AI and science
Work happening at MegaBrain and elsewhere, our collaborations with researchers and labs, and practical workflows for scientists using AI in their research.
Features
Detailed case studies of AI applied to real research problems.
Workflows
Practical, reproducible guides for scientists using MegaBrain Science.
Field notes
Roundups of what’s happening across AI and science.
Math’s Most-Watched AI Tracker Just Went Dark. Nobody Said Why.
On June 30, the GitHub wiki where Terence Tao logged nine months of AI contributions to Erdős’s open problems got one final commit: "freeze." 10 days later a rival claimed 64 parallel agents solved a 50-year-old conjecture nobody has verified. Where the real edge in parallel AI mathematics actually is, in numbers.
Sakana Just Found the Point Where Adding More AI Research Agents Stops Helping
The number is 100. Scale a discovery agent from 10 copies to 100 and it matches the human novelty baseline exactly. Push it to 1,000 and the score stalls while 10-20% of the output turns to noise, right at the scale the field is racing toward.
The First Peer-Reviewed AI Co-Scientist Gets Nearly Half Wrong. 10,000 Labs Use It Anyway.
Stanford's Biomni cleared peer review in Science on July 9 and is already running in 10,000+ labs. Its own benchmark: 57% accuracy across 443 questions. What that gap between adoption and accuracy means for reproducible science.
177x Faster, 12x Bigger, Same Model: What NVIDIA’s Science-Compute Week Actually Fixed
NVIDIA shipped 2 posts a day apart that make protein-complex alignment 177x faster, push the largest foldable complex 12x bigger, and cut molecular-dynamics time 46%, all without a new model. Plus the confirmed Tc numbers behind an ML-screened kagome superconductor.
160 Ideas, One Pipeline, Two AI Judges, Two Different Scores
A NASA-grounded research agent produced 160 hypotheses from 1,475 satellite datasets. Grading them with GPT-5.2 versus Claude Sonnet 4.6 left the rankings stable but moved the scores. Plus Microsoft’s Aurora 1.5 weather model and Flint, a deterministic chart language for agents.
Everyone’s Racing to Build a Smarter Model. The Data Says That Isn’t the Bottleneck.
Frontier agents beat published Nature-family results on 17.8% of tasks. Give the same model pre-built domain skills and completion jumps from 57.1% to 100%. What that gap says about where the AI-for-science race is actually won.
Anthropic Could Have Shipped a Bigger Model. It Shipped 60 Skills Instead.
Claude Science launched with 60+ curated skills instead of a bigger model. NatureBench, NVIDIA BioNeMo, and Microsoft Talos explain why that’s the smarter bet, and what shipped elsewhere this week.
Introducing the MegaBrain Science blog
We’re launching a blog about AI and science — the work happening at MegaBrain and elsewhere, our collaborations with labs, and practical workflows for scientists.
Coding agents in the social sciences
For the first time, core research tasks can be handed off to machines. What that means for empirical social science — and who is actually using agents so far.