AI labs may initially conceal a model's "chain of thought" for safety. However, when competitors reveal this internal reasoning and users prefer it, market dynamics force others to follow suit, demonstrating how competition can compel companies to abandon safety measures for a competitive edge.

The justification for accelerating AI development to beat China is logically flawed. It assumes the victor wields a controllable tool. In reality, both nations are racing to build the same uncontrollable AI, making the race itself, not the competitor, the primary existential threat.

The primary danger in AI safety is not a lack of theoretical solutions but the tendency for developers to implement defenses on a "just-in-time" basis. This leads to cutting corners and implementation errors, analogous to how strong cryptography is often defeated by sloppy code, not broken algorithms.

Many top AI CEOs openly admit the extinction-level risks of their work, with some estimating a 25% chance. However, they feel powerless to stop the race. If a CEO paused for safety, investors would simply replace them with someone willing to push forward, creating a systemic trap where everyone sees the danger but no one can afford to hit the brakes.

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.

A fundamental tension within OpenAI's board was the catch-22 of safety. While some advocated for slowing down, others argued that being too cautious would allow a less scrupulous competitor to achieve AGI first, creating an even greater safety risk for humanity. This paradox fueled internal conflict and justified a rapid development pace.

AI companies engage in "safety revisionism," shifting the definition from preventing tangible harm to abstract concepts like "alignment" or future "existential risks." This tactic allows their inherently inaccurate models to bypass the traditional, rigorous safety standards required for defense and other critical systems.

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.

The current approach to AI safety involves identifying and patching specific failure modes (e.g., hallucinations, deception) as they emerge. This "leak by leak" approach fails to address the fundamental system dynamics, allowing overall pressure and risk to build continuously, leading to increasingly severe and sophisticated failures.