Because software code is a language, LLMs are becoming superhuman coders. This makes them incredibly effective at finding system vulnerabilities for hacking (offense). However, this exact same capability makes them equally powerful for identifying and fixing those flaws (defense), leading to a rapid escalation in cybersecurity.

The core open-source belief that enough human experts will find all bugs is invalidated by AI discovering decades-old vulnerabilities in widely scrutinized code. This proves that high-level machine analysis is now essential for security, as human review alone is insufficient.

Advanced AI cyber tools like Anthropic's Mythos don't create new vulnerabilities; they excel at discovering existing, dormant bugs in human-written code. Their proliferation will catalyze a one-time, industry-wide upgrade cycle, ultimately hardening global infrastructure and leading to a more secure equilibrium between AI-powered offense and defense.

The shift to machine-versus-machine cyber warfare renders all human-written legacy software fundamentally insecure. This will trigger a global imperative to rewrite the world's operational software, not just for efficiency but for survival, with machines doing most of the coding to create impregnable systems.

AI has armed cyber attackers with a new weapon: swarms of coding agents. Unlike human attackers, these agents can exhaustively and rapidly review an entire codebase to find vulnerabilities, dramatically increasing the speed and scale of cyber threats. This necessitates a boom in AI-powered defensive tools.

A former OpenAI security expert argues that even if AI makes codebases more secure, hacking won't become harder. Attackers exploit the entire system—runtime behavior, configurations, authentication—not just static code. Looking only at code is like seeing a dinosaur's bones; you miss the muscles, feathers, and behavior that define the real-world attack surface.

For decades, software has contained vulnerabilities manageable only due to a limited number of human attackers. AI allows any individual to spin up hundreds of qualified "attackers" instantly, creating a massive force that will systematically exploit this historical security debt, leading to widespread chaos.

AI models are better at finding bad code than writing good code. This capability will rapidly uncover vulnerabilities in open-source, custom, and vendor software that would have otherwise taken 10 years to find. This creates an urgent, large-scale need for patching across all industries.

The traditional cybersecurity model of humans finding and patching vulnerabilities cannot keep pace with AI that discovers thousands of exploits in hours. This fundamental mismatch in speed and scale will require a complete overhaul of how software security is managed.