Pushmeet Kohli

SynthID timeline over the last few weeks has been momentous – from opening SynthID Detector to the public, to launching SynthID Bio to watermark functional, AI-designed proteins. Each moment brought us a step closer to our broader vision on provenance, or helping verify what is AI-generated and what isn’t. Recently, Dartmouth Professor Hany Farid and I discussed why solving provenance is vital to @GoogleDeepMind’s broader mission to develop AI to benefit humanity. In short: solving provenance is a highly complex, multi-disciplinary effort with watermarking technology at its core. It requires a collective mix of frontier research, interoperable open standards, and direct collaboration across platforms and developers. Read our full conversation on Google's Public Policy blog: t.co/w8MEhnywKy

At @GoogleDeepMind, we believe one of AI's most transformative impacts is empowering scientists to solve society's hardest problems. Over the past year, we've evolved our coding agent, @Google @Antigravity, to accelerate end-to-end scientific workflows combining our latest Gemini models with science-specific: • breakthrough models like AlphaFold and AlphaGenome • new interfaces like the Biomolecular Structure Viewer The impact of this integration has been transformative for the pace of our research. As an example, in a single session using Antigravity, our researchers working on antimicrobial resistance surfaced a novel antimicrobial peptide candidate. To support academic researchers leverage these tools, we've launched an Antigravity for Science credits program and are looking forward to what this enables: t.co/R4hlX3sgxL Read the antimicrobial resistance case study: t.co/aZKsOaaUiP

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A big thanks to @sebnowozin, @ynhnx and so many exceptional team members across Google who have contributed to this work.

I am proud to share that SynthID has been recognised by @TIME as one of the Best Inventions of 2026. When we first began developing this technology @GoogleDeepMind, the goal was to find a robust, imperceptible way to watermark AI-generated media directly at the moment of creation, without compromising content quality. It was no small research challenge. But three years into its use, SynthID has already watermarked over 180 billion images and videos, alongside 240,000 years of audio. We’ve also recently extended these watermarking principles to biology with SynthID Bio. SynthID isn't a silver bullet for misinformation and this isn't a challenge any one company can solve alone; but it does represent a step toward a more transparent information ecosystem. A sincere thank you to the teams across @Google for their contributions to making SynthID a reality, and also for the support the team has received from @demishassabis, @sundarpichai and the rest of the Google leadership on this journey. You can try the SynthID Universal Detector at t.co/DLhzDIJ2wS. Read more: t.co/g4f888rLh1

I am proud to share that SynthID has been recognised by @TIME as one of the Best Inventions of 2026. When we first began developing this technology @GoogleDeepMind, the goal was to find a robust, imperceptible way to watermark AI-generated media directly at the moment of creation, without compromising content quality. It was no small research challenge. But three years into its use, SynthID has already watermarked over 180 billion images and videos, alongside 240,000 years of audio. We’ve also recently extended these watermarking principles to biology with SynthID Bio. SynthID isn't a silver bullet for misinformation – and this isn't a challenge any one company can solve alone – but it represents a vital step toward a more transparent information ecosystem. A sincere thank you to the teams across @Google for their contributions to making SynthID a reality, and also for the support we have received from @demishassabis, @sundarpichai and the rest of the Google leadership on this journey. You can try the SynthID Universal Detector at t.co/DLhzDIJ2wS. Read more: t.co/g4f888rLh1

In rare disease research, scientists at @GamowLabs used Argon to analyse de-identified genomic data samples from newborns with rare developmental lung disorders. It successfully pinpointed a chromosomal rearrangement that routine software missed for years, and explained why it went undetected. Read more here: t.co/Mga7ct8s5f

In quantum computing, Argon is accelerating both error correction and algorithm design. It translated our AlphaQubit decoder into a single optimized kernel, nearly doubling its inference throughput so we can deploy larger, more capable models and scale to larger quantum devices, while continuing to meet strict real-time thresholds.

