just academic papers and an interest beyond AI for Science. For example, I’ve recently written about science communication, the economics, history, and importance of science instruments, and the geopolitics of AI for Science, while continuing to write about autonomous labs, which were part of my original research at Berkeley.

So it is no longer quite as accurate to pretend this is a Substack exclusively about AI for Science. Instead, consider this a brief update and recognition of what has already come to pass: this Substack is where I write1 about the broader Republic of Science2 — how science actually works as a social enterprise, its intersection with policy and politics, and of course, how AI is changing all of it. Inseparable from these perspectives will also be brief interludes about what I’m working on and building in this space.

I’m also finally retiring the awful ML4Sci domain3 and putting in a new logo.

The logo is a stylized diagram of the first cyclotron Ernest Lawrence designed, which I believe captures the present moment better than any other figure I could choose.

The cyclotron represents the spillovers of science, and how science can shape the arc of history, as Lawrence’s cyclotron technology was repurposed for uranium enrichment during the Manhattan Project.

It was also part of the birth of the U.S. national labs and the notion of scientific infrastructure as a national asset, helping catalyze the birth of Big Science and government-funded research.

Finally, the cyclotron represents the personality-driven trajectory of science. Ernest Lawrence himself played as central a role as his instrument did in shaping science policy. Lawrence was a strong advocate of “Big Science” and also prominently pushed for development of the hydrogen bomb, hence why he also has a weapons lab named after him (Lawrence Livermore National Lab).

Conveniently, the cyclotron also captures the underdiscussed perspective I’ve been focusing on recently — the way scientific instrumentation shapes the very means by which we perceive and understand reality, and the activities that constitute science.

In short, the cyclotron represents everything I want to write about here — science policy and funding, the history and people behind the science, scientific instrumentation and infrastructure, and the way science reshapes the world far beyond the lab.

Going forward, my aim is to publish roughly one post a week. As a sneak preview, the next few posts will be a critique of aesthetic notions of science and a history of how Warren Weaver and the Rockefeller Foundation shared the same view of tool-driven scientific progress that I’ve been articulating here.

In 2025, the Trump administration launched an unprecedented attack on public science funding, pausing and cutting billions of dollars of funding at National Science Foundation (NSF) and National Institute of Health (NIH), as well as firing significant amounts of non-political science agency staff.

There are a number of interrelated factors that led to this wanton destruction of U.S. scientific institutions, including an ineffective Congress, the political polarization of higher education, the increasing ossification of ivory tower academia, etc

But the question remains: how did we end up in a state of affairs where a popularly elected administration would so thoroughly torch the postwar consensus on public science funding?

The intellectually lazy answer—and frankly, the morally derelict one—is to say nothing could have been done, that one side is simply 'anti-science' and beyond reason.

Trump in many ways is merely a symptom, the harbinger for many sins. Universities and public institutions share responsibility for enabling this crisis. But one factor hasn't received sufficient attention: the utterly broken, antiquated state of science communications.1

Science Communications is Broken

During the assault on science funding last year, prominent defenders of Trump’s actions would ask “what did all this science funding ever do for us?” It is easy to dismiss this as ignorance. But the question itself is a symptom, evidence of just how thoroughly science communications have already broken down.

There has never been so great a chasm between the public and the scientist. Some of this is inevitable as the frontier of science becomes ever more specialized and esoteric. But the role of the scientific communicator is to bridge that gap, and today, they have become ineffective at that role.2 For too long, the complacent, unread press releases of university press offices were sufficient because the real power of universities were a small set of lobbyists in DC and a mostly bipartisan elite consensus.

But the Trump populist blitz demonstrated just how fragile and illusory that elite consensus was. Complacency and outdated advocacy models have left universities caught in a lurch, unprepared and untrained for the new environment they find themselves in. The meek response of Congress, ostensibly the voice of the people, in the face of such a crisis speaks to how fragile the underlying consensus was and how important it is to rebuild an effective public advocacy and communications machine for public science.

In the meantime, scientists continue to be ill-equipped by their institutions to engage with the public that funds their salaries. Instead, they are resorting to posting videos of robots doing science on Linkedin. Even incredibly impactful events, like a national lab AI hackathon with 1000 scientists and Anthropic & OpenAI, barely make a blip in the media landscape.3 As a result, public research today is completely disconnected from the very people it is meant to serve. How can a grateful public appreciate the exquisite U.S. scientific ecosystem when they know nothing about it?45

It would be a mistake to think that the status quo will simply return in 3 years. The dam has been broken. New forces, from the rise of billionaire philanthropy, the continued meteoric rise in startup VC funding, and the arrival of peak university enrollment means universities and academic research will continue to find themselves in a dynamic, evolving landscape. The urgency and need for modern science communications, one that can engage coherently and persuasively in the evolving bargain for public funded science, will only continue to increase.

New Media for New Science

So what would modern, effective science communications actually look like? The 2024 election confirmed what many already knew: we've entered a new media age, where audiences are reached through podcasts, video, and D2C channels like Substack.

Recognizing this, some fraction of the trillions of dollars in U.S. venture capital are spilling over to support a growing ecosystem of new media creators focused on telling the story of exciting new startups through new media. The result is a growing “charisma gap”: startups have compelling storytellers creating a mythos of glory, and science does not.

This isn't just a problem for funding - it's a problem for talent. When the most compelling narratives about technical work come from startups, that's where ambitious young people go. The absence of equally compelling stories about fundamental research means an increasingly self-selected cohort of students who choose to do publicly funded research.67

If we want to rebuild an enduring bipartisan coalition for public science funding, and an informed public on the value of public science, we will similarly need a new generation of science communicators.

A nascent ecosystem of new media science communicators is already emerging outside traditional institutional channels, from scientists and researchers who understand that reaching the public means meeting them where they are. But it is still mostly individual content creators doing passion projects at significant career risk. Notable examples include:

• organizing beautiful new art galleries of science in SF

• bringing their decades of experience in science policy to Substack

• on how biomedical research is improving our health

• Cleo Abrams on Youtube with science education shorts

• Jordan Taylor for engineering deepdives

Rebuilding public trust in science will require deliberate investment in people, platforms, and institutions skilled and trained in New Media for New Science. What today starts as side projects for passionate scientists and engineers could be scaled with sustained investment and new institutional pathways.8

If you’d like to support a new generation of science communicators, please reach out!

4

This incredible disconnect leads to a strange dichotomy: when I meet U.S. STEM graduate students, I am never more confident in our U.S. scientific talent and ability. But when I meet university administrators and science communicators, well, frankly I have greater sympathy for the Trump administration’s agenda.

This perspective article builds off many helpful conversations from a workshop I hosted in October 2025 on “Catalyzing Collaborations in Autonomous Science Instruments” in San Diego.

In the call to action, we make several observations:

First, autonomous labs are no longer a niche subcategory. In 2025, private venture capital markets have invested nearly $1B in startups developing autonomous labs to discover new materials, like room temperature superconductors, and accelerate drug discovery. At the same time, nearly $1B of public sector research funding has been announced for autonomous lab development from the U.S., U.K., and Canadian governments. This represents a transformative investment and recognition by both public science agencies and private markets of the critical role autonomous labs will play in scientific research, particularly in the development of large AI for Science models.

Second, while there have been significant advances in AI models and robotic arms, both in terms of cost accessibility and capability, less attention has been paid to the maturity of scientific instruments for integrating within autonomous labs. We highlight the inadequacy of science instruments today for the autonomous lab future, including the resulting open source hardware community that has evolved around autonomous labs, and put forward a call to action for a new generation of autonomous science instruments.

Concretely, we define autonomous science instruments as having three key attributes: open data and software API’s, design-for-automation, and instrument modularity.

Finally, we are releasing this call to action alongside a Renaissance Philanthropy press release, which features quotes from various AI and scientific leaders emphasizing the importance of this call to action, which I am reproducing in full below.

“Realizing this vision of autonomous science instruments will accelerate progress on some of the grand challenges of the 21st century, including compact fusion reactors, 600 mile per hour maglev trains, new therapies, materials that are stronger than steel and a fraction of the weight, and replacements for toxic “forever” chemicals,” said Tom Kalil, founder and CEO of Renaissance Philanthropy.

“AI is now starting to meaningfully accelerate scientific work—from contributing solutions to open math and physics conjectures, to helping scientists design better experiments and new materials. As that moves into the lab, AI can plan experiments and robotic systems can run them continuously, with the results streaming back to the AI to refine the experiment. Building instruments that are open and designed for this kind of AI-driven experimentation is a key part of enabling faster cycles of discovery across science.” - Kevin Weil, VP at OpenAI

“AI-enabled autonomous labs, including Carnegie Mellon’s AI Science Foundry, are ushering in a new era of science – one that changes how we fundamentally work and speeds our ability to drive innovations to impact,” said Theresa Mayer, Carnegie Mellon University’s vice president for research. “These labs demand robotic-controlled instruments and AI agents that are intelligent, interoperable and designed for continuous experimentation. By leading the development and standardization of these technologies, we’re enabling faster innovation, stronger collaboration and more reliable pathways from research to real-world applications.”

“We’ve invested heavily in AI models and autonomous experimentation, but we’re still trying to run them on instruments designed for a human sitting at a bench. That is now the limiting factor. Black-box hardware, closed APIs, and one-off integrations don’t just slow things down — they prevent reproducibility and scale.

By calling for open control, design-for-automation, and modular instruments, this paper lays out what’s actually required to move from impressive demos to durable, reproducible, AI-driven discovery. If we want AI to participate directly in experimentation — not just analyze the results after the fact — this is the blueprint.” - Andy Hickl, CTO of Allen Institute.

“To fully realize the promise of self-driving labs, we must move beyond bespoke solutions that address specific challenges and toward a standardized ecosystem of automation-ready tools. The Acceleration Consortium supports this call to action because open standards and modular design are critical missing links for generating the high-quality data required to train and validate the AI models that will accelerate scientific discovery. Effective standardization will also allow AI-driven discovery to be adopted broadly, facilitating innovation from the global scientific community”, Sean Caffrey, Executive Director, Acceleration Consortium.

“While we have seen tremendous progress in automated scientific pipelines where the substrate is coding or mathematically reasoning, it is clear that the next frontier is extending this to actual experiments. This will require the development of new types of instruments that can be used “in the loop” with automated reasoning” Michael Brenner, Harvard University Professor and Research Scientist at Google.

“The fundamental bottleneck of biotechnology today is that scientists must still run most of their experiments by hand at the lab bench. Ginkgo is committed to developing the modular, autonomous scientific instruments described in this paper that will free scientists from the bench and greatly accelerate their rate of discovery,” said Jason Kelly, CEO of Gingko Bioworks. “This article is a timely call to action as we enter the era of AI applied to scientific discovery.”

Science instrumentation is a key lever by which science progresses and are a legacy consolidated industry waiting for disruption.

If you are interested in helping realize this future, please do reach out.

With , I’m excited to introduce BenchSignal—a dataset tracking scientific instrument usage across millions of peer-reviewed papers.

We scanned OpenAlex, a repository of every open-access scientific article, and extracted the instruments mentioned in each one. We now have XX million papers with YY instrument mentions indexed, and the dataset grows daily.

Why build this? Over the past several months, I’ve developed two key convictions:

• the development of scientific instruments is an understudied lens for understanding scientific progress

• scientific instrumentation is a $500B industry waiting for disruption

With BenchSignal, we can identify vendor market share for different instruments in excquisite detail:

We can also identify which instrument modalities are growing in adoption. For instance, we can see the skyrocketing growth of Scanning Transmission Electron Microscopy (STEM) over more mature Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM).

We can also break out papers by country and identify where vendors have differentiated market access. For instance, Chinese researchers are more likely to use Japanese Transmission Electron Microscopes from JEOL, rather than U.S.-based Thermo Fisher.

We’re also expanding the dataset to include more granular institution-level tracking of higher-end instruments, along with citation-weighted metrics for both instruments and vendors. I won't be posting BenchSignal updates here, you can follow BenchSignal on Linkedin.

If you’re interested in commercial or academic applications for this data, reach out.

One thing great about working at Bell Labs, there are so many experts…you can have the best electron microscopists here, the best optical measurement of layers that you can have, and you can have the best fabrication of devices in the final structures, everything’s the very best…

Alfred Cho, MTS at Bell Labs

Bell Labs is the canonical example of a high-performance science organization, most known for its numerous Nobel Prize winning inventions and discoveries. What made it work so well? Plenty of ink has been spilled attempting to distill the secrets to its success, but the usual answer focuses on brilliant scientists given freedom to pursue fundamental research.

We tend to remember science as a series of discoveries, but we forget that discoveries often require new instruments—and new instruments often matter more than the discoveries they enable. New tools unlock new ways of seeing, which unlock new discoveries, which inform new tools. That suggests we should look deeper—to the engineers and toolmakers at Bell Labs as a constitutive ingredient for their success.

Looking at three Nobel Prizes won by Bell Labs, I find the same pattern again and again: the scientists get remembered, while the toolmakers who built the apparatus that made the experiments possible—and often went on to create the real commercial value—go unremembered.

If you want to recreate Bell Labs, you need to understand what actually made it work. And if you want to understand what we've lost without a new generation of Bell Labs, it's not just the Nobel Prizes.

Electron Diffraction and Vacuum Pumps

Bell Lab’s first Nobel Prize was to Clinton Davisson for experimentally demonstrating diffraction of electrons, and hence proving that like photons, electrons and all of matter also exhibit a wave-like nature.1 G.P Thompson, son of J.J. Thompson who won a Nobel Prize in 1906 for demonstrating electrons are particles, shared the Nobel with Davisson for similarly demonstrating electrons exhibit wave behavior.

The journey to demonstrating the diffraction behavior of electrons was long and full of serendipity, but the Bell Labs set up involved firing electrons at a nickel plate and measuring the scattering patterns of electrons. What is notable is that this set-up required precise control of the morphology of the nickel sample, the electron gun, the detector, and the vacuum needed to prevent electron scattering from atmospheric particles.

In this, Davisson had a collaborator, Lester Germer, who was responsible for developing the vacuum set-up. Co-Nobel laureate Thompson wrote this of the experimental set-up used by Davisson and Germer:

“[their] work was indeed a triumph of experimental skill. The relatively slow electrons [they] used are most difficult to handle. If the results are to be of any value, the vacuum has to be quite outstandingly good. Even now [34 years later] it would be a very difficult experiment. In those days it was a veritable triumph.”2

Indeed, several other European labs had been working on proving electron diffraction as well, the experiment idea itself not being novel, but could not crack the difficult experimental conditions demanded. So this Nobel Prize discovery was only possible because of highly skilled engineers, who were both scientists and technicians in their own right, who could create world-class experimental set-ups that no other lab could reproduce.3

While demonstrating the wave-like behavior of electrons was indeed a groundbreaking experimental confirmation of a critical quantum theory, it did not yield direct commercial value to Bell Labs. But the Davisson and Germer set-up was so unique, it was actually the first pioneering example of low-energy electron diffraction (LEED), a new surface characterization technique Germer would go on to pioneer in the 1960’s. The 30 year gap between their experiment and widespread commercialization was due to inadequate detector quality and the lack of commercially available high vacuum pumps, demonstrating just how far ahead of their time this experimental setup was. One description of the impact of how LEED changed the flow of science by unlocking new modes of perception:

Above all, practitioners came to better understand and reliably build low energy electron diffraction (LEED) instruments. The realization that LEED provided information about the topmost layers of atoms on a material, and that the structures of those layers differed significantly from the underlying bulk, opened new vistas of investigation. Also, through the ’60s, advances in computing power made it more tractable to provide theoretical analysis of LEED patterns. Thus, institutions with access to high-end computers and an interest in surface science – Bell Labs, IBM, and, to a lesser extent, Xerox and the Bureau of Standards – steered the field.

