Tell us a little about your career journey: How did you come to work in AI policy?

I stumbled into working on AI policy through an interest in China. I lived in China for about five years after graduating college, where I worked as a journalist and eventually wrote a book about China-California ties. During that time (2016-2018) technology was becoming increasingly central to U.S.-China relations, and AI was becoming increasingly central to both countries’ tech ecosystems.

There was a lot of hype at the time about China’s AI capabilities, and a lot of my work for the first few years was trying to see if that hype was grounded in reality. I looked at the different types of research output from the two countries and looked at how their data ecosystems compared. I used that project to transition from journalism to think tank work. Since then, I’ve kept digging deeper to try to understand their comparative capabilities and Chinese AI governance. 

What are some of the current AI policy challenges you’re working on?

I’m mostly trying to understand how China designs their AI governance rules, and using that as a basis to understand governance practices globally. I think we need to deeply understand what China is doing domestically because how it chooses to govern its own companies and its own labs is going to matter for people around the world. With that understanding of the domestic governance ecosystem, we can have a better sense of which international governance schemes connect or conflict with China’s priorities, and whether there can be some level of coordination between China and other countries when it comes to the international governance of AI. 

What advice do you have for those interested in a similar career path?

Bring some additional expertise to the table that’s not just AI policy. That could be a background in another country like China or in an institution like the European Union. It could be also training in law or it could be actual technical AI skills. I think plugging any of one of those things into AI policy questions allows you to add much more value. 

It’s possible, though, to go right into AI policy from the start. If you try that, I would recommend honing in on one very specific aspect of policy, one that you think is going to be really important in a few years. If you choose your research focus well, then when your issue rises to prominence, you will have done the foundational work to really add value to that conversation instead of following the conversation and repeating what everyone else is saying. 

What specializations do you think currently fall into that category? 

I think a deep understanding of international governance and regulatory institutions are going to be quite useful. Right now, there’s all these different institutions being set up and people trying to push AI governance into existing institutions like the United Nations. I was recently having a conversation about what an IPCC for AI would look like, and that conversation would really benefit from someone who actually knows deeply how the IPCC works. I think a lot of people who have spent their time deeply analyzing technical issues or domestic AI policy details are looking up and realizing that the international system doesn’t work according to the same mechanisms, and they are kind of scrambling to understand what can actually work and be useful on the international stage. 

What skills do you think are important for success in AI policy, and how could readers acquire them?

I really think there’s a role for every skill set out there. There’s a role for people who just want to bury their heads deep in one issue area for a long time. There’s a role for people who are just highly social and love organizing things and bringing people together. Perhaps the one unifying thread through all of these is you do eventually need to be able to communicate clearly,  whether you are doing deep policy research or organizing work. You need to be able to communicate your ideas clearly in both writing and speaking. 

How do you get those skills? I think the answers are somewhat boring in a way: it’s important to read more, write more and speak more. For the reading, I actually would not start with reading most DC think tank work, because a lot of the writing is quite bad. 

You should also be highly critical of your own writing and speaking. I don’t mean destructively self-critical in a way that prevents you from writing and speaking at all. I mean really examining what you’ve written or presentations you’ve made in the past. Forget what you know and try to see them through the eyes of someone who is in your target audience, usually someone who does not know nearly as much about this particular subject. What ideas or arguments are you taking for granted, but that they would need explained more clearly? Where are they likely to get lost? What sentiments and emotions are triggered by using the word X instead of the word Y? 

I think one principle that underlies almost all good writing is this: it’s not about getting ideas from your brain onto the page; it’s about getting the ideas from the page into someone else’s brain.

Are there any programs, resources, or books you’d especially recommend for those interested in AI policy?

For good translation and analysis of China’s AI ecosystem some of my favorites are: 

• ChinAI newsletter by Jeffrey Ding at George Washington University
• Recode China by Tony Peng, who does international PR for Baidu 
• If you read Chinese, just read lots of Chinese writing on AI in Mandarin

Additionally, if you’re interested in Chinese AI policy, it’s worth stepping back and just learning about China in general. Everything in Chinese AI policy looks super weird if you start from this one little corner and miss all of the wider context. Wider context on Chinese society is really important, and for that I’d recommend the books of Peter Hessler, From the Soil by Feitong Xiao, The Party by Richard McGregor, and Factory Girls by Leslie Chang. More than anything, I’d recommend spending some time in China getting to know people who have nothing to do with AI.

This is part of a series of career profiles, aiming to make career stories and resources more accessible to people without easy access to mentorship and advice. If you have suggestions for what questions you’d like to see answered in these profiles, please fill out our feedback form.