The emphasis on long-term, unprovable risks like AI superintelligence is a strategic diversion. It shifts regulatory and safety efforts away from addressing tangible, immediate problems like model inaccuracy and security vulnerabilities, effectively resulting in a lack of meaningful oversight today.

Rabois dismisses the AI safety movement by arguing it's driven by a cohort of thinkers who have a long track record of being incorrect on major societal issues like the environment. He sees it as a predictable pattern of using fear to enable bureaucratic interference with progress.

Prominent investors like David Sacks and Marc Andreessen claim that Anthropic employs a sophisticated strategy of fear-mongering about AI risks to encourage regulations. They argue this approach aims to create barriers for smaller startups, effectively solidifying the market position of incumbents under the guise of safety.

In China, academics have significant influence on policymaking, partly due to a cultural tradition that highly values scholars. Experts deeply concerned about existential AI risks have briefed the highest levels of government, suggesting that policy may be less susceptible to capture by commercial tech interests compared to the West.

The rhetoric around AI's existential risks is framed as a competitive tactic. Some labs used these narratives to scare investors, regulators, and potential competitors away, effectively 'pulling up the ladder' to cement their market lead under the guise of safety.

AI companies minimizing existential risk mirrors historical examples like the tobacco and leaded gasoline industries. Immense, long-term public harm was knowingly caused for comparatively small corporate gains, enabled by powerful self-deception and rationalization.

AI can produce scientific claims and codebases thousands of times faster than humans. However, the meticulous work of validating these outputs remains a human task. This growing gap between generation and verification could create a backlog of unproven ideas, slowing true scientific advancement.

A key feature making economics research robust is its structure. Authors not only present their thesis and evidence but also anticipate and systematically discredit competing theories for the same outcome. This intellectual honesty is a model other social sciences could adopt to improve credibility.

Other scientific fields operate under a "precautionary principle," avoiding experiments with even a small chance of catastrophic outcomes (e.g., creating dangerous new lifeforms). The AI industry, however, proceeds with what Bengio calls "crazy risks," ignoring this fundamental safety doctrine.