The same AI technology amplifying cyber threats can also generate highly secure, formally verified code. This presents a historic opportunity for a society-wide effort to replace vulnerable legacy software in critical infrastructure, leading to a durable reduction in cyber risk. The main challenge is creating the motivation for this massive undertaking.

Anthropic's new AI model, Mythos, is so effective at finding and chaining software exploits that it's being treated as a cyberweapon. Its public release is being withheld; instead, it's being used defensively with select partners to harden critical digital infrastructure, signifying a major shift in AI deployment strategy.

Anthropic's new AI, Claude Mythos, can find software vulnerabilities better than all but the most elite human hackers. This technology effectively gives previously unsophisticated actors the cyber capabilities of a nation-state, posing a significant national security risk.

The market panic selling cybersecurity stocks post-Anthropic's leak is illogical. The coming "agentic era"—with AI rapidly building and deploying code—will create an explosion of new security threats. This represents a golden age for cybersecurity companies, not a threat to their existence.

The long-term trajectory for AI in cybersecurity might heavily favor defenders. If AI-powered vulnerability scanners become powerful enough to be integrated into coding environments, they could prevent insecure code from ever being deployed, creating a "defense-dominant" world.

Generative AI's positive impact on cybersecurity spending stems from three distinct drivers: it massively expands the digital "surface area" needing protection (more code, more agents), it elevates the threat environment by empowering adversaries, and it introduces new data governance and regulatory challenges.

AI development is not just a commercial trend but a military arms race akin to the Cold War. National security imperatives will drive massive energy consumption for AI, overriding economic or public concerns about rising energy costs and creating an inevitable energy crisis.

Geopolitical competition with China has forced the U.S. government to treat AI development as a national security priority, similar to the Manhattan Project. This means the massive AI CapEx buildout will be implicitly backstopped to prevent an economic downturn, effectively turning the sector into a regulated utility.

The 2020 research formalizing AI's "scaling laws" was the key turning point for policymakers. It provided mathematical proof that AI capabilities scaled predictably with computing power, solidifying the conviction that compute, not data, was the critical resource to control in U.S.-China competition.