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Where biology, computation, markets, and philosophy collide.
The flagship long-form: rigorous essays that borrow from one field to see another more clearly — evolution and strategy, computation and cognition, the philosophy of science and the practice of building. The pieces that only this platform would publish.
MAMMAL posts state-of-the-art on nine benchmarks, but the result that matters is four potency predictions on drugs it never saw, confirmed by a real assay. Here's why that one experiment outweighs the leaderboard.
Companies deploy AI like installing software. The right model is introducing an organism into an ecosystem, and selection pressure predicts the failure modes the ROI math can't see.
The scaling hypothesis is the most successful empirical regularity in the history of machine learning and an explanation of nothing. The industry has bet its capital structure on a line it cannot explain continuing straight.
LLMs model the correlational structure of their training data with astonishing fidelity, but correlation is not causation and fluency is not truth. Knowing where that ceiling sits tells you what to trust them for and what the next paradigm must add.
Science is not hypothesis generation, which is cheap and always was. It is the disciplined killing of hypotheses against reality, plus the taste to pick which are worth testing — and neither is a text problem.
Most growth spikes companies celebrate and slumps they panic over are regression to the mean — statistical gravity, not signal. Mistaking it for causation rewards noise and punishes sense.
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