AI will find vulnerabilities at an unprecedented rate. The real crisis will be the organizational inability to patch them, especially in critical infrastructure with long update cycles and unsupported software where original developers are long gone. The problem shifts from finding flaws to fixing them at scale.

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.

AI models have solved vulnerability discovery so effectively they've exposed a new, larger bottleneck: remediation. With projects like Glasswing reporting a 10-to-1 ratio of bugs found to bugs fixed, the industry's challenge has rapidly shifted from finding flaws to having the human capacity to patch an overwhelming number of them.

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.

The massive increase in AI-generated code is simultaneously creating more software dependencies and vulnerabilities. This dynamic, described as 'more code, more problems,' significantly expands the attack surface for bad actors and creates new challenges for software supply chain security.

The emergence of AI that can easily expose software vulnerabilities may end the era of rapid, security-last development ('vibe coding'). Companies will be forced to shift resources, potentially spending over 50% of their token budgets on hardening systems before shipping products.

The plummeting cost of finding exploits via AI models means enterprises cannot simply patch vulnerabilities reactively. The necessary strategic shift is to build foundational security controls for each asset class, including a new, dedicated security layer specifically for the AI stack.

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.

Advanced AI models capable of finding complex code vulnerabilities are expected to be publicly available within months. This puts enterprises in an urgent race to find and patch their own security holes before malicious actors use the very same tools to exploit them.