Developer finds AI flags real earnings as fake due to outdated training data

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A software developer building an algorithmic trading system discovered that an AI reviewing financial reports incorrectly flagged a company's genuine earnings as 'contaminated data,' because the figures exceeded anything in the model's training history. A post-boom surge in the relevant industry had produced figures that would have seemed impossible by historical standards, but the AI had no knowledge of events after its training cutoff. The developer also uncovered a separate bug where financial data was being pulled under the correct ticker but labeled with a different company's name, due to independent data-lookup and naming processes failing silently on domestic ticker formats. Upgrading to a more capable language model improved reasoning quality on clean inputs, but made errors more dangerous when fed flawed data, as the smarter model produced confident, well-argued but incorrect verdicts. The developer fixed the naming bug before deploying the new model and added a safeguard that defaults to a neutral verdict whenever a company name cannot be resolved but an extreme conclusion is reached.

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