AI’s Impact on Jobs Demands a New Approach and New Public Policies

The impact of AI on jobs is too big and too complex to leave to market forces. What’s needed instead are new public policies.

So argue two MIT researchers in a recently published paper.

The two are Guy Ben-Ishai, Researcher and Senior Advisor at MIT FutureTech; and Neil Thompson, Principal Research Scientist at the MIT Computer Science and AI Lab and leader of the AI, Quantum and Beyond research group at the MIT Initiative on the Digital Economy.

Their paper, published in September by the Brookings Institution, is called Workforce Policy for the Age of AI: Recommendations from the Economic Literature. As the title suggests, it draws upon existing research, including a number of papers from IDE- and MIT Sloan-affiliated researchers, to provide data-backed strategies for navigating AI job disruption.

What’s at stake?

In the paper, the two researchers explain that the debate over AI and work is often framed as a choice between mass unemployment and widespread human augmentation. However, they add, neither of these conclusions is actually supported by the evidence.

Instead, the literature suggests that AI will produce sharply divergent labor-market outcomes on dramatically different timelines. Some forms of work will be displaced rapidly. Others will be augmented gradually. And these effects will likely vary across different occupations, industries and employers.

Letting market forces deal with the issue will be insufficient. Because, the researchers write, the impact of AI on employment is likely to be so profound, “a public policy response is imperative.” AI workforce policies are not just needed to accommodate individuals who may witness future job losses but are also central to a successfully deployment of AI and its integration in the economy, making such policies a U.S. strategic imperative, the researchers argue.

What would these policies look like? Ben-Ishai and Thompson propose five, ranging from apprenticeships to a national wage insurance program.

What does the prior research show?

To start their paper, the researchers reviewed the recent economic research. They came up with four key findings about AI’s impact on work:

1) AI exposure does not necessarily translate into commercially viable automation or augmentation.

It’s not clear whether AI can replace human labor every time it is deployed. Prior research finds that when large language models (LLMs) complete tasks that would take humans three to four hours, the LLMs’ success rate is only 65%.

By comparison, human error rates for routine and simple tasks are typically in the 2% to 9% range for simpler tasks, 16% for longer tasks. In other words, today’s LLMs are still less reliable for real-world commercial settings.

Also important is a task’s duration. Tasks that take longer are less likely to be completed successfully by AI. The research shows that a 10-fold increase in average task duration reduces AI model performance by 11%.

2) We need to go beyond asking, “Does AI create or destroy jobs?” to also ask, “How does AI change the value of human expertise?”

While technology can make jobs more pleasant, more interesting and less dangerous, it can also replace the parts that make those jobs meaningful. Automation can also remove the need for expertise. That’s important because expertise is how workers differentiate themselves and generate value. Expertise can also raise the barriers to entry; to be hired, a candidate must have the required skills.

The research also shows that with AI, some occupations actually generate more jobs, but at lower wages. Consider physicians. If AI could reliably diagnose and treat common medical conditions, it would reduce the value of those physicians’ expertise. Now the work could be done by lower-paid nurse practitioners. Therefore, AI would lower the barriers to entry, increasing the number of jobs. But it would also lower the average pay.

Conversely, with other occupations AI may lead to higher wages, but fewer jobs. For example, consider accountants. If AI automates bookkeeping but is too inconsistent when offering tax advice, this would increase the need for human expertise. There would be fewer jobs overall, but for those that remain, the pay would be higher.

3) Successful AI adoption requires knowing not only how to use AI, but also when to trust it.

AI systems are prone to errors, limited interpretability, and what are known as non-deterministic outputs, meaning the AI may give different answers when repeatedly asked the same question.

Due to these shortcomings, adopting AI involves a lot of “last-mile” customization. For example, adapting a general-purpose AI model to a specific banking application.

The training challenge involves more than just teaching humans how to use AI. People also need to understand when, where and how AI tools can reliably improve performance—as well as when AI tools are likely to fail.

4) Better AI enables more autonomous work across more tasks.

AI tends to improve over time. Prior research suggests LLM error rates are halving about every 2.5 years. If that holds, LLMs could achieve a 93% baseline success rate across most professional text-based tasks by 2029.

