Major AI labs are calling for collective cyber defense against AI threats. However, this is a strategic move where they create a dangerous technology, refuse to pause its development, and then position themselves to sell the solution (defensive AI). This self-serving cycle creates a perpetual market for their products while externalizing the risk.

When a private company creates a "digital skeleton key" capable of compromising critical national infrastructure, it fundamentally alters the balance of power. This moves the policy conversation beyond simple regulation and towards treating AI labs like defense contractors, with some form of government nationalization becoming a plausible endgame.

Leading AI companies, facing high operational costs and a lack of profitability, are turning to lucrative government and military contracts. This provides a stable revenue stream and de-risks their portfolios with government subsidies, despite previous ethical stances against military use.

AI's deep integration with major tech firms means a collapse would devastate the market. This economic risk, combined with the tech race against China, positions major AI players to argue for government bailouts as a matter of national security, regardless of their profitability.

A massive coalition led by NVIDIA argues open-sourcing AI is a net positive for security. They claim widespread access allows everyone to build defensive tools, countering the idea that open models are primarily an offensive threat. The recent hack of Hugging Face is their primary evidence.

Kevin O'Leary argues against taxing AI companies, clarifying they are currently unprofitable and burning through billions in venture capital. Their high valuations are based on a market-funded race for technological supremacy against rivals like China, not on current earnings.

Advanced AI models, like Anthropic's, that can identify deep cybersecurity risks and zero-day exploits transform the need for computing power from a commercial want to a national security imperative. This ensures that demand for compute will be funded regardless of economic conditions.

The U.S. government cannot develop leading AI in-house primarily because it lacks the technical talent. Crucially, it also cannot compete with the massive private capital mobilized for building data centers and training models. The commercial applications are so vast that they dwarf the defense sector's budget and influence.

Top AI companies are creating a "split screen" paradox by signing public letters that warn about the grave cybersecurity dangers of AI while simultaneously racing to develop even more powerful models. This dynamic of publicly acknowledging risk while privately accelerating it undermines the credibility of their commitment to safety.