The IMF Report on AI and Jobs is Alarming, But Education can Make a Difference

As educators, we must do everything we can to help our students develop AI skills

Related: Humanity Amplified: The Fusion of Deep Learning and Human Insight to Shape the Future of Innovation; Evidence of AI-Induced Unemployment Starts to Emerge

TLDR

*60% of jobs are exposed to AI; 33% of jobs will be highly redundant with AI
*The greatest exposure is in the US and the U.K.
*AI skills training is essential; college graduates have the greatest capability of adapting to this.
*Social assistance and job training will need to be made available.
*There is time to adapt, but we must use it.

Today, the International Monetary Fund (IMF) released its new projections related to the impact of AI technologies on employment.

The report is, frankly, alarming, and it’s consistent with what I’ve been saying since last spring: many existing forecasts underestimate the extent of potential job losses because they seem to assume somewhat tame technology development, and we are currently in a period of exponential growth in AI capabilities. This growth may even be faster than what the IMF assumes, as their report still largely focused only on the impact of generative AI technologies and we may be getting closer to AIs that can reason and plan, laying the foundation for AGI, which Sam Altman says is not that far way.

This would smash jobs and wages according to other economists.

The IMF report not an outlier report. As I’ve mentioned in other posts and in covered in our report, most future of work reports argue that AI is a significant threat to knowledge work, and we are starting to see this in forecasts.

In this new report, the IMF estimates that 60% of jobs in the US and the U.K. are exposed to AI.

In 27% of those cases, AI exposure is complementary, meaning that it will enhance the job. In some cases, this enhancement is artificial. For example, we may find that AI can adjudicate a trial as fairly as a judge, but judges are not exposed simply because people will not yet trust the AIs to do what judges do. This could also be true of medical diagnoses.

In 33% of those cases, it is low-complementary, meaning there is little to no additional value to the human contribution.

These include some lower-paying jobs (telemarketers) and some of the highest-paying and middle-class jobs that exist now (doctors, lawyers).

So, yes, 33% of jobs are at risk of being lost to AI.

Is there hope?  The report offers a bit of hope.

Training/retraining.  The report emphasizes the importance of “train(ing) the next generation of workers in these new technologies.” The report does not say that everyone who learns these skills will have a job (as this ridiculous tweet that has been going around has been saying), but those who have these skills will have a much better chance of getting a job. For many students, public schools are the only way to access instruction on these skills.

As educators, we should feel some pressure to develop these skills and help our fellow educators and our students develop the skills as well. This is something about the AI revolution we can control.

College. Historically, college-educated individuals have the best chance of adapting to such changes. It’s not clear how well that will hold this time around, as lower-skilled workers who can use AI well will be able to offset a lot of advantages for higher-skilled workers, but college students do have many opportunities to develop the “soft skills” and business skills needed to succeed in this environment.

Safety net. Governments will ultimately need to enact social safety nets, redistributing some of the wealth captured at the top (by the owners and investors of AI companies and those that are able to maximize profits with AI integration).  It will be interesting to see how that plays out in the US in a world of conservative or divided government and an already high federal debt ($34 trillion) and interest rates. The problem does not have to be solved by borrowing, and it could come from high taxation rates on these companies, but we’ll see if there is political support for that.

AI Literacy. While not mentioned in the report, I’ll put in another plug for AI Literacy. Our society is about to undergo some very significant transitions, with many AI scientists and AI leaders (Gates, Altman, Musk, Suleyman (talking about quantum computing)), including quite conservative thinkers on this issue (Choi), wondering if we will need to work at all in 20 years. As Reid Hoffman recently noted, we do have time to adapt — to train people to work with AI, to develop social support structures, to develop new caring professions (Russell) — as it takes a while for society to integrate technological advancements. The question is, will we? Will educators prepare people for what is coming?

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These are some key quotes from the report.

*In advanced economies, about 60 percent of jobs are exposed to AI, due to prevalence of cognitive-task-oriented jobs A new measure of potential AI complementarity suggests that, of these, about half may be negatively affected by AI, while the rest could benefit from enhanced productivity through AI integration.

*About 40 percent of workers worldwide are in high-exposure occupations; the share is 60 percent in advanced economies, which indicates potentially large macroeconomic implications. Advanced Economies have a greater share of high-exposure occupations, with either low or high complementarity, than emerging market economies and low-income countries (Figure 1, panel 1). In the average advanced economy, 27 percent of employment is in high-exposure, high-complementarity occupations, 33 percent in high-exposure, low-complementarity jobs.

*While historical waves of automation and the integration of information technology affected predominantly routine tasks, AI's capabilities extend to cognitive functions, enabling it to process vast amounts of data, recognize patterns, and make decisions. As a result, even high-skill occupations, which were previously considered immune to automation because of their complexity and reliance on deep expertise, now face potential disruption. Jobs that require nuanced judgment, creative problem-solving, or intricate data interpretation—traditionally the domain of highly educated professionals—may now be augmented or even replaced by advanced AI algorithms, potentially exacerbating inequality across and within occupations.

* On one hand, if low-complementarity positions, such as clerical jobs, serve as stepping stones toward high-complementarity jobs, a reduction in the demand for low-complementarity occupations could make young high-skilled workers’ entry into the labor market more difficult. On the other hand, AI may enable young college-educated workers to become experienced more quickly as they leverage their familiarity with new technologies to enhance their productivity. With the introduction of generative AI, the use of AI has itself become much easier.

* College-educated and younger people move more easily into high-complementarity jobs;

The potential implications of AI demand a proactive approach from policymakers geared toward maintaining social cohesion. While long-term productivity gains from AI are likely, during the transition, job displacement and changes in income distribution could have substantial political economy implications. History shows that economic pressures can lead to social unrest and demands for political change. Ensuring social cohesion is paramount. Policies must promote the equitable and ethical integration of AI and train the next generation of workers in these new technologies; they must also protect and help retrain workers currently at risk from disruptions.

*The field of AI is experiencing a swift evolution, especially with the advent of GenAI, which has broadened AI's potential applications. This suggests that its impact will expand to reshape job functions and the division of labor.

*For all economies, social safety nets and retraining for AI-susceptible workers are crucial to ensure inclusivity.

*College-educated workers are better prepared to move from jobs at risk of displacement to high complementarity jobs; older workers may be more vulnerable to the AI-driven transformation.

*Although many emerging market and developing economies may experience less immediate AI-related disruptions, they are also less ready to seize AI’s advantages. This could exacerbate the digital divide and cross-country income disparity

*Model simulations suggest that, with high complementarity, higher-wage earners can expect a more-than-proportional increase in their labor income, leading to an increase in labor income inequality. This would amplify the increase in income and wealth inequality that results from enhanced capital returns that accrue to high earners.

*Countries’ choices regarding the definition of AI property rights, as well as redistributive and other fiscal policies, will ultimately shape its impact on income and wealth distribution.

*AI adoption is expected to boost total income. If AI strongly complements human labor in certain occupations and the productivity gains are sufficiently large, higher growth and labor demand could more than compensate for the partial replacement of labor tasks by AI, and incomes could increase along most of the income distribution.

*AI adoption may lead to broad-based productivity gains, boosting investment and increasing overall labor demand, which may offset some of the decline in labor income caused by AI-induced labor displacement. As a result, the overall impact of AI on income levels and inequality will depend on the extent to which gains in economic activity generated by AI-induced productivity compensate for any labor income losses.