Pushmeet Kohli

I’m very pleased to introduce AlphaGenome 2, @GoogleDeepMind’s next-generation genomics model that deciphers how DNA changes affect cells. Cells are the basic units of life and key to our understanding of biology. It is an ongoing challenge to predict how DNA changes affect the functioning of different cells, such as for a heart muscle cell or an immune cell fighting an infection, and to uncover cellular drivers of disease. To make progress on this challenge, we needed to design the architecture of AlphaGenome 2 so its predictions were conditional on cell types and states. This new design enables AlphaGenome 2 to predict variant effects across hundreds of human cell types and adapt to unseen cell and disease states without retraining. When evaluated on the TenK10K immune cell dataset, AlphaGenome 2 demonstrated a major leap in accuracy over its predecessor. It reliably identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) compared to the original AlphaGenome. This step-change improvement in precision helps researchers pinpoint previously overlooked disease-relevant variants with high confidence. Using analysis on retrospective case studies, we were able to show AG2’s real world utility in disease understanding. A landmark study had previously shown that a variant in a distant regulatory switch drives irritable bowel disease by overactivating the ETS2 gene in gut macrophages. When used to make predictions across various immune cell types in the gut, AlphaGenome 2 singled out the exact causal variant and showed that it selectively boosts the ETS2 gene in gut macrophages, leaving other cell types and genes largely unaffected. I would like to thank my colleagues Adam Gayoso @adamgayoso, Žiga Avsec @Avsecz, and our entire genomics team for their incredible contributions and dedication to bringing this model to life. We are making AlphaGenome 2 available for non-commercial academic research via the AlphaGenome API, and soon we will release AlphaGenome 2 Skills in Google Antigravity. I’m incredibly excited to see how scientists around the world use this model to make progress on biological understanding and treating disease. Read more on our blog here: deepmind.google/blog/alphage…

Announcing a major milestone achieved by my team @GoogleDeepMind: we have proven key security properties of the machine code underlying the Signal messaging protocol. This is the first time the actual production code of a widely deployed protocol has been formally verified – by any method, with or without AI – and at this depth. More than a research breakthrough, it’s a prime example of how frontier AI can be a force multiplier to counter the speed and scale of modern cyber threats. Formal verification offers a higher standard of security; however, it’s extremely complex and time consuming, which is why in many cases it’s considered to be impractical. Similar efforts could take teams decades of combined effort. We did it in a month’s time. Using AlphaProof Code, Google DeepMind's new agentic framework for formal verification that runs on LLMs including Gemini Carbon, we verified the Signal protocol down to the actual code running on devices.  This milestone lays the groundwork for a future in which vital systems, from medical device firmware to power grid software, ship with mathematical proof that their protections work. Reaching that future will take continued research from our community. In that spirit, we're open-sourcing our formal specifications and verification artifacts so others can build on this work. On a personal note, this moment is of deep significance to me. I started my own research journey more than two decades ago as an intern working on checking the consistency of complex software systems. Seeing this grand challenge made practical with modern AI is extraordinary. I'm extremely proud of my team members: Swarat Chaudhuri, Andrew Ferraiuolo, Anton Kovsharov, Tej Chajed, Sam Schlesinger,  Karn Seth, Jonathan Katz, and Charles Sutton for taking on this grand challenge and delivering, and grateful to the Signal team for their partnership. deepmind.google/blog/formal-…

The @GoogleDeepMind Science team is sharing two key cryptographic advances today uncovering weaknesses in legacy 1024-bit RSA schemes. Working with leading cryptanalysis experts, our team showed how AI agents could make two significant cryptographic advances in just weeks, uncovering results which can bypass older digital signature protections and factor legacy 1024-bit RSA keys in bulk. While these results do not affect the security of modern encryption schemes such as RSA 2048, these results show why we must not assume the mathematical "hardness" of cryptographic schemes which we have relied upon in the past. It also shows how AI can help defenders improve security standards well ahead of future threats. I have written in more detail about our results, and why they are important for the future of security, in a longer essay which you can read here: xitter.twitterapi.xyz/pushmeet/status/211875…

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.

Delighted to see the launch of the UK’s new AI Biosecurity Lab, which @GoogleDeepMind will partner with and support. As life sciences enter a transformational era, it is essential for researchers, government, and industry to collectively strengthen our biological defences and guide these new capabilities responsibly. Congratulations to @KanishkaNarayan and the entire team that has worked on making this a reality.

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: publicpolicy.google/article/…

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 synthid.com. Read more: time.com/collection/best-inv…

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: blog.google/innovation-and-a… AMIE blog: blog.google/innovation-and-a…

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!

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: biohub.org/news/virtual-biol… And my previous essay here: linkedin.com/pulse/understan… @AllenInstitute, @BroadInstitute, @GladstoneInst, @humancellatlas, @ProteinAtlas, @sangerinstitute, @IsomorphicLabs, @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: blog.google/innovation-and-a…

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: biorxiv.org/content/10.64898… biorxiv.org/content/10.64898… github.com/google-deepmind/a…

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: xitter.twitterapi.xyz/pushmeet/status/210531…