Gemini 4 Argon is a step change in model performance. In recent weeks, it has had a transformative impact on the Science projects in our team at @GoogleDeepMind and our external collaborators, and helped make a number of remarkable advances across scientific disciplines. 🧵👇

In nuclear fusion, we found further counterexamples to Grad's conjecture, until recently a 60-year-old mystery in plasma physics. Argon helped discover new analytical solutions for stellarator 3D magnetic fields, helping improve code verification for stellarator design. Read more in @opalissan post:

Accelerating the development of tools to accelerate biological research has been a key focus of our teams @Google. In the almost ten years I have been at @GoogleDeepMind, our team’s research has resulted in a number of models and agents, from AlphaFold to AlphaGenome to Co-Scientist. And there's exciting progress in clinical AI evidence-building too: @Google’s first-ever paper in the Lancet was published last week, in collaboration with Beth Israel Deaconess Medical Center. In a prospective clinical feasibility study, our conversational medical AI system, AMIE, safely gathered patient history prior to primary care visits. There were zero safety stops required by overseeing physicians, and AMIE's differential diagnosis matched the final clinical diagnosis in 90% of cases within its top 7 possibilities . @ymatias and I have written how the tools developed by teams across Google are helping researchers better understand complex biology and real-world disease understanding. Understanding Diseases blog: t.co/TcxMNPE5sg AMIE blog: t.co/M0FcINli8I

Great results from an exceptional model. A great example of the positive impact of AI. This is why we at @GoogleDeepMind make advancing science a core focus!

Exceptional results from an exceptional model. A great example of the transformative positive impact AI can have on Science. This is why we @GoogleDeepMind have focused on Science as a key focus of our work.

I have always believed that advancing mathematics is about empowering mathematicians. From our earliest days at @GoogleDeepMind, our focus has been on deep collaboration with the mathematical community, pioneering new tools alongside one another to push the boundaries of what’s possible.

Our journey started with a remarkable collaboration with Professor Geordie Williamson from the University of Sydney, Professor Marc Lackenby and Professor András Juhász from the University of Oxford that demonstrated that AI systems can help mathematicians discover new conjectures. This work resulted in the paper Advancing mathematics by guiding human intuition with AI,  published in Nature in 2021.

Building predictive biology models is one of the most important scientific challenges of our time, but solving this requires new experimental biological data at an unprecedented scale. I’m pleased to announce that @GoogleDeepmind is partnering with @ChanZuckerberg’s Virtual Biology Initiative to create the multi-modal datasets needed for improved predictive biology models. I wrote recently about the need to discover how life works at many different scales, from DNA and individual proteins to living cells and whole-body health. As we learnt from AlphaFold and the Protein Data Bank, solving grand biological challenges will not be possible without high-quality open data and efforts like the @czbiohub initiative. Read the announcement here: t.co/RwWzG765lh And my previous essay here: t.co/3YzWDCEVNp @AllenInstitute, @BroadInstitute, @GladstoneInst, @humancellatlas, @ProteinAtlas, @sangerinstitute, @IsomorphicLabs, @Meta

Building predictive biology models is one of the most important scientific challenges of our time, but solving this requires new experimental biological data at an unprecedented scale. I’m pleased to announce that @GoogleDeepmind is partnering with @ChanZuckerberg’s Virtual Biology Initiative to create the multi-modal datasets needed for improved predictive biology models. I wrote recently about the need to discover how life works at many different scales, from DNA and individual proteins to living cells and whole-body health. As we learnt from AlphaFold and the Protein Data Bank, solving grand biological challenges will not be possible without high-quality open data and efforts like the @czbiohub initiative. Read the announcement here: t.co/RwWzG76DaP And my previous essay here: @AllenInstitute, @BroadInstitute, @Gladstone Institutes, @ Human Cell Atlas, @ Human Protein Atlas, @ Wellcome Sanger Institute, @Isomorphic Labs, and @Meta

