And Vijay Says…

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.

AI is making code cheaper to produce. That however doesn’t make software cheaper to own.

AI has dramatically increased the amount of code an engineer can produce. It hasn’t necessarily increased the amount of software an organization can safely own. Agents implement features, write tests, review their own work and fix the defects they find. So why should anyone read every line?

In Migrating Scarcity, I argued that when technology makes something abundant, scarcity moves to the next constraint. Code production is becoming abundant. The ability to establish that code is correct, safe and worth deploying is not expanding at the same rate. Code review needs to be rethought around that gap.

Passing tests is not the same as being correct

Let’s say we have an agent that implements a pricing change. It writes the code, generates the tests, runs static analysis and reports that everything passes.

Should a human read the diff? Maybe not. But someone still has to confirm the pricing rules are right. The implementation may faithfully execute a specification that left out an important business exception. The tests may cover every requirement in that specification. The agent may review its own work and find nothing wrong.

Everything passes, and the business still loses money.

Automated verification is only as good as the properties it checks. A type checker establishes certain facts about a program. A test suite shows that specific behaviors work under specific conditions. Neither shows that the right problem was solved. Automate verification wherever you can, but someone still has to decide what correct means. That decision matters more as code gets cheaper.

Three questions that decide where humans belong

Most organizations still run code review as a uniform ritual: someone writes, someone reads, the pull request merges. It was never a precise way to spend engineering judgment, and AI makes that obvious. A thousand lines of generated boilerplate may carry almost no risk. A five-line change to an authorization rule can expose customer data. Reviewing both the same way makes no sense.

I use three questions.

What happens if this is wrong? A defect in an internal prototype is not a defect in a payment system. Reversibility, blast radius and security exposure matter far more than line count.

Can correctness be verified independently? Some changes have clear contracts, strong tests and well-defined invariants. Others involve ambiguous requirements or business rules that are hard to state completely.

Who can recover the system when it breaks? A change can be correct today and still become a liability if nobody understands it well enough to diagnose it during an incident.

The principle that follows: the greater the consequence of failure, the stronger the independent evidence we should demand. Human attention belongs wherever that evidence is insufficient, whether that means reviewing code, specifications, architecture or operational readiness.

The 2 AM test

Imagine an agent builds a new subsystem. Every test passes, performance is excellent, security scans are clean. Six months later it fails at 2 AM.

Can the team explain the system’s critical assumptions, isolate the failure and recover service, even if the agents that built it are unavailable?

Nobody needs to understand every line. But if the team can’t operate the system without the agent that wrote it, what did the organization actually acquire? Cheap code with expensive ownership. In my native language Malayalam – we say you just bought an expensive dog that bites you 🙂

AI reviewing AI is not necessarily independent evidence

Suppose one agent writes the code, another writes the tests, a third reviews the implementation and a fourth fixes what the third finds. That sounds thorough.

But different agents are not necessarily independent reviewers. They may share training biases, assumptions or misunderstandings of the specification. Four checks can still miss the same error.

Agent review is valuable and should be used heavily. But count the confidence your checks justify, not the number of checks performed. Independent specifications, adversarial tests, production observation, fault injection and explicit business invariants can provide stronger evidence than asking a second model whether the first one did a good job.

The honest objection: human review was already unreliable

Plenty of human review is just rubber-stamping. Nobody reads a 3,000-line diff carefully. Approval is often a formality.

That is true, and it strengthens the case for reallocation. Reading everything produces the appearance of assurance. Reading the right things, with the authority to stop a release, produces the real thing. Humans should be reviewing specifications, business invariants, security boundaries, architecture and recoverability, and periodically sampling the automated process to catch failures in the process itself.

Accountability belongs here too. Every production change needs an accountable owner who accepts the evidence for deploying it and knows how the system will be operated. Removing manual review doesn’t remove that responsibility.

Who gets to learn?

Code review was never a perfect training mechanism, but it was one way engineers met unfamiliar code, questioned design choices and learned how a system fit together. If senior engineers only review the hardest problems and agents handle the rest, junior engineers lose the path to becoming the people who can handle those problems.

Watching agents succeed does not build judgment, and preserving low-value review rituals just to keep juniors busy is no answer either. Judgment comes from making decisions and living with the consequences. That means owning bounded changes, predicting failure modes before seeing an agent’s analysis, joining production diagnostics, and explaining why an implementation satisfies a business requirement.

This is no longer just a code review problem. Who gets the difficult work? How do we establish readiness for greater authority? Who pays to develop expertise when AI produces the immediate output more cheaply? Without deliberate answers, productivity can improve while engineering capability erodes.

The next scarcity

A company can have excellent automated tests and poor architecture. It can have secure code that nobody knows how to recover. It can have strong senior engineers today and no credible way to develop their successors. Reading every diff solves none of these, and neither does eliminating review.