Still, there’s a gap between what AI can technically achieve and what’s financially viable. The research shows that given AI integration costs, U.S. businesses would automate only about a quarter of the computer-vision tasks (23%) that could be performed by AI.

While AI capabilities are likely to improve, it remains unclear where AI will be affordable, feasible or practical.

What new policies do the researchers recommend?

Based on these prior findings, Ben-Ishai and Thompson propose five new policy directions. These recommendations do not require sweeping reforms of the U.S. social safety nets or labor markets. Rather, the researchers say, they are targeted, evidence-based programs designed to be fiscally sound, capable of attracting bipartisan support, and scalable if successful.

Their goal, they write, “should not be to predict every occupational change in advance, but to envision a workforce framework capable of responding before a temporary disruption becomes lasting economic harm.”

Here are their five policy recommendations:

1) Prioritize both displaced workers and high-productivity opportunities.

Workers facing displacement by AI should be a top priority. Prior research finds that workers displaced during periods of high unemployment have historically lost the equivalent of nearly three years of earnings.

For these workers, retraining alone is unlikely to be sufficient. What’s also needed is preemptive assistance, such as job-search help and wage insurance.

The researchers also call for a focus on transitions to high-productivity, high-impact sectors. In part because these can address two current challenges:

• U.S. employment is concentrated in slower-productivity sectors;
• An aging population increases the burden on a shrinking workforce.

Here, the best assistance will be training for high-quality opportunities.

2) Design domain-specific training in partnership with employers.

AI adoption involves more than just learning how to use the tools. Workers also need to assess AI reliability, exercise judgment, and integrate AI into their existing workflows.

Therefore, effective training will be both domain-specific and hands-on. In this way, workers can test outputs, recognize system limits, exercise judgment, and learn how to integrate AI into existing workflows.

These training programs should be developed in partnership with employers, the researchers recommend. That way, employers can help ensure that the training is both demand-driven and connected to actual hiring opportunities.

3) Align programs with shifts in human expertise.

Workforce transition programs, the researchers advise, should distinguish between two different risks: labor displacement of some occupations, wage erosion in others. These two outcomes require fundamentally different responses.

Where jobs are eliminated, policy should help workers find new jobs in other occupations and new sectors. This could include job-search assistance, wage insurance, and transitions to new occupations.

Where wages are lowered, policy should help workers preserve or improve earnings. This could include apprenticeships and transitions to higher-paid occupations.

4) Promote and expand apprenticeships.

Apprenticeships offer an effective mechanism for worker placement. They lower training costs, provide employers with a source of screening and signaling, and align training with real business needs.

Apprenticeships can also combine training with direct employer observation. This matters because otherwise, AI may make it more difficult for employers to identify which workers have the judgment, adaptability and learning potential to succeed in the role.

5) Establish a federal wage-insurance program.

The researchers recommend adopting policies that will help people who lose jobs but are reluctant to reenter the workforce at lower wages. Often, job loss results in the destruction of employer-specific skills that are not transferable to other jobs.

The idea of wage insurance may sound novel, but in fact the United States has prior experience. First created in 1962 and later run by the U.S. Labor Dept., as part of the Trade Adjustment Assistance operated until 2022. It compensated workers who had lost jobs as a result of international trade and increased imports.

More specifically, Trade Adjustment Assistance provided funds for both retraining programs and income support. It also included wage-insurance-type benefits, which the researchers say were effective at getting workers reemployed and could be run on a self-funding, budget-neutral basis. According to the Labor Dept., the program helped more than 2.5 million workers from 1974 to 2022.

Creating a similar program for today’s AI-driven job losses would need to be designed with flexibility, the researchers say. Today’s wage insurance could target verified cases where workers have been displaced by AI. The program could also be deployed in sectors where automation is significantly displacing jobs.

• Read the full paper: Workforce Policy for the Age of AI: Recommendations from the Economic Literature, by Guy Ben-Ishai and Neil C. Thompson

The post appeared first on MIT Initiative on the Digital Economy.