Please, no. Let’s not get too aggressive about regulating AI development and use.
Sure, certain kinds of AI regulation are worthwhile and probably unavoidable, but those theoretically fall within the bounds of existing law: the framework in which liability issues, property rights, and claims of fraud are settled. Otherwise, there may be a role for industry self-regulation, perhaps within the kind of framework outlined here. Beyond that, broader and more intrusive government regulation and oversight of AI companies would serve to slow or retard development, and thereby hamper progress on addressing safety and security (because ongoing development will help reveal weaknesses). It would also stifle industry competition in the U.S., and cede advantages to adversaries, particularly China.
Promise and Limits
My position on AI regulation flows from optimism about what AI can accomplish as well as recognition of AI’s inherent limits. It is a powerful technology, but it does not “reason”. It is capable of real innovation, but only to the extent that it mechanically and iteratively constructs solutions based on a catalogue of past knowledge. Of course, it does the latter exceptionally well, and its solutions can be quite novel, drawing upon complex links between phenomena across many dimensions, but that is not the same as creative genius.
Those who anthropomorphize AI tend to fall for superstitions about new technology that have plagued humanity for centuries and have, at times, hindered the advancement of human well being. Yet we have prospered from new technologies in spite of this noise.
Begging For Discipline
Unfortunately, recent warnings from some AI executives and developers have been taken to heart across the political spectrum, though not particularly within the Trump Administration. While I have been critical of many of Trump’s policy positions and initiatives, deregulation has been a bright spot (if very uneven), and the promise of a light touch on AI regulation is another plus. Yet Trump has allowed the Pentagon to prohibit defense contracts with Anthropic as a security concern. In addition, the proposed “AI Force” raises questions, especially in light of Defense (War!) Secretary Pete Hegseth’s Anthropic decision. Presumably, the AI Force would be a branch of Homeland Security. AI obviously has significant military and defense applications. But could such an AI Force remain above the regulatory fray even as it designates certain applications or models to be a threat to national security? And could such a designation be contested?
The “doomers” within the AI industry are an obvious irony. They warn that their work might have “existential” consequences but continue that work under what we might call a self-compelled burden of competence. If anyone and everyone is going to do AI, then they too must do it too, if only for the good of humanity. But they insist that joining forces with the federal government to regulate or even pause development will somehow save the day, despite the global nature of the competition.
Perhaps Deeper Motives
To me, their rationale for government regulation seems implausible. The big AI companies are massive enterprises, and whether or not one believes they could “capture” a new regulatory body, they are nonetheless highly interested in shaping the future of any rules and regulation to which they and their competitors would be subject.
Alex Tabarrokinsists that regulatory capture is typically confined to mature, regulated industries in his post “What Regulatory Capture Actually Looks Like”. He not only doubts that AI doomers have designs on capturing regulators; he also believes that the doomers expressed fears are completely sincere.
As one of Tabarrok’s commenters suggests, however, the AI executives’ behavior comports with the “bootleggers and baptists” theory of public choice, whereby politicians or influential parties claim the highest motives in serving the public interest and yet stand to reap economic gains from “protective” restrictions. In the case of AI, the executives may well be playing both roles. And they might happily accept both roles even if they believe their own warnings of doom. In fact, any rents the big AI concerns gain as a result of new regulations might be viewed as deserved, or unavoidable in any case.
Another plausible explanation for the pro-regulatory (and pro-pause) position taken by some players, notably Anthropic and OpenAI, is discussed by Francis Turner. It has to do with a desperate need for cash flow. One avenue would involve an effort to:
“… wriggle out of the optimistic compute commitments they have made. … both Open AI and Anthropic have made enormous commitments to purchase compute which has been what has driven the explosive growth in data centers. Unfortunately for Big AI the demand simply doesn’t seem to be there. Or at least the demand to pay the prices Big AI wants to charge.“
Turner also notes that a regulatory push to ban Chinese models would be seen as a competitive boost for these companies. Finally, Turner believes the big AI companies employ individuals with a personal drive to control the evolution and use of AI. That’s not hard to believe! A regulatory regime would offer ample opportunities for industry insiders to step into roles in which they could do just that. Possible motives are ample, whether borne of altruism, power-seeking, or the pursuit of economic gain. We could call this mere rent seeking or political bargaining, rather than regulatory capture, but some sort of capture might well be a goal.
Fearsand Risks
Again, cries for regulation are accompanied by a stoking of fears that AI presents existential risks for humanity. One prominent example is closely associated with Noah Smith, though I’m sure he’s not the first to discuss it. That’s the idea that AI will be used to design a deadly supervirus that will wipe out most of the human race. This fear is overblown and unrealistic. A number of extremely deadly pathogens already exist, ebola being the perhaps the most contagious and deadly. Local deployments could surely be used to infect a large number of people, but that would be difficult to pull off and we are capable of containment. It is hardly an existential threat to humanity. As to the creation and spread of deadly new bugs, practical obstacles drastically reduce the likelihood of such an outcome, particularly on an existential scale, as outlined by Claus Wilke in “I’m sorry, you’re not going to die from an AI-engineered supervirus”.
Perhaps the most significant risks presented by AI fall within the realm of cybersecurity. Certain security breaches could conceivably threaten large numbers of people, but they are unlikely to approach existential levels for humanity.
The potential for plain old fraud via AI is real, including the creation and propagation of misinformation at scale. This is probably as much a human or user problem as an AI problem. AI hallucinations are an aspect of it as well, but that’s really an obstacle to adoption, so AI companies are working hard to minimize the problem. Damages under all of these categories should fall within the traditional legal bounds of liability, whether the result of intent or negligence. The latter distinction should carry less weight given that AI decisions are often shrouded within a so-called “black box”. Of course, assigning liability assumes that a responsible party can be identified, but legally defining LLMs as products helps to solve that problem.
Turner also raises the possibility of severe financial damage from an AI agent given authority over large amounts of capital, especially if assigned malicious financial objectives. This category of damage falls within the scope of existing securities laws and regulation.
Finally, some have warned of a risk that humans will suffer “cognitive atrophy” from relying on AI. In fact, it’s said to be a reality even now, and that it will become more severe as humans rely on AI to solve more problems and perform tasks. Will AI cause humans to devolve and face a more brutish existence? While I accept that AI presents a risk of cognitive atrophy, there are also ways in which AI stimulates intellectual curiosity about all manner of things. Much depends on the spirit in which AI is approached. It must be used as a tool and a complement to human intellectual pursuit, not a substitute for it, but that depends in large part on the user. I’m confident that the human spirit of inquiry will remain as strong as ever.
Summary
To sum up my general view regarding the pleadings for regulation of AI, I offer this quote from Per Byland:
“The State’s core function, in both theory and practice, is to give the ruling class a reliable way to extract wealth from society while posing as selfless public servants. It’s an ideal setup for the greedy, self‑absorbed, and unproductive.“
Except in this case we see the ownership and employees within a highly productive sector of the economy ostensibly rooting for the state to make them less productive. Or are they really? If it’s safety they want, then it’s incumbent on them to make their products safe, whatever that might entail, or face legal consequences. Continued development is likely to reveal risks as well as better ways to mitigate those risks.
If it’s economic advantage that Big AI craves, that answer too is in their very hands: they must compete in the AI marketplace to produce capable, reliable, and safe products that buyers value. Adopting a rigid government regulatory solution or a pause in development would likely bring the sacrifice of U.S. competitive advantages in AI and ultimately a centrally-planned and ineffective approach to AI.
As for designs on gaining a political advantage, we can hope that AI doomers will ultimately go the way of all Luddites before them.
