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Slowing public releases of AI models for government review may not slow overall progress. This creates a scenario where labs advance internally for months, giving government agencies exclusive access while delaying public commercialization and the next cycle of investment.

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The exaggerated fear of AI annihilation, while dismissed by practitioners, has shaped US policy. This risk-averse climate discourages domestic open-source model releases, creating a vacuum that more permissive nations are filling and leading to a strategic dependency on their models.

While commendable, an AI company's refusal to sell models for controversial uses like mass surveillance is a temporary solution. Technology diffusion is so rapid that within 12-18 months, open-source models will match today's frontier capabilities. A government seeking these tools can simply wait and use a widely available open-source alternative, making individual corporate 'red lines' ultimately ineffective.

When companies like OpenAI and Anthropic pull products due to risk, it's a clear signal that they are unable to self-govern. This action is interpreted as a plea for government oversight, as relying on the social conscience of a few CEOs is an unsustainable model.

A pause on training new, more capable AI models could paradoxically increase risk. It would halt progress at the few, relatively safety-conscious frontier labs, allowing less scrupulous competitors to catch up. Meanwhile, compute stockpiling would continue, making any subsequent capability leap even faster and more dangerous.

Facing growing moral panic, the AI industry's plan appears to be moving so fast that regulation becomes impossible. By building data centers and deploying models at breakneck speed, companies aim to make their technology ubiquitous before any effective policy can form.

The decision to restrict powerful but dangerous AI models like Claude Mythos to a select group of large corporations for safety reasons risks creating a massive centralization of power. This gives these entities an insurmountable technological advantage over smaller players and the public.

Leaders at top AI labs publicly state that the pace of AI development is reckless. However, they feel unable to slow down due to a classic game theory dilemma: if one lab pauses for safety, others will race ahead, leaving the cautious player behind.

Governments face a difficult choice with AI regulation. Those that impose strict safety measures risk falling behind nations with a laissez-faire approach. This creates a global race condition where the fear of being outcompeted may discourage necessary safeguards, even when the risks are known.

AI is the first revolutionary technology in a century not originating from government-funded defense projects. This shift means policymakers lack the built-in knowledge and control they had with nuclear or space tech, forcing them to learn from and regulate an industry they did not create.

Drawing a parallel to Intel's early strategy, the immense capital costs of AI development necessitate serving the largest possible market (consumers and businesses). This private, market-driven approach inherently conflicts with government expectations for control, as the government becomes just one of many customers for a globally-scaled technology.