I’m pleased to announce SynthID Detector is now available as a universal verification platform. It’s an important step forward in helping people easily identify AI-generated content across the internet. Starting today, anyone can check whether images, audio, or video were created using models from @Google or our partners across the industry, including @OpenAI, @Nvidia, and @Kakao. Ever since our research teams at @GoogleDeepMind developed our first models, we understood that tracing the origin of digital content would become a central pillar of trust in the AI era. Today’s announcement combines the scientific achievements of SynthID with a shared commitment to making verification work seamlessly across platforms. I’m proud of our teams and partners for working together to make this possible. Try SynthID Universal Detector and read more about today’s update here: t.co/FVP2h6vVmq

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: t.co/tGklluPQFC t.co/2YGfFVzC9H t.co/Dvx7aESqna

It was such a pleasure to talk through our journey to make AI-generated artefacts identifiable with SynthID. @FryRsquared is not only a great communicator but she also has deep technical insight and innate curiosity. While @jeremydratcliff and I were representing the team, this work wouldn't be possible without the leadership and contributions of @sgowal @sdathath @FStimberg @BunelR @davidstutz92 @ImanisMind and so many exceptional team members at @GoogleDeepMind.

Very happy to announce that our team @GoogleDeepmind has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked. This proof-of-concept watermarking of the building blocks of life is enabled by SynthID Bio, our new protein watermarking method. It is designed to safeguard the new era of AI-powered generative biology and strengthen global biosecurity. You can read my thoughts here on why watermarking AI-designed proteins is an important research breakthrough:

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Earlier this year, I was invited by the U.S. Department of Energy’s Under Secretary for Science, @ScienceUnderSec, to serve as Vice-Chair of the Genesis Mission subcommittee, tasked with advising the DOE’s program on the use of AI to accelerate scientific breakthroughs. I am happy to share the Science Advisory Committee report, "Genesis Mission Frameworks for AI-Accelerated National Breakthroughs," which shows how we can make transformative discoveries across three priority areas: AI-enabled biology, accelerated fusion energy, and magnet sovereignty. Creating this roadmap with the Genesis Subcommittee was a rewarding and collective effort. Together with co-authors Drew Endy and Lara Jehi, and with significant contributions from Ali Douraghy and others, I had the privilege of leading the chapter on: "Harnessing Biology at Digital Speed". In it, we lay out a national campaign to shift biology from an observational science into a predictive, engineerable discipline to advance human health, bioenergy, and national resilience. For decades, our ability to sequence and read DNA has outpaced our ability to interpret and design it. We called for key investment in the following three pillars to close this gap: AI-enabled measurement/sensing of biology through high-throughput systemic multiomics, in-cell visual proteomics, and structural dynamics. AI-enabled modeling and prediction of molecular mechanics and whole-cell causal simulation. AI-enabled manufacturing and design through next-generation biofoundries and predictive scale-up, closing this gap by treating bioreactor physics and cellular mechanics as one integrated computational problem. Realising AI’s potential across these national priority areas will require enormous collaboration, and the Genesis Mission is enabling this across national laboratories, universities, and industry. A sincere thank you to the committee and our contributors. Read the full report here: t.co/xm2xlUYqSL

Earlier this year, I was invited by the U.S. Department of Energy’s Under Secretary for Science, @ScienceUnderSec, to serve as Vice-Chair of the Genesis Mission scientific committee, tasked with advising on the DOE’s program to use AI to accelerate scientific breakthroughs. I am happy to share the Science Advisory Committee report, "Genesis Mission Frameworks for AI-Accelerated National Breakthroughs," which shows how we can make transformative discoveries across three priority areas: AI-enabled biology, accelerated fusion energy, and magnet sovereignty. Creating this roadmap with the Genesis Subcommittee was a rewarding and collective effort. Together with co-authors Drew Endy and Lara Jehi, and with significant contributions from Ali Douraghy and others, I had the privilege of leading the chapter on: "Harnessing Biology at Digital Speed". In it, we lay out a national campaign to shift biology from an observational science into a predictive, engineerable discipline to advance human health, bioenergy, and national resilience. For decades, our ability to sequence and read DNA has outpaced our ability to interpret and design it. We called for key investment in the following three pillars to close this gap: AI-enabled measurement/sensing of biology through high-throughput systemic multiomics, in-cell visual proteomics, and structural dynamics. AI-enabled modeling and prediction of molecular mechanics and whole-cell causal simulation. AI-enabled manufacturing and design through next-generation biofoundries and predictive scale-up, closing this gap by treating bioreactor physics and cellular mechanics as one integrated computational problem. Realising AI’s potential across these national priority areas will require enormous collaboration, and the Genesis Mission is enabling this across national laboratories, universities, and industry. A sincere thank you to the committee and our contributors. Read the full report here: t.co/xm2xlUYqSL