Bad policy ideas are circulating that have been conceived amid hysteria over AI. These are interventionist approaches to the development and deployment of AI models, ranging from direct confiscation of AI capital, taxes on the flow of compute, various forms of regulation, and state and local efforts to forbid the construction of data centers. All of these actions would unnecessarily inhibit achievement of AI’s enormous potential benefits and present unnecessary national security challenges as well.
Land Use and Displacement
Emotionally I’m probably just as NIMBY as the next guy when it comes to developments in my vicinity that might offend my personal aesthetics or intrude on my privacy. But at a more rational level, I object to developments that will inflict external costs on me. I happen to live in a private community that provides some buffer against incursions of those kinds, but I deeply sympathize with anyone who finds their property will soon be next door to a large or obnoxious industrial, commercial or government facility, and I despise the use of eminent domain for almost any purpose.
But let’s step back and consider the case in which an owner of private property receives what they feel is just compensation on the sale of their land to a data center developer. This property might be in your close proximity, but you can’t prevent your neighbor from selling unless it’s by way of a larger political process to revoke his property rights. Of course, you can help organize or join a resistance group in an attempt to stop the development. That’s perfectly reasonable if you fear the prospect of having your property stranded in the middle of a new industrial or commercial development.
Ultimately, such efforts are likely to influence negotiations between communities and developers. In fact, developers of data centers can often be persuaded to work with communities in addressing public concerns, and some developers are eager to do so.
Water and Power Consumption
Aside from land use, potential displacement, and aesthetic issues (including plain-old NIMBYism), other underlying concerns exciting local opposition to data centers have to do with predicted strains on water and power supplies. These are no doubt critical issues in certain localities. However, on the whole these concerns are vastly overblown, as elucidated by Andy Masley at this link. In particular, water use by data centers is on the same order of magnitude as other industrial uses. Contrary to some claims, any water pollution by data centers is usually confined to the construction phase, if at all, and in that respect is very much like any other construction project. And as Masley points out, a data center can generate tax revenue for use in reducing water scarcity.
It should also be noted that data centers house the computational power of the entire internet. As the chart (from Masley) at the top of this post shows, AI represents an incremental need that is still relatively small relative to total data center power use. Incidentally, water cooling rather than air cooling reduces a data center’s power consumption.
Nevertheless, the power consumption of data centers is indeed a matter of critical importance and controversy. Referring again to the chart at the top, it’s evident that data center power usage is growing rapidly. However, developers are increasingly planning to produce their own power off-grid, often colocating with their power sources to minimize transmission costs. This includes locating alongside natural gas basins, installing wind and solar collection facilities nearby, and coming soon, incorporating modular nuclear reactors. The latter would provide base-load, dispatchable, zero-carbon power for data centers. Of course, modular reactors will be costly and might eat into returns from developing data centers, but other power sources are costly as well, and it is the one sure dispatchable, zero-carbon, off-grid solution.
Water and energy supplies for data centers are key to enabling broad contributions of AI to consumer welfare, productivity growth, and national security. Local interests should weigh other benefits that construction of data centers will bring to a community. Construction jobs and permanent data center jobs are obviously important considerations, as well as the aforementioned increases in local tax revenue.
State Regulation and Litigation
Of course, AI controversies are playing out at the national and state levels as well. First, there is the issue of AI regulation. AI legislation in all 50 states attempts to regulate various “threatening” aspects of AI. These bills address topics such as fraud prevention (e.g., deep fakes), chatbot safety, and restrictions on automated AI decisioning (e.g., hiring, insurance coverage and claims adjudication).
There is litigation and potential litigation at the state level related to alleged abuses by Open AI’s ChatGPT. These concern the use of customer data and alleged encouragement of self-harm, among other matters. And the New York legislature has passed a bill calling for a one-year moratorium on AI data center development.
These regulatory and legal efforts at the state and local level raise the prospect of fragmented treatment of AI in different jurisdictions that would be disruptive and costly for both AI companies and users. Federalist principles aside, economic efficiency argues for a more uniform approach to many concerns about AI. But whether it’s at the federal, state, or local level, tight regulatory control of AI risks compromising the healthy competitive development of AItechnology and the industry. That’s because politicians and bureaucrats cannot possess the knowledge of evolving competition, scarcity, and market incentives only revealed by free market processes.
Rooting for Regulation
Unfortunately, modern-day Luddites at the national level are calling for a moratorium on AI development. In fact, in 2023, fears of AI misalignment with human interests brought even Elon Musk to call for a six-month “pause” on development. Today, a number of industry insiders call for a “slowdown”, if only other countries go along with it (fat chance!).
Yes, AI is improving… fast, but the most consequential threats have to do with security protocols. Anthropic, in particular, almost begged for government control over its Mythos product, which recently gripped the AI and cybersecurity communities with its advanced ability to identify software vulnerabilities. The Fable version is said to incorporate “guardrails”, but reportedly Fable is vulnerable to “jailbreaks”. In what should not have surprised Anthropic after its own warnings, the federal government imposed export controls, restricting access by foreign nationals. And now, Anthropic has withdrawn availability of the models worldwide..
Be Careful What You Ask For
Perhaps Anthropic got what it deserved, but sadly, the Trump Administration seems to have crossed a threshold from a “light touch” approach to regulating AI to something more severe. Let’s hope the Mythos/Fable affair doesn’t presage a permanent transition from private governance to state control. Thatwouldinhibit development and present risks likely to rattle some of AI’s most important customers, .
The last link cites Timnit Gebru’s critique that AI labs have made a huge miscalculation:
“She argues that AI labs have consistently used ‘dangerous AI’ narratives for marketing, investor appeal, and competitive advantage, only for the narrative to backfire when actual state power intervenes. (on X)”
It’s possible that Anthropic and a few of its competitors have fallen for the same mistaken notion that central planning by government bureaucrats can improve upon market processes. Statists on the right and the left have been eager to join the chorus for regulatory control.
Dean Ball channels Friedrich Hayek in the following tweet on the mistaken impression that government must impose a “strategy” and “plan” AI.
“I think part of it, at least vis a vis US/China competition, is that US and western chattering classes find it hard to believe that the market-driven outcome of frontier AI could possibly be right. They basically believe, in their hearts, that the Chinese system, with its ‘industrial strategy,’ has eclipsed capitalism. So they harbor the same inferiority complex toward the Chinese system that many Americans once harbored toward the EU’s system. Their heuristic is that the industrial strategists of China have grasped the whole picture of the technological competition in a way that US industrialists, with their ‘profit maximizing incentives,’ could not possibly have matched. And so any outcome in the economy that is not the result of ‘strategy’ is therefore prima facie worse than what the ‘strategists’ have concocted. They also believe the Chinese strategists possess awesome powers of foresight and the ability to evade all tendencies of financial and economic gravity, due of course to ‘strategy,’ really it’s almost a kind of orientalism.”
National security is an important consideration, of course, but AI development should not be hamstrung for fear of the ever-present need for improved encryption or by the prospect of threats from autonomous weapons systems. Indeed, AI can and should be put to use defending against all such threats to national security without compromising its promise as a revolutionary technology with a wide range of applications. Again, Trump’s purported intent to encourage U.S. AI development is undercut by his fixations on controlling trade and “taking stakes”. And do foreign customers want to deal with this confusing state of affairs? Or simply go to China?
AI and Capital Redistribution
Another nest of controversies has to do with the widespread presumption that AI will be negative for labor markets. Prescriptions from the populist left and right include various kinds of AI taxation, redistribution, and even nationalization.
Bernie Sanders and Donald Trump both want a sovereign wealth fund, and Sanders wants to fund it with a one-time 50% tax on AI stock. Sanders, the High Prince of Economic Parasites, is sponsoring a bill he claims would allow the American public to take a role in determining the future of AI, whatever that means. What he hopes to create is a mechanism for wealth redistribution, since the fashionable view is that AI will be a catastrophe for labor. While the AI industry is far from profitable at the moment, many AI stocks have soared in value. And Sanders’ target “AI industry” might fairly broad, including chip manufacturers and other producers of AI infrastructure.
