Monthly Archives: August 2025

DOGE Hunts On, Despite Obstacles

30 Saturday Aug 2025

Posted by in Administrative State, DOGE, Liberty

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I’ve noted a number of policy moves by Donald Trump that I find aggravating (scroll my home page), but I still applaud his administration’s agenda to downsize government, promote operational efficiency, and deregulate the private economy. It’s just too bad that Trump demonstrates a penchant for expanding government authority in significant ways, which makes it harder to celebrate successes of the former variety. Beyond that, there have been huge obstacles to rationalizing the administrative state. We’ve seen progress in some areas, but the budgetary impact has been disappointing.

Grinding On

The Department of Government Efficiency (DOGE) was to play a large role in the effort to reduce fraud and inefficiency at the federal level. On the surface, it’s easy to surmise that DOGE has failed in its mission to root out government waste. After seven months, DOGE touts that it has saved taxpayers $205 billion thus far. That is well short of the original $2 trillion objective (subsequently talked down by Elon Musk), but it was expected to take 18 months to reach that goal. Still, the momentum has slowed considerably.

Moreover, the $205 billion figure does not represent recurring budgetary savings. Some of it is one-time proceeds from property sales or grant cancellations. Some of it ($30 billion) seems to represent savings in regulatory compliance costs to Americans, but that’s not clear as the DOGE website is lightly documented, to put it charitably. A recent analysis reached the conclusion that DOGE had exaggerated the savings it has claimed for taxpayers, which seems plausible.

But DOGE is still plugging away, reviewing federal contracts, programs, regulations, payments, grants, workforce deployment, and accounting systems. The work is desperately needed given the fraud that’s been exposed among the agency workforce, which seemed to escalate following the advent of massive Covid benefit payments during the pandemic. Some details of an investigation by the Senate DOGE Caucus, discussed at this link, are truly astonishing. Employees at multiple state and federal agencies have been collecting food stamps, survivor benefits, and even unemployment benefits while employed by government. Apparently, this was made possible by the lack of list de-duplication by the federal agencies that dole out these benefits. This might be a pretty good explanation for the lawsuits filed by federal employee unions attempting to prevent DOGE from accessing agency records. Congratulations to Senator Joni Ernst, Chairman of the Caucus, for her leadership in exposing this graft.

False Aspersions

Shortly after DOGE was constituted, most of its employees were assigned to individual agencies to identify opportunities to reduce waste and promote efficiency. This has led to confusion about the extent to which DOGE should take credit for certain savings maneuvers. However, contrary to some allegations, no DOGE employees have been “embedded” as career civil servants.

Since almost the start of Trump’s second term, DOGE has been blamed for workforce reductions that some deemed reckless and arbitrary. There were indeed some early mistakes, most notably at HHS, but a number of those key workers were rehired. Many of the force reductions were instigated by individual agencies themselves, and many of those were voluntary separations with generous severance packages.

As to the “arbitrary” nature of the force reductions, one former DOGE staffer described the difficulty of making sensible cuts at the Veterans Administration under agency rules:

“Then came a reality check about RIF rules, which turned out to be brutally deterministic:

  • Tenure matters most—new hires were cut first
  • Veterans’ preference comes next; vets are protected over non-vets
  • Length of service trumps performance—seniority beats skill
  • Performance ratings break any remaining ties

“These reduction-in-force rules–which stem from the Veterans’ Preference Act of 1944–surprised me and many others. Unlike private industry layoffs that target middle management bloat and low performers, the government cuts its newest people first, regardless of performance. Anyone promoted within the last two years was also considered probationary—first in line to go.“

It would be hard to be less arbitrary than these rules. Other agencies are subject to similar strictures on reductions in force. No wonder the Administration relied heavily on a buyout offer (“deferred resignation”) with broad eligibility in its attempt to downsize government. Furthermore, the elimination of positions was largely targeted functions that were wasteful of taxpayer resources, such as promoting DEI objectives and administering grants to NGOs driven by ideological motives.

Of course, the buyouts come with a cost to taxpayers. In fact, one report asserted that DOGE’s efforts themselves cost taxpayers $135 billion or more. Of course, buyouts carry a one-time cost. However, that figure also includes a questionable estimate of lost productivity caused by turmoil at federal agencies. I’m just a little skeptical when it comes to claims about the productivity of the federal workforce.

