When I moved to the Chinese city of Xi’an in 2010, I lived in a neighborhood called Gaoxin, the "high-tech development zone" of the city. At the time, the title sounded aspirational at best and laughable at worst.
Xi’an was bristling with activity, but none of it was very high-tech: buildings were getting torn down, karaoke joints were opening up, and hawkers were selling baked sweet potatoes on every corner. The Gaoxin neighborhood was littered with offices of semiconductor companies that shuttered after subsidies ran out. On the sign for the abandoned Xi’an High-Tech Development Zone Innovation Center, the H had fallen off and the Z was hanging by a wire.
This grim picture fit neatly with the narrative I’d grown up around in Silicon Valley. Technology innovation requires personal freedoms, and government involvement—whether restrictions or subsidies—only messes things up. That narrative always rested on some convenient historical amnesia, but it took China’s remarkable rise to upend U.S. assumptions about how technology is built and how it impacts geopolitics.
My work as a journalist and researcher has chronicled that transformation, looking around the corner at what comes next. I’m fascinated by what makes tech ecosystems tick: what combination of money, timing, people, and policies can kickstart innovation?
That question grew out of what I witnessed in China, but finding the answers requires looking from Bangalore to Jakarta to Austin. But in Washington, DC, China’s rise in artificial intelligence (AI) sparked a different set of questions. When China released its national AI plan in 2017, there was lots of concern about China eclipsing the United States in tech capacity, but there was also little data on where the two countries actually stood. For the past four years, I’ve worked alongside other scholars to fill that gap, breaking down AI into its building blocks—training data, research talent, and semiconductors, to name a few—and developing metrics for comparison. Today, we have a more grounded picture of this global AI landscape, a foundation on which to make good policy.
At Carnegie, I will focus especially on two aspects of AI: proactive policy recommendations for fostering U.S. competitiveness and a deeper look at China’s emerging AI governance framework. For years, many people refused to take Chinese technology seriously, and that unwillingness set back U.S. policy by years. I fear that we’re repeating that mistake today by refusing to take Chinese AI governance seriously.
I hope to leverage Carnegie’s tremendous international networks and the deep expertise of my colleagues to shed a little light on this, and nudge the field of AI in a positive direction.