If the public wants to kill AI investment in the U.S., tank equity markets, and give politicians an excuse for more profligate spending, then Sander’s bill is a grand idea. It would be an outright expropriation of wealth. The impacts on economic growth, productivity, American competitiveness, and national security would be unambiguously negative. And lest you think such a redistribution is necessary to compensate for job losses caused by AI, that issue is far from settled. In fact, it’s highly likely that the job realignment certain to take place will result in growth from a variety of occupations previously unimagined, just as technological advances have in the past.
The Compute Tax
Others (including Sanders) have also broached the idea of a “compute tax”, or as Brian Albrecht explains:
“… a levy on computational resources. Think GPU hours, processing power, data center electricity, or some similar proxy for AI work.”
Albrecht believes the real intent is to tax the stock of physical AI capital, as opposed to a flow of input services rendered for AI. But consider the number of goods and services whose values are likely to be enhanced by the use of AI as an input. And also consider the innovation and discovery that will be made possible by AI. Albrecht wisely questions the logic of adding to the cost and discouraging this value added via taxation. In the context of killing the golden goose, he cites two rules of optimal taxation: don’t tax intermediate goods and don’t tax capital. When the supply of capital is elastic, he notes, taxing it is more likely to harm workers than to help them. And one can reasonably argue that the external benefits expected to flow from AI would justify a compute subsidy rather than a tax. Finally, Albrecht cautions that a compute tax, unless it is very broad and at a very high rate, won’t raise much revenue.
Trump’s Confusion
Bernie Sanders deserves plenty of condemnation for his infantile, class-warfare rhetoric and interventionist approach to economic policy, including state ownership of the means of production. But in practice Donald Trump isn’t much better. He’s been busy partially nationalizing several different industries, including steel, semiconductors, nuclear energy, rocket motors, quantum computing, and critical mineral supplies, often with direct reins on business decisions (e.g., the “Golden Share” in U.S. Steel). Now, he’s angling to acquire equity stakes in AI companies. The Senate Armed Services Committee is ready to help him out with a bill that would establish a Department of Defense Equity Investment Account at the Treasury.
These are all part of the sovereign wealth fund Trump has decided is in the fiscal and national security interests of the U.S. Again, government ownership stakes in private companies invite cronyism, political interference, and regulatory capture. In the case of AI, it is an invitation to censorship and government surveillance. Moreover, spare government funds would be better spent paying down our burgeoning public debt, reducing government obligations and interest expense at zero risk. In contrast, the value of private equity stakes and their returns are fully at risk, while leaving government debt, interest expenses, and interest rate rollover risks in place.
Trump is now inveigling the likes of Sam Altman (OpenAI), Dario Amodei (Anthropic), Sundar Pichai (Google), and even Elon Musk (xAI) to accept his vision of public ownership of AI stock. It’s effectively a trap and a prescription for competitive failure, but Trump doesn’t get it.
Superabundance?
Many AI industry leaders have indeed bought into some version of an AI wealth transfer, primarily because they accept the notion of superabundance along with heavy losses of remunerative work for humans. But in fact they don’t understand the economics of capital deepening and the contradictions implied by their position.
First, savings and funds available for capex are scarce, and any given project for AI buildout must compete with many other valued uses. The working world will not be monopolized by AI robots any time soon, even given dramatic cost reductions. AI may well increase the productivity of human workers (along with their wages) in greater proportion than other forms of physical capital. But some forms of labor are likely to be in surplus, and that will cause the wages in those occupations to become more competitive relative to the cost of potential AI-augmented substitutes. In fact, occupations in which humans are more competitive than machines will persist. Here is Albrecht on this point:
“And comparative advantage always pops up fighting against [human job losses]. When automation makes some things cheap, the things that remain expensive tend to be the things that are hard to automate. And the things that are hard to automate are, almost by definition, the things where humans still have comparative advantage. The saved dollar drifts toward where humans are still worth paying. That’s not optimism. That’s what comparative advantage means.“
A second contradiction of the superabundance job-loss narrative is, as I’ve said, that there will be many inventive new occupations available for humans. At worst, job losses will be a transitional phenomenon. Third, superabundance itself implies drastically lower prices, which would ultimately benefit wage earners and consumers, obviating the need for government intervention on their behalf.
I had to laugh when I read this quote of Rebecca Lowe, who has an amusing and sensible reaction to the “AI will take all the jobs” narrative:
“I think a large part of this is you don’t really get experts in their particular domains writing about AI. Instead, you get ‘the AI expert’, and they want to reinvent the wheel. You see this when they write about economics, or when they write about philosophy. You talk to an AI person and suddenly they’re like, ‘I’ve just discovered this thing!’ And it turns out they’re talking about, like, supply and demand. And you’re like, oh my God.”
CCP Interference
I’ll briefly touch on one other controversy: whether the anti-AI/data center furor is being instigated by the Chinese in an attempt to undermine U.S. leadership in AI. The House Energy and Commerce Committee claims to have evidence that strongly suggests the CCPs involvement in attempts to hamstring substantial U.S. leadership in AI. Apparently no details on that evidence have been made public, however. It would not be surprising or uncharacteristic of the CCP, and if true would constitute another tension in the attempt to safeguard national security while avoiding government obstruction in AI development.
Summary
Artificial intelligence is animating economic controversies at the local, state and federal levels. Like other forms of industrial development, opponents are roused by claims of strains on local resources as well as displacement of property owners. Some of these claims are exaggerated or can be resolved via negotiation or technological solutions.
There are also fears that AI can be used in a variety of nefarious ways. There may be legitimate dangers, and AI companies themselves are actively working to address so called “alignment” issues. Nevertheless, there are increasing calls for state and/or federal regulation of AI. These proposals must be approached cautiously or they could easily derail U.S. progress on perhaps the most promising technologies to ever come down the line. That would indeed represent an economic and national security failure.
Finally, fear that AI will lead to large-scale job losses and widening inequality has prompted calls for taxes on AI capital, or even partial nationalization, with redistribution of future profits to the public. This would be a colossal mistake. Nothing could stanch AI development more effectively than such a policy. Unfortunately, even Donald Trump has called for the government to take equity stakes in AI companies pursuant to “national priorities” and supposedly for the benefit of American taxpayers. In fact, this partial nationalization has already begun. This is a prescription for destructive regulation, planning failures, and corruption.
The key lesson in all this is that we’ll all be better off if government stays out of the way of AI development.
I’m an AI enthusiast, and while I have econometric experience and some knowledge of machine learning techniques, I’m really just a user — I really lack deep technical expertise in the area of AI. However, I use it frequently for research related to my hobbies and to navigate the kinds of practical issues we all encounter day-to-day. With one good question, AI can transform what used to require a series of groping web searches into a much more efficient process and informative result. In small ways like this and in much greater ways, AI will bring dramatically improved levels of productivity and prosperity to the human race.
Still, the fear that AI will be catastrophic for human workers is widely accepted. Some claim it’s already happening in the workplace, but the evidence is thin (and see here and here). While it’s certain that some workers will be displaced from their jobs by AI, ultimately new opportunities (and some old ones) will be available.
I’ve written several posts (here, here, here, and here) in which I asserted that a pair of phenomena would ensure continuing employment opportunities for humans in the presence of AI: an ongoing scarcity of resources relative to human wants, and the principle of comparative advantage. Unfortunately, the case I’ve made for the latter was flawed in one critical way: reciprocal comparative advantages across different factors of production are not guaranteed. In trade relationships, trading partners have reciprocal cost advantages with respect to the goods they exchange, and I extended the same principle to factors of production in different sectors. Unfortunately, that analogy with trade does not always hold up, in part because the owners of productive inputs don’t fully engage in direct trade with one another.
