Frontier labs are building a science vertical: models, acquisitions, and partnerships aimed at scientific discovery itself — drug design, materials, formal mathematics. It is quieter than the chat and coding businesses, and the capital behind it is larger than anything else the labs are betting on outside their core models. Which fields, how much money, and what does the math say?
Follow the disclosed dollars
Add up every disclosed AI-for-science capital commitment from the last ~18 months — fundraises, acquisitions, and partnership ceilings, from the sources listed at the end — and the pie is not really a pie:
Structural biology and drug discovery take $6.4B of the $7.2B traceable — 88.9%. Isomorphic Labs alone accounts for $5.0B of it: a $2.1B Series B, the largest in AI-drug-discovery history, plus partnership ceilings of $1.7B with Eli Lilly and $1.2B with Novartis. Add Lilly's $1B co-innovation lab with NVIDIA and Anthropic's $400M acquisition of Coefficient Bio — an eight-month-old computational-biology startup — and the field's dominance is total.
Materials, chemistry and physics get the other 11.1% — essentially one company, Periodic Labs, founded by ChatGPT architect Liam Fedus (ex-OpenAI) and Ekin Dogus Cubuk (ex-DeepMind). Roughly $0.3B raised, another $0.5B in progress at a $7.5B valuation — a near-sixfold valuation jump in about a year.
Everything else — formal mathematics, weather, co-scientist agents, health-data labs — rounds to zero disclosed capital. The largest traceable deal in the remainder is Doctolib's €20M clinical-AI laboratory — about $0.02B, or 300× smaller than Isomorphic's Series B alone. Not zero activity; zero deals. That asymmetry is the subject of this post.
The deal math: 2.6% upfront, 97.4% promise
Look inside the headline numbers and the structure is striking. Lilly's $1.7B partnership paid $45M upfront — that is 2.6% cash, 97.4% contingent milestones. The sector's direct revenue remains small because the economics are milestone-deferred: the money arrives if and when molecules pass trials. Market forecasts price that promise aggressively — one projection runs $24.5B (2026) to $160.5B (2035), which works out to a 23.2% compound annual growth rate — but the honest reading is that drug discovery today is an option-value business: enormous if Isomorphic's first internal candidates, targeted for human trials by end of 2026, actually work.
Which makes the small slice more interesting than it looks.
The 11% slice is the one with revenue
The most telling detail in the numbers: Periodic Labs — the materials company — already has paying customers in the semiconductor industry. While drug discovery waits a decade for milestones, materials R&D pays now, because industrial customers don't need FDA approval to use a better electrolyte, catalyst, or high-temperature superconductor candidate. Periodic's approach is the structurally important part: autonomous robotic laboratories running thousands of physics and chemistry experiments to generate proprietary training data. The moat is not the model — it's the experimental data nobody else has.
That logic extends directly to every industrial R&D organization sitting on decades of lab notebooks, test campaigns, and legacy reports. Those archives are that same kind of proprietary dataset — most organizations just can't query them yet. It's why the materials slice looks undervalued at 11%: the drug-discovery giants are betting on future molecules, while the materials players are monetizing the data flywheel today.
The zero-dollar vertical that changes daily work: formal math
Formal mathematics attracted no disclosed capital at all, yet it's the science vertical whose cost curve moved most violently — and the only one visible in the arcade dataset (Leanstral and Leanstral 1.5: 2 of 206 releases, 1.0%). The numbers: Leanstral 1.5 solves 87.4% of PutnamBench (587/672) at roughly $4 per problem, versus ~$300 for the closed Seed-Prover — 75× cheaper, a 98.7% cost reduction, on an Apache-2.0 model with a free endpoint. And its Lean 4 formal-verification runs found five previously unreported bugs in open-source repositories.
Why does theorem proving matter commercially? Because formal verification is the extreme end of a spectrum every regulated lab lives on: answers you can check versus answers you must trust. In nuclear, aerospace, or medical devices, an unverifiable answer is a useless answer. The formal-math vertical is quietly building the tooling for machine-checkable science, and the likely monetization is not mathematics but verified software and safety cases for exactly the industries that can't accept "the model said so."
Why the big labs need science (the token business won't carry them)
Tie this back to the open-weights analysis: open models now deliver ~90% of closed-frontier capability at ~6× lower cost per call, and Chinese open models route three tokens for every US closed one. Token-serving is commoditizing. Science is the escape, because its moats are physical: Isomorphic's target data, Periodic's robot labs, Verge Labs' decade of proprietary brain-tissue data. An open-weight competitor can clone your benchmark scores; it cannot clone your wet lab. And the prize scale is different: an approved drug is a $1B+/year asset — no API pricing tier compares.
There's also a European angle. Science AI in regulated sectors — nuclear, health, defense-adjacent materials — cannot simply ship its data to a US API. That's the same sovereignty current running through Soofi S and the Mistral–Microsoft air-gapped deployments: for this vertical, EU-hosted and on-premise isn't a compliance checkbox, it's the entry ticket.
Europe's most instructive live experiment is Doctolib's clinical-AI research lab: €20M in 2026, a research team associated with Inria, Inserm, and Université Paris Cité, and a first project — improving care pathways — that will train on the health data of more than 50 million French patients starting August 2026. It's the same moat logic as Isomorphic and Periodic — proprietary data nobody else holds — at 1/300th the budget. And it comes with the tension the US players don't face at home: the project runs on an opt-out basis under the CNIL's MR004 methodology, which has already made it controversial. European science AI gets its data moats only by navigating consent frameworks that are themselves part of the competitive landscape.
What to expect next
- Materials revenue beats drug revenue for years. Milestone-deferred pharma economics versus semiconductor customers paying today; the 88.9/11.1 capital split will not match the revenue split before 2030.
- Every frontier lab owns wet-lab capacity by end of 2027. Anthropic bought Coefficient Bio; Periodic built robot labs; the proprietary-experiment flywheel is too obviously the moat.
- Verification becomes a product category. The Leanstral cost curve (75× in one generation) is what commoditization looks like before productization — machine-checked safety cases for software and engineering will be sold the way OCR is sold today.
- The pie diversifies slowly. Drug discovery's 88.9% reflects where patents pay best, not where AI helps science most. The daily-work reality — literature, legacy data, reporting — is spread across every field and shows up in no funding announcement.
Method: capital figures are disclosed fundraises, acquisitions, and partnership ceilings from the references below, summed per field ($7.2B total traced); shares, deal ratios, and CAGRs computed from those figures. Model-release statistics from the arcade dataset (206 entries).
References: Forbes — Isomorphic Labs' $2.1B fundraise · BioPharm International — Series B and partnerships · MedCity News — milestone-deferred economics · Presenc — IsoDDE and the 2026 trial timeline · Drug Target Review — 2026 predictions, Lilly×NVIDIA · 1BusinessWorld — Anthropic × Coefficient Bio · Contrary Research — Periodic Labs breakdown · TechFundingNews — Periodic's $7.5B valuation round · Towards Healthcare — AI drug-discovery market sizing · PR Newswire — Verge Labs launch · Turing Post — 12 AI co-scientists of 2026 · Mistral — Leanstral model card · Maddyness — Doctolib's €20M AI lab · DSIH — Doctolib care-pathways project · Usine Digitale — the opt-out controversy