This made LEED indispensable to surface science; as new technologies were developed, such as specimen preparation tools and an alphabet soup of spectroscopies and other analytic techniques, they were laboriously coordinated with LEED – for instance, a new specimen preparation technique could only be seen to be affecting the structure of a surface if it produced a change in the LEED pattern.

C.C.M. Mody, 2004

Davisson was responsible for proving De Broglie’s hypothesis and experimentally demonstrating the wave behavior of electrons. But it was Germer who provided the experimental set-up that made the Nobel Prize experiment possible. And the real commercial value was not in the experiment or discovery itself, but in the technique and toolmaking underlying the revealing of new physics. It would be Germer, who though he did not win a Nobel Prize, would translate the techniques he developed into a product and a tool that would transform surface science and enable a new generation of microscopy.

Gordon Teal & the Junction Transistor

Perhaps Bell Labs’ most famous invention was the invention of the transistor, for which Bell Labs’ John Bardeen, Walter Brattain, and William Shockley won the 1956 Nobel Prize in Physics. Bardeen and Brattain developed a simple point-contact transistor in 1947, while Shockley theorized the junction transistor in 1948, which would eventually go to form the basis for the first modern semiconductor.

Shockley fabricated the first junction transistor in 1949, sufficient as a proof-of-concept, but the polycrystalline germanium used in this simple device had poor real-world performance, due to the crystal grain boundaries inhibiting charge diffusion.

Gordon Teal, another Member of Technical Staff, had worked on germanium during his PhD and recognized the need for high-purity single crystals to realize semiconductors with acceptable performance. Following Bardeen and Brattain’s first transistor result, he sent several memos to management requesting funding and time to improve Czochralski’s method for growing high-purity metal ingots. He even compared the need for high-purity crystals as analogous to the importance of pure vacuum in tubes.

But Teal had been assigned to varistors, a related semiconductor project, and his proposals were “met with indifference… Shockley believed that the germanium the lab used was adequate and that single crystal germanium would offer no advantage.” Bell Labs management sided with Shockley. His dismissal of Teal’s work—he later called the effort needed to improve junction transistor performance “practically negligible at the Laboratories”—perhaps reflects his theoretical physics background, which contrasted with Teal’s more practical engineering understanding of the substantial process knowledge needed to improve a device’s performance.

It was not until late 1948 that Teal found another Bell Labs engineer, John Little, who needed germanium for a separate project and worked with him to fabricate monocrystal ingots. They used a modified version of the Czochralski method, which involved dropping a small seed of germanium into a “melt” of liquid germanium, then slowly pulling the seed out as it cooled, forming an ingot with uniform crystal structure.

Despite this success, management only allowed Teal to work on the project in off hours. For most of 1949, Teal would experiment on his ingot puller in evenings until 2 or 3 am, stowing his equipment away before the metallurgical staff arrived in the morning.

It was not until Shockley fabricated the first junction transistor and Teal demonstrated that his high-purity germanium improved the lifetime of charge carriers in the transistor by 20-100x that Teal’s work was finally brought into the light and allowed him to finally file a patent for his ingot-puller and modified Czochralski’s methods.4 It was Teal who modified his ingot-puller to also include the precise doping required to create p-n junctions.5

In 1952, AT&T hosted the transistor technology symposium on its semiconductor research, available to any company that paid the $25,000 license fee. Teal gave a talk on his crystallization work and impressed the CEO of a Geophysical Services Incorporated, a small Texas company that sold seismic equipment to oil and gas but wanted to pivot into semiconductors. Their CEO strongly believed not only in semiconductors as the future, but in the importance of vertically integrating, rather than selling end products through component assembly. He invited Teal to join and help stand up and run his own R&D lab at the company. Presented with the opportunity to return to his home state of Texas, and the autonomy to run his own research after having his research neglected by management at Bell Labs, Teal accepted.

GSI rebranded to Texas Instruments and would become a leader in U.S. semiconductor industry. While Bardeen and Brattain would have a successful academic physics career, and Shockley would go on to found the Shockley Semiconductor Corporation, it was Teal who took the toolmaking and process knowledge he developed to help create a U.S. semiconductor giant.

Charge-Coupled Device Cameras

The more recent 2009 Nobel Prize in Physics went to Bell Labs William Boyle & George Smith “for the invention of an imaging semiconductor circuit – the Charge-Coupled Device sensor” in 1970. This breakthrough semiconductor-based imaging platform was a paradigm shift from bulky film photography. While the other two examples identify engineering techniques and spillovers from Nobel Prize experiments, this Nobel Prize was immediately controversial in who it left out.

In the late 1960s, Bell Labs was already investigating “magnetic bubble memory”, an alternative to hard disk drives that could also provide a form of non-volatile memory storage. To read from bubble memory, an external magnetic field would drive each magnetized "bubble" to move sequentially down an array until it could be read by a pickup. Boyle and Smith’s original paper cited by the Nobel Prize committee simply used electrons, rather than magnetized domains, for similar memory storage applications.

But it was Michael Tompsett, another Bell Labs engineer, who took the idea of “Charge Coupled Devices” and designed the first imaging architecture using CCD. By adding a photosensitive semiconductor layer on top, electrons would be generated when photons hit the surface. These electrons could be stored in the capacitor array and read off as pixel values when placed under an external voltage. Tompsett is the sole author listed on the patent for a CCD imaging sensor and his exclusion from the Nobel Prize for the primary, indeed only application from Boyle & Smith, was immediately a source of controversy following the 2009 Nobel Prize announcement.6

The CCD camera would go on to be developed by Fairchild, after poaching one of Tompsett’s collaborators, Gil Amelio, and later on by Japanese manufacturers. Amelio would describe his motivation for leaving as: “the only thing you get frustrated about at Bell Labs is that you invent everything but you can't make anything…you can't put anything in production, you can't see it commercialized, you can't see it go out into the world and…I wanted to build something.”7

This Nobel snub speaks not only to the continued neglect of engineers in the Western scientific paradigm but also Bell Labs’ specific inattention to the novel and commercial engineering applications for its Nobel Prize winning technologies. The development of CCD would disrupt space-based surveillance during the Cold War, enable digital cameras for the masses, and power the Hubble Telescope. Like LEED before it, it demonstrates how engineering applications of Nobel Prize discoveries—not the discoveries themselves—become the tools that enable the next generation of science.

A Bell Labs for Scientific Instruments

Many remember Bell Labs for the incredible scientists and their Nobel Prizes. All three discoveries I’ve discussed—electron diffraction, the transistor, and the CCD—were recognized for fundamental breakthroughs.

But these vignettes show that skilled scientists and new discoveries alone are necessary but not sufficient to recreate the magic of Bell Labs. True breakthrough discoveries require not just great scientists, but also engineers who can build the advanced instrumentation and tooling required to conduct experiments and build prototypes, as Germer did for Davisson.8

And in terms of commercial value, it is often those same engineers, rather than Nobel Prize winning scientists, who are most instrumental in producing commercial value from those discoveries.9 Indeed, the phenomenon of simultaneous discovery — like Thompson’s simultaneous discovery of electron wave behavior as Davisson — suggests that scientific discovery is a poor moat for commercialization. Arguably the real competitive moat lies in having adjacent engineering talent that can see the engineering applications of new discoveries, like Tompsett did with the CCD or Germer with the prototype LEED developed for Davisson’s experiment.

Finally, there is increasing interest in recursive self-improvement with AI. In many ways, scientific discoveries that inform new scientific tooling are the ultimate example of recursive self-improvement. New discoveries lead to new and more powerful tools, which unlock more discoveries. There is tremendous opportunity to infuse scientific instruments with AI & autonomy capabilities, but it will require new shapes of scientific organizations and companies to develop and adopt new ways of doing science in the age of AI.

Anduril and the Last Supper

By now, many will be familiar with the story of defense prime consolidation. After the Cold War ended, 51 prime contractors merged down to 5, leading to less competition, slower development timelines, and ballooning costs. The government became dependent on a fragile oligopoly that struggles to surge production or innovate quickly.

Anduril, a venture-backed defense tech startup now valued at $30B, was founded to disrupt these legacy primes. Their bet: that the need for good software and autonomous assets created an opportunity to disrupt the legacy defense primes still building fragile, exquisite platform systems.

Whether or not Anduril succeeds, the diagnosis and theory of change is striking—and as I’ve been digging into the science instrument industry, I found a surprisingly similar story playing out.

Consolidation in the Science Instrument Industry

When I first started my journey into autonomous labs1, I did so out of a conviction that they would play an important role in how AI accelerates scientific discovery. I quickly became convinced that science instruments—with their poor software APIs and lack of automation-friendly hardware—would be the primary constraint to realizing autonomous labs, particularly compared to the rapid progress in AI models and robotics.

Which is why I’ve been more recently writing about automation-native science instruments and even hosted a 2-day workshop on autonomous science instruments earlier this year.

What surprised me as I learned more about the science instrument industry, was how often I ran into a familiar story: legacy primes who acquired and rolled up smaller companies, leading to expensive products with horrible software and high-cost recurring service contracts. Large science companies like Thermo Fisher are closer to science conglomerates, with bundles of subsidiaries acquired through acquisition.2 I was able to recreate the Last Supper M&A graph for the science instrument industry, looking at acquisitions from Agilent ($40B market cap), Bruker ($7B), Danaher ($160B) and Thermo Fisher ($200B). And the graph below is only a selected set of acquisitions.

Danaher is particularly unabashed about the role M&A plays in their ability to compete. Their annual report segments the company into Life Sciences, Biotechnology, and Diagnostics. Here is how each section begins:

Danaher established the life sciences business in 2005 through the acquisition of Leica Microsystems and has expanded the business through numerous subsequent acquisitions, including the acquisitions of AB Sciex and Molecular Devices in 2010, Beckman Coulter in 2011, Pall in 2015, Phenomenex in 2016, IDT in 2018, Aldevron in 2021 and Abcam in 2023.

Danaher established the Biotechnology segment through the acquisition of Pall in 2015, and expanded the business through the acquisition of Cytiva in 2020

Danaher established the diagnostics business in 2004 through the acquisition of Radiometer and expanded the business through numerous subsequent acquisitions, including the acquisitions of Vision Systems in 2006, Beckman Coulter in 2011, Iris International and Aperio Technologies in 2012, HemoCue in 2013, Devicor Medical Products in 2014, the clinical microbiology business of Siemens Healthcare Diagnostics in 2015 and Cepheid in 2016

This pattern of acquisitions and consolidation is often used by primes to improve their competitiveness in specific instrument lines.

The Liquid Chromatography Mass Spectrometry (LCMS) sector offers a textbook example of strategic consolidation. Agilent, a long-time leader in Gas Chromatography MS (GCMS), acquired Varian in 2010 to in-house Varian’s vacuum pumps (a component in LCMS) and their LC column product lines. Meanwhile, Thermo Fisher—already the leader in Mass Spec—acquired Dionex to secure the ‘front-end’ liquid chromatography hardware, creating a fully integrated product offering. Then there is Danaher, the most aggressive acquisition player, which bought AB Sciex to enter the LCMS hardware game and Phenomenex to capture revenue from the consumables that run LCMS.

The Federal Trade Commission (FTC) does occasionally take action in these acquisition deals. In the LCMS case, FTC required Varian and Agilent to spin off several higher end MS product lines to Bruker before allowing the 2010 acquisition to proceed.

The FTC has also taken action in other acquisitions, including Thermo Fisher’s 2014 acquisition of Life Corp (divest several products to GE Healthcare) and in the 2006 creation of Thermo Fisher (Thermo Electron merger with Fisher Scientific). In other words, the $200B world leader in scientific equipment was born through an antitrust-challenged merger.

Natural Gravity Towards Scale

There are real economic reasons why consolidation makes sense in the science instrument industry, and it would be intellectually dishonest to ignore them.

Sales and support overhead: Science instruments require extensive pre-sales consultation, application support, and field service. A single electron microscope might cost several million dollars and require specialist engineers for installation and maintenance. Building out a global sales and service network is enormously expensive, and there are genuine economies of scale in spreading these fixed costs across a broader product portfolio.

Low manufacturing margins: Instrument manufacturing itself operates on thin profit margins. Companies face strong incentives to create vendor lock-in, as the higher profit margin business lines are through providing integrated solutions and selling consumables. The margin incentive structure then naturally biases companies to expand beyond selling instruments towards growing into other business lines that provide higher margins.

Downsides and Disruption

But this consolidation comes with costs that affect the future of autonomous science. The clearest symptom is the poor state of software for scientific instruments. For instance, Thermo Fisher markets “automated electron microscopy” solutions, but to actually automate anything you need to pay a license to use their Python API and their proprietary IDE. The state of the science instrument industry is such that in 2025, you pay millions for an instrument and a recurring service contract, then pay again for the privilege of having programmatically control over your instrument.

And this is not just Thermo Fisher or electron microscopy, but represents the broad trend across science instruments. During my workshop on autonomous science instruments, we heard from researcher after researcher on the pain of working with instrument software:

• poor API documentation, if there even is an API

• non-standard data formats, or even vendors encrypting instrument data that can only be decrypted with proprietary software

• 9-18 months of upfront development time spent just getting instruments to talk to each other through homemade drivers

Autonomous labs will not realize their full potential until there is a robust portfolio of science instruments that ship with open software APIs, standardized data formats, and hardware designed for robotic integration.

While antitrust scrutiny of acquisitions can help preserve competitive pressure, it often is only able to rearrange business units between the same set of oligopolistic primes.

To truly build automation-native science instruments will require Anduril-like disruption to a legacy industry that has not seen a new prime in 40 years.

Today, there is a billion-dollar opportunity to build automation-native science instruments: modern software and instrument hardware designed for robotic integration, built to power the future of autonomous labs. A few early-stage teams are starting to chip away at building autonomous instruments in specific product lines. But building the next Thermo Fisher will require understanding the market dynamics that let incumbents survive—and what it takes to break through.

If you’re interested in chatting more about an “Anduril for Science Instruments” — reach out!

