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

What AI Job-Related Studies Say

Apr 11, 2019

Predicting the scale of AI-induced job losses has become a cottage industry for economists and consulting firms the world over.

Depending on which model one uses, estimates range from terrifying to totally not a problem. Here I give a brief overview of the literature and the methods, highlighting the studies that have shaped the debate. Few good studies have been done for the Chinese market, so I largely stick to studies estimating automation potential in the United States and then extrapolate those results to China.

By Dr. Kai-Fu Lee

What AI Can and Can’t Do

Apr 09, 2019

When it comes to job replacement, AI’s biases don’t fit the traditional one-dimensional metric of low-skill versus high-skill labor.

Instead, AI creates a mixed bag of winners and losers depending on the exact content of job tasks performed. While AI has far surpassed humans at narrow tasks that can be optimized based on data, it remains stubbornly unable to interact naturally with humans or imitate the dexterity of our fingers and limbs. It also cannot engage in cross-domain thinking on creative tasks or ones requiring complex strategy, jobs whose inputs and outcomes aren’t easily quantified. What this means for job replacement can be expressed simply through two X–Y graphs, one for physical labor and one for cognitive labor.

By Dr. Kai-Fu Lee