Assemblyman Alex Boris argues against copying California's AI safety bill (SB53). Unlike state-specific data privacy laws, such a bill wouldn't grant new rights to New Yorkers, as any company large enough to be affected in New York is already subject to the California law, making the effort redundant.
Related Insights
When lobbying against New York's RAISE Act for AI safety, the industry's own estimate of the compliance burden was surprisingly low. They calculated that a tech giant like Google or Meta would only need to hire one additional full-time employee, undermining the argument that such regulation would be prohibitively expensive.
The US President's move to centralize AI regulation over individual states is likely a response to lobbying from major tech companies. They need a stable, nationwide framework to protect their massive capital expenditures on data centers. A patchwork of state laws creates uncertainty and the risk of being forced into costly relocations.
When addressing AI's 'black box' problem, lawmaker Alex Boris suggests regulators should bypass the philosophical debate over a model's 'intent.' The focus should be on its observable impact. By setting up tests in controlled environments—like telling an AI it will be shut down—you can discover and mitigate dangerous emergent behaviors before release.
The bill regulates not just models trained with massive compute, but also smaller models trained on the output of larger ones ('knowledge distillation'). This is a key technique Chinese firms use to bypass US export controls on advanced chips, bringing them under the regulatory umbrella.
The idea of individual states creating their own AI regulations is fundamentally flawed. AI operates across state lines, making it a clear case of interstate commerce that demands a unified federal approach. A 50-state regulatory framework would create chaos and hinder the country's ability to compete globally in AI development.
Contrary to its controversial reputation, New York's RAISE Act is narrowly focused on catastrophic risks. The bill's threshold for action is extraordinarily high: an AI must contribute to 100 deaths, $1 billion in damage, or a fully automated crime, far from regulating everyday AI applications.
Laws like California's SB243, allowing lawsuits for "emotional harm" from chatbots, create an impossible compliance maze for startups. This fragmented regulation, while well-intentioned, benefits incumbents who can afford massive legal teams, thus stifling innovation and competition from smaller players.
California's push for aggressive AI regulation is not primarily driven by voter demand. Instead, Sacramento lawmakers see themselves as a de facto national regulator, filling a perceived federal vacuum. They are actively coordinating with the European Union, aiming to set standards for the entire U.S. and control a nascent multi-trillion-dollar industry.
Both Sam Altman and Satya Nadella warn that a patchwork of state-level AI regulations, like Colorado's AI Act, is unmanageable. While behemoths like Microsoft and OpenAI can afford compliance, they argue this approach will crush smaller startups, creating an insurmountable barrier to entry and innovation in the US.
Advocating for a single national AI policy is often a strategic move by tech lobbyists and friendly politicians to preempt and invalidate stricter regulations emerging at the state level. Under the guise of creating a unified standard, this approach effectively ensures the actual policy is weak or non-existent, allowing the industry to operate with minimal oversight.
