Drawing on work at Endura Therapeutics, Adrian Sanborn divides AI's impact on science into two categories: Foundries use next-generation sequencing, high-throughput microscopy and physical automation to reduce experimental measurement costs by an order of magnitude, while Navigators apply cheaper thinking to decisions and processes.
Radical Numerics co-founder and CEO Eric Nguyen argues that models enhancing biological capabilities can also support defence and advocates more aggressive frontier research. His team's Evo and Evo 2 genomic language models were used by Arc/Stanford researchers to generate complete phage genomes and synthesise functional viruses.
Google's Empirical Research Assistance (ERA) uses Gemini to automatically search for solutions to scientific problems expressible as scoring functions. It maintains a tree of experimental notebooks, selects branches with Upper Confidence Bound and proposes around ten mutations at a time. Between Gemini 2.0 and 2.5, it went from unusable to highly effective.
MIT Technology Review and Times of San Diego spent 15 months creating the first comprehensive map and analysis of deaths near US border surveillance towers, examining migrant deaths since 2015. They requested records from 17 Texas county sheriff's offices and obtained over 4,000 pages from 14 counties, used Anthropic's Claude API to extract coordinates where remains were found, then manually checked samples.
Hugging Face published its open-source model observatory report for January–August 2026. Hub data shows Chinese labs released the largest open-source models by parameter count in most months, with Chinese monthly peaks between 754 billion and 2.78 trillion parameters, while US models stayed below 130 billion in five of seven months.
Hugging Face's blog argues AI's next real constraint is GPU utilisation, rather than intelligence. GPUs are billed by calendar hours but produce only in compute hours, mirroring the cost structure of grounded aircraft.
As inference costs fell from around $30 per million tokens for GPT-4-class models in early 2023 to under $1 today, and below $0.10 at some providers, UC Berkeley's Aditya G. Parameswaran and colleagues proposed three directions for data systems in the agent era: For Agents, Of Agents and By Agents.
Jack Clark estimates in Import AI 455 a roughly 60% chance of AI R&D autonomously training successor models without human involvement by the end of 2028, and around 30% in 2027.