The irony is that AI makes engineering judgment more important while potentially removing the work through which engineers learned to develop it.

The goal is not less human review. It is putting human judgment where it changes the outcome, and being able to say why the rest can be trusted.

Stop measuring how much code humans review. Start asking what justifies trusting the code they don’t.

In 2019, I wrote a blog called Reality Check on Promotions. My argument was that promotions depend on three things: performance, potential, and feasibility. Being excellent at your current job doesn’t automatically mean you are ready for the next one. And being ready doesn’t mean there is a position available.

I still believe that. But the framework assumes we know what the next job looks like. Seven years later, I am not sure we can make that assumption anymore.

What exactly are we promoting people into?

Consider two people. One manages 200 employees delivering a business process. The other manages ten people and a collection of AI agents delivering better outcomes at substantially lower cost.

Who deserves the bigger title? Who should earn more?

Most companies would look at the org chart before answering. Headcount has been a convenient proxy for responsibility, status, and compensation. That proxy is becoming less useful.

But AI challenges all three parts of my original framework.

Performance

Strong output has traditionally been evidence of individual capability. Not perfect evidence, but useful.

AI makes that signal less reliable.

Two analysts can produce equally impressive recommendations. One understands the business, challenges the assumptions, and uses AI to accelerate the work. The other accepts a plausible answer without understanding its limitations. Their presentations might even look identical.

I would absolutely reward people who use AI to deliver better outcomes. Productivity matters. There is no virtue in doing manually what technology can do better.

But rewarding outcomes and assessing readiness are different decisions. Reward the results, but don’t automatically treat them as evidence that someone is ready for greater authority.

Potential

This is the bigger problem.

We have traditionally developed people through increasingly difficult assignments. They did the work, made mistakes, received feedback, and gradually earned the right to make bigger decisions.

In The Corner Case, I explore what happens when AI removes the routine work that also helped people develop expertise.

There are two problems, not one.

First, we may stop producing the experiences needed for the next level. Second, AI-assisted output makes it harder to recognize who has developed genuine judgment.

Consider a junior analyst who delivers an excellent recommendation. Would she have caught an incorrect assumption? Could she defend the recommendation when challenged? Does she understand what could go wrong. Reviewing the presentation by itself won’t tell us.

We need to create opportunities to exercise judgment deliberately, and evaluate how people make decisions, not just what they deliver. And we need to keep revisiting what we mean by potential. Being ready for today’s senior job may not prepare someone for the job that exists five years from now.

Feasibility

AI lets individuals create more value without proportionately larger teams. At the same time, organizations can operate with fewer management layers.

The gap between individual contribution and organizational position will widen.

We have had technical and managerial career ladders for decades. But managerial roles still tend to carry greater authority, compensation, and prestige.

That was already questionable. AI makes the mismatch much larger.

An exceptional engineer or analyst equipped with AI might generate more economic value than their manager. Why should that person need a management title to earn more?

Measuring individual contribution remains difficult, especially when people and AI systems work together. We should evaluate outcomes owned and decisions made, not simply count deliverables. That won’t eliminate disagreements about fairness, but headcount isn’t a particularly good measure of value either.

Companies will need ways to increase compensation and responsibility without waiting for management vacancies. Otherwise, their best AI-enabled contributors may leave for organizations willing to reward their economic impact without requiring them to manage a team.

Legacy HR grading systems tie pay bands directly to direct reports. As long as compensation scales with span of control rather than economic impact, companies will keep forcing their best AI-enabled builders into bad management roles

Who develops the next generation?

There is another problem companies cannot solve individually.

Every company has an incentive to automate routine work. But if that work also develops future experts, the industry could eventually find itself short of experienced people.

Each company improves its economics while collectively weakening the talent pipeline. Hiring experienced people from competitors doesn’t solve that problem. It just moves the scarcity around.

Companies will need to invest deliberately in development through rotations, supervised decisions, simulations, and opportunities to handle difficult work. Those investments may not produce an immediate productivity return.

Someone still has to pay for them !

In the past, junior work was the tuition an enterprise paid to produce senior talent. Automating that work removes the tuition cost, but it also cancels the classes

Hierarchy doesn’t disappear. Organizations still need people who allocate capital, resolve conflicts, and accept accountability.

In Migrating Scarcity, I argued that when something becomes abundant, scarcity moves to the next constraint. As AI makes execution and analysis cheaper, judgment, problem selection, and accountability become more important.

Those capabilities don’t necessarily correspond to the next box on an org chart.

The old promotion framework assumed a relatively stable set of jobs arranged in a hierarchy. AI challenges the stability of both the jobs and the hierarchy.

We may be entering a world where promotion is no longer the primary measure of career progress. If that happens, the question isn’t just who deserves the next promotion. It’s whether the next promotion is even the right thing to offer.