Former DOJ antitrust chief Jonathan Cantor suggests that AI companies asking to coordinate on safety might be motivated by a desire to slow the cash-burning race to the frontier, allowing them to stabilize their economics before going public.
Jonathan Cantor argues that new AI-specific laws aren't immediately necessary. Companies can already be held responsible for their AI's actions under established product liability principles, just as they are for faulty products or employee misconduct.
The argument that US AI companies need to consolidate or receive special treatment to compete with China is flawed. America's strength lies in its dynamic, competitive markets, not in mimicking China's state-backed national champion model.
The debate over AI and Big Tech power is causing a political realignment, with figures like libertarian David Sachs and progressive Lina Khan finding common ground. This bipartisan concern suggests a fundamental shift in how both parties view tech monopolies.
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.
The chaotic approach to AI regulation reflects a larger systemic breakdown. The established New Deal framework—creating agencies to promulgate rules—is defunct, replaced by an ad-hoc executive branch and an ineffective Congress, leaving major policy questions in a "jump ball."
The true promise of antitrust in tech is to safeguard moments of disruptive change. The focus should be on preventing today's giants from squashing the next generation of innovators, ensuring competition has its greatest chance at these inflection points.
Jonathan Cantor reveals that a key source for antitrust investigations isn't just the public or government. It's other tech companies, who secretly help regulators build cases against monopolists because they want an opportunity to compete on the merits.