Intelligent Machines (Audio) IM 865: Mythic - Too Dangerous to Release?
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Apr 9, 2026 Daniel Meissler, security expert and creator of Unsupervised Learning, weighs in on Anthropic’s Mythos and its surprising ability to find long-missed zero-days. He discusses how such capabilities can leak and spread, the tug-of-war between concentrated control and wide release, and what rapid capability jumps mean for security, work, and governance.
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Mythos Is A General Leap Not A Niche Tool
- Mythos isn't narrowly trained for security; it's a generally stronger model that excels at many knowledge-work tasks, so security gains are a side effect of overall capability.
- Daniel Meissler observed Anthropic tested Mythos on code and exploit-finding but emphasized its broad improvement over Opus, not special cybersecurity training.
AI Advances Spread Fast Through Small Tricks
- Powerful model improvements tend to diffuse rapidly across the field through small reproducible tweaks and shared practices, not just secret breakthroughs.
- Meissler compared it to many labs discovering the same calculus-like advances or swapping tiny hyperparameter tricks that accumulate into a big lead.
Harden Critical Infrastructure Before AI Floods Attacks
- Prepare for an insecure transition: expect many compromises before infrastructure hardens under constant automated attacks.
- Meissler warns critical systems (power, water, desalinization) risk disruption during the shift from occasional to near-constant exploitation.