But as the AGI bubble has matured into a more product- and returns-oriented phase, AI companies have begun to shift their focus to actual practical applications.

One of the clear beneficiaries of this shift is AI for Science. Just in the past year at OpenAI and Anthropic:

• Sam Altman has said the AI application he is most excited about over a 10-year horizon is AI for Science

• Kevin Weil shifted from CPO to head of AI for Science projects

• Liam Fedus, former OpenAI VP of post-training, left to co-found an autonomous lab for scientific discovery company

• Anthropic launched Claude for Life Services last month

• Anthropic is now hiring an AI for Science writer, along with an AI for economics writer.

This is a remarkable vibe shift from a few years ago, when AI for Science was a mere pit stop on the destination to AGI. Now, AI for Science is the goal — from Anthropic’s editorial perspective, the entire economy and science are roughly co-equal in focus.

Governments are now undergoing the same realignment. As a result, Q4 2025 has produced major AI-for-Science announcements from the U.S., U.K., and China.1 What follows is my summary of these national strategies on AI for Science — and my assessment of where each country now stands.

U.S.

The Trump White House recently released an executive order (EO) on “Launching the Mission Genesis”, which tasks the Department of Energy (DOE) with marshalling its 17 national labs, supercomputers, datasets, and scientific infrastructure to build AI for Science foundation models and robotic labs, in partnership with private partners.2 It is notable that DOE, rather than the National Science Foundation, is taking the lead; equally notable is that the EO provides no meaningful funding. This EO follows on the AI Action Plan published earlier this year, which included several notable references to programmable cloud labs3 and AI for semiconductor R&D.

Despite the ongoing and unprecedented cuts to federal science funding, the U.S. continues to benefit from its world-leading AI talent and private capital markets. Particularly in AI for Science, these are often in partnership with public science institutions. A few examples just from this past year:

• Deepmind and National Hurricane Center partnered to deploy Deepmind’s weather forecasting model, which outperformed even human experts and ensemble models at predicting the trajectory of Hurricane Melissa

• Meta, Lawrence Berkeley National Lab, and Lawrence Livermore National Lab partnered together to produce the Open Molecules 25 computational dataset, a massive Density Functional Theory (DFT) computational materials dataset which has been described as like “AlphaFold for materials science”.

• And as I wrote about earlier this year, the U.S. has seen $1B in private venture capital investment in developing autonomous labs

Key Takeaway: The race remains the U.S.’s to lose. Despite federal science funding cuts, immigration rollbacks, and an exodus of agency staff, the U.S. still retains deep capital markets and extraordinary talent and entrepreneurship. The Trump administration has taken several steps to emphasize AI for Science — including the Genesis EO — but whether these efforts materialize into meaningful substance remains unclear, given broader policy headwinds and acute staffing and funding constraints across agencies

U.K.

Just a few days before the Trump White House released the Mission Genesis EO, the U.K. Department for Science, Innovation, and Technology (DSIT) released their AI for Science strategy.4 Compared to the White House EO, the UK DSIT strategy is far more comprehensive and is accompanied with £137m in actual funding. I also found it notable the UK DSIT strategy explicitly couples the AI for Science strategy with the UK Modern Industrial Strategy, demonstrating at least greater awareness of the importance of technology development and industrial competitiveness.

Overall, the pace of U.K. investment and action on AI for Science, and autonomous labs, has been remarkably impressive. In July 2025, I wrote a profile on 5 things the U.K. is doing on AI for Science and they have continued to set the pace throughout the year. Their AI for Science push is part of a broader whole-of-government push for AI, including AI growth zones and investments in compute access and AI hardware innovation5.

Just in the 4 months since I published my profile in July, the U.K. has also:

• UK DSIT Sovereign AI unit released an open call for autonomous lab market engagements.

• ARIA also recently closed a funding call for “AI scientists”, which funded 9-month sprints to derisk early-stage technologies around AI-powered scientific discovery

• Commissioned Isambard-AI, a new supercomputer at University of Bristol, equipped with 5.4k GH200’s, making it 10x faster than the next-fastest supercomputer in the U.K. and #11 on the Top500 supercomputer list6

Key Takeaway: In hindsight, I believe we will look back upon 2023-2025 as the beginning of a remarkable investment from the U.K. in revitalizing their scientific apparatus, with the creation of ARIA and DSIT backed with actual public capital to invest in transformative projects. But the U.K. lacks the same structural advantages the U.S. enjoys and faces broader macroeconomic headwinds. On AI for Science specifically, limited compute and a far smaller talent pool than the U.S. or China will continue to constrain progress,7 even as public sector investment accelerates.

China

Two months ago, China released their AI+ diffusion plan [see Jeff Ding and Matt Sheehan’s far more in-depth coverage], which included AI for Science and Technology as one of the 6 key sectors for diffusion. Claude-translation [h/t ] of that particular section:

(1) “AI+” Science and Technology

1. Accelerate the process of scientific discovery. Speed up exploration of new scientific research paradigms driven by AI, and accelerate the process of major scientific discoveries “from 0 to 1.” Accelerate the construction and application of scientific large models, promote the intelligent upgrading of basic research platforms and major science and technology infrastructure, build open and shared high-quality scientific datasets, and improve the ability to process complex multimodal scientific data. Strengthen AI’s role as a cross-disciplinary driver, and promote the integrated development of multiple disciplines.

2. Drive innovation in R&D models and efficiency improvement. Promote the integrated and coordinated development of AI-driven technology research and development, engineering implementation, and product deployment; accelerate the landing and iterative breakthroughs of technologies “from 1 to N”; and promote the efficient transformation of innovative achievements. Support the promotion and application of intelligent R&D tools and platforms, strengthen collaborative innovation between AI and areas such as biomanufacturing, quantum technology, and sixth-generation mobile communications (6G), use new scientific research achievements to support scenario applications, and use new application needs to drive breakthroughs in scientific and technological innovation.

3. Innovate methods of research in philosophy and the social sciences. Promote a shift in research methods in philosophy and social sciences toward human–machine collaborative models, explore new organizational forms of philosophy and social science research suited to the AI era, and broaden research horizons and perspectives of observation. Conduct in-depth research on the deep impacts and mechanisms of AI on human cognition and judgment, ethical norms, and other aspects, explore the formation of a theoretical system of “AI for good,” and promote AI’s better service to humanity.

China’s policy ecosystem is more bottom-up: central government sets high-level targets (e.g., 70% AI penetration in each sector by 2027) and delegates actual implementation to provincial and municipal governments. Case in point: Beijing municipal government got a head start, publishing their municipal AI for Science Strategy in July 2025. [Claude translation here]

Also notable is the now hallmark recognition by the Chinese government of pairing both “from 0 to 1” and “from 1 to N” innovation, diffusion being a demonstrated strength of theirs the past few decades in automotive and energy manufacturing. And of course, “AI for Science” is not only the direct product of “AI for Science” efforts but of broader macro considerations. In this case, China seeks to take advantage of the U.S. exodus of scientific talent through their new K-Visa for STEM talent, even despite local opposition.

Key Takeaway: Given the language and cultural barrier, along with a unique political system, China is always more opaque and difficult to grok. But it is undeniable that their patient investment in science talent are bearing fruit — they are now home to the majority of the top 10 research universities and have an almost spontaneous flourishing of competitive open source AI models. I have a much fuzzier sense of what is happening specifically in China on AI for Science (someone should really dig into this!), but they have all the ingredients for success.

I will close with a reminder of what I view to be the real lesson of AlphaFold: patient federal investment in the 1990s created an open protein-structure dataset that scientists — especially in the U.S. — contributed to for decades. It was only a few years ago that the value of this dataset was realized by another U.S. company to create AlphaFold. That story is still unfolding but it is worth remembering that the seeds for it were planted decades ago by public science funding.

Now, more than ever, public science funding is critical. The recent history of AI for Science shows that private companies rarely generate their own scientific datasets; they rely on them as public goods. Building the datasets needed for AI foundation models and adapting existing scientific infrastructure — from autonomous labs to high-performance computing, beamlines, and particle accelerators — is the essential task for nation states. Just as with the dawn of the electronic computer, the race is very much afoot: countries that move decisively now will define scientific and technological leadership in the midst of this historic disruption.

Addendum on the hermeneutics of government plans

Having spent some time in government, I unfortunately am cursed with too many opinions on government strategies and reports.

To be clear: the posting of a document on a white house website, or agency website, does not mean “Mission Accomplished”.

A report or executive order or strategy is meaningful insofar as it represents a policy consensus on a particular set of directions or actions, and actually empowers agency staff to actually carry out activities listed in the order. One astute observer of government bureaucracy referred to these orders as “hunting licenses” — but getting a license is merely the first step, and usually is insufficient without funding. A simpler way to put it is, “It’s an implementation game” (and a money one).

Each national strategy exists in the unique political economy of each country. By themselves, they merely suggest particular focuses or directions. Hence why I try to present the broader context that those strategies are placed in, both in terms of the policy environment and broader macro conditions for each country. Ultimately, real progress will proceed slowly, invisibly through the harnessing of infrastructure and bureaucracies to create public value through datasets, compute, or other investments.

Addendum — reflections on AI for Science

Almost exactly 6 years ago, I started this somewhat wonky substack8 as an aspirational Berkeley undergrad researcher as a way to force myself to read more research papers on how AI was being used in various scientific fields. Several jobs, writing hiatuses, and model releases later, AI for Science is now at the forefront of national science and tech policy and geopolitical competition. It has been an unexpected journey in many ways, but I feel fortunate to be able to play a small role in how the field progresses.

The popular imagination pictures the lone scientist who discovers a new breakthrough drug or dreams up a new theory that disrupts conventional wisdom. In every field, we can name an intrepid discoverer who advanced Science through discovery: Einstein, Edison, Doudna, Curie, etc.

This narrative of how Science progresses is deeply steeped in our culture today. Even the metascience and progress studies discourse, which generally features a more online, usually non-scientist audience, centers their entire project around concerns of a slowing pace of discovery.

This discovery-centered view of how Science is advanced also shapes our imagination for how Science may change in the future. For instance, today we are enamored with new AI models that can parse literature and make millions of inferences, with the promise to accelerate new discoveries. But this situates AI within the existing framing of Science advancing through new discoveries and theories, simply at a different rate and with a different agent.

I want to offer a different perspective on how Science advances, which is not by individuals pushing one discovery or theory, but rather Science advancing on the basis of new instruments, which unlock an entire suite of capabilities for a scientific field writ large.

The Value of New Perspectives

What value do new perspectives on how science advances offer? Conceptualizing Science as advancing through new instruments and tools can1:

• encourage us to reconceptualize what a scientist looks like and does. We might be more inclined to look for the tinkerer, the misfit engineer, who hacks together a new microscope or instrument with an eye towards commercialization, rather than just the inventor/discoverer of an object of interest.

• encourage us to reconceptualize the role of science institutions. Rather than imagining universities as bundles of labs within departments, what if we imagine them as core research facilities serving a variety of independent researchers? Similarly, such a perspective moves DOE national lab user facilities closer to the fore of the nation’s scientific capabilities.2

• refocus science funding policy conversation away from what kinds of ideas we fund and bring renewed interest to the underdiscussed dimension of what kind of infrastructure do we fund. From national lab user facilities, which are the crown jewel of scientific infrastructure, to incubating a small but vibrant startup ecosystem of science hardware founders, the ends of science policy could be expanded with new perspectives on how Science advances.

• add a new dimension to scientific competitiveness. Almost every index today for country-level comparison either looks at funding or patents, papers, citations, awards, etc. But if we consider Science advancing through instruments, then we may look to the scale and capacity of our scientific infrastructure, and the pace of innovation in new science instruments.
  Notably, science instrument manufacturing is one of the few manufacturing sectors that China has not yet broken into.

• allow us to consider the sociological constructs by which scientists accept and adopt new tools and instruments. The diffusion of new instruments and infrastructure within science is not a new question, but certainly has added importance in the question of the manner by which scientists adopt various AI models and workstreams.

• new perspectives on how Science advances provides us additional frameworks to grasp onto for thinking about how AI will change the way we do Science. For instance, I had previously written about how AI foundation models could be analogized to particle accelerators - massive upfront CapEx that allows us to “see farther” than we can previously - and could operate on a user facility model.

What are some examples of when instruments advanced science? I will use three instruments which have won a Nobel Prize for their “invention” and provide some context on how they have advanced their respective fields in the modern era.

3️⃣ Case Studies: How Instruments Changed Science

Transmission Electron Microscopy for Nickel Superalloys

Ernst Ruska won the 1986 Nobel Prize in Physics for developing the first transmission electron microscopes.

For centuries, scientists used optical microscopes, but visible light’s wavelength fundamentally limits the resolution to hundreds of nanometers at best.3 Rather than using visible light photons, electron microscopes use electrons as the medium for characterizing samples. Transmission Electron Microscopes (TEM) fire a beam of electrons at a sample and then measure the scattering of electrons that are “transmitted” through the sample (as opposed to Scanning Electron Microscopes, which look at the “reflected” electrons) and were the first major electron microscope technique to be commercialized.

While TEMs were first developed in the 1930s, their precision and reliability significantly improved in the 1950s and 1960s. This higher resolution allowed scientists to examine metal microstructures with unprecedented detail. As a result, TEM was instrumental in explaining the exceptional performance of newly developed nickel superalloys— materials engineered during the 1950s to withstand the high-temperature, high-strength conditions needed in new jet turbine engines. More broadly, U.S. development of TEMs during the 1960s was critical not only for jet turbines but also for semiconductor development and understanding silicon defect control.

Cyclotrons and Uranium Enrichment

Ernest Lawrence won the 1939 Nobel Prize in Physics for developing the cyclotron

Making small particles move very fast turns out to be quite useful. For beamlines, an electron beam is accelerated around a massive ring. The beam’s continuous turn emits highly intense X-ray beams (synchrotron radiation), which are directed to various stations for characterizing samples. Particle colliders operate similarly but accelerate protons and other particles, usually to collide at very high energies.

Lawrence, of Lawrence Berkeley National Lab fame, invented the first cyclotron, which used magnets operating under high vacuum to direct and guide accelerated particles. Notably, Lawrence’s contribution to the Manhattan Project was reconfiguring this exact same technology stack to enrich Uranium. By relying on the mass difference between Uranium isotopes, the technology was adapted to separate them under acceleration in a magnetic field. This is one useful example of how an ecosystem of instrument-oriented science innovators can serve as dual-use engineering talent.

Today, particle accelerators are massive scientific infrastructure hosted at national labs and international facilities, such as the famous CERN facility in Switzerland.4 Their efficient construction and operation often requires continual projects and upgrades—a critical practice that sustains the engineering community and prevents the technical knowledge base from degrading or dissipating.5 Functionally, they operate on a user facility model, providing a public service and serving as a schelling point for various scientific sub-fields whose research relies on these unique capabilities.