Thus, so-called reciprocity of opportunity costs cannot guarantee future employment for humans in a world with AI-augmented capital. Nevertheless, there is a strong case that reciprocity of comparative advantages will exist, whether labor and capital are (less than perfect) complements or substitutes. This is likely to hold up even though human labor and AI-augmented capital could well become more substitutable in the future.
Below, I’ll start by reviewing the principles of scarcity, opportunity costs, and input selection. Then I’ll turn to a couple of other rationales for a more sanguine outlook for human jobs in a world with widely dispersed AI in production. Finally, I’ll provide more detail on whether reciprocal input opportunity costs are likely to exist in a world with AI-augmented capital, and the implications for continued human employment.
Scarcity and Advantages
Scarcity must exist for a resource to carry a positive price. That price is itself a measure of the resource’s degree of scarcity as determined by both demand and supply. And ultimately an input’s price reflects its opportunity cost, or the reward foregone on its next-best use.
Labor and capital are both scarce inputs. Successful integration of AI into the capital stock will make capital more productive, but it will not eliminate the fundamental scarcity of capital. There will always be more use cases than available capital, and particular uses will always have positive opportunity costs.
If capital is more productive than labor in a particular use, then capital has an absolute advantage over labor in that line of production. If capital and labor are perfect substitutes in producing good X, then capital can be substituted for labor at a constant rate, say 1 unit of capital for every 2 units of labor, in a straight line without any change in output.
One might expect the producer in this scenario to choose to employ only capital in production. That’s the general argument put forward by AI pessimists. They appeal to a presumed, future absolute advantage of AI (or AI combined with robotics) in each and every line of production. In fact, the pessimists treat the AI robots of the future as perfect substitutes for labor. That’s not a foregone conclusion, however, and even if it were, absolute advantages are not reliable guides to economic decision-making.
Physical tradeoffs in a line of production are one thing, but opportunity costs are another, as they depend on rewards in other lines of production. In the example above, if a unit of capital costs slightly less than two units of labor, then it would indeed be rational to employ all capital and zero labor in producing good X. Then, capital has not just an absolute advantage in X, but also a sufficient cost advantage over labor (or else labor would be more highly valued elsewhere). In this example, the labor share of income from producing X is zero. The capital share is 100%.
Income Shares
The simple case just described is the same as the one examined by Brian Albrecht in his recent analysis of “Capital In the 22nd Century”, an essay by Philip Trammell and Dwarkesh Patel. The controversial conclusion in the latter essay is that capital taxation will be necessary in a world of strong AI, because labor’s share of income will approach zero.
Albrecht is rightfully skeptical. He examines the case of capital and labor as perfect substitutes, as above, and the “corner solution” with all capital and no labor in production.
Albrecht notes that empirical estimates show that capital and labor are not even close to perfect substitutes. In fact, on an economy-wide basis, capital and labor have a fairly high degree of complementarity. But this varies across sectors, and Albrecht acknowledges that substitutability might increase in a world of strong AI.
Albrecht covers a second avenue through which AI-augmented capital could displace labor: rapid growth in the capital stock fueled by stubbornly high returns to capital. While Albrecht’s main interest is in whether capital taxation will one day be necessary, his analysis is obviously a useful reference for thinking about whether labor will be completely displaced by AI-augmented capital.
Again, capital is a scarce resource. For it to grow unbounded in AI-augmented forms, its real yield (and marginal product) must always and forever resist diminishing returns while also exceeding rates of time preference. It also must stay ahead of depreciation on an ever-expanding stock of existing capital. Albrecht is of the opinion that AI-augmented capital might be especially prone to rapid obsolescence. For that matter, it remains to be seen whether the many moving parts of humanoid robots will be highly vulnerable to wear and tear in the field. Perhaps the use of AI in materials research and robotics design can ease those physical constraints.
There are other obstacles to complete AI dominance in the labor market. Institutions of almost all kinds will always face AI deployment risks. On this point, an interesting piece is “Persuasion of Humans Is the Bottleneck”. The author, Erik Schiskin, says that in addition to investment in physical capital:
“AI deployment is capital-intensive in a different way: admissibility—what institutions can rely on, defend, insure, audit, and appeal without taking unbounded tail risk.“
Of course, this too increases the cost of AI deployment.
A “One-Good” Analysis Is Inadequate
Albrecht essentially confines his analysis of inputs and incomes shares to a world in which thee is only one kind of final output, and yet he makes the following assertion:
“Remember this is a model of the whole economy, so that would mean there’s not a single thing produced that humans have a comparative advantage.“
That kind of aggregation is not possible in a world with comparative advantages, however. A mental model with only one good cannot describe a world with opportunity costs. Capital and labor are both scarce resources. Their alternate uses cannot be buried within a single aggregation without appealing to the “idle state” as an alternative use.
With more than one good, the opportunity cost of using an addition unit of capital to produce good X is what must be foregone when that unit of capital is not deployed to its next-best use producing some other good.
And to return to our earlier example, if capital is the exclusive input to the production of Good X, that’s because 1) capital is perfectly substitutable for labor in that line of production; 2) capital is more productive than labor in producing good X; and 3) capital’s relative cost for producing good X is sufficiently low to favor its use.
Factor Intensities
Now I’ll revisit my earlier rationale that for labor’s continuing role in a world with AI-augmented capital. I began to have doubts abouthowinput substitutability might play out as AI is deployed (see other views here and here, as well as Albrecht’s post). So I enlisted the assistance of two AI tools, Grok and ChatGPT, to help identify relevant economic literature bearing on the durability of the “reciprocity” phenomenon given a technical shock. There were differences in the conclusions of the two AI tools when certain embedded assumptions were overlooked or not initially made plain. Considerable push-back against these analyses by yours truly helped to align the conclusions. I’ll be skipping over lots of gory details, but I’d welcome any and all feedback from readers with insight into the issues, or with deeper knowledge of this type of economic research.
Reciprocal input opportunity costs (and comparative advantages) depend on parameters that help determine factor intensity and income shares. A paper by Ronald W. Jones in 1965 helped delineate conditions that preserve the relative rankings of factor intensities across sectors in a closed economy. Those conditions can be extended to the context of reciprocal input opportunity costs. I’ll briefly discuss those conditions in the next couple of sections.
For now, it’s adequate to say that when capital’s comparative advantage in one sector is offset to some degree by a reciprocal comparative advantage for labor in another, we need not conclude that human labor will become obsolete given a positive shock to the productivity of capital. Again, however, in earlier posts I mistakenly asserted that this kind of reciprocity was a more general phenomenon. It is not, and I should have known that. That said, the specifics of the conditions are of interest in the context of AI-augmented capital.
Non-Reciprocity
First, let’s cover cases that are the least conducive to reciprocal opportunity costs: when capital and labor are perfect substitutes in the production of all goods, and when they are perfect complements in the production of all goods. While there are many cases in which inputs are used in fixed proportions in the short run, or where one input can easily be substituted for another in a particular task, it’s still safe to say we don’t generally live in either of those worlds. Nevertheless, they are instructive to consider as extreme cases.
The case of perfect substitutes was discussed above in connection with Albrecht’s post. Then cost minimization yields corner solutions involving 100% capital and zero labor for both goods if capital is everywhere more productive (relative to its cost) than labor. There is no reciprocity of input opportunity costs across goods except by coincidence, and labor will be unemployed.