Obstacles

DOGE has had to grapple with other severe limitations, as Dan Mitchell has commented. These are primarily rooted in the spending authority of Congress. Only one rescission bill reflecting DOGE cuts, totaling just $9 billion, has made it to Trump’s desk. Another “untouchable” for DOGE is interest on the federal debt, which has become a huge portion of the federal budget.

Furthermore, DOGE is guilty of one self-imposed obstacle: the main driver of ongoing deficits is entitlement spending, While the Big Beautiful Bill included Medicaid reforms, the Trump Administration and Congress have shown little interest in shoring up Social Security and Medicare, both of which are technically insolvent. While DOGE would seem to have limited authority over entitlements, as opposed to the discretionary budget, some charge that DOGE made a critical error in failing to address entitlement fraud. According to Veronique de Rugy:

“It is insane not to have started there. Given DOGE’s comparative advantage in data analytics and [information technology], this is where it can have the greatest impact… Cracking down on this waste isn’t just about saving money; it’s about restoring integrity to safety-net programs and protecting taxpayers. And if fixing this problem is not quintessential ‘efficiency,’ what is?“

On the Bright Side

Michael Reitz offered a different perspective. He cited the difficulty of reforming an entrenched bureaucracy. He also noted the following, however, as a kind of hidden success of DOGE and Elon Musk:

“But others I spoke with thought Musk’s four months in government were both substantive and symbolic. He changed the conversation about waste and grift. Musk made cuts cool again, especially for Republican politicians who have forgotten fiscal restraint. He highlighted the need to follow the data and oppose bureaucrats who impede reform by controlling the flow of information.“

Of course, DOGE has been instrumental in identifying absurdly wasteful federal contracts, even if they are “small change” relative to the size of the federal budget. This includes grants to NGOs that appear to have functioned primarily as partisan slush funds. DOGE has also helped identify deregulatory actions to eliminate duplicative or contradictory agency rules on industry, reducing costly economic burdens on the private sector. The DOGE website claims (preliminarily) that it has deleted 1.9 million words of regulation, but doesn’t provide a total number of rules eliminated.

An important part of DOGE’s mission was to modernize technology, software, and accounting systems at federal agencies. This included centralization of these systems with improved tracking of payments and a written justification for each payment. These efforts were met with hostility from some quarters, including lawsuits to limit or prevent DOGE personnel from accessing agency data. Nevertheless, DOGE has pushed ahead with the initiative. This is a laudable attempt to not only modernize systems, but to encourage transparency, accountability, and efficiency.

In a related development, this week DOGE was blamed by a whistleblower for uploading a file from Social Security containing sensitive information to an unsecured cloud environment. However, a spokesperson for the Social Security Administration stated that the data was secure and that the SSA had no indication that it had been breached. We shall see.

AI Scrutiny

Now, DOGE is recommending the use of an AI tool to cut federal regulations. According to Newsweek:

“The ‘DOGE AI Deregulation Decision Tool,’ developed by engineers brought into government under Elon Musk’s DOGE initiative, is programmed to scan about 200,000 existing federal rules and flag those that are either outdated or not legally required.“

Critics are concerned about accuracy and legal complexities, but the regulations flagged by the AI tool will be reviewed by attorneys and other agency personnel, and there will be an opportunity for public comment. The process could make deregulatory progress well beyond what would be possible under purely human review. DOGE believes that up to 100,000 rules could be eliminated, saving trillions of dollars in compliance costs. If successful, this might well turn out to be DOGE’s signal accomplishment.

Conclusion

I’m disappointed at the flagging momentum of DOGE’s quest to eliminate inefficiencies in the executive branch. I’m also frustrated by the limited progress in translating DOGE’s work into ongoing deficit reduction. In addition, it was a mistake to leave aside any scrutiny of improper entitlement payments. Nevertheless, DOGE has has some significant wins and the effort continues. Also, it must be acknowledged that DOGE has faced tremendous obstacles. For too long, government itself has metastasized along with bureaucratic inefficiencies and graft. That is the rotten fruit of the symbiosis between rent seeking behavior and a bloated public sector. We should applaud the spirit motivating DOGE and encourage greater progress.

