Anthropic on letting AI build scientific models

Summary

An Anthropic research essay breaks scientific modeling into three steps—building the model, constraining its parameters to data, and interpreting it—and describes AI's role in each. Over the past two years, systems have appeared where LLM-based agents write candidate model code, run it, compare outputs with real data, and revise, taking over design work once thought to be human-only. The central argument is that many models can fit the same data, so researchers should ask for the full distribution of plausible models rather than a single best fit. The authors also state the limitation that such systems have mostly been tested on rediscovering known models, with sparse evidence on open problems.

Why it matters for our work

The essay draws a sharp line between AI producing an answer and producing understanding, and offers a practical workflow: compare surviving models to see which parts of a system are pinned down by data and where the next experiment should aim.

Translated from the Korean original. Summaries may be translated and edited. Commentary reflects our perspective; forecasts remain the source’s views.