Earlier this year I was invited by the U.S. Department of Energy’s Under Secretary for Science @dariogil to join the Genesis Mission scientific committee tasked to advise on DOE’s program to use AI to accelerate breakthroughs in Science as its Vice-Chair. I am happy to share the Science Advisory Committee report, "Genesis Mission Frameworks for AI-Accelerated National Breakthroughs," which shows how we can make transformative discoveries across three priority areas: AI-enabled biology, accelerated fusion energy, and magnet sovereignty. Creating this roadmap with the Genesis Subcommittee was a rewarding and collective effort. Together with co-authors Drew Endy and Lara Jehi, and with significant contributions from Ali Douraghy and others, I had the privilege of leading the chapter on: "Harnessing Biology at Digital Speed". In it, we lay out a national campaign to shift biology from an observational science into a predictive, engineerable discipline to advance human health, bioenergy, and national resilience. For decades, our ability to sequence and read DNA has outpaced our ability to interpret and design it. We called for key investment in the following three pillars to close this gap: AI-enabled measurement/sensing of biology through high-throughput systemic multiomics, in-cell visual proteomics, and structural dynamics. AI-enabled modeling and prediction of molecular mechanics and whole-cell causal simulation. AI-enabled manufacturing and design through next-generation biofoundries and predictive scale-up, closing this gap by treating bioreactor physics and cellular mechanics as one integrated computational problem. Realising AI’s potential across these national priority areas will require enormous collaboration, and the Genesis Mission is enabling this across national laboratories, universities, and industry. A sincere thank you to the committee and our contributors. Read the full report here: t.co/xm2xlUYqSL

The developments of the last few weeks are a powerful reminder of the sheer pace of AI progress. It highlights the urgent need for us to build better coordination mechanisms to govern and harness this technology for the benefit of human civilization.

1/5 🧬 Today, our team @GoogleDeepMind is taking another step on our mission of deciphering the genome. We are releasing AlphaGenome Atlas, a massive (petabyte-scale) resource containing AlphaGenome predictions for every possible single-letter DNA change in the human genome — 9 billion in total. t.co/Lf9DWRfWCu

2/5 To navigate this scale, we're introducing the AlphaGenome Variant Impact (AVI) score. By combining AlphaMissense and AlphaGenome into a single metric, AVI helps researchers quickly prioritize high-impact variants and unravel disease mechanisms.

Every once in a while, science reaches a horizon that changes everything. We are standing at that exact threshold with AI and biology today. The promise of understanding life at its most fundamental level to cure disease and safeguard our world is what inspires our team @GoogleDeepMind every day. Yet, the greater the capability, the greater the duty to ensure it is developed safely. Today, I'm sharing my thoughts on how we can responsibly accelerate AI biology to unlock its full potential:

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Really proud of the relentless progress our weather prediction team is making. WeatherNext 3 sets a new standard for prediction fidelity, accuracy and coverage. Great example of how AI can help all of us plan better for the future! blog.google/innovation-and…