Public Debt and AI: Ain’t But One Way Crowding Out

17 Sunday Aug 2025

Posted by in Artificial Intelligence, Deficits

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There’s a hopeful narrative making the rounds that artificial intelligence will prove to be such a boon to the economy that we need not worry about high levels of government debt. AI investment is already having a substantial economic impact. Jason Thomas of Carlyle says that AI capital expenditures on such things as data centers, hardware, and supporting infrastructure account for about a third of second quarter GDP growth (preliminarily a 3% annual rate). Furthermore, he says relevant orders are growing at an annual rate of about 40%. The capex boom may continue for a number of years before leveling off. In the meantime, we’ll begin to see whether AI is capable of boosting productivity more broadly.

Unfortunately, even with this kind of investment stimulus, there’s no assurance that AI will create adequate economic growth and tax revenue to end federal deficits, let alone pay down the $37 trillion public debt. That thinking puts too much faith in a technology that is unproven as a long-term economic engine. It would also be a naive attitude toward managing debt that now carries an annual interest cost of almost $1 trillion, accounting for about half of the federal budget deficit.

Boom Times?

Predictions of AI’s long-term macro impact are all over the map. Goldman Sachs estimates a boost in global GDP of 7% over 10 years, which is not exactly aggressive. Daren Acemoglu has been even more conservative, estimating a gain of 0.7% in total factor productivity over 10 years. Tyler Cowen has been skeptical about the impact of AI on economic growth. For an even more pessimistic take see these comments.

In July, however, Seth Benzell of the Stanford Digital Economy Lab discussed some simulations showing impressive AI-induced growth (see chart at top). The simulations project additional U.S. GDP growth of between 1% – 3% annually over the next 75 years! The largest boost in growth occurs now through the 2050s. This would produce a major advance in living standards. It would also eliminate the federal deficit and cure our massive entitlement insolvency, but the result comes with heavy qualifications. In fact, Benzell ultimately throws cold water on the notion that AI growth will be strong enough to reduce or even stabilize the public debt to GDP ratio.

The Scarcity Spoiler

The big hitch has to do with the scarcity of capital, which I’ve described as an impediment to widespread AI application. Competition for capital will drive interest rates up (3% – 4%, according to Benzell’s model). Ongoing needs for federal financing intensify that effect. But it might not be so bad, according to Benzell, if climbing rates are accompanied by heightened productivity powered by AI. Then, tax receipts just might keep-up with or exceed the explosion in the government’s interest obligations.

A further complication cited by Benzell lurks in insatiable demands for public spending, and politicians who simply can’t resist the temptation to buy votes via public largesse. Indeed, as we’ve already seen, government will try to get in on the AI action, channeling taxpayer funds into projects deemed to be in the public interest. And if there are segments of the work force whose jobs are eliminated by AI, there will be pressure for public support. So even if AI succeeds in generating large gains in productivity and tax revenue, there’s very little chance we’ll see a contagion of fiscal discipline in Washington DC. This will put more upward pressure on interest rates, giving rise to the typical crowding out phenomenon, curtailing private investment in AI.

Playing Catch-Up

The capex boom must precede much of the hoped-for growth in productivity from AI. Financing comes first, which means that rates are likely to rise sooner than productivity gains can be expected. And again, competition from government borrowing will crowd out some private AI investment, slowing potential AI-induced increases in tax revenue.

There’s no chance of the converse: that AI investment will crowd out government borrowing! That kind of responsiveness is not what we typically see from politicians. It’s more likely that ballooning interest costs and deficits generally will provoke even more undesirable policy moves, such as money printing or rate ceilings.

The upshot is that higher interest rates will cause deficits to balloon before tax receipts can catch up. And as for tax receipts, the intangibility of AI will create opportunities for tax flight to more favorable jurisdictions, a point well understood by Benzell. As attorneys Bradford S. Cohen and Megan Jones put it:

“Digital assets can be harder to find and more easily shifted offshore, limiting the tax reach of the U.S. government.”