Cryo-EM & Protein Data Bank

Jacques Dubochet, Joachim Frank, Richard Henderson won the 2017 Nobel Prize in Chemistry for developing cryogenic electron microscopy.

While electron microscopy was commonly used in materials science, it couldn’t be used for biological samples because the powerful electron beams would cause the samples to degrade. The key innovation that unlocked Cryo-EM was the ability to vitrify samples, preserving biological structures in a special amorphous (glass-like) ice structure. Suddenly, a new high resolution imaging technique for proteins was available. Cryo-EM was particularly suited for large, uncrystallized proteins, which other protein characterization techniques like Nuclear Magnetic Resonance (NMR) and X-Ray diffraction (XRD) crystallography could not handle.

Notably, contributions to the protein databank (PDB) from Cryo-EM have significantly increased in the past several years. Readers may recall the PDB is the same dataset which AlphaFold was trained on. Cryo-EM’s ability to provide imaging of more complex multi-structure interactions is a good example of how adoption of new instruments can unlock new data generation capabilities for AI for Science models.

Future Work

A few examples of research questions with this new perspective I’d love to see more public writing on:

• What is the role of public compute facilities e.g. DOE supercomputer user facilities, in supporting scientific innovation?6

• how will new automation-first science instruments be adopted in the context of autonomous labs? What does the “go-to-market” look like for new science instruments, and is there a venture capital case for such companies?

• how have global development philanthropies like Clinton Health Access Inititative driven cost-of-care improvements through scaling diagnostic instruments?

• How might this reshape philosophy of science? e.g. instead of Kuhnian revolutions or Polanyi’s Republic of Science, might we conceptualize science as ecosystems of practice that are advanced by the diffusion of new instruments and tools?

And more! Do reach out if you’d be interested in writing on this topic.

5

Despite their scale, they also exhibit scaling laws over time:

Autonomous labs, which integrate robotics into the scientific lab, are one of the most exciting AI-enabled scientific systems. This past year, a significant amount of venture capital has flowed towards developing autonomous labs and there is an increasingly robust lab automation startup ecosystem. At the same time, the U.S. government has also taken some steps towards supporting autonomous lab development, though this has slowed, due to Trump science funding cuts.

With private capital accelerating development, what can public and philanthropic investment do to ensure the U.S. leverages its AI leadership to accelerate discovery and sustain global technological competitiveness?

In this post, I outline several different kinds of programs that philanthropy could support to accelerate scientific discovery through autonomous lab development and harness AI for scientific progress.

Education and Talent

Identifying and training young scientific talent for a future of autonomous experimentation is one clear area that private companies strictly benefit from, rather than provide. AI is disrupting the way we do science and we need to ensure science education meets the moment. We can also build pipelines that guide early-career scientific talent towards addressing the key problems for autonomous labs.

Reimagining Undergraduate Chemistry Education for Self-Driving Labs

Undergraduate chemistry courses still teach inductive reasoning through static experiments and manual lab work. Yet AI and automation demand a different skillset—students who can connect theory with robotics, sensors, and code. This program would fund curriculum and materials for “frugal self-driving labs”: simple automation platforms undergraduates can assemble to gain hands-on experience in robotics, AI, and chemistry. For instance, one proposal reimagines the standard titration chemistry lesson as an autonomous experiment, linking chemical theory with software control and feedback.

SDL undergraduate fellowship

Building and operating self-driving labs requires rare, interdisciplinary fluency across automation hardware, AI, and domain science—a combination few students currently possess. This creates a critical ramp-up period for new graduate students to ramp up the needed interdisciplinary skills for developing autonomous labs, which hinders research productivity. This program would fund a summer fellowship placing top undergraduates in leading autonomous lab research groups, where they would gain direct experience integrating software, robotics, and experimental workflows. By cultivating a cohort of hands-on researchers early in their careers, this fellowship would expand the talent pool capable of advancing SDL technology and incubate a model for training the next generation of AI-fluent scientists.

Science Instrument Innovation Fellows

“We don’t have the right software to make our machines talk to one another…Hardware is built for human interaction, not for autonomous or robotic interaction, so most of the things you try to automate are hard to do because they are not set up for robots.”
Benji Maruyama, NASEM workshop on autonomous labs

Scientific instrumentation remains dominated by legacy firms with outdated software, closed interfaces, and designs optimized for manual use. This fellowship would identify graduate researchers who have built novel instruments as part of their academic work and support them in transitioning toward commercialization and autonomy-native design e.g. like a targeted SpecTech BRAINS fellowship. Through entrepreneurial mentorship and technical acceleration, the program would cultivate a new generation of founders building interoperable, automation-ready instruments—incubating the next Thermo Fisher or Agilent for 21st-century science. The program can also help catalyze broader venture capital interest in this critical hardware industry and inject competition and modernization into the science instrument industry.

Materials Data Factory

AI foundation models in materials science remain constrained by the scarcity of high-quality, large-scale experimental datasets. For instance, the Alphafold breakthrough was only possible because of the protein data bank, a large-scale experimental dataset generated by Brookhaven national lab. Self-driving labs can autonomously generate standardized, high-throughput data while exploring new material candidates. Publicly funded materials data factory would generate massive, experimental datasets for fundamental material classes and open-source the data, helping advance AI for science foundation models.

This initiative would fund a consortium of academic and national lab SDLs to produce and openly publish vast datasets across key domains. Building this distributed “materials data factory” would establish a cornerstone public dataset resource, accelerating clean-energy innovation and catalyzing open-science collaboration globally.

Example material classes could include:

• lithium electrolyte formulations

• metal organic frameworks for carbon capture and catalytic properties

• perovskite solar active material formulations

• metal alloy and corrosion testing

Metascience of Self-driving Labs

More clearly quantifying the benefits of self-driving labs, and their impact on the way we do science, will make their benefits more legible to future funders. Such metascience studies could also inform the evolving thinking on how AI will impact scientists and the nature of scientific research.1

SDL reproducibility study

Despite their promise to enhance reproducibility by automating experimental execution, there has not yet been any systematic studies that have tested whether self-driving labs (SDLs) indeed yield more consistent results across institutions. This project would fund a coordinated, multi-lab reproducibility experiment in which identical SDL setups conduct the same protocols across sites. By quantifying performance variation and identifying sources of drift—both mechanical and computational—the study would provide the first rigorous baseline for reproducibility in autonomous experimentation. The findings would strengthen confidence in SDLs as a shared, interoperable research infrastructure capable of scaling reliable scientific discovery.

SDL acceleration study

While self-driving labs promise to accelerate scientific discovery, there has not yet been a systematic evaluation of the actual acceleration SDLs can unlock in an experimental setting. This metascience effort would track the development of a SDL, breaking down upfront development time and operational/maintenance costs for a SDL, in comparison to standard experimental efforts. The findings would make the costs and benefits of SDL more legible and allow for clearer cost-benefit analysis of investing in SDLs and identifying bottlenecks to reduce friction in SDLs.

Policymaking

Elevating autonomous labs within federal science policy would help attract greater public investment in their development. These labs should be treated as core national infrastructure—alongside synchrotrons, supercomputers, and other shared research facilities.2 A coordinated strategy that connects AI model development with autonomous experimentation would strengthen U.S. scientific competitiveness and renew the case for public science funding in the AI era.

Autonomous Science Video Series

Private corporations’ advances in large language models (LLMs) are increasingly being used to justify sweeping cuts to public science funding. Yet autonomous labs represent a cornerstone of modern scientific infrastructure—especially in the age of AI—because they generate the vast, high-quality experimental data needed to train scientific foundation models. It has been particularly frustrating to me how many policymakers remain remain unaware of both the critical role autonomous labs play and the progress being made at universities and national laboratories over the past decade.3

This project proposes a series of concise, high-production video features (~10 minutes each), designed for native distribution across major social platforms, to showcase breakthroughs in autonomous lab research. The series would not only highlight scientific achievements but also reframe the public narrative around AI—positioning it as a tool to strengthen, rather than supplant, public science. Over time, this campaign can help galvanize support for modernizing and expanding automated scientific infrastructure across America’s research institutions. Current video documentation of autonomous labs is limited to supplemental figures of scientific papers [thread here].

If you are interested in funding any of these programs, feel free to reach out here.

Materials are not charismatic technologies like cars or computers. Yet they enable almost every one of humanity’s technical achievements: rebar unlocked the skyscrapers of the 1920s; chemically strengthened glass delivered us smartphones; and stainless steel, not created until 1913, brought with it the clinical equipment upon which modern medicine depends.

Ben Reinhardt

Innovations in materials science drive technological competitiveness. They are among the most tangible ways basic R&D spending benefits consumers. From semiconductor design breakthroughs to steady advances in battery and solar materials, a healthy materials research ecosystem is essential for staying at the frontier of industrial technologies.

In the past decade, China has become a clear leader in material science research, through a combination of decentralized industrial policy subsidizing manufacturing investments, and investing heavily in science funding for key material science sectors.12 China has leveraged and coordinated the various arms of the government science apparatus to plug key material vulnerabilities, such as helium3 and rare earth refining4.

Meanwhile, the U.S. struggles to pass federal budgets on time, creating chronic uncertainty for federal science funding. The National Science Foundation’s (NSF) budget has stayed largely flat, eroded by inflation. 5 Layoffs and cancelled grants have further weakened the system, and visa restrictions have driven away international researchers who once sustained America’s talent advantage.6

So what does the turmoil in Washington DC have to do with San Francisco venture capitalists?

In the past year, the frothy AI venture capital investors have begun to normalize their AGI expectations and settle on AI for Science as one of the key growth markets for advanced AI technology, outside of code generation. For instance, just in the past few months at OpenAI:

• Sam Altman has said AI for Science is the AI application he is most excited about over the next 10 years.

• Kevin Weil, formerly head of product at OpenAI, has transitioned to lead their AI for Science effort.

• Liam Fedus, former VP of post-training at OpenAI, left to found an AI for Science startup (specifically, Periodic Labs).

AI for Science is in the air.

One critical part of enabling AI for Science is autonomous labs. As I have been writing about for years now, autonomous labs, which integrate robotics into science experiment workflows, are an important technology for accelerating material discovery, generating real-world training datasets for foundation model training, and giving AI agents autonomy in the physical world. While university researchers and national labs have been developing autonomous labs for several years, the startup ecosystem for autonomous labs is also increasingly active:

While VC funding historically went to large AI model developers like OpenAI and Anthropic, this past year has seen a dramatic shift towards also funding AI applications like AI for Science — to the benefit of autonomous lab development.

In this past year, the amount of venture investment into startups building autonomous labs for materials and chemistry is comparable to the annual NSF budget for materials, chemistry, and physics.789 10

Most of the VC funding to build autonomous labs is in Lila Sciences ($550M) and Periodic Labs ($300M), with a long tail of smaller startups.

It is somewhat beautiful that San Francisco VC’s are subsidizing scientific infrastructure build-out, such as autonomous lab development. It is even more ironic when considering that a non-trivial amount of VC in the U.S. today is foreign capital seeking American VC returns.

But let me be clear: public science funding, while never perfect, can not be replaced by VC money.

We do not need to present false choices between VC funding and public science funding. We should strive to have both robust public science funding and recognize the positive spillover from VC funding in AI for Science. We can recognize the unique capital markets America is endowed with and the importance of supporting public science funding. If we are serious about competing with China on the global stage for technological innovation, then we need to leverage every part of the toolkit.

Public funding remains essential for autonomous lab development. It operates on different incentives and timelines, supporting the long-term, cumulative work that startups depend on. Venture capital doesn’t train undergraduate or graduate students in materials science, or teach them to run precision instruments—that infrastructure exists because of public investment.

While frothy VC bubbles come and go—and are famously wasteful—public science funding remains the stable bedrock for sustained investment and talent. It anchors research and talent development across generations. Autonomous labs, while still underrated, are only one piece of the materials innovation ecosystem. National labs and university user facilities—home to the world’s largest beamlines and supercomputers—form billion-dollar public infrastructures equally vital to materials discovery.

Autonomous-lab startups will produce enormous quantities of experimental data, much of it kept proprietary. Publicly funded researchers, by contrast, have a mandate to share their data. For instance, AlphaFold was only possible because of open datasets generated at Brookhaven National Laboratory. If we surrender all autonomous lab development to the private sector, we risk starving the broader AI for Science ecosystem of the public data that fuels it.

In the coming posts, I’ll write about what philanthropy and public science funding can do to support the autonomous lab ecosystem.

Thanks to H.W. for inspiring this post.

1

In this 2021 CSET paper, they survey granular research clusters and identify which country has disproportionate leadership in each research cluster. China is disproportionately leading in material science and other STEM fields (shown below) whereas U.S. leads in far fewer research clusters, mostly in social science and biology.

8

NSF budget from here and here.

As with any emerging field, the optimal firm boundaries are still in flux and a powerful direction of experimentation.2 Startups are experimenting not only with new technologies, but with new business models — staking out different positions across the lab automation value chain in search of where value accrues and how best to capture it.

This post maps the current landscape of lab automation startups, organized by the strategic archetypes they represent. It also highlights one critical gap and a thesis I believe is increasingly important to the future of the field.

Discovery-as-a-Service

Example Companies: Ginko Bioworks, Radical AI, Zymergen (acq by Ginko), Recursion, Lila Sciences, Insitro, Periodic Labs, Yoneda Labs

These companies bundle lab automation infrastructure with in-silico methods to operate full-stack discovery pipelines. Rather than selling tools or infrastructure, they aim to capture value by directly commercializing or licensing the discoveries their platforms generate.

The core challenge is that discovery and commercialization often involve fundamentally different competencies. While there’s a well-established model for commercializing scientific discoveries, it’s less clear how to directly monetize the acceleration of discovery itself. These companies not only carry the high CapEx of building lab automation infrastructure, but also take on the scientific and commercial risk of identifying which discovery programs are most viable.

The likely best in-road here is biotech, where there is a clear process for commercializing discoveries and where there are some new firms (likely not all listed) who are adding in lab automation to their workflows.

Infrastructure-as-a-Service

Example Companies: Strateos, Emerald Cloud Lab

Rather than capturing value through discoveries themselves, lab automation IaaS companies provide remote access to automated lab infrastructure. These “cloud labs” allow researchers to design and execute experiments virtually without physically entering a lab.

While this model lowers barriers to scientific experimentation, it still demands significant CapEx to build and maintain fully automated lab environments. Moreover, because most scientific instruments were not designed for programmatic control, achieving true end-to-end automation remains difficult. In practice, many of these platforms still rely on human technicians behind the scenes, abstracted away from the user interface.

Lab Orchestration Software

Example Companies: Labric, Tetrascience, Benchling Connect, Artificial, Biosero, Pedal AI, Atinary

Rather than wrapping around discovery commercialization or infrastructure leasing, these companies focus on the software wrapper layer of lab automation — orchestrating experiments, exfiltrating data, and providing software analytic wrappers. Rather than building hardware, they offer cloud platforms that sit atop existing lab equipment. For newer entrants in the space, they are often layering in AI for data analysis and workflow optimization.