Matrix multiplication is the basic computational operation that powers modern computing (including AI). Yet, the theoretical fastest speed at which computers can multiply matrices (omega ω) is still unknown and has been a longstanding challenge for complexity theory and computer science. Today, we announce a new record for omega (ω<2.371177). This is the result of a great team effort between @GoogleDeepMind, our academic collaborators, and our Gemini-powered coding agent AlphaEvolve! 🧮 t.co/5c7E8W1F1d

Great to see @OpenAI join @ElevenLabs in adopting @Google's SynthID for audio, enabling their users to have access to the same robust, imperceptible safeguards that power our own products. For years, we at @GoogleDeepMind have been pioneering research on SynthID watermarking to address the risks of AI-generated media (particularly acute for audio and voices). We had successfully integrated SynthID to safeguard our product surfaces - Gemini Live, Lyria, and Veo, but protecting the ecosystem requires an industry-wide effort to build foundational safety infrastructure together which is now gaining momentum! SynthID: t.co/XqcVCcj9sb OpenAI Verification Portal: t.co/sLgYvYIo12 ElevenLabs: t.co/Dbfhgq0axk

Great to see @OpenAI join @elevenlabsio in adopting @Google's SynthID for audio, enabling their users to have access to the same robust, imperceptible safeguards that power our own products. For years, we at @GoogleDeepMind have been pioneering research on SynthID watermarking to address the risks of AI-generated media (particularly acute for audio and voices). We had successfully integrated SynthID to safeguard our product surfaces - Gemini Live, Lyria, and Veo, but protecting the ecosystem requires an industry-wide effort to build foundational safety infrastructure together which is now gaining momentum! SynthID: t.co/XqcVCcjHhJ OpenAI Verification Portal: t.co/sLgYvYIVQA ElevenLabs: t.co/Dbfhgq0ImS

Ten years ago, I crossed paths with a guy on a remarkable journey. When I told him of my desire to understand the world, he invited me along, suggesting that I might find my answers where he was going. I soon realized I had joined a true polymath who had assembled an unparalleled vessel of minds - thinkers, builders, philosophers, and artists. As he transitions to the next phase of this important mission, I am left with nothing but gratitude for the profound adventures we've shared. Thank you, my friend @demishassabis, for what you have built and for the path ahead.

The @GoogleDeepMind Science team has not pivoted; it had expanded its work. We are continuing to tackle the hardest problems in Science and Biology—from understanding protein function and deciphering the genome to enabling de-novo protein and enzyme design, while also accelerating the process of scientific discovery by developing Gemini-powered agents. The culture of @GoogleDeepMind is to empower our scientists. They have agency over which challenging goals they want to pursue, and this is true for the AlphaFold team as well.

The @GoogleDeepMind Science team is not pivoting; it is expanding its work. We are continuing to tackle the hardest problems in Science and Biology—from understanding protein function and deciphering the genome to enabling de-novo protein and enzyme design, while also accelerating the process of scientific discovery by developing Gemini-powered agents. The culture of @GoogleDeepMind is to empower our scientists. They have agency over which challenging goals they want to pursue, and this is true for the AlphaFold team as well.

The @GoogleDeepMind Science team is not pivoting; it is expanding. And we are continuing to tackle the hardest problems in Science and Biology; from understanding protein function and deciphering the genome to enabling de-novo protein and enzyme design. The culture of @GoogleDeepMind is to empower our scientists. They have agency over which challenging goals they want to pursue. The large majority of the AlphaFold team members continue working on hard scientific challenges and accelerating the process of scientific discovery by developing Gemini-powered agents for Science based on their research interests and expertise.

