Gemini 4 Argon is already unlocking new scientific breakthroughs in quantum computing, nuclear fusion, protein design and beyond. 🧵

Gemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.” Created in @Antigravity, it animates orbiting electrons, atomic data, and light spectra.

We did a lot to understand Gemini 4's capabilities and propensities, and to improve our mitigations for this launch -- you can get a tl;dr here blog.google/innovation-and-a… and dive into the report storage.googleapis.com/deepm…

Four Gemini 4 Argon agents beat Minecraft from scratch in 27 hours. Operating in a sandbox using only code and text, they wrote 2,783 programs, divided up jobs over chat, shared notes, and debugged errors together - all with no human help ↓

Together with scientists around the world, our models and tools are helping make disease more detectable, treatable, and preventable. 🧬 From pinpointing rare genetic variants to speeding up drug discovery, here’s how we’re helping turn lab breakthroughs into real-world care → goo.gle/4i48Cmr

Last week, Hurricane Isaias made landfall in the Florida Panhandle. Our thoughts are with everyone affected and with the recovery efforts still underway. Earlier and more accurate forecasting can help protect communities by giving them more time to prepare, and we are grateful that Google’s WeatherNext model was able to assist the @NWSNHC 's expert forecasters. Based on initial evaluations, here's how our model supported the response to #Isaias: - Tracking the Hurricane’s Path: WeatherNext was the most accurate model on the storm’s track. While traditional physics models predicted landfall closer to New Orleans, WeatherNext correctly predicted a path further east. This allowed NHC forecasters to highlight the potential for wind and storm surge impacts in the Florida Panhandle from their very first forecast. - More Time to Prepare: Up to three days before landfall, our model consistently predicted its eventual location in the Florida Panhandle to within 60 km or less, supporting the NHC as they honed in on a track toward the Florida Panhandle and issued hurricane watches and warnings to give officials in Florida, Alabama, and Mississippi more time to prepare. - Early Indications about Intensity: Early on, WeatherNext accurately predicted the intensification to a major hurricane. Alongside the regional hurricane models HWRF and HMON, WeatherNext’s intensity forecasts helped NHC forecasters to predict that the Isaias would approach the coast as a category 2 hurricane from the very first advisory. Giving communities more time and more accurate warnings is critical for protecting lives and livelihoods. While model performance varies from storm to storm, Isaias reflects our broader findings. In our recent @Nature paper co-authored with NHC forecasters, evaluating 200+ global tropical cyclones, we found our model provides ~1 day of additional lead time for both track and intensity. It’s been an honor for the @GoogleDeepMind and @GoogleResearch teams to work alongside NHC forecasters. By combining AI, physics models, satellite data, and the crucial expertise of meteorologists, we're excited to keep pushing the boundaries of forecasting to help protect communities.

Explore the latest advances in native audio models and the challenges of building better real-time voice experiences with @GoogleDeepMind + @SierraPlatform. Learn about conversational latency, multilingual code-switching, agentic voice tasks, and more →goo.gle/4hKV9B5

SynthID has been named one of @TIME's Best Inventions of 2026. With over 180 billion images and videos already watermarked, it’s making it easier for people to distinguish AI-generated content from other media → goo.gle/4jsZxWF

Over half of reported crimes in Singapore use sophisticated social engineering. With @GovTechSG, we’re building on-device fraud detection using our open model Gemma. By fine-tuning it on local scam tactics, it identifies threats in real time without compromising personal privacy → goo.gle/4ygVkJa

New essay for the @GoogleDeepMind Institute with James Manyika on AI x Science, "Bending the Curve of Discovery". While accelerating existing scientific practices is certainly useful, the real promise of AI is its potential to act as an invention of a method of invention (IMI) a la Griliches--e.g., the microscope or statistical inference--which would unlock questions and whole modes of discovery that were previously beyond human reach. Today, LLMs and specialized models like AlphaFold act as economic complements. LLMs handle analysis, coding, and writing, while specialized tools handle domain-specific predictions. Most handoffs between them run through the scientist. What may the future of science look like? LLMs orchestrating those handoffs automatically--prompting specialized models, auditing outputs, and looping until either the question is answered or a non-automated stage is reached (e.g., wet lab testing). The scientist's role shifts from running each step to designing this loop. We saw a glimpse of this workflow with Anthropic’s enzyme discovery a few weeks ago, and we're seeing this in our own work too. This raises foundational epistemic questions: 1. What will scientific understanding look like when discoveries are made by black-box models whose output is increasingly difficult to interpret? 2. How do we extract underlying mechanisms, not just outputs, as science becomes more automated? What will theory look like? 3. If AI automates the writing and junior lab work, how do we train the next generation of scientists to push the frontier? 4. What will motivate scientists in this epistemic reality? There's an economics angle too. Hypothesis generation gets cheap, so bottlenecks move downstream to verification and to choosing which questions are worth asking. Realizing AI’s full potential will take investment in infrastructure and institutional reform--making this as much an organizational challenge as a technical one We don't have the answers yet, and I'd love to hear what others think. Paper here: bit.ly/ai-in-science

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