Monthly digests of new tools, pricing changes, and research developments — focused on what actually matters for researchers, not AI hype.
Google Gemini for Science launches with Co-Scientist multi-agent hypothesis engine; an open-source model beats GPT-4 on citation accuracy; Nature reveals pharma's private protein data advantage; a new model predicts protein-protein interactions. Plus: Deep Research expands to ChatGPT Plus, Evo 2 enters standard practice, and Overleaf AI exits beta.
Evo 2's 270B-parameter genomic model is the new reference point for sequence-level biology, ECMWF's ensemble weather AI extends forecast skill further, and three major tool pricing overhauls are worth knowing about.
A Nature investigation reveals that AI protein models trained on proprietary pharmaceutical data from over 20,000 structures outperform public-data models including AlphaFold variants — raising questions about who benefits from AI advances in structural biology.
Generative crystal design moves from research papers to usable tools, Elicit ships Research Agents and a public API, and autonomous lab platforms cross from industry into academic reach.
Argonne extends its battery-materials foundation model toward molecular crystals, ECMWF's AIFS remains the operational benchmark, NOAA's GraphCast-derived models keep outperforming legacy systems, and a 45-year GraphCast hindcast archive opens up new climate research.
GenCast's peer-reviewed Nature results are genuinely strong — beating ECMWF's ensemble system on 97.2% of verification targets. Now NVIDIA's open-source Earth-2 suite claims to beat GenCast too, but that comparison isn't independently verified yet.
At Google I/O 2026, Google unveiled Gemini for Science — a platform combining Co-Scientist (multi-agent hypothesis generation), AlphaEvolve (self-improving algorithms), and Empirical Research Assistance (ERA) into a unified AI research workflow for scientists.
Researchers report an AI system that predicts how proteins interact with each other — a harder problem than single-protein structure prediction — with accuracy levels that could accelerate cancer biology and drug discovery research.
A Nature news report describes a smaller, fine-tuned open-source model that outperforms major commercial LLMs on scientific literature review accuracy, matching human experts on citation correctness — a significant result for researchers who need verifiable outputs.
New tools, pricing updates, and what's actually working in research AI. One email a month.
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