We stand at an inflection point where the convergence of advanced AI and scientific research promises to unlock a new golden age of discovery. @Google is equipping Genesis Mission scientists with our frontier models and agentic tools. This will act as a force multiplier for human ingenuity - boosting American scientific productivity and empowering top researchers to solve pressing global challenges faster than ever. t.co/rbYlSZ29ef

Great to see launch of the Gemini 3.5 Flash Cyber model! 🛡️ By combining Gemini Cyber with the Code Mender harness, we're giving frontline software defense teams a head start to efficiently uncover and secure critical vulnerabilities in complex production systems. @GoogleDeepMind Learn more ⬇️ t.co/bQUpVQ0fbN

Great to see launch of the Gemini 3.5 Flash Cyber model! 🛡️ By combining Gemini Cyber with the Code Mender harness, we're giving frontline software defense teams a head start to efficiently uncover and secure critical vulnerabilities in complex production systems. Learn more ⬇️ t.co/bQUpVQ0fbN

Fascinating application of @GoogleDeepMind's AlphaFold in protein engineering! 🧬 As we approach 5 years since the AlphaFold Database was launched, it’s great to see it being used to fill in structural blind spots. While still early, it’s a brilliant example of how open access to AI-predicted structures is empowering experimental biologists to target complex health challenges.

Tackling the most complex scientific challenges requires a proactive approach to safety. Today, together with @IsomorphicLabs, we're sharing our joint approach to bio-resilience. Learn more: deepmind.google/blog/our-appro…

Developing increasingly powerful AI necessitates a rigorous checks and balances approach to ensure it benefits humanity. Really glad to see @demishassabis proposing this framework for the responsible development and deployment of AI systems. x.com/demishassabis/…

Our teams across @GoogleDeepMind and @googlecloud are working together to bring AlphaEvolve, our algorithm discovery and code optimization agent, to scientists and engineers solving the world's hardest technical problems. Looking forward to the new discoveries powered by AI! 👇 x.com/googlecloud/st…

@JohnJumperSci , you have been a fantastic colleague. I am deeply grateful for all your invaluable contributions to the @GoogleDeepMind Science program. From the mission-focused execution on AlphaFold to the amazing Nobel ceremony- you leave us with a lot of memories. We wish you the best, my friend.

The next era of AI isn't just about writing code faster; it's about discovering entirely new ways to solve complex engineering bottlenecks. @GoogleDeepMind's evolutionary coding agent, AlphaEvolve, is now deployed on @googlecloud, acting as an autonomous research engineer for enterprise infrastructure. Instead of just generating boilerplate, it systematically explores and refines algorithmic search spaces. We are already seeing incredible empirical results in production: Logistics: @BASFAgro achieved an 80% accuracy improvement over their existing supply chain models. Core Systems: @jetbrains saw a 15-20% performance boost to their core IDE B-tree indexing stack. Read the full technical reports on how these algorithms are evolving here: 🔗 JetBrains: t.co/a47rq5DC5F 🔗 BASF: t.co/IRtTSmw57J

When I was asked by the American Academy of Arts and Sciences to write an essay on my thoughts on how AI will accelerate Science, I felt honored but also felt that it would require a lot of thoughtfulness and diligence to distill my thoughts on paper. The essay has now been

AI agents are advancing research-level math. 🚀 I’m thrilled to share @GoogleDeepMind’s AlphaProof Nexus - an agentic framework for formal proof search powered by Gemini. When applied to a set of open formal math problems, our agent autonomously solved: ✅ 9 open Erdős

See the formal proofs (in lean) discovered by the AlphaProof Nexus agent: github.com/google-deepmin…

Really happy to see @OpenAI adopt @GoogleDeepMind's SynthID for watermarking AI generated images. We need more such cross industry partnerships for enabling responsible use of AI systems. x.com/OpenAI/status/…

The results of the research happening in my team @GoogleDeepMind have convinced me that the next era of scientific discovery will be aided by AI agents acting as force multipliers for human ingenuity. That’s why I’m proud to introduce Gemini for Science - a collection of

The future of Math is mathematicians and AI agents working together. Very pleased to introduce @GoogleDeepMind's AI co-mathematician: a multi-agent system designed to actively collaborate with human experts on open-ended research mathematics. Mathematicians testing the agent

Read more in our technical paper: arxiv.org/abs/2605.06651 See the Frontier Tier 4 problem evaluation at: epoch.ai/frontiermath/t…