I think the most important AI question is, at some level, how do you deploy it so that it is a genuinely positive force across a wide spectrum of people.
I like to tell a story to describe Why Are Things This Way, for some wide hand wave of the world right now and it goes like this: The post-Cold War era marked a renaissance in global trade, what some people call the Pax Americana. This period of globalization rested on two American pillars and one Chinese: the U.S. dollar’s status as the world’s reserve currency, the U.S. Navy’s command of maritime shipping lanes and the rapid development of highly scaled manufacturing.
Underpinning this system was a constellation of technologies: containerization1, ERP systems, advanced telecommunications, the financialization of assets, and cheap energy. China’s accession to the WTO in 2001 was the culmination of its Reform and Opening policy, with leaders like Hu Jintao embodying a sense of forward momentum. We were at the apex of Francis Fukuyama’s “end of history”: the belief that liberal democratic capitalism represented the final stage of human political evolution.
This felt like a rising tide that might, for once, actually lift all boats. Growth did materialize. We witnessed a substantial economic expansion that lifted millions out of poverty, most dramatically in China but also across dozens of countries where GDP and living standards surged.
It was easy to look at this trend line and extrapolate upwards. The most common objection to that extrapolation was that it relied on non-renewable, extractive energy and materials 2. But I think this argument was a mistake on both sides: globalization represented a step change: a one-time shift enabled by a unique convergence of technologies that amplified the principles of specialization and trade to an unprecedented scale.
These technologies were big leaps, but their diffusion unfolded gradually, over decades. This extended rollout created an illusion of continuous growth. As Jeffrey Ding argues in his excellent 2024 book Technology and the Rise of Great Powers, the critical factor is not which nation invents technology first, but which spreads it through their economy faster. The same principle applies globally: diffusion creates the feeling of growth, but its just the future being unevenly distributed. We thought the end of the Cold War ended history, but in reality it just gave us a really good logistics stack.
The Great Financial Crisis of 2008 was the first major crack, exposing a disconnect between elite consensus and public experience. Contagion from the U.S. subprime mortgage market rippled worldwide, shattering faith in both institutions and experts. Austerity measures inflicted deep pain on the median voter, while ZIRP boosted GDP figures and asset valuations, widening the gap between elite enrichment and broad-based prosperity. COVID-19 extinguished any lingering illusion of elite competence. Chains collapsed across critical sectors from masks to electronics to, oddly, toilet paper.
Today, in the post-pandemic, post-austerity landscape, we’ve seen a decisive shift toward realpolitik and narrow, short-term domestic political calculation: people are less trusting of “the system” and more receptive to those actively disrupting it.
If you ask a random person in Hayes Valley they’ll say that AI is a similar step change, maybe even larger: it could lead to flourishing prosperity or possibly doom everyone to being consumed by a rogue instance of Claude obsessed with the Golden Gate bridge. Unlike the internet or mobile phones AI is emerging in a volatile, multilateral world with a broken trust environment.
AI needs vast resources: data, compute, electricity, technical skills, integration and political support. The US and China have adopted somewhat divergent approaches to how to manage that.
The U.S. model emphasizes corporate AI accountability and regulation that favors large incumbents, restriction over compute resources through export controls, and voluntary safety frameworks developed largely by industry. In essence, they are asking the public to trust corporate institutions to manage AI safely, and to deliver the long-term societal benefits to consumers. It’s downstream of the way the tech giants like Microsoft, Google and Apple have navigated government before: hands off during rapid growth, then clear regulations to offer a stable business environment.
China’s Internet companies are under no question of who is in charge, particularly after the crackdowns on gaming and social media a few years back. The Chinese Communist Party is caught in a bind: AI aligns very well with the kind of hard science, foundational technology they want to prioritize, but is dependent on foreign technology and needs the kinds of data and skills that exist within the big social conglomerates they just tried to reign in. China is running a playbook of rapid diffusion and explosive competition, with the government putting heavy hands on the scale: who can buy which GPUs, what kind of content controls must exist, and a national level AI plan. It says to the public: trust in the party, and we will ensure AI delivers social benefit, for our definition of “social benefit”
A recent New York Times opinion column by Michelle Goldberg (“An Anti-A.I. Movement Is Coming. Which Party Will Lead It?”) asked which US political party might adopt an anti-AI stance.
One major question, going into 2026, is which party will speak for the Americans who abhor the incursions of A.I. into their lives and want to see its reach restricted. Another is whether widespread public hostility to this technology even matters, given all the money behind it. We’ll soon start to find out not just how much A.I. is going to remake our democracy but also to what degree we still have one.
Goldberg is asking who will promise to assert state control for AI: who will bring the Chinese model to America. The fundamental problem for me is I’m not sure that the public trust the government much more than they do corporations, or the media.
If AI is a global-scale step change it requires global coordination, which is expensive: it’s something folks can engage with when everything is going well. When times are tougher, economic interdependence morphs into leverage for coercion and control. Blackwells and rare earths and SWIFT messages become chips on the bargaining table.
Billions of people use large language models, which gives the creators of those models influence in how people act. But thus far the influence on the models from their users is very indirect: aggregate usage patterns or occasional thumbs up/thumbs down feedback. Open Weight and Open Source models offered folks more control, but despite the slow death of scaling the ability to train and operate a true frontier model remains a very large hurdle.
What would it take for people to believe that this power is being used in a way that includes them, instead of being done to them? In a high-trust world you can do that with credentials and commitments. In this world you can’t. People don’t need safety and impact reports from labs or promises of benevolence from the state. They need leverage.
Trust can’t scale, but verification maybe can. That requires independent auditing, liability, and transparency around capabilities and how those capabilities are being deployed. Open weights help, competition helps, and national strategies help, but none solve the whole problem. We’re building machines that can reason. We also need to build systems where the people who own the machines can’t silently rewrite the terms of everyone else’s lives.