"The End of Prediction" paper uses GPT-6 Astra to argue against its own conclusions
A paper argues that AI buy/sell signals are worthless and that models should instead find anomalies, read filings, and have a heavy reasoning model rebut the result; the author ran the counter-thesis with GPT-6 Astra, cutting cost from $60,000 to $450.
This is f*cking gold.. Chinese researchers just released a paper with a brutal title: "The End of Prediction". The case is simple: an AI that outputs BUY or SELL is finished as a product. Any signal a model can read is priced in before it finishes reading, and active managers already lose to the index over any multi-year window. The question a model answers well is different: what changed, and what is the evidence that it is real rather than noise. So the architecture flips. Plain code finds the anomaly, a million-token model compares eight quarters in one pass, and the heavy reasoning model is spent only on arguing against the result. 10,000 companies a day become 40 deep reads and 8 reasoning passes -- about $450 of inference where a naive full pass costs $60,000. Flagged events hit 37% precision against a 15 to 20% base rate. A real edge, and still nowhere near a forecast. The output has no score and no call. Only what changed, sourced, and what contradicts it. That is exactly the pipeline I built and wrote up below: Robinhood data, Kimi K3 for the reading, GPT-6 Astra for the counter-thesis. The entire paper in one line: you are selling research velocity, not prediction ↓
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