Empiricists vs. Extrapolators

Samuel Hammond · 18 Feb 2026

Samuel Hammond did not write this page.

Every claim below was made in this piece, quoted word for word and numbered in the order the piece makes them, so you can read it there rather than take our word for it. The sentence above each quote is our reading of the claim, not their wording. Each quote was checked against a stored copy of the page at build time; where the two differ, the quote is the fact.

Our reading
People who extrapolate from first-principles models have a stronger track record predicting AI progress than those who wait for empirical proof.
Mental modelFirst principles

Their wordsExtrapolators have a remarkable track record in the AI field, being repeatedly early to trends and capabilities that empiricists believed were still decades away.
Our reading
Complex systems are usually governed by a small number of stable invariants, which makes long-run outcomes more predictable than short-run details.

Their wordsThe world may be complex, but complex systems are typically controlled by a small number of highly stable invariants.
Our reading
Forecasting AI capability progress is more like solving a thermodynamics equation than predicting chaotic, short-term dynamics.

Their wordsForecasting AI capabilities is closer in kind to solving a thermodynamics equation than solving the three-body problem.

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