MIT Technology Review

In the final weeks of 2025, the battle over regulating artificial intelligence (AI) in the US reached a boiling point. On December 11, after Congress failed twice to pass a law banning state AI laws, President Donald Trump signed a sweeping executive order seeking to handcuff states from regulating the booming industry.

Governments plan to pour $1.3 trillion into artificial intelligence (AI) infrastructure by 2030 to invest in “sovereign AI,” with the premise being that countries should be in control of their own AI capabilities. The funds include financing for domestic data centers, locally trained models, independent supply chains, and national talent pipelines. This is a response to real shocks: COVID-era supply chain breakdowns, rising geopolitical tensions, and the war in Ukraine. But the pursuit of absolute autonomy is running into reality.

An artificial intelligence (AI) large language model’s (LLM) parameters are often said to be the dials and levers that control how it behaves. Think of a planet-size pinball machine that sends its balls pinging from one end to the other via billions of paddles and bumpers set just so. Tweak those settings and the balls will behave in a different way. OpenAI’s GPT-3, released in 2020, had 175 billion parameters. Google DeepMind’s latest LLM, Gemini 3, may have at least a trillion—some think it’s probably more like 7 trillion—but the company isn’t saying.

When you think of Big Tech these days, Yahoo is probably not top of mind. But for a 62-year-old Chinese dissident named Xu Wanping, the company still looms large—and has for nearly two decades. In 2005, Xu was arrested for signing online petitions relating to anti-Japanese protests. He didn’t use his real name, but he did use his Yahoo email address. At the time, Yahoo was among Silicon Valley’s largest and most influential technology companies. And Yahoo China, its Chinese subsidiary, was busy tapping into the country’s nascent but potentially enormous internet market.