While valuable, this approach relies heavily on integrating with a fragmented and constantly evolving landscape of vendor-specific hardware, file formats, and APIs. It is unclear what the barrier to entry or competitive moats here are. As a result, competition in this category may hinge less on technical defensibility and more on cloud ecosystem integrations and go-to-market velocity.

Lab-in-a-Box

Example Companies: OpenTrons, ChemSpeed, Trilo Bio, Tecan, Mito Robotics, Flow Robotics, North Robotics, Unchained Labs

Lab-in-a-Box companies sell automation products that individual labs can purchase and deploy onsite. These systems, typically focused on liquid handling, sample prep, or modular automation, allow researchers to configure and run their own workflows locally, without relying on centralized cloud infrastructure. In some cases, this includes enclosed workcells or robot-arm-equipped “lab boxes” that aim to generalize across multiple experimental steps.

The tradeoff is that these systems primarily provide workflow automation, not end-to-end experimental capability. Outside of liquid handling, most units lack integrated synthesis or characterization tools, which researchers must supply and integrate themselves. As a result, users often bear significant responsibility for instrument compatibility, workflow setup, and maintenance, particularly when working outside of standardized applications.

Robotic Experimentalist

Example Companies: Medra AI, Zeon systems

Related to Lab-in-a-Box, these companies build on a stronger thesis about general-purpose robotics. Rather than designing enclosed, task-specific lab systems, Robot Experimentalist startups aim to leverage commercial robotic platforms and train them to directly manipulate existing science instruments and lab environments.

The promise is compelling: bypass the need to redesign every instrument or build a custom box by dropping in a flexible robot that can “learn” how to use existing lab hardware. But it's still unclear whether robotics are yet robust, dexterous, or affordable enough to natively integrate with heterogeneous lab setups. Adoption will likely depend on whether these systems can generalize across workflows, reduce configuration overhead, and hit acceptable cost-performance thresholds. These startups are riding the broader wave of humanoid and general-purpose robotic platforms — their success may hinge less on lab-specific insight and more on timing the maturity curve of this larger technological wave.

Automation-Native Science Instruments

Example Companies: ?

A conspicuous gap in the lab automation startup ecosystem is companies building science instruments designed from the ground up for automation. Most existing startups abstract over the limitations of today’s fragmented instrument landscape. Discovery-as-a-Service (DaaS) and Infrastructure-as-a-Service (IaaS) players integrate around legacy tools, shouldering the cost and complexity themselves. Lab-in-a-Box and ScienceData-as-a-Service models, meanwhile, offload the integration problem to researchers — requiring them to retrofit workflows or wrap software layers around hardware never built for automation in the first place.

The core thesis here is that none of these models fully overcome the brittleness of the underlying hardware ecosystem. IaaS and DaaS companies absorb too much integration debt with inadequate science instruments. Lab-in-a-Box and Lab Orchestration leave critical gaps in synthesis and characterization, and do not fully solve adoption challenges. And general-purpose robotics will not generalize fast or cheaply enough to serve as a universal abstraction layer for all physical labs.

Even if everything in the above paragraph is off-base, the case for purpose-built, automation-native instruments still stands. Every company in this market — from cloud labs to orchestration platforms to robotic experimentalists — would benefit from complementary science instruments that expose robust pythonic APIs and are natively designed to be controlled by autonomous agents.

The challenge here is that it requires new science hardware development. It is a product-based approach, at least initially, and consequently also offers low returns. But I think both of these issues are quite surmountable with the right team and business model. While most startups work around the limitations of today’s instrument ecosystem, this path aims to disrupt it entirely — replacing a legacy industry built for a slower, more manual era of science.

This is a thesis I intend to be exploring more in the coming months – stay tuned!

In this episode, I sit down with Robert Underwood, a staff scientist at Argonne National Laboratory. We dive into Argonne’s mission as an open science lab, the power of its new exascale supercomputer Aurora, and how these resources are being harnessed to drive the future of AI for science. We discuss the AuroraGPT project, which aims to adapt AI models for scientific data, as well as the challenges of handling massive scientific datasets generated by facilities like the Advanced Photon Source.

We also talk about how Argonne is collaborating with initiatives like the Trillion Parameter Consortium to push the boundaries of AI at scale, while staying focused on scientific workflows and reproducibility. Here are three takeaways from our conversation:

1. Aurora: Public Compute at Scale
   At the heart of Argonne National Lab’s leadership computing facility is Aurora, a public exascale supercomputer purpose-built for large-scale scientific computation. Unlike commercial cloud GPU clusters, Aurora is optimized for running massive, coordinated jobs across tens of thousands of nodes—something essential for many forms of modern science, from fluid dynamics to materials modeling. As Robert Underwood explains, Aurora also supports mixed-precision compute, allowing researchers to exploit AI workloads as well. The lab’s role as an open science facility means this capability is available to academic and public researchers, not just private industry, and reflects a broader vision of compute as national infrastructure.

2. AuroraGPT: AI-for-Science Models
   AuroraGPT is Argonne’s initiative to adapt foundation models for scientific domains—ranging from high-dimensional physics simulations to sparse bioinformatics graphs. Rather than build one giant model, the team is developing a family of models tailored to specific scientific questions and modalities. Robert notes that this effort is constrained not by compute—Argonne has secured DOE-scale allocations on Aurora—but by personnel, with only a ~30 person team. Argonne is also one of the key backers of the Trillion Parameter Consortium, composed of other scientific and industry leaders, working on building a trillion parameter AI model for science.

3. Managing the Scientific Data Deluge
   Unlike commercial LLMs that scrape a finitely sized internet and now rely on synthetic data, science faces the opposite challenge: an overwhelming flood of data generated by experimental infrastructure like the Advanced Photon Source. Each beamline can generate up to a terabyte per second. To handle this, Argonne is pioneering a hybrid edge-HPC architecture—compressing real-time generated scientific data using GPUs and FPGAs at the beamline before routing it to supercomputers like Polaris for further analysis. This vision of autonomous experimentation—AI models directly interfacing with scientific instruments—marks the future of how we’ll do science at scale.

Transcript

Charles Yang

Okay, awesome. Today I have the pleasure of having Robert Underwood join us. Robert is a staff scientist at Argonne National Lab. Robert, thanks for coming on.

Robert Underwood

Yeah, thank you for having me.

Charles Yang

Great. So maybe first it'd be helpful if you could give our listeners a sense of Argonne National Labs mission and history and focus. Not everyone might be familiar with kind of what the national labs and Argonne in particular does.

Robert Underwood

Sure. The national lab system traces its origins to the Manhattan Project—so these labs go all the way back to the development of the atomic bomb. Argonne was one of the original labs, alongside Los Alamos. But since then, the labs have evolved significantly. Their mission today is much broader: developing energy and science capabilities for the benefit of the nation.

That includes a wide range of research domains, and increasingly, that means working on AI for science.

Argonne is what’s called an open science lab, meaning our facilities are available to external researchers. We're one of the three major computing labs in the DOE ecosystem, alongside Oak Ridge and NERSC. These three house some of the world’s most powerful computing infrastructure.

At Argonne, that’s the Argonne Leadership Computing Facility, or ALCF. The crown jewel there is Aurora, one of the world’s largest open science supercomputers. It delivers over one exaflop of double-precision floating point performance—which is a staggering amount of computational power.

But what’s especially interesting about Aurora is its flexibility: it can also compute in lower precision formats, which makes it uniquely valuable for machine learning and AI workloads. That’s one of the key areas we’re exploring—and something we’ll talk more about today.

Charles Yang

You mentioned that Argonne is an open science lab. That’s in contrast, of course, to the weapons labs under DOE that aren’t quite as open, shall we say.

You also brought up Aurora, which I believe came online just a few months ago and recently ranked number one in the world on the Top500 high-performance computing benchmark [Postscript: Aurora is now #3 on Top500, behind Frontier at Oak Ridge National Lab and El Capitan at Lawrence Livermore National Lab]. Could you walk us through how a system like Aurora compares to what we’re seeing in the commercial space—particularly the new cloud GPU clusters companies are building, like the ones from CoreWeave or Lambda? Those also have a lot of compute. So what’s the real difference?

Robert Underwood

Yeah, great question. There are a few important differences.

First, national lab systems like Aurora tend to emphasize specialized hardware characteristics. We typically use high-performance interconnects—that’s becoming more common in commercial AI supercomputers, but it’s still a differentiator. We also rely heavily on parallel and distributed file systems, which offer different consistency models than what you’ll find in commercial cloud environments.

Another major distinction is job structure and scale. We design our systems to run single, extremely large jobs—things that may need the entire machine to run. That’s a core part of the mission of the Argonne Leadership Computing Facility: to enable one-of-a-kind science that simply isn’t possible on other infrastructure.

With Aurora, for example, we’re talking about jobs that span 10,000+ nodes, with something like 60,000 GPUs all working in tandem on a single simulation or model. You just don’t get access to that kind of coordinated compute outside the lab environment. For many scientific applications, it’s the only viable way to run these workloads.

Charles Yang

That makes sense. I do want to dive into the details of the AI-for-science work you’re doing, but maybe one more tangent on compute architecture.

You mentioned that Aurora—and high-performance computing (HPC) more broadly—tends to focus on high-precision float types, like double precision. But many modern AI workloads are now shifting toward lower or mixed precision to scale better.

Do you see a divergence emerging between the needs of traditional HPC and the requirements of AI workloads? It feels like there are two increasingly distinct paradigms for large-scale compute, and I wonder whether the labs will need to start rethinking how they architect future systems to support both.

Robert Underwood

I mean, from my perspective, what I see is that industry is actually getting closer to us. It’s not so much that industry cares about double precision, but if you look at the other exascale machines in the United States, they also use GPUs where, if you want to get the maximum possible computational performance out of the machine, you have to use these lower-precision floating point units. This would be like Tensor Cores on NVIDIA hardware.

But AMD and Intel each have their own equivalent—something like BFLOAT16-style computational capacity. And if you want to fully leverage that, you need to use lower-precision formats.

So while we talk about Aurora as being an exaflop machine, I think if you use the 16-bit precision, if I’m not mistaken, it gets close to 12 exaflops of performance. So if you’re really looking to take advantage of the peak power of the machine, you’re going to be using these low-precision representations.

Charles Yang

Right. Well, so maybe let's talk about the project that you guys announced over a year ago now called AuroraGPT. What is it?

Robert Underwood

So AuroraGPT is Argonne's effort to prepare for a future where AI and science are much more heavily integrated. One way we think about that is by asking: what does it mean to leverage data that’s unique and specific to scientific applications and workflows in the context of AI?

That data often looks very different from what you typically see in most industrial use cases. For example, we might need to represent higher-dimensional data—like 5D or 6D tensors—for certain kinds of physics problems. We might need to handle very large graph data, or work with sparse and unsparsed grids or meshes, which are often used in things like finite element codes.

So there are many ways in which the labs have unique data structures that are extremely valuable for solving specific scientific problems, but which haven’t really been explored by most major AI players in the industry. That’s where we see a niche: how do we adapt AI models and tooling to scientific workflows and applications?

Charles Yang:

Yeah, and I think that data modality point is really important. The kinds of examples you’re describing—those aren’t things ChatGPT is going to be able to help with. Or maybe it could, but the dimensionality just isn’t the right shape for that kind of model.

So is AuroraGPT a single big model that you’re training? You mentioned a bunch of different modalities and different kinds of scientific applications. What does the progression look like so far?

Robert Underwood

So what we're really looking at is kind of a series of models, each aimed at answering one or more different kinds of scientific questions. For example, we might want to understand whether a model trained on substantially more biological sciences information is better at answering questions in that domain. That might be one of the questions we’re trying to answer.

So we would train not only on papers and standard reference materials available in biology, but also look at adapting various other resources. For example, Argonne has something called the BVBRC, which I believe is the Bacterial and Viral Bioinformatics Resource Center, which is one of these major resources that we're using for trying to do these experiments around bio. The BVBRC is a multimodal database containing both tabular information and other forms of data. It includes descriptions of actual in-lab experiments—experiments that people have done using different materials and biological samples—as well as simulations involving similar or sometimes the same materials. So you can imagine this is a very rich dataset, and we’re trying to explore how we can take all of that and make it accessible to scientists working with AI.

Charles Yang:

That’s really interesting—especially this biological dataset you mentioned. How does that compare to something like Arc’s Evo model?

The scale-pilled thesis, of course, is: if you throw enough tokens at it, the model can learn a lot of the underlying relationships. And on the biology side, a lot of that work focuses on tokenizing gene or DNA sequences. Is the dataset you’re describing different in that regard? And how does it stack up against what we’re seeing in industry?

Robert Underwood

Yeah, so my perception not having looked into the details of the EVO model specifically is that Arc is doing some very interesting things kind of in the materials space. They've done some kind of techniques where they look at kind of equivariant neural networks to adapt kind of my understanding is like MD style simulations of the different materials and particles and then adapting those to models.

I think that that gets you some of the way there, but my impression is that there are kind of richer forms of data that might be available from simulations that the labs maybe have access in greater quantities or greater varieties than what companies may have in this particular space.

So one way in which we can have access to a large amount of data is we have a large facility also at Argonne called the Advanced Photon Source. This allows us to take imaging, essentially, of different materials and understand information as it's being the structure of the materials that we're imaging.

So as we're studying the structure and better understanding these more fundamental properties of the materials and the biological samples that we're studying, that then goes back and informs the next set of experiments that one might run. So there's a deep interconnectedness to this that might extend beyond what you might be able to contain in, say, a single simulation about a single material, but finding these deeper relationships that might exist. Now, it's possible that you can get at that with, as you described it, this just scaling with additional tokens. But I think the better way to think about it is like, is it better to provide a more concise, richer data source or a larger, less rich data source? And I think that's kind of an open question that we'll see be answered over the next coming years.

Charles Yang

And the good point about the APS, I mean, I think the UK announced a very similar project run using their hard light source in their national lab to generate a protein ligand data set. So certainly do want to talk about the role that scientific infrastructure plays in generating data for AI models. But before we leave the aura GPT, it sounds like you are developing a number of kind of foundation models that are specifically geared towards this kind of high fidelity experimental data that might not be easily tokenizable by the current class of industry models.

What's the kind of state of the effort? I mean, like how many people are working on it? What kind of compute systems are you all using? What's the scale of the models you guys are working with?

Robert Underwood

So at this time, I think we have like order of 30-ish people that work some percentage of their time on AuroraGPT. So if we compare that to industry efforts, they're going to have a lot more people because they have a much larger budget for these kinds of things. But the idea was that we want to kind of use the small amount of resources that we do have and leverage them for the biggest impact that we can.

