Antirez argues that AI-driven cybersecurity is fundamentally different from proof-of-work systems. Unlike hash collision finding where more compute guarantees eventual success, bug discovery with LLMs hits a ceiling determined by model intelligence, not compute volume. Using the OpenBSD SACK bug as a case study, he demonstrates that weaker models hallucinate bug patterns without truly understanding the multi-step reasoning required to identify real vulnerabilities. Stronger models hallucinate less but still can't find the bug without sufficient intelligence. The conclusion: in AI security, model quality wins over raw GPU power.
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