Thanks Prof. Abhay Karandikar and @PrinSciAdvGoI Prof. Ajay Sood for inviting me to speak at the conclave about the work we do @GoogleDeepMind. I was particularly happy to hear @PMOIndia @narendramodi speak about the importance India places on advancing Science and Research for tackling global challenges. Given India's young and growing scientific community, I am very optimistic about the role Science will play in driving the country's growth.

Congrats Harsha and the @MicrosoftAI team on the @ScienceMagazine publication of the excellent Diagnostic AI work! Great to also see another independent validation of the promising health capabilities of Gemini 4 Argon :)

Great to see @GoogleDeepMind's investments in health and bio stand out in Ortet's interesting new evaluation. Gemini 4 Argon takes the numero uno spot overall on the Health Index and tops biological research (turns out not refusing routine bio questions helps 😅). And Gemma 4 shining too. More soon :)

Gemini 4 Argon is an absolute genius when it comes to science! Incredible! I asked it to propose a new unifying theory of biology by 2040, connecting seemingly different fields, from immunology to neuroscience, through first principles, mathematical rules, and information processing. Its response was the most mind-boggling and complex scientific explanation I have ever seen! I couldn't even understand the math, so I asked it to turn the theory into a poster and then explain it in simpler terms for us mere mortals!

Gemini 4 Argon has been transformative for our @GoogleDeepMind work in science and medicine. More from Pushmeet below. We are only getting started :) We are also annoucing an early access program for scientists to get the highest tier access to Gemini 4 Argon in @antigravity Please apply here and can't wait to see what the community will build and discover -

New paper! Science is constrained by physical and economic bottlenecks such as hypothesis testing, especially as many discoveries involve long chains of smaller steps and hypotheses, each requiring validation. As the marginal cost of hypothesis generation goes down, the physical validation cost is likely to remain inelastic in the absence of major investments in scientific infrastructure. Autonomous science is therefore not merely a question of raw capabilities - the allocation of limited validation capacity matters just as much. We argue that this will require deliberately designed markets and institutions, both to allocate scarce validation capacity to the most valuable work and to ensure public good research isn’t crowded out. With @weballergy , @FranklinMatija , @Dr_Atoosa , @vivnat , @alan_karthi , @sindero and I.

Delighted to see our real-world clinical study of a conversational diagnostic AI system published in @TheLancet, alongside coverage in @nytimes last week. Looking back, AI in medicine might feel inevitable but it actually takes deliberate, uncertain leaps to get there. I have been fortunate to be part of two such moments. The first was early 2022 (pre ChatGPT) when @alan_karthi and I got access to our first @Google LaMDA and PaLM models. That convinced us to go all in on LLMs in medicine, sparking the Google Brain moonshot leading to Med-PaLM. The second moment leading directly to this Lancet study, was late summer 2023. @taotu831 showed me the early AMIE models he was training with our self-play loop, targeting our upcoming OSCE study with patient actors, later published in @Nature 2025. At that exact time, I was navigating a personal health condition. Testing myself, the consultation the AI performed was so natural and clinically sound that I was convinced it was ready for the real world. So I reached out to Alan and together with the team we asked how we could test this in the real world quickly, safely and responsibly. We needed a prospective clinical trial with a safety endpoint and all roads led to @AdamRodmanMD at @bethisraellahey @harvardmed . Adam was incredibly gracious with his time kicking off our first discussions in the fall of 2023. But clinical trials take a long time; years of painstaking work. There are so many critical people and moments that make them happen, far beyond the glitzy PR materials and the main authors. - For example, a discussion with @greg_corrado sparked the design of the oversight setup underpinning the safety layer in the study. - Kavita Kulkarni (now at BioHub) spent months negotiating the initial contracts with BIDMC; it was uncharted territory for both organizations. - @_khaledsaab (now rocking at OpenAI!) working tirelessly to improve the AI, together with @taotu831, @apalepu13, @RyutaroTanno, @ckbjimmy, @valentinlievin, @elahevedadi and many more folks. - @Mysiak, Alessio Orlandi, Petar Sirkovic and team sharing the seamless serving infrastructure they built for Co-Scientist, which we could re-use for much of this study. - @SSaraMahdavi making sure the study team was well organized :) This is just to name a few. Prospective trials are a massive team sport and I am proud of what our teams achieved. While the AI's clinical reasoning was strong, seeing the patient trust, safety, and satisfaction data, with ZERO safety stops from our supervising physicians, is what truly stands out. At the same time, this is still a stepping stone. We are moving from AI as a tool to an active care team member - a guardian angel amplifying clinicians and staying with patients throughout their health journey. More to come soon, and as always, a pleasure to work with so many great folks across @GoogleDeepMind , @GoogleResearch and @bethisraellahey . Paper - NYT -

2+ years ago, @vivnat and I asked a simple question with our amazing team @GoogleDeepMind @GoogleResearch: what would it take for conversational diagnostic AI to safely enter into a real medical clinic? That sparked many London<>Boston flights over the pond, enduring friendship and collaboration with @AdamRodmanMD @BIDMChealth and our amazing teammates, rigorous safe-stopping protocols, and our first prospective study now in @TheLancet:

Real-world AI goes to the urgent care setting. A prospective feasibility assessment of patient-facing conversational Al held up very well according to patients and physicians for accuracy of diagnosis, among many other metrics Just published @TheLancet @AdamRodmanMD @PeterBrodeurMD @alan_karthi @GoogleDeepMind

Getting from promising hypotheses to precise targets can take years of experimental work. See how Professor Clare Bryant’s lab at the University of Cambridge is using Co-Scientist to help accelerate that process:

In collaboration with the @francesarnold and @pengliu_group research groups, we @GoogleDeepMind are proud to release AlphaProtein Novo, a pipeline for small-molecule enzyme design. We developed AlphaProtein Novo on a few standard model reactions, and validated designs in our own wet lab. We then wanted to challenge this system against nascent chemistries, so we asked leading scientists in biocatalysis whether we could partner on the reactions they cared about.

Today, our team is sharing a preprint on our new system for generative de-novo enzyme design, AlphaProtein Novo. At @GoogleDeepMind, we have long focused on the building blocks of life (proteins). We developed AlphaFold to predict structure, trained language models to understand function, and built AlphaProteo to design de-novo binders. But proteins play many important roles in nature, such as enzymes that catalyze chemical reactions. Designing entirely new enzymes from scratch to catalyze custom reactions has long been a holy grail for scientists. Our team has shown how AlphaProtein Novo can produce new-to-nature enzymes with state-of-the-art activity on 2 benchmark reactions. We’ve also designed custom enzymes to synthesize the pharmaceutical motif piperidine, and others to degrade the environmental plasticizer toxin DEHP. While there is much more work to do in this direction, we’re excited to have demonstrated the ability to design functional proteins that go beyond what is known in nature. Huge congratulations to @jueseph, @ZvxyWu, Josh Abramson, the @GoogleDeepMind protein design team, and our collaborators in the @francesarnold & Liu Group! 🎉 🔗 Preprints and code:

this is the most fun I have had with a model in a long, long time. gemini 4 argon is a true leap across many axes spanning capabilities and safety. as always, super excited about the progress we will make on science and medicine with this. more to come very very soon :)