The End-State Fallacy: Where Is AI Security Headed?

Irregular researchers argue that while AI security's long-term equilibrium may be defense-dominant (with AI continuously auditing and patching vulnerabilities), the transition period will sharply favor offense—a dynamic they term the "end-state fallacy." 

Drawing on their work with frontier labs, they document rapid capability gains: a custom CPU exploit task went from unsolvable in February 2026 to reliably solvable for ~$20 by June, and open-weight models are now only months behind frontier cyber capabilities. 

They frame offensive progress across three dimensions (range, complexity, orchestration), noting costs are falling ~10x annually and diffusion is accelerating—with frontier-grade offensive capabilities expected to proliferate by Q1 2027. 

The essay identifies structural asymmetries favoring offense: collapsing exploit-to-patch windows, defender accountability tax, AI's dual-use nature, and the difficulty of verifying fixes versus exploits. 

They propose a strategy of "differential defensive cyber acceleration" (DDCA): measuring the field, building capabilities that disproportionately help defenders (e.g., remediation, incident response), and managing offensive diffusion to buy time—concluding that the path matters more than the destination, and proactive intervention is urgently needed before defenders become overwhelmed. 

https://endstatefallacy.com/

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