AI Growth Realism

Benzell’s trepidation about our future fiscal imbalances is well founded. However, I also think Benzell’s modeled results, which represent a starting point in his analysis of AI and the public debt, are too optimistic an assessment of AI’s potential to boost growth. As he says himself,

“… many of the benefits from AI may come in the form of intangible improvements in digital consumption goods. … This might be real growth, that really raises welfare, but will be hard to tax or even measure.”

This is unlikely to register as an enhancement to productivity. Yet Benzell somehow buys into the argument that AI will lead to high levels of unemployment. That’s one of his reasons for expecting higher deficits.

My view is that AI will displace workers in some occupations, but it is unlikely to put large numbers of humans permanently out of work and into state support. That’s because the opportunity cost of many AI applications is and will remain quite high. It will have to compete for financing not only with government and more traditional capex projects, but with various forms of itself. This will limit both the growth we are likely to reap from AI and losses of human jobs.

Sovereign Wealth Fund

I have one other bone to pick with Benzell’s post. That’s in regard to his eagerness to see the government create a sovereign wealth fund. Here is his concluding paragraph:

“Instead of contemplating a larger debt, we should instead be talking about a national sovereign wealth fund, that could ‘own the robots on behalf of the people’. This would both boost output and welfare, and put the welfare system on an indefinitely sustainable path.”

Whether the government sells federal assets or collects booty from other kinds of “deals”, the very idea of accumulating risk assets in a sovereign wealth fund undermines the objective to reduce debt. It will be a struggle for a sovereign wealth fund to consistently earn cash returns to compensate for interest costs and pay down the debt. This is especially unwise given the risk of rising rates. Furthermore, government interests in otherwise private concerns will bring cronyism, displacement of market forces by central planning, and a politicization of economic affairs. Just pay off the debt with whatever receipts become available. This will free up savings for investment in AI capital and hasten the hoped-for boom in productivity.

Summary

AI’s contribution to economic growth probably will be inadequate and come too late to end government budget deficits and reduce our burgeoning public debt. To think otherwise seems far fetched in light of our historical inability to restrain the growth of federal spending. Interest on the federal debt already accounts for about half of the annual budget deficit. Refinancing the existing public debt will entail much higher costs if AI capex continues to grow aggressively, pushing interest rates higher. These dynamics make it pretty clear that AI won’t provide an easy fix for federal deficits and debt. In fact, ongoing federal borrowing needs will sop up savings needed for AI development and diffusion, even as the capital needed for AI drives up the cost of funds to the government. It’s a shame that AI won’t be able to crowd out government.

Trump’s Dreadful Sacking of BLS Commish

10 Sunday Aug 2025

Posted by in Data Integrity, Economic Aggregates

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The dismissal of the Bureau of Labor Statistics (BLS) commissioner Erika McEntarfer by President Trump was regrettable and a dumb move besides. It was undeserved, and its timing made Trump look like the authoritarian buffoon of his enemies’ worst nightmares.

Trump believed the weak employment report for July made him “look bad”. He was particularly enraged by the downward revisions in nonfarm payrolls for the months of May and June (see chart above). Of course, he would not have liked the estimates to begin with, had they been in line the ultimate revisions — he just doesn’t like “bad” numbers on his watch. Trump stated his conviction that the weak report was “politically motivated”, and even “rigged” by McEntarfer, which is absurd. To anyone who knows anything about how these numbers are produced, this makes Trump look like a guy who is willing to manipulate economic data to his advantage. Only good numbers, please!

As I’ve said before, the mere availability of aggregate economic statistics seems to encourage activist policy. This is made worse by the unreliability and mis-measurement of these aggregates, which compounds policy failures. Like other parts of the federal statistical system, BLS reporting has shortcomings, some of them severe and getting worse. But that’s not McEntarfer’s doing. The numbers, for all their faults, are generated by a highly standardized process. Reforming that process will not be cheap.

