The Scientific Challenge Atlas

Overview

A mixed landscape, and no map of it

A basic difficulty is that the landscape is mixed. Some scientific advances may be reachable by a sufficiently strong intelligence working from humanity’s current scientific record, public data, and executable computation. Others still require gathering new information about the world through new experiments or observations. We do not yet have a map of which advances fall into which category.

The distinction

Two categories of challenge

It is worth pausing on the significance of that distinction.

The registry

What the atlas would do

The Scientific Challenge Atlas would provide a map of that class. It would be a public registry of important open scientific questions in the intelligence-reachable part of the landscape. Some entries may be solved by exhaustive synthesis. Others may require a new proof, model comparison, bound, or conceptual reframing. The common feature is that the needed information about the world is already available.

The atlas would:

1. Show the span of meaningful scientific advances that strong AI could plausibly make without first waiting for new experiments or observations.
2. Identify challenges whose solution may now be limited mainly by compute, search, and verification.
3. Estimate the rough reasoning, search, coding, and compute burden required for each advance.
4. Provide a public, disciplined way to evaluate the significance of AI for meaningful scientific advances.

The findings would inform important decisions, including how strongly nations should support the use of frontier AI for advances that would improve the prosperity and security of their people.

Precedent

What the Erdős problems showed

This idea is inspired by the role that Erdős problems have played in mathematics. Paul Erdős posed many important problems in compact form. Thomas Bloom’s Erdős Problems database now lists 1,217 problems, with 555 marked solved as of July 24, 2026.

Recent AI efforts have shown why this matters. On May 21, 2026, OpenAI announced an AI-assisted breakthrough on Erdős’s planar unit distance problem. Another large-scale formal-proof effort resolved 9 of 353 open Erdős problems at a cost of a few hundred dollars per problem. The broader lesson is that a good public problem list creates a focal point for the community to attack challenges. Science needs an analogous map.

Criteria for entry

The Form

A useful feature of trying AI on the Erdős problems is that it does not require resolving difficult debates about topics like the nature of knowledge or the character of human creativity. They provide the question, the answer shape, and the verification criterion (a sound mathematical proof).

The atlas aims for the same simplicity. Problems that cannot be stated with those criteria, for example because there is no plausible verification path, are excluded from the atlas.