U.S.-China Comparison: Moravec’s Revenge

Apr 25, 2019

We've been talking about AI-related job loss or creation in the U.S. What about China?

How will China's workers fare in this brave new economy? Few good studies have been conducted on the impacts of automation here, but the conventional wisdom holds that Chinese people will be hit much harder, with intelligent robots spelling the end of a golden era for workers in the “factory of the world.” This prediction is based on the makeup of China’s workforce, as well as a gut-level intuition about what kinds of jobs become automated.

By Dr. Kai-Fu Lee

The Bottom Line for Employment with AI

Apr 23, 2019

Putting together percentages for the two types of automatability—38 percent from one-to-one replacements and about 10 percent from ground-up disruption—we are faced with a monumental challenge.

Within ten to twenty years, I estimate we will be technically capable of automating 40 to 50 percent of jobs in the United States. For employees who are not outright replaced, increasing automation of their workload will continue to cut into their value-add for the company, reducing their bargaining power on wages and potentially leading to layoffs in the long term. We’ll see a larger pool of unemployed workers competing for an even smaller pool of jobs, driving down wages and forcing many into part-time or “gig economy” work that lacks benefits.

By Dr. Kai-Fu Lee

Two Kinds of Job Loss: One-to-One Replacements and Ground-Up Disruptions

Apr 18, 2019

Beyond that disagreement over methodology regarding studies that predict potential job loss from AI technology, which I explored in my two previous posts, I believe using only the task-based approach misses an entirely separate category of potential job losses: industry-wide disruptions due to new AI-empowered business models. Separate from the occupation- or task-based approach, I’ll call this the industry-based approach.

Part of this difference in vision can be attributed to professional background. Many of the above studies are done by economists, whereas I am a technologist and early-stage investor. In predicting what jobs were at risk of automation, economists looked at what tasks a human completed while going about their job and asked whether a machine would be able to complete those same tasks. In other words, the task-based approach asked how possible it was to do a one- to-one replacement of a machine for a human worker.

By Dr. Kai-Fu Lee

What the AI Job-Loss Studies Missed

Apr 16, 2019

While I respect the expertise of the economists who pieced together the estimates about AI-related job loss, which I described in my previous post, I also respectfully disagree with the low-end estimates of the Organization for Economic Cooperation and Development (OECD).

That difference is rooted in two disagreements: one in terms of the inputs to their equations, and one major difference in the way I envision AI disrupting labor markets. The quibble causes me to go with the higher-end estimates of PwC, and the difference in vision leads me to bring that number up higher still.

By Dr. Kai-Fu Lee