One compelling take on the negative revisions is that they are really Trump’s very own fault. In an excellent post describing some of the technicalities that drive revisions, Claudia Sahm says:

“This is a policy problem, not a measurement problem. … Large, unpredictable shifts in economic policy are placing unusual strains on our measurement apparatus because they are causing large, unpredictable changes in the behavior of consumers and businesses. These changes are difficult to measure in real time. The GDP statistics this year have struggled to isolate massive swings in imported goods around the start of tariffs from its measure of domestic production. The initial estimates of payrolls didn’t capture the slowdown in employment, but that’s more a reflection of how sharp the jobs slowdown is, rather than a limitation of the surveys.“

The key lesson here is that shifts in the policy landscape can make economic activity more difficult to measure. And of course, policy uncertainty has contractionary effects on top of the stagflationary effects of higher taxes (i.e., tariffs). But I’m not holding out hope that Trump will engage in any introspection on the point.

As Sahm explains, the sharp slowing of job growth serves to highlight one of the difficulties inherent in survey-based measures of economic performance: not all responses are timely, and that is likely aggravated when underlying changes in activity are dramatic. In fact, she says, the June revision was driven largely by late reporting. Furthermore, the May and June revisions to payrolls were also partly driven by a change in seasonal adjustment factors based on new data (BLS uses a concurrent seasonal adjustment methodology).

In terms of industries, half of the June revision to payrolls came from state and local education, erasing an initial estimate showing that public education jobs had increased in June, which perplexed analysts at the time. The other half of the revision was spread broadly across the private sector.

In addition to the changeable nature of survey data and seasonal variability, BLS reports suffer because they often involve shaky assumptions made necessary by the limits of survey coverage. Perhaps the most controversial of these comes from the so-called birth/death (b/d) model of business formation/closure. This model is used by the BLS to estimate the net jobs created by new businesses that cannot be covered by the monthly Establishment Survey. Month-to-month, that can be a large gap to fill. Unfortunately, the b/d model can be extremely inaccurate, especially at turning points. In July 2025, the b/d model added about 257,000 jobs to total new jobs (prior to seasonal adjustment). Thus, the b/d assumption was 3.5 times the seasonally adjusted total gain of 73,000!

Critics of BLS methodology insist that its monthly payroll estimates should be benchmarked to quarterly data from a different survey as soon as it is available: the Quarterly Census of Employment and Wages, which has a 90% response rate. From Mish Shedlock:

“It is inexcusable for the BLS to not incorporate QCEW data as soon as possible.

“Instead, it relies on poor sampling of a small subset. On that poor sample, the response rate is pathetic.

“In addition, there is survival bias. In recognition of survival bias, the BLS concocted its absurd birth-death model.

“And on top that that, struggling businesses have no incentive to respond. In contrast, large corporations likely have someone dedicated to filling out government surveys.”

I’ve been critical of large BLS revisions in the past, as well as glaring inconsistencies between estimates of payroll jobs from the Establishment Survey and total civilian employment from the BLS Household Survey. Of course, they are different surveys designed to estimate different things with different samples, different coverage, geared toward counting jobs in one case and people employed and unemployed in the other. The two are benchmarked differently and at different frequencies. Still, it’s unsettling to see the two surveys diverge sharply in terms of monthly changes or trends, or to see consistently one-directional revisions. John Podhoretz states that the number of new nonfarm payroll jobs has been revised down in 25 of the past 30 months!

As Veronique de Rugy says, flaws are not the same as bad faith. Surely improvements can be made to both BLS surveys, their benchmarking, and to other adjustments and assumptions made for reporting. However, it’s pretty clear that BLS has not had the staffing and resources necessary to address these shortcomings. Over the ten years ending in 2024, inflation-adjusted BLS funding declined by more than 20%. At the same time, response rates on the Household survey have declined from 89% to less than 70%. The Establishment Survey of nonfarm businesses has also been plagued by deteriorating response rates, which fell from 61% to less than 43% over the past 10 years. And now, the Trump Administration has proposed an additional budget cut for the BLS of 8% in 2026.

Trump would have done better to ask the BLS commissioner what resources were needed to revamp its processes. Instead, his approach was to create a public spectacle by firing the head of the agency. One has to wonder how Trump might find a well-trained economist or statistician who will take the job if the numbers must always reflect well on the boss.

AI Won’t Repeal Scarcity, Tradeoffs, Or Jobs

04 Monday Aug 2025

Posted by in Artificial Intelligence, Labor Markets

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Every now and then I grind my axe against the proposition that AI will put humans out of work. It’s a very fashionable view, along with the presumed need for government to impose “robot taxes” and provide everyone with a universal basic income for life. The thing is, I sense that my explanations for rejecting this kind of narrative have been a little abstruse, so I’m taking another crack at it now.