So in terms of of model sizes, we've looked at kind of 7 billion parameter models, and we're looking at like 70 billion parameter models kind of scaling up from there. These are kind of sizes that are useful in terms of being able to fit on existing info. So if you look at kind of across the space, you'll see a series of both 7 billion, 9 billion kind of parameters, just kind of this kind of smallish, but still useful size.

And then you'll kind of see kind of the 70-ish billion parameter size that roughly corresponds to like a single DGX node worth of hardware. And those are kind of common sizes that you typically see. Argonne also is affiliated with something called the trillion parameter consortium. So we have aspirations to eventually go bigger, but we're kind of starting small and building and experimenting with these techniques at smaller scales to see where they can eventually go.So at this time, I think we have on the order of 30-ish people who spend some percentage of their time working on AuroraGPT. If you compare that to industry efforts, they’re going to have a lot more people, simply because they have much larger budgets for this kind of work. But the idea for us is to use the relatively small amount of resources we do have and try to leverage them for the biggest possible impact.

In terms of model sizes, we’ve looked at 7 billion parameter models, and we’re starting to scale up to 70 billion parameter models. These sizes are useful because they can still fit on existing infrastructure. If you look across the space, you’ll see a bunch of models in the 7 to 9 billion parameter range—smallish, but still quite useful.

And then there’s the 70-ish billion parameter size, which roughly corresponds to a single DGX node’s worth of hardware. That’s another common checkpoint you see across the industry.

Argonne is also affiliated with something called the Trillion Parameter Consortium. So we do have aspirations to eventually go bigger. But for now, we’re starting small—building up our tooling and experimenting at these more tractable scales to see how far we can push the techniques.

Charles Yang

Yeah, I mean, have you all had any results come out of it that you can talk about now?

Because my general concern is—it’s 2025, and industry models are getting larger and larger. But even now, they haven’t really proven much directly. Though, to be fair, groups like Future House in San Francisco are starting to productionize some of them in more domain-specific ways.

When you talk about 7 billion and 70 billion parameter models—granted, these are very different kinds of architectures, especially when you're dealing with higher-fidelity data—but it still feels like the pacing and the level of resources going into testing this hypothesis you’re describing seems kind of disproportionate or maybe inadequate relative to the broader conversation.

Do you all have a timeline for when you're expecting results? And what would you need to see to feel like the hypothesis is validated—or, on the flip side, to conclude it’s not the right path?

Robert Underwood

So I would say that we're working very actively to have kind of the first set of results that we're ready to talk about publicly. We're not ready to do that at this stage. But what I can say is that this is a very large problem and I have confidence that it will not be solved by the time that we publish our results. So even outside of the context of like actual models being released, we are making efforts to make kind of methodological contributions and kind of other contributions around the evaluation of AI. So a good example of this would be the EAIRA paper.

So the evaluation team here at Argonne has recently put out a paper called EAIRA, which looks at proposing a methodology for evaluating AI models in the context of science. So if you kind of look at that methodology, it proposes kind of two existing components of most methodological stacks, which are multiple choice questions. And that, but we look at them specialized at science for scientific purposes.

And then we kind of look at where are there gaps between existing benchmarks that are used for evaluating these kinds of models? as well as kind of more specific. Then we kind of move into more like open generation style questions or free response style questions. But then the kind of the last two things that we look at that are kind of, I think, kind of different than what you see a lot of the evaluations doing right now is you see that there is a desire to look at what we call lab style experiments or kind of think of these like case studies or so these are like very long form experiments where we have domain experts working on a very hard cutting edge problem.

We bring them in for multiple hours and we have them work with state of the art models from across the different vendors. And we ask the same similar sets of questions to each model and we evaluate where are there gaps. And while it's not the same thing as producing a model per se, it's a meaningful contribution in terms of describing where there are gaps in the methodology for evaluating these models.

So another kind of thing that you kind of see, which is towards the fourth category proposed in the paper, which is this notion of what we call field style experiments. So these are large scale experiments. So you may have heard of something called the thousand scientists jam that was organized by the US Department of Energy and OpenAI and Anthropic earlier this year. So part of that effort is looking at how do we scale up this kind of idea of a lab style experiment, but to a larger community and kind of building automated tools and scalable evaluation methodologies to quickly assess across the corpus, maybe even the size of the entire DOE, what are invaluable problems that we want to solve. So while building a model is like a piece of our mission, it's not the only piece of our mission.

Charles Yang

Yeah. I certainly think, yeah, to the point about evaluation, mean, that's something that we've seen, like, you know, open as fund a lot of work around AI for math, where they are essentially trying to pull out like benchmarks that they can then use to benchmark their models against, right? And that takes a lot of work for mathematicians to kind of get involved in. It's certainly a form of labor at the very least. I mean, and I think to the broader point around AI for science models, you've seen, you know, metas come out the Evo and the OMOL models. Google Deepmind come out the Graphcast and their AI for weather forecasting models.

So, certainly the thesis, I think, is supported by many that there are differentiated classes of models for scientific data specifically. But that message certainly gets lost a lot, I think, in the discourse of not all AI models are born the same or trained the same way.

Robert Underwood

Yeah. Yeah, but the other thing is, if you look at a model like Olmo, example, Olmo is in many ways trying to solve a very different problem than what you might compare to with a llama3, right? Because one of the distinct purposes of the Olmo model specifically is that they want the entire process to be fully reproducible.

And having kind of a fully reproducible model stack all the way down to the data is actually really important if you're wanting to meaningfully measure like what are the performance differences for, for example, injecting a bunch of biological sciences data. Cause you know exactly what was in the training set. And if you want to go back and audit, where did this weird generation come from? You, you, if you're doing this with a llama based model, you don't have a prayer. whereas if you have a model that you've trained from scratch, whether it be based off of or some other kind of data set where you have this full provenance. This is something that's really, really valuable in a scientific context, whereas in a business context, you may or you may not care about that full level of traceability.

Charles Yang

Right. No, I think that's definitely another point as well about the differences between these kinds of models, how we bake these models. OK, last question on AuroraGPT, and then I do want to talk about the data generation side. What do you think is kind of like the primary limitation right now? I'm certainly very supportive of this whole effort. Podcast is really around focusing on AI for science, and I think having a public capacity to do that is obviously important. What are like kind of the primary limitations you think to scaling up the success of AuroraGPT and Argonne National Labs involvement in the AI for Science. It people? Is it compute? Is it something else?

Robert Underwood

My impression is that compute is by far our biggest scaling, or not compute, personnel is our largest scaling constraint that we have right now. As I said, we're a very small team. And if you look at just like, for example, Meta, my impression is that there were order of a thousand people involved with the llama three paper. could have, you know, roughly miscounted there, but like they're at least an order of magnitude, if not two orders of magnitude larger than the effort that we have. So if we're wanting to kind of demonstrate comparable style outputs and comparable style efforts, we're going to need more people than we probably have now. Now, what's kind of...

Charles Yang

But I mean, are you all trying to compete on llama or are you all trying to compete on, I mean, I do want to distinguish like what is like the right benchmark of reference here, right? Because if you start saying we need a thousand people like meta to do a llama style thing, people are gonna ask why is Argonne building a llama style model.

Robert Underwood

I mean, I guess that's a fair assessment, but at the same time, like there are a lot of different science domains and very few, if any of them have robust treatments in science. So I don't know if a thousand people is necessarily the right number, but my point is if you, if you want to see larger and faster progress out of the labs on these kinds of efforts, we are going to need more people to do that kind of work.

Charles Yang

Yeah, definitely. Well, and it's interesting to you say personnel not compute, because I know for some other companies that that is like the primary limitation.

Robert Underwood

I mean, compute will eventually become a limitation, but I think, like, for example, we're able to secure an INCITE proposal. So INCITE is a program within the DOE to get large scale allocations of core hours on machines like Aurora. And like, while we are definitely making use of our INCITE allocation, I think if we had more people, we could make even more effective use of that allocation. So I think personnel right now is our biggest constraint.

Charles Yang

Yeah. OK. I mean, I think that's going to be helpful for many folks to hear. OK. Let's talk about, I mean, in the other hat that you wear at Argonne, you also do a lot of work on scientific data compression. Why does that matter? What's the kind of motivation there?

Robert Underwood

So scientific data compression is really important for a lot of different domains. So if you look at these exascale applications, they can produce mind-numbing volumes of data. So if I'm not mistaken, the hack simulation, hack farpoint that was ran, I think it generated on the order of 2.3 petabytes of data. If you look at things like the APS upgrade at Argonne or LNLC's [Linac Coherent Light Source] upgrade at SLAC, these facilities are like on pace to produce order of a terabyte of data per beamline in some cases, which is just a mind numbing volume of data. And with that much data, you really have to have a careful and thoughtful approach as to what you're going to do with that data in the long run. So data compression is one of a variety of ways by which you can approach that problem.

And what's interesting about data compression is that it allows you to retain the original dimension of the data, so the original sizing information of the data, and the original number of the data. So you're not reducing the featuredness or the richness of the data. You're not really producing the number of data. What you're reducing is the precision. And in many cases, for applications, it's better to lose precision on data, especially if you can control exactly how much precision you lost and where you lost it from. So for example, if you're looking at, for example, fine-grained features on a subsurface, you might have a large portion of the data that's relatively sparse, and you can kind of compress that very aggressively, because there's not a lot of scientific content in that sparser region.

But where you have kind of a turbulent boundary condition that exists kind of between two points, maybe you need to have a more conservative style compression approach that gets used in that region where there's a lot more scientific content. kind of using compression, you can kind of address concerns both of data rate. So basically how fast are you producing data? So like these APS or LCLS style use cases, but you can also address use cases where you need to do large scale data archiving. So kind of deployed at scale, you can imagine data compression would allow you to dramatically reduce the needs for long-term storage of data, not because you're actually storing dramatically less data, but the footprint of that data on storage is dramatically smaller. So that's where compression kind of plays a role and can be very helpful.

Charles Yang

Yeah, I mean, I think that's an interesting contrast because, I mean, with a lot of these conventional industry models, they've kind of reached data limits now of the known set of tokens in the world and people are doing synthetic data and all these complicated things. But in the world of science, like we're actually drowning in data in some sense, right? Like there's too much data being generated by these massive particle accelerators and hard light sources that there's this whole field that is looking to understand how to grapple with all that data in a way that's like sort of more manageable.

We talked with Sergei Kalinin who does autonomous microscopes and he talked about the massive amount of data being generated by each microscope nowadays at the leading frontier. Do you kind of see a heterogeneous or hybrid architecture in the future for scientific instruments where they are running, or maybe this is already the case, like massive data compression at this point where the data is being generated at the facility.

Robert Underwood

So just to kind of define terms really quickly. So what I'm hearing from you is that you're saying that you're going to have kind of some edge facility where you're producing data at a very large rate. And then maybe you have a set of edge devices that are going to kind of process or accept that data, transfer it over a network, and then maybe you have some large computing resource where you're going to then kind of do further processing on it after potentially you've either restored it from an archive or after you've transmitted across a wide area network. So if that's what you're describing, Like we already do those kinds of techniques. Yeah.

Charles Yang

That's what I'm assuming. Yeah. So it's already happening. Yeah. Do you want to talk a little bit more about like maybe for the advanced photon source at Argonne, which is one of the brightest light sources, I think, at least in the country, what does that kind of look like in terms of data flows and where the data is being processed?

Robert Underwood

Yeah, so in the case of the APS, so you can have large different experiments that are conducted at one of many different beam lines that exist on the APS. think there's an order of 80 different beam lines. And the way that you can think about this is each beam line specializes in a particular class of experiments. So you might have some experiments that are performing something called tomography. So this is, as I understand, MRI-style images where you're looking at trying to understand the structure of a material. You may have other ones that are looking at trying to understand more like subatomic style interactions that exist on different materials. And for each of these, you'll use either different wavelengths of the X-rays, or you'll use different intensities of the X-rays to kind of study or different ways of capturing the X-rays as they're going off of the sample. So maybe in some cases, you're shooting directly through the sample and you're studying direct. In some cases, you're looking at backscattering.

So you have different kinds of ways of conducting these experiments. And these can each produce very large volumes of data. So in the case of small angle scattering, like wagon angle scattering experiments, you could potentially generate up to a terabyte of data every second. So in that case, the team that I'm working with as part of another project called Illumine is looking at how can we design specialized compression techniques that will allow us to kind of take the data that comes directly off of these detectors and make it small enough that we can get it from the detectors to intermediate storage where we can then potentially recall it on either a locally available HPC resource or a further away HPC resource. In the case of APS, we frequently will use the ALCF resources.

It's actually kind of interesting. at the ALCF on machines like Polaris, which is one of our other large computing resources that we have, there's actually a special queue that's called the demand queue. And what the demand queue is for this particular block of racks, if a job comes in from the advanced photon source that needs to be processed in near time, we can actually kind of prioritize the jobs that are coming off of the APS for that set of racks that are dedicated towards this demand queue. And then other jobs can kind of run at a lower priority in the background when there's not an APS job just to keep machine busy. So having this ability to kind preempt the computation on the ALCF resource when you have kind of an urgent need for compute is kind of an interesting and exciting way that you can kind of combine these large scale facilities together.

Charles Yang

So, I mean, that's an interesting, like, sort of, I guess, partnership between the fact that you have this leadership compute facility at Argonne and, like, of the, including one of the world's largest supercomputers and one of the world's largest beam lines or hard light sources that's also sending data back and forth. Do they process any data locally at the APS or is it always sent to Polaris?

Robert Underwood

So this is actually a good question. So depending on what the task is, they will perform certain operations at the edge. So for example, if you're looking at, example, this is a technique that's used at LCLS, not at the APS. But if you're doing a technique called serial 50 second crystallography, you might, for example, conduct kind of initial peak detection at the edge. So basically, there are certain regions of this data that contain particular, I'll call them bright spots. I think the scientific term for them is brag spots. And these brag spots are kind of bright, scientifically significant pieces of the overall image that you have. So the reason why you might look for these on the edge is that you can perform this technique called non-hit rejection. So if you have a frame that you take a picture of, and in or across this frame or across this detector, you don't see that there are any peaks on this particular frame.

You can actually discard that frame entirely, which reduces the amount of data that you have to transfer. in some ways, it's kind of an adaptive sampling technique, kind of based off of how much information is in the frame. And then the second thing you frequently will do is you will then apply compression. So you're doing the compression near the beam line, and then you're going to do your analysis further away.

So you're trying to leverage like what techniques do I need to have nearest to the beam line where I can handle them both, where I can perform them both in terms of their complexity at a very high rate. But also where can I leverage the fact that I haven't had to transfer that data yet in order to make the most effective use of it.

Charles Yang

And so for beamlines that are each generating up to one terabyte a second and doing this kind of compression, mean, does this mean each beamline has a CPU server that's dedicated to servicing the data and compression needs? And roughly, what scale are we talking about here?

