A key, informal safety layer against AI doom is the institutional self-preservation of the developers themselves. It's argued that labs like OpenAI or Google would not knowingly release a model they believed posed a genuine threat of overthrowing the government, opting instead to halt deployment and alert authorities.

Countering calls for government-mandated slowdowns, Zuckerberg's position is that AI labs already face sufficient incentives to ensure safety. He argues that model safety is a product feature that is economically rewarded, and that the reputational and financial consequences of releasing an unsafe model are enough to enforce responsible pacing by individual companies.

Security leaders don't wait for government mandates; they adopt market-driven standards like SOC 2 to protect their business and customers. AI governance is following a similar path, with companies establishing robust practices out of necessity, not just for compliance.

Despite intense commercial pressure to be first to market, pharmaceutical companies adhere to strict, self-regulated safety protocols. This model of industry-wide cooperation to ensure public trust and avoid catastrophic failure provides a hopeful analogy for how competing AI labs could collectively enforce safety standards.

Vance argues that AI companies creating potentially dangerous models have a responsibility to build and release defensive countermeasures. He views their calls for government regulation as an attempt to shirk this responsibility, rather than a genuine safety effort.

The existence of internal teams like Anthropic's "Societal Impacts Team" serves a dual purpose. Beyond their stated mission, they function as a strategic tool for AI companies to demonstrate self-regulation, thereby creating a political argument that stringent government oversight is unnecessary.

The tech industry is backing a self-regulatory body (SRO) to pre-empt a government agency that could take 5-9 years to approve new AI models. This proactive step aims to prevent a bureaucratic slowdown that would cede the US's innovation speed advantage to competitors like China.

Unlike past tech waves where companies resisted government oversight, today's AI leaders are actively inviting it. This is a strategic move to shape regulations in their favor, creating barriers to entry for smaller players and open-source competitors under the guise of safety and responsibility.

The need for AI safety shouldn't be seen as a roadblock to progress. Instead, it's an innovation challenge. Companies should be incentivized to engineer safer products from the outset, which will ultimately lead to better technology.