We show that powerful autonomous learning does not ensure trading dominance in competitive markets, even when humans hold systematically biased beliefs. We characterize how information structure and market sophistication bound reinforcement-learning traders’ performance relative to humans. Humans receive private signals, potentially including soft information unavailable to algorithms, and differ in their ability to extract information from prices and anticipate others’ trading, as captured by level-k reasoning. AI traders learn demand schedules from prices and realized profits and may also receive private signals. Despite heterogeneous initial conditions and persistent differences in learning parameters, all AI traders converge to a common probabilistic trading policy, generating algorithmic herding, which amplifies the equilibrium-price response to aggregate AI demand and compresses profits. Without an informational advantage, AI is outperformed by the most sophisticated humans because it learns from market data generated by average, rather than frontier, human sophistication. Even the least sophisticated humans can outperform AI when their private information is either very precise or very imprecise. AI sophistication rises with that of the humans who compete with and train it.