Legislators are crafting AI regulations based on the narrow, outdated use case of chatbots (e.g., protecting kids from predators). This misses the far more significant paradigm of locally-hosted, open-source AI agents. The current policy debate is fighting the last war and risks creating irrelevant or harmful laws.

A Chinese government policy banning after-school human tutors, intended to reduce academic pressure, had an unintended consequence: it created a market vacuum filled by AI tutors. This regulatory action unintentionally accelerated a large-scale societal experiment in AI-driven education, far outpacing adoption in the West.

By applying export controls—a tool for military hardware—to a consumer-facing AI model, the government set a new, unpredictable standard. This blunt instrument makes any AI company vulnerable to having its products instantly restricted based on political whims rather than a clear regulatory process, spooking the entire industry.

China employs a dual strategy for AI. Domestically, its Cyberspace Administration rigorously penalizes unlabeled deepfakes to maintain social control. Abroad, its companies like ByteDance face no such constraints, allowing them to use foreign IP freely and creating a significant regulatory arbitrage advantage over Western competitors.

For companies like ByteDance, the primary obstacle in launching new AI models globally isn't simply blocking copyrighted content, but implementing guardrails that are refined enough not to reject legitimate, unrelated prompts. This highlights a difficult engineering problem: ensuring safety and compliance without frustrating users and limiting the model's utility.

The EU's AI Act has been so restrictive that it has largely killed native AI development in Europe. The regulation is so punitive that even major American companies like Apple and Meta are choosing not to launch their leading-edge AI capabilities there, demonstrating the chilling effect of preemptive, overbearing regulation.

Overly-specific regulation focused on AI tools (e.g., model size) risks accidentally stifling valuable, unforeseen use cases. A better policy focuses on outcomes. For example, prosecute fraud committed with an LLM, but don't regulate the LLM itself, thereby protecting innovation while punishing misuse.

Undersecretary Rogers warns against "safetyist" regulatory models for AI. She argues that attempting to code models to never produce offensive or edgy content fetters them, reduces their creative and useful capacity, and ultimately makes them less competitive globally, particularly against China.

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.