Robert Underwood

So if you look at different beam lines have different needs. So for example, not all beam lines necessarily generate a terabyte a second. But the ones that do, at least at Argonne, they actually have essentially Polaris nodes that are deployed at the edge. They're like the very similar kind of hardware. They may have slight differences in terms of the network interface, for example. But otherwise, they look very, very similar to the kinds of resources we already use on the supercomputer. So there's just fewer of them.

So if you want to kind of look at kind of a more forward-looking example of this, you might look at, example, LCLS is doing at Slack. In their case, they're actually looking at kind of taking data directly off of FPGAs, field programmable gate arrays, and then communicating that directly to a GPU where they do some preliminary processing. And then after that, they then use a technique where they send that data directly from the GPU across the network interface to either long-term storage or for further analysis. So you can potentially get these really integrated beamline designs where you're very carefully understanding each of the stages of the pipeline, you're kind of deploying them as a collective whole.

Charles Yang

Awesome. And so these are GPU-based workloads.

Robert Underwood

Yes, so because of the data rates that these systems have, you're very frequently going to be moving towards GPUs for many of the different frameworks that you have.

Charles Yang

That's awesome. So we're basically running a, I guess this is the dichotomy of both scientific simulations, video games, and AI training are all kind of similar style workloads. Okay, so we're basically doing like GPU, like pre-processing of the flood of scientific data being generated at each of these beam lines.

Robert Underwood

Yeah, GPU plus FPGA. So some of the tasks are even actually being done on FPGAs because, for example, if you're doing this non-hit rejection, that might be something that you really want to do it in hardware, given the throughput constraints that are involved with that particular part of the process. So if you can build it sometimes, FPGAs also play a very important role in these kinds of real-time scenarios.

Charles Yang

And roughly what kind of data compression rate are we talking about? Is it like on there of like compressing 5 % or is it more like a 10-fold compression?

Robert Underwood

So the goal set out for us by the different beam lines is usually to achieve at least a 10x in compression. In some cases, it's a 20x in compression relative to the raw data stream. And in many cases, we have been able, and if you are interested, can point you towards some papers where we've done this exact kind of technique on a variety of different beam lines and approaches.

Charles Yang

Yeah, I mean, that's awesome. And I think really, again, striking the difference in how these fields think about it where the AI world is turning to synthetic data in the scientific world is compressing up to 20x of its data because it can't deal with the amount that's being generated at these facilities. What do you think is kind of a promising area? mean, so, you know, Argonne has all these large pieces of scientific infrastructure like beam lines that are generating vast amounts of data.

What do you see as kind of a particular fields or applications that you're excited about in where AI could potentially play a role. mean, this is sort of going back to the Aurora GPT conversation. We talked earlier about UK OpenBind competition as one example of what the role of the scientific infrastructure can play. Have you come across any others that you think would be exciting or perhaps not enough folks know about?

Robert Underwood

So what I would say is we're as part of our AuroraGPT, we're actually actively working with both beam lines at the event on source, proton source, in addition to kind of the biological data group that we have here at Argonne. so I think that definitely as you kind of progress and it's kind of the development of science, you'll see increasingly that we're going to be linking AI systems up to things like self-driving labs, where we're going to utilize either robotics to conduct experiments and then collect the results from those experiments and then interpret them using AI. Or maybe we use AI to guide where is the next most promising experiment to perform. So there's a lot of opportunities here as we interface automatable infrastructure with AI systems.

Charles Yang

Awesome. And certainly self-driving lab is something we've talked a lot about on this podcast as well. So great to hear. mean, that's quite the vision of both generating mass amounts of data at these beam lines, running large scale AI models on the leadership class facilities that you have at the supercomputers, and then using the self-driving lab infrastructure at Argonne to kind of then iterate from there and generate more data. And the beam lines have some degree of automation themselves as well,

Robert Underwood

Yeah, and that's actually something that we're trying to improve with projects like Illumine. So if you look at what the project has, there's roughly three thrusts. And two of those thrusts, broadly speaking, have to deal with kind of what's sometimes referred to as integrated research infrastructure. So how do we more actively communicate kind of and make decisions in an automated fashion, both at like the kind of each frame detection scale. So that's like one really tight real time kind of set of constraints.

But also kind of broader optimization style constraints that might happen at the order of several seconds or several minutes. So you have kind of different reinforcement loops that are happening at these different timescales, making different kinds of decisions about how the experiment potentially will progress. So I think it's a very exciting area. It's a project I'm excited to be a part of.

Charles Yang

Awesome. I can't think of a better way to end than that. Robert, thanks for the time.

Robert Underwood

Yeah, appreciate it.

In this episode, I sit down with Ken Ono, professor of mathematics at the University of Virginia, to explore the evolving relationship between artificial intelligence and mathematical research. We discuss the cultural shifts in mathematics over the last 30 years, as well as what kinds of reasoning and creativity are uniquely human. Ken reflects on how AI models perform in mathematical benchmarks, the surprising ways they already assist with research tasks, and the real challenges of evaluating their capabilities.

We also talk about the Spirit of Ramanujan project, formal proof assistants like Lean, and why large language models might soon become the default lab partner for pure mathematicians. Here are three takeaways from our conversation:

1. Mathematics has shifted from solo practice to collaborative, cross-disciplinary research

In the past, mathematicians were often discouraged from collaborating Today, partnerships are the norm, and fields like number theory now intersect with areas like physics, biology, and computer science. Cultural and institutional incentives, like NSF REU programs, have helped embed collaboration into how mathematics is practiced and taught.

2. AI is a powerful assistant in mathematical research, but it’s not yet creative

Large language models can quickly summarize unfamiliar areas of math, identify relevant literature, and even debug incorrect reasoning traces. While they outperform human researchers at scanning and synthesizing existing knowledge, they don’t yet generate fundamentally new ideas.

3. Benchmarking mathematical AI is harder than it looks, and is often misunderstood

Benchmarks can misrepresent how scientists work in practice; real research isn’t solving puzzles, it’s building ideas across messy, creative processes. The real challenge isn’t beating the model on a hard problem but rather designing a benchmark problem that is human-solvable, numerically checkable, and likely to remain unsolved by AI for 5–10 years. This effort is less about competition and more about probing model reasoning in a measurable, reproducible way.

Transcript

Charles
Ken, thanks for joining us.

Ken Ono
Charles, wonderful to be here.

Can you describe your research in combinatorics and number theory? (00:50)

Charles
Maybe we could start by talking broadly about your work. I know your background is in combinatorics and number theory. Some people might recognize those from undergrad courses, but could you give us a layman's explanation of the kind of research you do?

Ken Ono
I'm originally trained as a pure mathematician and have been working as a scientist for almost 30 years at various universities. The questions that inspired me early in my career were the famous problems in number theory and combinatorics. I started my PhD in the early 1990s at UCLA. Around that time, Andrew Wiles, who was then a professor at Princeton, announced a proof of the very famous problem known as Fermat's Last Theorem.

It's hard to believe it's been 30 years since that was proven. The theorem says that aⁿ plus bⁿ can never equal cⁿ for integers a, b, and c that are non-zero, and for n greater than 2. By contrast, the Pythagorean theorem tells us that a² plus b² can equal c², and there are lots of integer solutions like 3, 4, and 5. But Fermat's claim was that once you go beyond squares, no such solutions exist. Wiles famously proved that, although there was a hiccup early on.

This kind of work falls into the realm of very abstract mathematics, often based on questions that go back centuries. Over the last 30 years, I've continued to work in pure mathematics. I’ve also gotten deeply involved in representation theory, which grows out of abstract algebra. I've thought a lot about the application of number theory to physics, especially string theory and the distribution of black holes.

More recently, my research has shifted a bit. I still write papers on number theory. I just wrote one on how to use partitions to detect prime numbers, which was recognized by the National Academy as a runner-up for the Cozzarelli Prize. But my research portfolio has also expanded to include data applications in athletics. I’ve worked with the U.S. Olympic swimming team, and more recently, the role of AI in mathematics as a partner in scientific discovery.

How much do mathematicians specialize versus work across different fields throughout their careers? (04:30)

Charles
Awesome. That's certainly a wide-ranging career. On that point, I’m curious: people like mathematician Terence Tao are known for spanning many fields. It sounds like you’ve also worked across a number of different areas, both academic and applied. How common is that in mathematics? Do most people move around between different fields, or do they tend to specialize?

Ken Ono
When I started graduate school in the late 1980s, it was typical for students and faculty to specialize. Even saying you were a number theorist was considered broad. You were expected to narrow further, like focusing on the Langlands program or multiplicative number theory.

Solo papers were the gold standard back then. Collaboration among students was actually discouraged. But that has changed dramatically. Today, most mathematics papers are the result of partnerships, three or four authors are common, and there are even large-scale collaborations. You may have heard of the Polymath Project, where some papers have two or three dozen authors.

So while many professors still specialize in a particular domain, it’s now much more common to see mathematicians using their expertise across fields. Terry is a good example, but so is my friend Jordan Ellenberg, who works in arithmetic geometry, AI, statistics, and more. There’s a growing appreciation for mathematicians who can span disciplines.

Charles
What do you think drove that change over the last 20 or 30 years?

Ken Ono
Part of it is cultural. About 20 years ago, we began encouraging undergraduates to do research, especially through programs like the NSF's REU program. Those programs built their models on teamwork, consisting of groups of undergraduates working with postdocs and faculty. That collaborative mindset carried forward.

We also see more institutional support for cross-disciplinary work. As the STEM advisor to the provost at UVA, part of my role is to identify opportunities for collaboration across departments. That kind of work is increasingly encouraged by university leaders, by academic societies, and by the science itself.

Look at the recent Nobel Prize in Chemistry, awarded for solving the protein folding problem using AI. That was a major chemistry question answered with tools from machine learning, which originated in electrical and computer engineering. A few years ago, it would have been hard to imagine that happening.

So this shift has both social and scientific roots. There's greater openness to partnership, and the problems we're trying to solve increasingly demand a broader range of tools. We’re in a new era of scientific discovery. I don’t think anyone doubts that AI, machine learning, and large language models are now central to research. That’s part of what makes your podcast so timely. It helps us navigate these changes, especially as we think about how to train the next generation of scientists.

Nobody wants to replace scientists. That’s not the goal. But we do have to think carefully about how to work alongside AI and how to make sure current scientists don’t feel threatened by it. These are big questions, and it’s good that we’re talking about them.

Charles
Yeah, that’s certainly part of the thesis of this podcast, that in some sense, everyone is an interdisciplinary scientist now, with AI as a paradigm large enough to potentially disrupt every field. That’s part of the goal here: to try to make sense of that.

Ken Ono
I wouldn’t go so far as to say every field. In fact, I don’t believe that large language models are anywhere close in many areas of mathematics. And it’s not due to a lack of interest in trying to apply AI to mathematics — a lot of people are thinking deeply about that. But at the cutting edge of mathematics, where progress depends on the invention and creation of entirely new ideas, I don’t think it’s clear yet whether AI will be useful.

I have a strong opinion that this is still the realm of the pure mathematician, whose main purpose is to generate entirely new ideas, not extensions or adaptations of existing ones. Most professional mathematicians probably don’t work at that level of abstraction, and I’d like to explain what I mean by that.

Throughout history, there have always been a small number of mathematicians — like Alexander Grothendieck, Peter Scholze, or Jean-Pierre Serre — whose ideas seem almost spiritual in origin. They give birth to entire new fields of mathematics, often from nowhere. And I’ve seen no evidence that AI can create ideas we haven’t already glimpsed in some form.

That said, AI absolutely has a role to play in certain areas of mathematics. It can be a useful partner, especially in the types of work most mathematicians actually do.

How would you describe what mathematicians do? (15:20)

Charles
Interesting. Notably, you’re making a distinction between different classes of mathematical work. For the majority of mathematicians, how would you conceptualize the work they do? That’s a relevant starting point if we want to understand how AI might integrate with or disrupt the field.

Ken Ono
This is important to clarify because many of the 700- or 800-word stories that pop up on social media every day don’t define the fields they’re talking about. It can be misleading, and I’ve been burned by that myself — nuance gets lost, and we end up with this kind of hysteria. The world is indeed changing quickly, but let’s take a deep breath and try to get it right.

So when we ask, “What do mathematicians do?” — the answer isn’t simple. Math is broad. You have applied mathematicians working on problems in engineering, such as designing bridges, spacecraft, or climate models. On the other end, you have pure mathematicians who are inventing new ideas in fields that may be so specialized that only two or three dozen people worldwide can understand them. These ideas may not have practical applications today, though they might in the future.

The vast majority of pure mathematicians, though, are working within their fields or adjacent ones, trying to answer open questions. Mathematics is a cumulative discipline, and we take pride in building on the knowledge that came before us.

In my case, I work in number theory, which is easier to describe: I study numbers. But other mathematicians might work on things like schemes, varieties, or topological spaces. Whether or not those are accessible to you depends on your training.

What do pure mathematicians do? We prove theorems. That’s our goal. Our aim is to confirm guesses as true mathematical facts, based on axioms and logical deduction.

Here’s a trivial example: every even number is divisible by two. You could define what “even” means in different ways, but it always comes back to the idea that dividing by two gives you a whole number.

But things get much more complex. You might ask: given a family of equations in variables x and y, what are all the solutions? For example, y = 5x + 3 is a line — everyone knows that. But make a small change to the equation, and it can go from high school algebra to territory no one understands.

Before AI, a pure mathematician might use a computer to look for examples and patterns. Then we’d build a theory and try to prove something like: every equation of this type has only solutions with certain properties.

Replace “equation” with “scheme” or “topological space” or “quantum invariant,” and the work is the same. What are the rules that govern the behavior of these objects? And can we confirm those rules with certainty? That’s what pure mathematicians strive to do. Not sometimes, but always: to establish truth.

Would you say most mathematicians are essentially selecting tools and strategies from the literature to tackle open problems? (21:15)

Charles
So I guess one model I have for pure mathematicians is that there’s a set of existing tools and theorems already present in the literature, and they’re essentially trying to find the right set of tools and chart a path toward some currently unproven object. Would you say that’s a fair high-level description of what most pure mathematicians do?

Ken Ono
Yes, every professional mathematician does a lot of that, but it would be a mistake to characterize pure mathematics as just that. When we talk about how models and AI can assist us, I’m going to come back to exactly what you just said. But let me break it down. What you described can be thought of in three major tasks.

First, a professional mathematician must know the literature and the main tools used in their field. For your listeners who’ve taken an introduction to proofs course, you’ve likely seen techniques like proof by induction or proof by contradiction, or assembling lemmas that together prove a theorem. These are foundational skills. As you progress and specialize, you move beyond basic techniques to very high-level theorems. You might be working under assumptions like the Riemann Hypothesis or using things like Hilbert’s Theorem 90. The level of depth increases, but the core idea is the same: understanding the landscape of results and how to combine them. That’s the first piece: knowing the literature and how to parse problems.

