Can America create economic opportunities as fast as AI transforms existing work?
The midterm elections have given Democrats control of both chambers of Congress while Republicans retain the White House. I expect plenty of political disagreements over the next two years. But I would hope both sides can agree on one objective: maximizing economic growth and productive employment.
AI gives us an extraordinary opportunity to do both. But there is a question I don’t think we are paying enough attention to.
What happens if AI eliminates existing work faster than the economy creates valuable new work?
Productivity doesn’t guarantee employment
The classical economic argument is compelling. Technology lowers costs. Competition lowers prices. Purchasing power increases, creating demand for other goods and services. New businesses emerge and employment follows.
I think AI will create these benefits too. But there is a complication.
Suppose AI makes software development ten times more productive. We could create ten times more software and entirely new applications. But AI might also develop, test and maintain most of that additional software.
We could end up with dramatically more output without a corresponding increase in employment.
History gives us plenty of examples of technology creating new work. It doesn’t prove that new work will emerge fast enough this time, especially when AI can automate much of that new work too.
I think the real economic challenge is to expand the frontier of valuable human activity faster than AI reduces the labor required for existing activities.
An early signal worth taking seriously
Stanford researchers Brynjolfsson, Chandar and Chen found that employment among workers aged 22 to 25 in highly AI-exposed occupations was approximately 19% below where it would have been if it had grown at the same rate as employment among less-exposed peers.
The difference was primarily associated with reduced hiring. The researchers also found no widespread economy-wide displacement and cautioned that the evidence doesn’t establish AI as the cause. Still, I think this deserves serious attention.
Work produces two things: economic output and human capability.
Junior engineers write code. Junior accountants reconcile accounts. Analysts build models. They gradually develop the judgment needed to handle more difficult work.
AI can remove those learning opportunities along with the work.
An individual company may rationally hire fewer juniors. But if enough companies do that, where will the next generation of experienced professionals come from?
This is something I explored in The Corner Case. A company can buy experienced talent instead of developing it. An entire industry cannot keep doing that forever.
We need more exploration, not just exploitation
Companies have strong incentives to use AI to make their existing operations more efficient. And they absolutely should capture those productivity gains.
But I think the bigger opportunity is in discovering economic activities that were previously impossible or uneconomic.
AI could make personalized education, preventive healthcare and entirely new types of scientific research commercially viable. The problem is that cheaper technology alone doesn’t create new businesses.
A startup might build an excellent application at a fraction of the historical cost but still struggle to reach customers. The years of litigation between Apple and Epic illustrate how valuable control of distribution can be, even when building software becomes cheaper.
New businesses still need capital, infrastructure, customers and permission to operate.
And there is another problem. Markets may underinvest in exploration when the benefits spill over to competitors or when the returns are too uncertain for conventional financing. We need established businesses to become more productive. We also need new businesses to discover what else becomes possible.
Three things I would ask Congress to do
1. Make creating and financing new businesses easier.
Reduce unnecessary regulatory fragmentation, improve market access and remove infrastructure bottlenecks, especially in electricity and transmission.
Fund foundational research that benefits multiple industries. Experiment with SBIR-style awards and prize challenges to encourage commercialization of new ideas.
I would rather see government help create more businesses than keep subsidizing the same large companies.
2. Invest in developing human capability.
I would propose an illustrative five-year, $2 billion apprenticeship experiment supporting 40,000 paid developmental positions annually, with public funding capped at $10,000 per position.
Test employer incentives against worker-directed funding. Require actual paid work, employer contributions and portable skills. Use randomized or phased enrollment to find out whether these programs create additional jobs or simply subsidize hiring that would have happened anyway.
There is good reason to be skeptical of subsidies. A 2026 NBER study found no detectable effect on hiring, earnings or retention from the Work Opportunity Tax Credit in Wisconsin. The authors identified eligibility-screening concerns and organizational inertia as possible explanations.
A better-designed program might work. We should test that assumption rather than take it on faith.
Congress should decide upfront what an acceptable cost per additional sustained job looks like. If an approach doesn’t deliver, stop funding it.
The Senate HELP and House Education and Workforce committees could develop the pilot, with funding through appropriations.
This addresses the entry-level pipeline. Displaced mid-career workers need attention too, but that’s a different problem requiring different interventions.
3. Reexamine incentives for investing in people versus technology.
Employers pay payroll taxes on wages, while qualifying equipment and certain software investments can benefit from 100% first-year depreciation.
That doesn’t automatically mean the tax system favors automation. Labor costs are deductible too, and employees may bear some of the economic burden of employer payroll taxes through lower wages.
But I would like Congress to examine whether targeted payroll-tax offsets for additional developmental employment could improve hiring without discouraging automation. Any such offset should be funded elsewhere so Social Security and Medicare are unaffected.
We should make technology cheaper to deploy. We should also make it economically attractive to develop people.
Why I still care about employment
There is a reasonable counterargument here.
If AI can produce more output with fewer workers, why not distribute the purchasing power and let people work less?
Maybe that will eventually be part of the answer.
But employment does more than generate income. It develops capability, creates opportunities for advancement and gives people a way to participate productively in the economy.
I don’t think we should preserve obsolete jobs. I certainly don’t think maximizing hours worked is a sensible goal either.
What I would like to maximize is the opportunity for people to contribute economically, develop valuable capabilities and earn higher real incomes.
What should we measure by 2028?
I would like Congress to establish an annual AI and Economic Opportunity report that tracks productivity, real wages, new employer businesses and early-career employment.
The apprenticeship experiment should have predefined measures of success, including cost per additional sustained job. We could evaluate implementation in 2028 and longer-term employment outcomes in 2029 and 2030.
For the broader economy, I would keep asking three questions.
Are we creating more productive businesses?
Are more Americans getting opportunities to develop valuable capabilities?
Are real earnings rising alongside productivity?
If AI dramatically increases output and profits while opportunities for productive employment keep shrinking, I would find it hard to call that a complete economic success.
We should be aiming for all three.