The other case certain to have non-reciprocal opportunity costs (except by coincidence) is when capital and labor are perfect complements. Then, the rigidity of resource pairings lead to indeterminate input prices and an inability to absorb unemployed resources. However, note that if perfect complementarity were to persist under strong AI, as unlikely as that seems, it would not lead to a capital share of 100%.
A Paradox of Substitutability
Now I turn to more plausible ranges of substitutabilty. There’s a notion that capital with AI enhancements will become more substitutable for labor than it has been historically. And if that’s the case, there’s a fear that humans will be out of work and produce a zero labor share of income. This same line of thinking holds that future prospects for human employment and labor income are better if capital and labor remain somewhat complementary.
That framing of the future of work and its dependence on complementarity vs. substitutability is fairly intuitive. Paradoxically, however, a higher degree of substitutability might not have any impact on human comparative advantages, or might even strengthen them, as long as the elasticity of substitution is not highly asymmetric across sectors.
The Ronald Jones paper referenced above shows that under certain conditions, factor intensities for different goods will retain their relative rankings after a shock to factor prices or technology. By implication, comparative advantages will be preserved as well. So if capital has a greater intensity in producing X than in producing Y, that ranking must preserved after a shock if capital and labor are to retain their reciprocal comparative advantages. Jones shows this is satisfied when the inputs in both sectors are equally substitutable, or when changes in substitutability across sectors are equal. If those changes are not greatly different, then reversals in factor intensity are unlikely and reciprocity is usually preserved. Therefore, if augmenting capital with AI increases the elasticity of substitutability between capital and labor broadly, there is a good chance that many reciprocal comparative advantages will be preserved.
Another general guide implied by the Jones paper is that factor intensities and reciprocal comparative advantages are more likely to be preserved when production technologies differ, input proportions are stable, and differences in substitutability are similar or differ only moderately.
Empirically, elasticities of substitution between capital and labor vary across industries but are typically well within a range of complementarity (0.3 to 0.7). Starting from these positions, and given increases in substitutability via AI-augmented capital, factor proportions aren’t likely to change drastically, and rankings of capital intensity aren’t likely to be altered greatly, thus preserving comparative advantages for most sectors.
Restatingthe Last Section
Starting from a world in which inputs have reciprocal comparative advantages (and reciprocal opportunity costs), a technological advance like the augmentation of capital via AI might or might not preserve reciprocity. The return on capital will increase, and widespread capital deepening is likely to drive up the rental rate of a unit of capital relative to its higher marginal product. If capital intensities increase in all sectors, but relative rankings of capital intensities are preserved, then labor will retain comparative advantages despite the possible absolute advantages of AI-augmented capital. Labor’s share of income will certainly not fall to zero.
If the substitutability of capital and labor increase, ongoing reciprocity can be preserved if the change in substitutability does not differ greatly across sectors. This is true even for large, but broad, increases in substitutability. However, should large increases in substitutability be concentrated to some sectors but not others, reciprocity could fail more broadly. If greater substitutability implies greater dispersion in substitutabilities, then reciprocity is likely to be less stable.
Of course, regardless of the considerations above, there are certain to disruptions in the labor market. Classes of workers will be forced to leverage AI themselves, find new occupations, or reprice their services. Nevertheless, once the dynamics have played out, labor will still have a significant role in production.
Recapping the Whole Post
Here are a few things we know:
Capital will remain scarce, even more so if its return reaches impressive heights via AI augmentation. Another way of saying this is that interest rates would have to rise in order to induce saving. Depreciation and obsolescence of capital will reinforce that scarcity, and there are now and always will be too many valued uses for capital to become a free good.
Capital and labor are not perfect substitutes in most tasks and probably won’t be, even given strong AI-augmentation.
Capital and labor are not perfect complements, though they have been complementary historically. Their complementarity might well be moderated by AI.
Besides capital scarcity, there will be a continuing series of bottlenecks to AI deployment, some of which will demand human involvement.
We start our transition to a world of AI-augmented capital with different inputs having comparative advantages in producing some goods and not others. In general, at the outset, there is a reciprocity of input comparative advantages and opportunity costs across sectors, much as reciprocal opportunity costs exist in cross-country trade relationships.
A technological shock like the introduction of strong AI will alter these relationships. However, as long as factor intensities in different sectors maintain their rank ordering, reciprocal opportunity costs will still exist.
If substitutability increases with the introduction of AI-augmented capital, reciprocal opportunity costs will be preserved as long as the changes in the degree of substitutability do not differ greatly across sectors.
My earlier contention that reciprocal opportunity costs were the rule was incorrect. However, it’s safe to say that reciprocity will persist to one degree or another, even if more weakly, as the transition to AI goes forward. That means labor will still have a role in production, despite many areas in which AI-augmented capital will have an absolute advantage. And we haven’t even discussed preferences for “the human touch” and the likelihood that AI will spawn new opportunities for human labor as yet unimagined.
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 beenevenmore 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 asanimpediment 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.
As of February 2026, I’m adding this short preamble to a few older posts on the subject of AI and future prospects for human labor. In the original post below (and a few others), I overstated the case that the law of comparative advantage would assure a continued role for humans in production. I still think the case is strong, mind you, but now I’m convinced that the outcome depends on elasticities of input substitution and how those elasticities might shift given the advent of AI-augmented capital. You can read my most recent thoughts on the matter here.
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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.
As of February 2026, I’m adding this short preamble to a few older posts on the subject of AI and future prospects for human labor. In the original post below (and a few others), I overstated the case that the law of comparative advantage would assure a continued role for humans in production. I still think the case is strong, mind you, but now I’m convinced that the outcome depends on elasticities of input substitution and how those elasticities might shift given the advent of AI-augmented capital. You can read my most recent thoughts on the matter here.
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In this case, the “A” stands for Altman. Now Sam Altman is no slouch, but he’s taken a few ill-considered positions on public policy. Altman, the CEO of Open AI, wrote a blog post back in 2021 entitled “Moore’s Law For Everything” in which he predicted that AI will feed an explosion of economic growth. He also said AI will put a great many people out of work and drive down the price of certain kinds of labor. Furthermore, he fears that the accessibility of AI will be heavily skewed against the lowest socioeconomic classes. In later interviews (see here and here), Altman is somewhat demure about those predictions, but the general outline is the same: despite exceptional growth of GDP and wealth, he envisions job losses, an underclass of AI-illiterates, and a greater degree of income and wealth inequality.
Not Quite Like That
We’ve yet to see an explosion of growth, but it’s still very early in the AI revolution. The next several years will be telling. AI holds the potential to vastly increase our production possibilities over the course of the next few decades. For that and other reasons, I don’t buy the more dismal aspects of Altman’s scenario, as my last two posts make clear (here and here).
There will be plenty of jobs for people because humans will have comparative advantages in various areas of production. AI agents might have absolute advantages across most or even all jobs, but a rational deployment would have AI agents specialize only where they have a comparative advantage.
Scarcity will not be the sort of anachronism envisioned by some AI futurists, Altman included, and scarcity of AI agents (and their inputs) will necessitate their specialization in certain tasks. The demand for AI agents will be quite high, and their energy and “compute” requirements will be massive. AI agents will face extremely high opportunity costs in other tasks, leaving many occupations open for human labor, to say nothing of abundant opportunities for human-AI collaboration.
However, I don’t dismiss the likelihood of disruptions in markets for certain kinds of labor if the AI revolution proceeds as rapidly as Altman thinks it will. Many workers would be displaced, and it would take time, training, and a willingness to adapt for them to find new opportunities. But new kinds of jobs for people will emerge with time as AI is embedded throughout the economy.
Altman’s Rx
Altman’s somewhat pessimistic outlook for human employment and inequality leads him to make a couple of recommendations:
1) Ownership of capital must be more broadly distributed.
