Very nice. OpenAI's field report says AI coding agents are helping scientists fix old research software， speed up slow programs， and build tools that previously took too much time.
One project cut an RNA-seq task， a common way of measuring which genes are active， from about 15 hours to about 15 minutes.
Another updated MHCflurry， a tool used in immune-system research， and cut one training job from about a week to about an hour while retaining 99.9% accuracy.
Scientists still had to decide what the software should do and check whether the results were correct， because the agents sometimes sounded confident even after making clear mistakes.
Scientists use this software to study huge amounts of data， including DNA and gene activity， but much of it is old， slow， and difficult to maintain.
The 8 projects were mostly in life sciences， with 5 using Codex alone and 3 combining Codex with Claude Code.
However， the report found that agents could declare success while hiding subtle numerical or logical defects， especially in broader rewrites without exact reference outputs.
Researchers therefore had to define acceptance criteria， compare against existing tools or simulated data with known answers， and investigate the last-mile discrepancies.
The ultimate gain is less routine implementation work for scientists， but adoption will depend on verification systems and maintainers that remain accountable after release.