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Google DeepMind and Meta Join Biohub's $1.8 Billion AI Biology Push

What changed

Alphabet (GOOGL) and Meta Platforms (META) are buying into AI biology. On Wednesday, October 7, Google DeepMind, Alphabet’s drug discovery unit Isomorphic Labs, and Meta agreed to put a combined $300 million into Biohub’s Virtual Biology Initiative. Biohub is the science group backed by Mark Zuckerberg and Priscilla Chan. With the U.S. Department of Energy and the National Institutes of Health also in, total commitments now reach $1.8 billion. What the Money Buys The initiative will measure how cells respond across far more conditions than scientists have ever tested, then use that data to train AI models that predict cell behavior. The first dataset is due in about a year, and the goal is accurate predictive models within five. The funding breaks down like this: - Biohub: $500 million, committed in April - Department of Energy: more than $500 million over five years - NIH: more than $500 million in earlier federal funding, now being standardized for the project - Google DeepMind, Isomorphic Labs, and Meta: $300 million combined In return, the three companies see the data before the public does. Commercial funders get a one-year embargo on the data they help pay for, while government-funded data has none. “We have to have some incentive for commercial players to be a part of this, and the embargo period creates that,” Biohub head of science Alex Rives said. $300 Million Is a Rounding Error for Alphabet and Meta The check is tiny next to what these two already spend. Alphabet spent $68.97 billion on research and development over the last 12 months, and Meta spent $71.63 billion. Together that’s $140.6 billion, or about $385 million a day. So the entire $300 million commitment is less than one day of their combined research budgets. Both companies have more than doubled R&D since 2021, and Meta now outspends Alphabet. Capital spending makes the gap even wider. Alphabet spent $91.45 billion on capex in 2025 and Meta spent $69.69 billion, each roughly 3.7 times its 2021 level. Both companies have tied most of that spending to AI data centers. Biology is a small add-on to that spending. Nothing in this deal moves either company’s earnings. Why Early Access Is the Real Asset What the money really buys is time. A one-year embargo on commercially funded datasets gives participating companies a head start in training models before those datasets become public. That…

Sources and evidence

外部发现 · Google DeepMind · Original source ↗ · Site entry ·

Researchers fear being scooped by AI, as OpenAI's announcement timing draws questions

What changed

As AI agents’ ability to do science autonomously continues to improve, scientists are facing a once-unthinkable question: will I get scooped by a bot? At least twice in the past five weeks, researchers have said that they had been working for an extended period on a particular research question, only to learn that artificial-intelligence companies had either answered the question before them or announced results before they were ready to do the same. In response, researchers who are worried that AI tools might scrape their unpublished work and scoop them are limiting their use of such tools. Anthropic’s AI biolab finds ‘CRISPR-like’ DNA in viruses. What’s next? Nevertheless, some AI researchers, although not all, say that AI agents are increasingly likely to reach conclusions before scientists can do so. Researchers are “going to be scooped, but not because they’ve uploaded a manuscript”, says Geoffrey Irving, chief scientist at Resolution, an AI-safety research organization in Berkeley, California. “They’re going to get scooped because the AIs are very good at solving problems, and they’re going to get better and better.” Controversial findings On 7 September, Tristan Buckmaster, a mathematician at New York University in New York City, posted online that he and a collaborator had made progress on the Navier–Stokes problem, a long-standing open question in fluid dynamics. According to Buckmaster’s account, he and Levent Alpöge, a mathematician at the AI company Anthropic, based in San Francisco, California, whose work with Buckmaster was a personal project, had been pursuing the problem for a year. Buckmaster’s 7 September post also included a paper with the solution to a simpler version of the problem. The next day, 8 September, OpenAI, also in San Francisco, announced that its agents had solved the puzzle — a timing that Buckmaster has publicly questioned. Who gets credit in the AI era? OpenAI maths bombshell sparks debate Buckmaster raised the possibility that information that he and Alpöge had uploaded to an OpenAI tool could have been used to train the company’s models. OpenAI disputed this scenario. The company said in a statement that an investigation had confirmed that Buckmaster’s “prompts” in the two months before the 8 September announcement “could not have influenced the system in any way, including through training”. The statement also said tha…

Sources and evidence

Nature Portfolio · Nature · Original source ↗ · Site entry ·

OpenAI's Navier–Stokes Claim Puts Mathematicians in a Reckoning at Heidelberg Forum