2) Capital and land must be taxed, potentially replacing income taxes, but primarily to fund equity investments for all Americans.
Here I agree with the spirit of #1. Broad ownership of capital is desirable. It allows greater participation in the capitalist system, which fosters political and economic stability. And wider access to capital, whether owned or not, allows a greater release of entrepreneurial energy. It also diversifies incomes and reduces economic dependency.
Altman proposes the creation of an American Equity Fund (AEF) to hold the proceeds of taxes on land and corporate assets for the benefit of all Americans. I’ll get to the taxes in a moment, but in discussing the importance of educating the public on the benefits of compounding, Altman seems to imply that assets in AEF would be held in individual accounts, as opposed to a single “public” account controlled by the federal government. Individual accounts would be far preferable, but it’s not clear how much control Altman would grant individuals in managing their accounts.
To Kill a Golden Goose
Taxes on capital are problematic. Capital can only be accumulated over time by saving out of income. Thus, as Michael Munger points out, as a general proposition under an income tax, all capital has already been taxed once. And we tax the income from capital at both the corporate and individual level. So corporate income is already double taxed: corporate profits are taxed along with dividend payments to shareholders.
Altman proposed in his 2021 blog post to levy a tax of 2.5% on the market value of publicly-traded corporations each year. The tax would be payable in cash or in corporate shares to be placed into the AEF. The latter would establish a kind of UnLiquidated Tax Reserve Accounts (ULTRA), which Munger discusses in the article linked above (my bracketed x% in the quote here):
“Instead of taking [x%] of the liquidated value of the wealth, the state would simply take ownership of the wealth, in place. An ULTRA is a ‘notional equity interest.’ The government literally takes a portion of the value of the asset; that value will be paid to the state when the asset is sold. Now, it is only a ‘notional’ stake, in the sense that no shared right of control or voting rights exists. But for those who advocate for ULTRAs, in any situation where tax agencies are authorized to tax an asset today, but cannot because there is no evaluation event, the taxpayer could be made to pay with an ULTRA rather than with cash.”
This solves all sorts of administrative problems associated with wealth taxes, but it is draconian nevertheless. Munger quotes an example of a successful, privately-held business subject to a 2% wealth tax every year in the form of an ULTRA. After 20 years, the government owns more than a third of the company’s value. That represents a substantial penalty for success! However, the incidence of such a tax might fall more on workers and customers and less on business owners. And Altman would tax corporations more heavily than in Munger’s example.
A tax on wealth essentially penalizes thrift, reduces capital accumulation, and diminishes productivity and real wages. But another fundamental reason that taxes on capital should be low is that the supply of capital is elastic. A tax on capital discourages saving and encourages capital flight. The use of avoidance schemes will proliferate, and there will be intense pressure to carve out special exemptions.
A Regressive Dimension
Another drawback of a wealth tax is its regressivity with respect to returns on capital. To see this, we can convert a tax on wealth to an equivalent income tax on returns. Here is Chris Edwardson that point:
“Suppose a person received a pretax return of 6 percent on corporate equities. An annual wealth tax of 2 percent would effectively reduce that return to 4 percent, which would be like a 33 percent income tax—and that would be on top of the current federal individual income tax, which has a top rate of 37 percent.”
… The effect is to impose lower effective tax rates on higher‐yielding assets, and vice versa. If equities produced returns of 8 percent, a 2 percent wealth tax would be like a 25 percent income tax. But if equities produced returns of 4 percent, the wealth tax would be like a 50 percent income tax. People with the lowest returns would get hit with the highest tax rates, and even people losing money would have to pay the wealth tax.“
Edwards notes the extreme inefficiency of wealth taxes demonstrated by the experience of a number of OECD countries. There are better ways to increase revenue and the progressivity of taxes. The best alternative is a tax on consumption, which rewards saving and capital accumulation, promoting higher wages and economic growth. Edwards dedicates a lengthy section of his paper to the superiority of a consumption tax.
Is a Wealth Tax Constitutional?
The constitutionality of a wealth tax is questionable as well. Steven Calabresi and David Schizer (C&S)contend that a federal wealth tax would qualify as a direct tax subject to the rule of apportionment, which would also apply to a federal tax on land. That is, under the U.S. Constitution, these kinds of taxes would have to be the same amount per capita in every state. Thus, higher tax rates would be necessary in less wealthy states.
C&S also note a major distinction between taxes on the value of wealth relative to income, excise, import, and consumption taxes. The latter are all triggered by transactions entered into voluntarily. They are avoidable in that sense, but not wealth taxes. Moreover, C&S believe the founders’ intent was to rely on direct taxes only as a backstop during wartime.
The recent Supreme Court decision in Moore v. United States created doubt as to whether the Court had set a precedent in favor of a potential wealth tax. According to earlier precedent, the Constitution forbade the “laying of taxes” on “unrealized” income or changes in wealth. However, in Moore, the Court ruled that undistributed profits from an ownership interest in a foreign business are taxable under the mandatory repatriation tax, signed into law by President Trump in 2017 as part of his tax overhaul package. But Justice Kavanaugh, who wrote the majority opinion, stated that the ruling was based on the foreign company’s status as a pass-through entity. The Wall Street Journalsays of the decision:
“Five Justices open the door to taxing unrealized gains in assets. Democrats will walk through it.”
In a brief post, Calabrisi laments Justice Ketanji Brown Jackson’s expansive view of the federal government’s taxing authority under the Sixteenth Amendment, which might well be shared by the Biden Administration. But the Wall Street Journal piece also describes Kavanaugh’s admonition regarding any expectation of a broader application of the Moore opinion:
“Justice Kavanaugh does issue a warning that ‘the Due Process Clause proscribes arbitrary attribution’ of undistributed income to shareholders. And he writes that his opinion should not ‘be read to authorize any hypothetical congressional effort to tax both an entity and its shareholders or partners on the same undistributed income realized by the entity.’”
Growth Is the Way, Not Taxes
AI growth will lead to rapid improvements in labor productivity and real wages in many occupations, despite a painful transition for some workers requiring occupational realignment and periods of unemployment and training. However, people will retain comparative advantages over AI agents in a number of existing occupations. Other workers will find that AI allows them to shift their efforts toward higher-value or even new aspects of their jobs. Along the same lines, there will be a huge variety of new occupations made possible by AI of which we’re only now catching the slightest glimpse. Michael Strain has emphasized this aspect of technological diffusion, noting that 60% of the jobs performed in 2018 did not exist in 1940. In fact, few of those “new” jobs could have been imagined in 1940.
AI entrepreneurs and AI investors will certainly capture a disproportionate share of gains from an AI revolution. Of course, they’ll have created a disproportionate share of that wealth. It might well skew the distribution of wealth in their favor, but that does not reflect negatively on the market process driving the outcome, especially because it will also give rise to widespread gains in living standards.
Altman goes wrong in proposing tax-funded redistribution of equity shares. Those taxes would slow AI development and deployment, reduce economic growth, and produce fewer new opportunities for workers. The surest way to effect a broader distribution of equity capital, and of equity in AI assets, is to encourage innovation, economic growth, and saving. Taxing capital more heavily is a very bad way to do that, whether from heavier taxes on income from capital, new taxes on unrealized gains, or (worst of all) from taxes on the value of capital, including ULTRA taxes.
Altman is right, however, to bemoan the narrow ownership of capital. As I mentioned above, he’s also on-target in saying that most people do not fully appreciate the benefits of thrift and the miracle of compounding. That represents both a failure of education and our calamitously high rate of time preference as a society. Perhaps the former can be fixed! However, thrift is a decision best left in private hands, especially to the extent that AI stimulates rapid income growth.
