Karl Popper argued in 1959 that the demarcation between science and non-science is falsifiability. A theory is scientific if and only if it makes predictions that could, in principle, be proven wrong. A theory that accommodates every possible outcome, that is revised after the fact to explain whatever happened, is not a theory at all. It is a narrative.¹

Most prediction systems violate this principle. An analyst who says "I think oil will go up because of geopolitical tensions" has not made a falsifiable prediction. There is no specified price level, no time horizon, no mechanism by which the claim would be proven wrong. If oil goes up, the analyst claims credit. If oil goes down, the analyst claims "the market hasn't realized it yet." The prediction is irrefutable, which means it is worthless.

The falsifier architecture. Adversarial falsification inverts the standard approach. For every prediction the system generates, it also generates three to five specific conditions that would kill the prediction. These are not vague reservations. They are specific, observable, time-bounded claims:

"If Kazakhstan announces production exceeding 65 Mlbs for 2026, the enrichment bottleneck prediction weakens because primary supply is higher than modeled."

"If TENEX offers new enrichment contracts to Western utilities, the effective Western enrichment capacity is higher than the graph assumes."

"If three or more new ISR mines enter production before 2028, the depletion rate model is too pessimistic."

Each falsifier is connected to the fact pipeline. When a new fact arrives, news article, SEC filing, government report, the system checks whether it matches any active falsifier's keywords. If a match is found, a quick automated check determines whether the falsifier has been triggered. If two or more falsifiers on the same prediction trigger, the prediction is automatically resolved as incorrect.²

This architecture makes the prediction system anti-fragile. Every failed prediction improves the model because the falsifier identifies which specific assumption was wrong. The enrichment edge coefficient was too low. The depletion rate was too aggressive. The delay function for mine development was too long. Each correction makes the next simulation more accurate. The system does not learn from being right. It learns from being wrong in specific, diagnosed ways.

The practice of adversarial falsification also combats confirmation bias at the architectural level. A human analyst naturally seeks evidence that confirms their thesis. A system that generates its own kill conditions and actively watches for disconfirming evidence has no such bias. It is trying to prove itself wrong with the same vigor that it tries to prove itself right. This is the computational implementation of what Tetlock identified as the strongest predictor of superforecasting accuracy: intellectual humility combined with relentless self-correction.