In our latest feature for Discourse magazine, Connor Haaland and I explore the question, “Should the U.S. Copy China’s Industrial Policy?” We begin by noting that:
Calls for revitalizing American industrial policy have multiplied in recent years, with many pundits and policymakers suggesting that the U.S. should consider taking on Europe and China by emulating their approaches to technological development. The goal would be to have Washington formulate a set of strategic innovation goals and mobilize government planning and spending around them.
We continue on to argue that what most of these advocates miss is that:
China’s targeting efforts are often antithetical to both innovation and liberty, and involve plenty of red tape and bureaucracy. China has become a remarkably innovative country for many reasons, including its greater tolerance for risk-taking, even as the Chinese Communist Party continues to pump resources into strategic sectors. But most Chinese innovation is permissible only insomuch as it furthers the party’s objectives, a strategy the U.S. obviously wouldn’t want to copy.
We discuss the problems associated with some of those Chinese efforts as well as proposed US responses, like the recently released 756 page report from the National Security Commission on Artificial Intelligence. The report takes an everything-and-the-kitchen-sink approach to state direction for new AI-related efforts and spending. While that report says the government now must “drive change through top-down leadership” in order to “win the AI competition that is intensifying strategic competition with China,” we argue that there could be some serious pitfalls with top-down, high price tag approaches.
Jump over to the Discourse site to read the full essay, as well as our previous essay, which asked, “Can European-Style Industrial Policies Create Tech Supremacy?” These two essay build on the research Connor and I have been doing on global artificial intelligence policies in the US, China, and the EU. In a much longer forthcoming white paper, we explore both the regulatory and industrial policy approaches for AI being adopted in the US, China, and the EU. Stay tuned for more.
The Technology Liberation Front is the tech policy blog dedicated to keeping politicians' hands off the 'net and everything else related to technology.
Running List of My Research on AI, ML & Robotics Policy
by Adam Thierer on July 29, 2022 · 0 comments
[last updated 4/3/2025 – Check my Medium page for latest posts]
This a running list of all the essays and reports I’ve already rolled out on the governance of artificial intelligence (AI), machine learning (ML), and robotics. Why have I decided to spend so much time on this issue? Because this will become the most important technological revolution of our lifetimes. Every segment of the economy will be touched in some fashion by AI, ML, robotics, and the power of computational science. It should be equally clear that public policy will be radically transformed along the way.
Eventually, all policy will involve AI policy and computational considerations. As AI “eats the world,” it eats the world of public policy along with it. The stakes here are profound for individuals, economies, and nations. As a result, AI policy will be the most important technology policy fight of the next decade, and perhaps next quarter century. Those who are passionate about the freedom to innovate need to prepare to meet the challenge as proposals to regulate AI proliferate.
There are many socio-technical concerns surrounding algorithmic systems that deserve serious consideration and appropriate governance steps to ensure that these systems are beneficial to society. However, there is an equally compelling public interest in ensuring that AI innovations are developed and made widely available to help improve human well-being across many dimensions. And that’s the case that I’ll be dedicating my life to making in coming years.
Here’s the list of what I’ve done so far. I will continue to update this as new material is released: Continue reading →