The second piece is strategizing. Given the enormous number of theorems and ideas produced by over two centuries of mathematics, it’s not enough to know what exists. You need to figure out how to creatively pull together ideas into a coherent proof. Many modern papers are over 100 pages long. Some major results involve hundreds of pages assembled over several years. This takes skill and subtlety.

The third skill is getting your hands dirty with examples. You have to test out low-hanging examples to get a feel for the landscape of a problem. You don’t just set out to solve a famous problem without a germ of an idea. You need some initial steps that give you a foothold, even if the final proof is hundreds of pages long.

Of course, for the deepest work — like the Local Langlands Correspondence, which made big news recently — even those three tasks are not enough. That kind of breakthrough involves inventing entirely new ideas. That’s the fourth task. It’s very rare, but some individuals like Grothendieck or Serre contribute in that way.

Most mathematicians don’t need to reach that level to be successful. You can be world-famous without ever having a groundbreaking idea that looks completely unlike anything seen before. That’s normal.

You can win a Nobel Prize in physics or chemistry without inventing a new kind of experiment. In science, we build on existing work. You might not even prove something yourself, but if you find a phenomenon that drives discovery for decades, that’s still hugely valuable.

Einstein is a great example. You can still win the Nobel Prize today for experimentally confirming a prediction of Einstein’s. That sort of contribution is common across the sciences, and it exists in math too. You might become famous for solving an old problem with ideas that escaped others. Or you might be the one who knows the literature better than anyone and finds a rare combination of arguments that works in exactly the right order. All of those are valid paths to success in mathematics.

How do entirely new mathematical ideas come about? (28:20)

Charles
Can you give us a better sense of how those novel ideas are born? For instance, I know the proof of Fermat’s Last Theorem involved ideas from very different areas. Is it usually something like unexpected connections or is it more like a new class of techniques coming out of nowhere?

Ken Ono
Great question. Let me give you two examples that reflect very different types of creativity.

First, Srinivasa Ramanujan. He was born in the late 19th century in South India and was a two-time college dropout. He was self-taught and kept thousands of formulas in three notebooks. He believed his ideas were revealed to him in dreams by a Hindu goddess. He didn’t value proof in the way professional mathematicians do because, to him, these ideas were given.

Fortunately, he wrote letters to professors in England, or he might have been lost to history. The formulas he recorded are still being explored today. We know how to prove them now, but we don’t always understand their full importance. Some of his formulas, written months before his death in 1920, are used in modern black hole physics, decades before black holes were even understood. His creativity was almost artistic, like a new era of painting. He saw beauty and meaning in expressions others overlooked. His reasons for caring about a formula often had no connection to why it would later become important.

Second, Andrew Wiles. Very different story. Wiles had formal training. He earned his PhD at Cambridge and worked in a field called Iwasawa theory, which deals with elliptic curves. In the early 1980s, German mathematician Gerhard Frey suggested a wild idea: if you could relate elliptic curves to modular forms — a bridge between two very different areas of math — you might be able to prove Fermat’s Last Theorem.

Japanese mathematician Yutaka Taniyama had earlier proposed what that bridge might look like. Frey speculated, “If someone builds this bridge, maybe we could solve Fermat’s problem.” At the time, nobody thought that was realistic.

But Wiles, working privately in his attic, actually did it. He built the tools and proved the theorem. The creativity of Taniyama and Frey to imagine the connection was remarkable. But what Wiles did was something else entirely. He had the vision and audacity to follow through and build the bridge himself. That kind of creativity is rare.

It was like watching someone climb Everest for the first time, or landing on the moon. The level of insight and perseverance Wiles and his student Richard Taylor showed that was a once-in-a-lifetime achievement.

What are the main ways AI can be used in mathematics today? (37:15)

Charles
I know Terence Tao has given a few talks on AI and math, and he’s kind of laid out two or three approaches we can use. One is using machine learning and optimization to find counterexamples or generate connections between different examples. Another is using formal proof solvers like Lean to compile theorems and reasoning in a formal way. And then, of course, there's using large language models. Is that how you think about the different ways AI can be used in math? Are there particular approaches you've found especially promising or not so promising?

Ken Ono
Yeah, great question. Circling back to the Nobel Prize-winning work of Jumper and Hassabis on protein architecture, it's clear to many of us in mathematics — and likely to all scientists — that the use of AI in combinatorial construction problems is both undeniable and extraordinary. The computer's ability to explore possible constructions and mathematical architectures is inhuman. There are certain problems that humans really shouldn't be doing when a computer can do it better through clever brute-force strategies.

There are many examples of that. AlphaGeometry is one direction, but we don't need to go down that path right now. That work is very precise. It's similar to how AI discovered new openings in Go or chess, and those kinds of skills are useful beyond games, in drug discovery, and even in developing conjectures in pure mathematics.

Now, the work I've been most interested in lately is how AI could go beyond what we already expect computers to do. You mentioned Lean and the formalization of proof. Circling back to what I said earlier: pure mathematicians, not applied ones, rigorously prove theorems. That is the bread and butter of the field.

Kevin Buzzard, a friend and well-known number theorist, has been a strong advocate for using tools like Lean. He’s working toward a future where computers have a vast library of mathematics they can use to verify proofs. And that’s amazing. Just last week, there was a formalized proof of something called the ABC conjecture. It's a great piece of work in analytic number theory. We'd love to know if there are truly no counterexamples, but this Lean-based result tells us that any such exceptions are extremely rare. That’s a big deal. It shows how computers can help check our work, not do it for us, but that checking is incredibly valuable.

I wish we could introduce something like Lean into federal government processes or airline operations. It’s that kind of precision.

Now, on the daily work of mathematicians: there's a lot of hype that AI will be everywhere, and I don’t disagree. AI can read MRIs, x-rays, and do many things better than people. But I haven’t yet seen AI come up with ideas that we haven’t at least glimpsed before.

That said, I’ve been working with a company called Epoch AI, which is developing benchmark problems to test mathematical reasoning in large models. Since late fall, I’ve been working with them and using cutting-edge models like Gemini 2.5 Pro and a high-end version of GPT-4, not the version most people have access to.

Our task has been to create a large set of benchmark math problems across fields — from topology to algebraic geometry to combinatorics to number theory — to test these models. These are not problems meant to mirror the research process of a mathematician. Instead, they are precisely defined problems where we know the answer in advance and can check them numerically. That’s key. These problems must have verifiable numerical answers, even if those numbers are a hundred digits long. And the bar for what counts as a good benchmark problem is very high.

The media misunderstood this a bit. They thought we were struggling to find problems AI could solve. But the challenge wasn’t that: we can easily find problems AI can't solve. The goal isn't model versus mathematician. The goal is to understand what reasoning capabilities these models already have. We're trying to figure out how AI can be a partner, an assistant, a co-pilot in scientific discovery.

That said, I'm deeply impressed by where the best models are today. I don’t use AI in my research yet, but I wouldn't be surprised if I start within three or four months. It still makes lots of mistakes, but you have to look more deeply at what's going on.

When it comes to mathematics, LLMs are astonishing. It's far beyond where I thought we would be just six months ago. Using the best models, I can confidently say that I can type in a question in high-level mathematics in any field and, within a couple of minutes, the model will identify the relevant literature, name the leading researchers, and generate a three- or four-page summary of the topic with remarkable accuracy.

I’ve been quoted as saying these models outperform the best graduate students at top universities. I regret putting it that way. It would have been more accurate to say that, in some respects, they outperform my own abilities as a professional mathematician with 30 years of experience.

Because if you asked me questions from all over math, I’d have to admit there are only a few areas where I truly feel like an expert. In all the others, I couldn’t begin to tell you what’s currently considered cutting-edge, even if some of the world experts work down the hall from me. If someone were to talk nonsense in one of those fields but used the right terminology, I wouldn’t know how to tell the difference. What’s impressive is that a large language model, in every scientific discipline, can get you into the ballpark.

Where does it start to make mistakes? When the questions become very difficult or subtle. And some of my colleagues take comfort in seeing a model fail. But when those failures happen in areas outside my expertise, I don't find it comforting. I realize I would have gotten it wrong too. The model sounds like it knows what it’s talking about.

Models often resemble a strong beginning graduate student. They know the terminology, they know the statements of theorems, but they’re overconfident, and they miss the subtleties and the importance of details. But they’re still very good.

Now, I don’t want us to feel comfortable just because the models make mistakes. That can be misleading. You can trace the reasoning. You can see what the model is trying to do. And in fields with a saturated literature, the model has read the papers. It knows the theorems and even the lemmas and can follow the inner workings.

And when it makes a mistake, you can say, "I think you're wrong." It pauses, reflects — "the user thinks I’m wrong" — and then it will correct itself. It might say, "I accidentally made this mistake. Let me try again." If you approach the model like a fallible human partner, then you realize it can already function as a kind of research collaborator. Not as a black box that gives you an answer, but as a tool to access the accumulated wisdom of science and have a conversation with it. You can challenge it, and it will adjust.

People talk about writing good prompts to get the most out of these models. But as scientists, we can take it further. The model doesn’t just recognize patterns in the field you ask about. it brings in methods from related fields and applies them in new ways. That’s a skill I didn’t expect. I didn’t think, for example, that ideas from statistics could so quickly be brought into number theory. I assumed that in 2025, the models would only scrape directly relevant papers and get stumped by anything outside their niche. But they’ve already gone beyond that.

What makes benchmark problems for mathematical reasoning models so challenging to build? (55:15)

Charles
It sounds like the challenge in making benchmarks is creating problems that are machine-readable ones that can be incorporated into a benchmark suite. That’s hard, right? Not just finding open problems that AI can’t solve. There are plenty of those. It’s more that they wouldn’t make sense as benchmarks.

Ken Ono
Charles, that's right. At Epoch AI, Elliot Glazer, who leads the Frontier Math team, gave us a very specific challenge: we want the problems we create to remain unsolved by AI for five to ten years. That’s a high bar.

It’s not that I’m racing against a computer and can’t solve something by August. We’re looking for problems that are human-solvable today, that have verifiable numerical answers, but where the AI’s reasoning trace shows it's far off. We want to believe those problems will still be out of reach for models five to ten years from now.

That’s an incredibly hard challenge. And I wish the media would get that right. It’s not about me or the other mathematicians going head-to-head with large language models. It’s about that broader task. Though the media is right to ask, “Is this really where we are now—that it’s so hard to find problems like this?” And yes, it really is.

But this isn’t the typical work we do as mathematicians. That kind of framing is more like sport. Sure, we can come up with questions that the models will fail on, but it’s still remarkable how much they get right.

Charles
And I think that’s the other challenge with benchmarks. They try to measure model performance, but they don’t actually capture the full scope of scientific activity. People often point to AlphaFold as an endpoint but really that’s just the beginning. How scientists actually use AlphaFold is a whole other question.

Ken Ono
Exactly. I don’t think of my AI work as being mostly about benchmarking. I don’t see myself as some kind of AI hero either. But I’m happy to say that I fully expect these models to become useful research assistants in the near future.

Google changed everything by making information easily searchable. Now, at a high level, all of science is at our fingertips. These models are like a supercharged Google, but with the added ability to compute, generate low-level examples, and understand strategies within specific fields.

One of the most impressive things I’ve encountered while working with these models is that sometimes we pose a problem that it can’t solve, even after significant nudging. It spins its wheels and gets lost. And when that happens, I like to end the session by asking: “So, what have you learned from your mistakes?”

And the model responds beautifully. It might say, “I learned that if I jump to conclusions under these hypotheses, I can make mistakes when given examples with certain properties.” It can thoughtfully summarize its missteps. I run these models in temporary mode, so they’re not being trained on these interactions. But if they were, they’d already be improving. That’s one reason I believe models like GPT-4 mini or GPT-5 could serve as real lab assistants, possibly by the end of this year.

How are you preparing your students to use AI in their research? (1:01)

Charles
So you mentioned you don’t currently use AI in your own research. But do your students? How do you think about preparing graduate students to work with these tools?

Ken Ono
I haven’t explicitly asked my graduate students and postdocs whether they’re using AI in their work. Maybe I should. But I’d be surprised if they are, mainly because these advanced models aren’t cheap, and our graduate students and postdocs aren’t exactly well paid. Most likely, they’re not using the same tools I’ve been testing. That might change. Even if prices stay high, capabilities will go up, so the value proposition could make it worthwhile.

I’d guess they’re not using these tools in their research, but maybe they’re using them for daily tasks like email writing or other routine things. I’d be surprised if they weren’t using them in some way.

Charles
Yeah, it would be an interesting survey, and maybe even something universities could offer as a benefit in the future. Imagine giving all graduate students access to a high-level model as part of their education.

Ken Ono
That’s a very controversial idea. Some schools will jump on it quickly, others will resist. But I agree with you, it’s going to be important.

How did your experience with the film The Man Who Knew Infinity lead to founding the Spirit of Ramanujan project? (1:03:00)

Charles
Yeah, we’ll see how it plays out. Maybe just one last question. You worked on the movie The Man Who Knew Infinity, about Ramanujan, and started the Spirit of Ramanujan project to identify emerging math talent around the world. How was that experience, and how do you think about cultivating talent in young people?

Ken Ono
Being part of The Man Who Knew Infinity was one of the most exciting things I’ve ever done. It just came out of the blue, and I’m so grateful it happened.

Hollywood decided to make a film about mathematics, and they chose Ramanujan’s story. When we screened the film in Silicon Valley, we had some well-known supporters. The Breakthrough Foundation helped fund it, and we screened it at Yuri Milner’s house. Dev Patel was there, Stephen Fry was there, and we did a Q&A afterward.

What stood out was how many audience members were involved in SETI: the search for extraterrestrial intelligence. I said, “You all are searching the stars, but I think the story of Ramanujan tells us we should also be searching planet Earth for terrestrial intelligence.”

That struck a chord. The president of the Templeton World Charity Foundation came up to me afterward and said, “You’re right. What could you do with $550,000?” That’s how the Spirit of Ramanujan project was born.

Since then, we’ve supported 125 young scholars from around the world, from about three dozen countries. The program is on pause right now due to funding issues, but through last year, it’s been amazing.

Charles
That’s awesome. How did you find the students? I know Ramanujan famously sent a letter to a professor in England.

Ken Ono
Yes, it’s funny. It’s an application process, but that story inspires it. Ramanujan’s letter to G.H. Hardy is what launched his career, and we like to think of our application process as a modern version of that. Students write us letters or essays that are very much in the spirit of Ramanujan’s message to Hardy. Of course, we also require recommendation letters and personal statements, which you’d expect these days, but it is about capturing that same spirit.

Charles
Awesome. Well, Ken, thanks so much for taking the time. This has been a fascinating conversation.

Ken Ono
Great. Have a good day, Charles.

I’m hosting an autonomous science lightning talk series in Boston on Thursday July 24, 5-7pm, hosted out of Glasswing Venture’s lovely office. We have some great speakers lined up, ranging from using self-driving labs to discover new polymers to AI-accelerated chemistry simulations. Come out / share with any friends in Boston!

You can RSVP here.