Discussion on Hacker News challenges the analogy that AI‑driven security will behave like proof‑of‑work mining. antirez argues that while hash collisions surface the same pattern. LLMs explore code paths until the state space saturates, after which additional tokens no longer increase discovery probability. Teams that rely on brute‑force scanning will find diminishing returns as model intelligence becomes the limiting factor.

The thread cites the OpenBSD SACK vulnerability as a concrete example. Running a weaker model indefinitely failed to expose the chain of errors—missing start‑window validation, an integer overflow, and a null‑node branch—that together trigger the bug. Stronger models, even when large, tend to hallucinate less, so they miss the superficial patterns without grasping the underlying exploit logic. Thus model depth directly influences false‑positive rates.

Consequently, cybersecurity will reward access to more capable LLMs rather than raw GPU throughput. antirez warns against claims that cheap, open‑source models, even a GPT-120B instance, can reliably find complex bugs; they may surface false positives but lack the depth to craft working exploits. The takeaway is investment in model quality and inference speed will dictate advantage. Speedy inference contracts the window for attackers.