Killer Regulation
Altman also supports AI regulation, and I’ll cut him some slack by noting that his motives might not be of the usual rent-seeking variety. Maybe. Anyway, he’ll get some form of his wish, as legislators are scrambling to draft a “roadmap” for regulating AI. Some are calling for billions of federal outlays to “support” AI development, with a likely and ill-advised effort to “direct” that development as well. That is hardly necessary given the level of private investment AI is already attracting. Other “roadmap” proposals call for export controls on AI and protections for the film and recording industries.
These proposals are fueled by fears about AI, which run the gamut from widespread unemployment to existential risks to humanity. Considerable attention has been devoted to the alignment of AI agents with human interests and well being, but this has emerged largely within the AI development community itself. There are many alignment optimists, however, and still others who decry any race between tech giants to bring superhuman generative AI to market.
The Biden Administration stepped in last fall with an executive order on AI under emergency powers established by the Defense Production Act. The order ranges more broadly than national defense might necessitate, and it could have damaging consequences. Much of the order is redundant with respect to practices already followed by AI developers. It requires federal oversight over all so-called “foundation models” (e.g., ChatGPT), including safety tests and other “critical information”. These requirements are to be followed by the establishment of additional federal safety standards. This will almost certainly hamstring investment and development of AI, especially by smaller competitors.
Patrick Hedger discusses the destructive consequences of attempts to level the competitive AI playing field via regulation and antitrust actions. Traditionally, regulation tends to entrench large players who can best afford heavy compliance costs and influence regulatory decisions. Antitrust actions also impose huge costs on firms and can result in diminished value for investors in AI start-ups that might otherwise thrive as takeover targets.
Conclusion
Sam Altman’s vision of funding a redistribution of equity capital via taxes on wealth suffers from serious flaws. For one thing, it seems to view AI as a sort of exogenous boon to productivity, wholly independent of investment incentives. Taxing capital would inhibit investment in new capital (and in AI), diminish growth, and thwart the very goal of broad ownership Altman wishes to promote. Any effort to tax capital at a global level (which Altman supports) is probably doomed to failure, and that’s a good thing. The burden of taxes on capital at the corporate level would largely be shifted to workers and consumers, pushing real wages down and prices up relative to market outcomes.
Low taxes on income and especially on capital, together with light regulation, promote saving, capital investment, economic growth, higher real wages, and lower prices. For AI, like all capital investment, public policy should focus on encouraging “aligned” development and deployment of AI assets. A consumption tax would be far more efficient than wealth or capital taxes in that respect, and more effective in generating revenue. Policies that promote growth are the best prescription for broadening the distribution of capital ownership.
As of February 2026, I’m adding this short preamble to a few older posts on the subject of AI and future prospects for human labor. In the original post below (and a few others), I overstated the case that the law of comparative advantage would assure a continued role for humans in production. I still think the case is strong, mind you, but now I’m convinced that the outcome depends on elasticities of input substitution and how those elasticities might shift given the advent of AI-augmented capital. You can read my most recent thoughts on the matter here.
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I was happy to see Noah Smith’s recent post on the graces of comparative advantage and the way it should mediate the long-run impact of AI on job prospects for humans. However, I’m embarrassed to have missed his post when it was published in March (and I also missed a New York Timespiece about Smith’s position).
I said much the same thing as Smith in my post two weeks ago about the persistence of a human comparative advantage, but I wondered why the argument hadn’t been made prominently by economists.I discussed it myself about seven years ago. But alas, I didn’t see Smith’s post until last week!
I highly recommend it, though I quibble on one or two issues. Primarily, I think Smith qualifies his position based on a faulty historical comparison. Later, he doubles back to offer a kind of guarantee after all. Relatedly, I think Smith mischaracterizes the impact of energy costs on comparative advantages, and more generally the impact of the resources necessary to support a human population.
We Specialize Because…
Smith encapsulates the underlying phenomenon that will provide jobs for humans in a world of high automation and generative AI: “… everyone — every single person, every single AI, everyone — always has a comparative advantage at something!” He tells technologists “… it’s very possible that regular humans will have plentiful, high-paying jobs in the age of AI dominance — often doing much the same kind of work that they’re doing right now …”
… often, but probably transformed in fundamental ways by AI, and also doing many other new kinds of work that can’t be foreseen at present. Tyler Cowen believes themost important macro effects of AI will be from “new” outputs, not improvements in existing outputs. That emphasis doesn’t necessarily conflict with Smith’s narrative, but again, Smith thinks people will do many of the same jobs as today in a world with advanced AI.
Smith’s Non-Guarantee
Smith hedges, however, in a section of his post entitled “‘Possible’ doesn’t mean guaranteed”. This despite his later assertion that superabundance would not eliminate jobs for humans. That might seem like a separate issue, but it’s strongly intertwined with the declining AI cost argument at the basis of his hedge. More on that below.
On his reluctance to “guarantee” that humans will have jobs in an AI world, Smith links to a 2013 Tyler Cowen post on“Why the theory of comparative advantage is overrated”. For example, Cowen says, why do we ever observe long-term unemployment if comparative advantage rules the day? Of course there are many reasons why we observe departures from the predicted results of comparative advantage. Incentives are often manipulated by governments and people differ drastically in their capacities and motivation.
But Cowen cites a theoretical weakness of comparative advantage: that inputs are substitutable (or complementary) by degrees, and the degree might change under different market conditions. An implication is that “comparative advantages are endogenous to trade”, specialization, and prices. Fair enough, but one could say the same thing about any supply curve. And if equilibria exist in input markets it means these endogenous forces tend toward comparative advantages and specializations balancing the costs and benefits of production and trade. These processes might be constrained by various frictions and interventions, and their dynamics might be complex and lengthy, but that doesn’t invalidate their role in establishing specializations and trade.
The Glue Factory
Smith concerns himself mainly with another one of Cowen’s “failings of comparative advantage”: “They do indeed send horses to the glue factory, so to speak.” The gist here is that when a new technology, motorized transportation, displaced draft horses, there was no “wage” low enough to save the jobs performed by horses. Smith says horses were too costly to support (feed, stables, etc…), so their comparative advantage at “pulling things” was essentially worthless.
True, but comparing outmoded draft horses to humans in a world of AI is not quite appropriate. First, feedstock to a “glue factory” better not be an alternative use for humans whose comparative advantages become worthless. We’ll have to leave that question as an imperative for the alignment community.
Second, horses do not have versatile skill sets, so the comparison here is inapt due to their lack of alternative uses as capital assets. Yes, horses can offer other services (racing, riding, nostalgic carriage rides), but sadly, the vast bulk of work horses were “one-trick ponies”. Most draft horses probably had an opportunity cost of less than zero, given the aforementioned costs of supporting them. And it should be obvious that a single-use input has a comparative advantage only in its single use, and only when that use happens to be the state-of-the-art, or at least opportunity-cost competitive.
The drivers, on the other hand, had alternatives, and saw their comparative advantage in horse-driving occupations plunge with the advent of motorized transport. With time it’s certain many of them found new jobs, perhaps some went on to drive motorized vehicles. The point is that humans have alternatives, the number depending only on their ability to learn a crafts and perhaps move to a new location. Thus, as Smith says, “… everyone — every single person, every single AI, everyone — always has a comparative advantage at something!” But not draft horses in a motorized world, and not square pegs in a world of round holes.
AI Producer Constraints
That brings us to the topic of what Smith calls producer-specific constraints, which place limits on the amount and scope of an input’s productivity. For example, in my last post, there was only one super-talented Harvey Specter, so he’s unlikely to replace you and keep doing his own job. Thus, time is a major constraint. For Harvey or anyone else, the time constraint affects the slope of the tradeoff (and opportunity costs) between one type of specialization versus another.