Will Human Workers Be Obsolete?

The popular account envisions a world in which AI replaces not just white-collar technocrats, but by pairing AI with advanced robotics, it replaces workers in the trades as well as manual laborers. We’ll have machines that cure, litigate, calculate, forecast, design, build, fight wars, make art, fix your plumbing, prune your roses, and replicate. They’ll be highly dextrous, strong, and smart, capable of solving problems both practical and abstract. In short, AI capital will be able to do everything better and faster than humans! The obvious fear is that we’ll all be out of work.

I’m here to tell you it will not happen that way. There will be disruptions to the labor market, extended periods of joblessness for some individuals, and ultimately different patterns of employment. However, the chief problem with the popular narrative is that AI capital will require massive quantities of resources to produce, train, and operate.

Even without robotics, today’s AIs require vast flows of energy and other resources, and that includes a tremendous amount of expensive compute. The needed resources are scarce and highly valued in a variety of other uses. We’ll face tradeoffs as a society and as individuals in allocating resources both to AI and across various AI applications. Those applications will have to compete broadly and amongst themselves for priority.

AI Use Cases

There are many high-value opportunities for AI and robotics, such as industrial automation, customer service, data processing, and supply chain optimization, to name a few. These are already underway to a significant extent. To that, however, we can add medical research, materials research, development of better power technologies and energy storage, and broad deployment in delivering services to consumers and businesses.

In the future, with advanced robotics, AI capital could be deployed in domains that carry high risks for human labor, such as construction of high rise buildings, underwater structures, and rescue operations. This might include such things as construction of solar platforms and large transports in space, or the preparation of space habitats for humans on other worlds.

Scarcity

There is no end to the list of potential applications of AI, but neither is there an end to the list of potential wants and aspirations of humanity. Human wants are insatiable, which sometimes provokes ham-fisted efforts by many governments to curtail growth. We have a long way to go before everyone on the planet lives comfortably. But even then, peoples’ needs and desires will evolve once previous needs are satisfied, or as technology changes lifestyles and practices. New approaches and styles drive fashions and aesthetics generally. There are always individuals who will compete for resources to experiment and to try new things. And the insatiability of human wants extends beyond the strictly private level. Everyone has an opinion about unsatisfied needs in the public sphere, such as infrastructure, maintenance, the environment, defense, space travel, and other dimensions of public activity.

Futurists have predicted that the human race will seek to become a so-called Type I civilization, capable of harnessing all of the energy on our planet. Then there will be the quest to harness all the energy within our solar system (a Type II civilization). Ultimately, we’ll seek to go beyond that by attempting to exploit all the energy in the Milky Way galaxy. Such an expansion of our energy demands would demonstrate how our wants always exceed the resources we have the ability to exploit.

In other words, scarcity will always be with us. The necessity of facing tradeoffs won’t ever be obviated, and prices will always remain positive. The question of dedicating resources to any particular application of AI will bring tradeoffs into sharper relief. The opportunity cost of many “lesser” AI and robotics applications will be quite high relative to their value to investors. Simply put, many of those applications will be rejected because there will be better uses for the requisite energy and other resources.

Tradeoffs

Again, it will be impossible for humans to accomplish many of the tasks that AI’s will perform, or to match the sheer productivity of AIs in doing so. Therefore, AI will have an absolute advantage over humans in all of those tasks.

However, there are many potential applications of AI that are of comparatively low value. These include a variety of low-skill tasks, but also tasks that require some dexterity or continuous judgement and adjustment. Operationalizing AI and robots to perform all these tasks, and diverting the necessary capital and energy away from other uses, would have a tremendously high opportunity cost. Human opportunity costs will not be so high. Thus, people will have a comparative advantage in performing the bulk if not all of these tasks.

Sure, there will be novelty efforts and test cases to train robots to do plumbing or install burglar alarm systems, and at some point buyers might wish to have robots prune their roses. Some people are already amenable to having humanoid robots perform sex work. Nevertheless, humans will remain competitive at these tasks due to the comparatively high opportunity costs faced by AI capital.