What changed

On 13 September, leading luminaries and up-and-coming talents in mathematics and computer science congregated at the annual Heidelberg Laureate Forum in Germany for a week of discussion, networking, and—naturally—wurst. Having attended several of these events in the past, the mathematicians normally focus their chatter on which famed researchers are present, or what interesting problems they have been working on. But instead, every snippet of conversation caught in passing or any debate accidentally overheard was about how AI companies such as OpenAI, Anthropic, and Google are steamrollering their way through mathematics. And there is good reason for these fervent discussions. Mathematics is the perfect testing ground for AI, involving step-by-step logical reasoning and answers that are automatically and objectively verifiable. This has led tech giants to develop their AI mathematical capabilities at a terrifying rate this year, leading to both new solutions to previously unsolved problems and a reckoning within the community as to what it means to do math in the age of AI. Tech Giants Target the Millennium Problems In short order, AI has gone from struggling with everyday research-level problems to solving a whole raft of teasers posed by prolific Hungarian mathematician Paul Erdős, then verifying the proof of Fermat’s Last Theorem, and—most recently and famously—OpenAI announcing it had solved the Navier-Stokes existence and smoothness problem. As attendee and young researcher Ailsa Robertson, of the University of Amsterdam, put it: “AI and LLMs set the math community on fire over summer.” Of the seven extremely difficult Millennium Prize Problems posed in the year 2000 by the Clay Mathematics Institute, only one, the Poincaré conjecture, has been solved by humans so far. OpenAI’s claim to have solved a second, the Navier-Stokes existence and smoothness problem, if verified, represents a watershed moment for automated reasoning. Fields Medalist Jacob Tsimerman, of the University of Toronto, was keen to recognize this achievement during a press conference at the forum: “We’ve seen rapid increases in capabilities, even faster than many people, including myself, expected,” he said. “Is AI doing something new? I don’t know the details, but…solving Navier-Stokes feels pretty definitive.” Since this announcement, rumors have swirled about which of the remaining…

Sources and evidence

外部发现 · OpenAI · Original source ↗ · Site entry ·

insitro's Machine Learning Models for Small Molecule in vivo Pharmacokinetic Prediction Now Available in Lilly TuneLab

What changed

Share SOUTH SAN FRANCISCO, Calif. – October 6, 2026 – insitro, the AI company unlocking causal human biology to systematically generate high-impact therapeutics, today announced that the advanced machine learning models that insitro built with Eli Lilly and Company (Lilly) to predict in vitro and, critically, in vivo properties of small molecules can now be used by certain biotech companies in Lilly TuneLab™. Trained on multi-species preclinical data that Lilly generated over decades of research from hundreds of thousands of unique molecules, the models offer a powerful alternative to today’s industry standard method of extrapolation from in vitro assays, providing an enterprise-grade capability traditionally available only to organizations with the scale to generate such data volumes themselves. The models will be updated as the dataset continues to expand. Announced in September 2025, the collaboration paired insitro’s computational and AI/ML expertise with Lilly’s proprietary preclinical dataset of in vitro and in vivo measurements from a wide array of compounds with established ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, derived from decades of Lilly drug discovery, and representing a world-class dataset in quality, consistency and scale. This collaboration demonstrates the power of insitro’s platform by combining insitro’s AI and ML expertise with Lilly’s data to unlock and advance small molecule development. “Nine in 10 programs that enter the clinic fail, most often when we first test for efficacy. That happens when the mechanism did not drive the disease, but also when the molecule never reaches the tissue at the right concentration for the right duration,” said Daphne Koller, Ph.D., founder and CEO of insitro. “The question of a molecule’s in vivo behavior has been an empirical question, answered experimentally, slowly, one compound at a time. These models make it a computational question first.” The downstream implications extend to patients. Most small molecule candidates fail, often for pharmacokinetic or safety reasons that surface late, after significant time and capital have been committed. Better prediction at the design stage may mean that fewer candidates advance on flawed structures, promising molecules reach the clinic on shorter timelines, and program development becomes more tractable. Improved in…

Sources and evidence

insitro · News · Original source ↗ · Site entry ·

Photonic neuromorphic learning via generalized in situ physical gradient descent

What changed

Physics-based neuromorphic computing promises major gains in performance and energy efficiency, but training remains largely in silico, requiring costly physical modeling and suffering from fabrication-induced errors. Here we introduce on-chip, in situ physical gradient descent (INSPIRE), a general training method for photonic integrated circuits. INSPIRE uses on-chip synthetic time-reversal holography to measure the full complex fields of bidirectional photonic modes, enabling gradient computation and parameter updates directly in the physical system. The framework is topology-agnostic and compatible with diverse optical circuits. Experiments yield trained matrices with a relative error of 0.26%, and we demonstrate in situ training through scattering media for matrices larger than the number of native tunable elements. INSPIRE also enables meta-photonic circuits to perform in situ meta-learning, realizing single-shot photonic learning with 251-fold model compression and 136-fold task-specific training acceleration. This work offers a practical route toward adaptive and efficient intelligent photonic systems. This study reports a physics-based in situ learning method that trains artificial neural networks directly within the photonic integrated circuits, turning diverse optical circuits into efficient AI learning hardware.

Sources and evidence

Nature Portfolio · Nature Computational Science · Original source ↗ · Site entry ·