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conflict of interest\n  Verdict: fails to establish | still open — needs track record of calibrated forecasts, not a single seat\n  Repair: aggregate multiple independent forecasters; score them against past calibration; treat timelines as probability distributions\n\nArgument B: AI → doom or utopia / one is implausible / therefore the other\n  Structural: —\n  Linguistic: vague terms make the dilemma feel exhaustive\n  Cognitive: —\n  Rhetorical: false dichotomy — ignores the large middle of outcomes\n  Verdict: fails to establish | still open — enumerate the full outcome space, assign probabilities to each\n  Repair: replace binary with a distribution over scenarios; argue each on its own evidence\n\nArgument C: one viral demo of task X / therefore reliable at X\n  Structural: hasty generalization from a filtered sample of one\n  Linguistic: —\n  Cognitive: availability heuristic — vivid demo feels typical\n  Rhetorical: —\n  Verdict: fails to establish | still open — needs pass rate over many unfiltered trials\n  Repair: measure success rate on a held-out, contamination-checked test set; report variance and failure modes\n\nArgument D: if intelligent then passes B / passed B / therefore intelligent\n  Structural: affirming the consequent (invalid form)\n  Linguistic: equivocation on \"intelligent\" (rich sense vs. thin task sense)\n  Cognitive: —\n  Rhetorical: —\n  Verdict: fails to establish | still open — define \"intelligent\" operationally, test generalization beyond B, rule out benchmark contamination\n  Repair: use held-out tasks the model wasn't optimized for; check for train/test leakage; specify which capability the benchmark actually measures\n\nFallacy-fallacy check: no conclusion is asserted"}]}}