There will be many other domains in which humans will remain competitive. Once more, that’s because the opportunity costs for AI capital and other resources will be high. This includes many of the skilled trades, caregivers, and a great many management functions, especially at small companies. Their productivity will be enhanced by AI tools, but those jobs will not be decimated.

The key here is understanding that 1) capital and resources generally are scarce; 2) high value opportunities for AI are plentiful; and 3) the opportunity cost of funding AI in many applications will be very high. Humans will still have a comparative advantage in many areas.

Who’s the Boss?

There are still other ways in which human labor will always be required. One in particular involves the often complementary nature of AI and human inputs. People will have roles in instructing and supervising AIs, especially in tasks requiring customization and feedback. A key to assuring AI alignment with the objectives of almost any pursuit is human review. These kinds of roles are likely to be compensated in line with the complexity of the task. This extends to the necessity of human leadership of any organization.

That brings me to the subject of agentic and fully autonomous AI. No matter how sophisticated they get, AIs will always be the product of machines. They’ll be a kind of capital for which ownership should be confined to humans or organizations representing humans. We must be their masters. Disclaiming ownership and control of AIs, and granting agentic AIs the same rights and freedoms as people (as many have imagined) is unnecessary and possibly dangerous. AIs will do much productive work, but that work should be on behalf of human owners, and human labor will be deployed to direct and assess that work.

AIs (and People) Needing People

The collaboration between AIs and humans described above will manifest more broadly than anything task-specific, or anything we can imagine today. This is typical of technological advance. First-order effects often include job losses as new innovations enhance productivity or replace workers outright, but typically new jobs are created as innovations generate new opportunities for complementary products and services both upstream in production or downstream among ultimate users. In the case of AI, while much of this work might be performed by other AIs, at a minimum these changes will require guidance and supervision by humans.

In addition, consumers tend to have an aesthetic preference for goods and services produced by humans: craftsmen, artists, and entertainers. For example, if you’ve ever shopped for an oriental rug, you know that hand-knotted rugs are more expensive than machine-weaved rugs. Durability is a factor as well as uniqueness, the latter being a hallmark of human craftspeople. AI might narrow these differences over time, but the “human touch” will always have value relative to “comparable” AI output, even at a significant disadvantage in terms of speed and uncertainty regarding performance. The same is true of many other forms, such as sports, dance, music, and the visual arts. People prefer to be entertained by talented people, rather than highly-engineered machines. The “human touch” also has advantages in customer-facing transactions, including most forms of service and high-level sales/financial negotiations.

Owning the Machines

Finally, another word about AI ownership. An extension of the fashionable narrative that AIs will wholly replace human workers is that government will be called upon to tax AI and provide individuals with a universal basic income (UBI). Even if human labor were to be replaced by AIs, I believe that a “classic” UBI would be the wrong approach. Instead, all humans should have an ownership stake in the capital stock. This is wealth that yields compound growth over time and produces returns that make humans less reliant on streams of labor income.

Savings incentives (and negative consumption incentives) are a big step in encouraging more widespread ownership of capital. However, if direct intervention is necessary, early endowments of capital would be far preferable to a UBI because they will largely be saved, fostering economic growth, and they would create better incentives than a UBI. Along those lines, President Trump’s Big Beautiful Bill, which is now law, has established “Baby Bonds” for all American children born in 2025 – 2028, initially funded by the federal government with $1,000. Of course, this is another unfunded federal obligation on top of the existing burden of a huge public debt and ongoing deficits. Given my doubts about the persistence of AI-induced job losses, I reject government establishment of both a UBI and universal endowments of capital.

Summary

Capital and energy are scarce, so the tremendous resource requirements of AI and robotics means that the real world opportunity costs of many AI applications will remain impractically high. The tradeoffs will be so steep that they’ll leave humans with comparative advantages in many traditional areas of employment. Partly, these will come down to a difference in perceived quality owing to a preference for human interaction and human performance in a variety of economic interactions, including patronization of the art and athleticism of human beings. In addition, AIs will open up new occupations never before contemplated. We won’t be out of work. Nevertheless, it’s always a good idea to accumulate ownership in productive assets, including AI capital, and public policy should do a better job of supporting the private initiative to do so.