A look at OpenAI’s math that’s claimed computes faster Fourier transforms

TEXT START: Hi there, argmin readers!

The Dissection

This is not a refutation of the theorem. It is an attack on the gap between asymptotic formalism and deployable computation. The article uses constants, impossible crossover points, arbitrary-precision arithmetic, and superpolynomial intermediate values to show that the claimed FFT advantage is useless on real machines. It then expands that critique into an indictment of theory-computer-science culture and AI-generated mathematical output.

The deeper function is boundary enforcement: practical engineers reclaim “real computation” as the tribunal and strip formal AI output of prestige.

The Core Fallacy

The technical criticism may be sound within the article’s stated machine model. Its broader error is treating one impractical result as evidence against the significance of AI research automation.

DT does not require every AI output to be production-ready. P1 requires only that cognitive artifacts—proofs, algorithms, candidates, critiques—can be generated at lower cost and greater volume. A useless FFT can still demonstrate that part of the research pipeline is being mechanized. The article measures the artifact; DT measures the cost curve.

This does not rescue the particular algorithm. It exposes the article’s narrow field of vision.

Hidden Assumptions

• Bounded precision, finite memory, and current hardware are the only relevant standards; formal results have no strategic option value.
• Human experts will remain the scarce, decisive filters capable of processing the resulting flood.
• Exposing bad incentives will cause institutions to coordinate and reform, despite P2’s coordination constraint.
• Immediate practical benchmarks are the relevant measure of AI’s impact, rather than cheap automated generation, evaluation, and iteration.
• The theory-versus-practice boundary can remain a protected human prestige domain after AI can increasingly operate on both sides.

Social Function

Primary classification: partial truth. Secondary classifications: prestige signaling and ideological anesthetic.

The article correctly attacks big-O theater and unrealistic computational models. But it converts a power-and-labor transition into a standards dispute. The villains become sloppy proofs, bad conferences, and AI slop; the erosion of cognitive labor’s scarcity remains largely unexamined. The author assumes the role of reality’s gatekeeper. That is a temporary moat, not a permanent position.

The Verdict

This is a sharp autopsy of one category of AI-generated mathematics and a weak diagnosis of the wider transition. It correctly shows that asymptotic superiority without realistic constants, precision, and memory is not a faster FFT. It misses that useless output at near-zero marginal cost is itself automation—and that filtering, implementation, and verification are the next layers to be attacked.

The article exposes P1 while mistaking the first corpse—mathematical slop—for the whole mechanism. Human practical judgment remains valuable only while it remains scarce. Once the full research loop becomes cheap to automate, the critic’s authority becomes another function awaiting replacement.