Draft horses operated under the constraints of land, stable, and feed requirements, which can all be viewed as long-run variable costs. The alternative use for horses at the glue factory did not have those costs.
Humans reliant on wages must feed and house themselves, so those costs also represent constraints, but they probably don’t change the shape of the tradeoff between one occupation and another. That is, they probably do not alter human comparative advantages. Granted, some occupations come with strong expectations among associates or clients regarding an individual’s lifestyle, but this usually represents much more than basic life support. In the other end of the spectrum, displaced workers will take actions along various margins: minimize living costs; rely on savings; avail themselves of charity or any social safety net as might exist; and ultimately they must find new positions at which they maintain comparative advantages.
The Compute Constraint
In the case of AI agents, the key constraint cited by Smith is “compute”, or computer resources like CPUs or GPUs. Advancements in compute have driven the AI revolution, allowing AI models to train on increasingly large data sets and levels of compute. In fact, by one measure of compute, floating point operations per second (FLOPs), compute has become drastically cheaper, with FLOPs per dollar almost doubling every two years. Perhaps I misunderstand him, but Smith seems to assert the opposite: that compute costs are increasing. Regardless, compute is scarce, and will always be scarce because advancements in AI will require vast increases in training. This author explains that while lower compute costs will be more than offset by exponential increases in training requirements, there nevertheless will be an increasing trend in capabilities per compute.
Every AI agent will require compute, and while advancements are enabling explosive growth in AI capabilities, scarce compute places constraints on the kinds of AI development and deployment that some see as a threat to human jobs. In other words, compute scarcity can change the shape of the tradeoffs between various AI applications and thus, comparative advantages.
The Energy Constraint
Another producer constraint on AI is energy. Certainly highly complex applications, perhaps requiring greater training, physical dexterity, manipulation of materials, and judgement, will require a greater compute and energy tradeoff against simpler applications. Smith, however, at one point dismisses energy as a differential producer constraint because “… humans also take energy to run.” That is a reference to absolute energy requirements across inputs (AI vs. human), not differential requirements for an input across different outputs. Only the latter impinge on tradeoffs or opportunity costs facing an inputs. Then, the input having the lowest opportunity cost for a particular output has a comparative advantage for that output. However, it’s not always clear whether an energy tradeoff across outputs for humans will be more or less skewed than for AI, so this might or might not influence a human comparative advantage.
Later, however, Smith speculates that AI might bid up the cost of energy so high that “humans would indeed be immiserated en masse.” That position seems inconsistent. In fact, if AI energy demands are so intensive, it’s more likely to dampen the growth in demand for AI agents as well as increase the human comparative advantage because the most energy-intensive AI applications will be disadvantaged.
And again, there is Smith’s caution regarding the energy required for human life support. Is that a valid long-run variable cost associated with comparative advantages possessed by humans? It’s not wrong to include fertility decisions in the long-run aggregate human labor supply function in some fashion, but it doesn’t imply that energy requirements will eliminate comparative advantages. Those will still exist.
Hype, Or Hyper-Growth?
AI has come a long way over the past two years, and while its prospective impact strikes some as hyped thus far, it has the potential to bring vast gains across a number of fields within just a few years. According to this study, explosive economic growth on the order of 30% annually is a real possibility within decades, as generative AI is embedded throughout the economy. “Unprecedented” is an understatement for that kind of expansive growth. Dylan Matthews in Vox surveys the arguments as to how AI will lead to super-exponential economic growth. This is the kind of scenario that would give rise to superabundance.
I noted above that Smith, despite his unwillingness to guarantee that human jobs will exist in a world of generative AI, asserts (in an update) at the bottom of his post that a superabundance of AI (and abundance generally) would not threaten human comparative advantages. This superabundance is a case of decreasing costs of compute and AI deployment. Here Smith says:
“The reason is that the more abundant AI gets, the more value society produces. The more value society produces, the more demand for AI goes up. The more demand goes up, the greater the opportunity cost of using AI for anything other than its most productive use.
“As long as you have to make a choice of where to allocate the AI, it doesn’t matter how much AI there is. A world where AI can do anything, and where there’s massively huge amounts of AI in the world, is a world that’s rich and prosperous to a degree that we can barely imagine. And all that fabulous prosperity has to get spent on something. That spending will drive up the price of AI’s most productive uses. That increased price, in turn, makes it uneconomical to use AI for its least productive uses, even if it’s far better than humans at its least productive uses.
“Simply put, AI’s opportunity cost does not go to zero when AI’s resource costs get astronomically cheap. AI’s opportunity cost continues to scale up and up and up, without limit, as AI produces more and more value.”
This seems as if Smith is backing off his earlier hedge. Some of that spending will be in the form of fabulous investment projects of the kinds I mentioned in my post, and smaller ones as well, all enabled by AI. But the key point is that comparative advantages will not go away, and that means human inputs will continue to be economically useful.
I referenced Andrew Mayne in my last post. He contends that the income growth made possible by AI will ensure that plenty of jobs are available for humans. He mentions comparative advantage in passing, but he centers his argument around applications in which human workers and AI will be strong complements in production, as will sometimes be the case.
A New Age of Worry
The economic success of AI is subject to a number of contingencies. Most important is that AI alignment issues are adequately addressed. That is, the “self-interest” of any agentic AI must align with the interests of human welfare. Do no harm!
The difficulty of universal alignment is illustrated by the inevitability of competition among national governments for AI supremacy, especially in the area of AI-enabled weaponry and espionage. The national security implications are staggering.
A couple of Smith‘s biggest concerns are the social costs of adjusting to the economic disruptions AI is sure to bring, as well as its implications for inequality. Humans will still have comparative advantages, but there will be massive changes in the labor market and transitions that are likely to involve spells of unemployment and interruptions to incomes for some. The speed and strength of the AI revolution may well create social upheaval. That will create incentives for politicians to restrain the development and adoption of AI, and indeed, we already see the stirrings of that today.
Finally, Smith worries that the transition to AI will bring massive gains in wealth to the owners of AI assets, while workers with few skills are likely to languish. I’m not sure that’s consistent with his optimism regarding income growth under AI, and inequality matters much less when incomes are rising generally. Still, the concern is worthy of a more detailed discussion, which I’ll defer to a later post.
As of February 2026, I’m adding this short preamble to a few older posts on the subject of AI and future prospects for human labor. In the original post below (and a few others), I overstated the case that the law of comparative advantage would assure a continued role for humans in production. I still think the case is strong, mind you, but now I’m convinced that the outcome depends on elasticities of input substitution and how those elasticities might shift given the advent of AI-augmented capital. You can read my most recent thoughts on the matter here.
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You might know someone so smart and multi-talented that they are objectively better at everything than you. Let’s call him Harvey Specter. Harvey’s prospects on the labor market are very good. Economists would say he has an absolute advantage over you in every single pursuit! What a bummer! But obviously that doesn’t mean Harvey can or should do everything, while you do nothing.
Fears of Human Obsolescence
That’s the very situation many think awaits workers with the advent of artificial general intelligence (AGI), and especially with the marriage of AGI and advanced robotics (also see here). Any job a human can do, AGI or AGI robots of various kinds will be able to do better, faster, and in far greater quantity. The humanoid AGI robots will be like your talented acquaintance Harvey, but exponentiated. They won’t need much “sleep” or downtime, and treating wear and tear on their “health” will be a simple matter of replacing components. AGI and its robotic manifestations will have an absolute advantage in every possible endeavor.
But even with the existence of super-human AGI robots, I claim that work will be available to you if you want or need it. You won’t face the same set of pre-AGI opportunities, but there will be many opportunities for humans nonetheless. How can that be if AGI robots can do everything better? Won’t they be equipped to meet all of our material needs and wants?
Specter of the Super Productive
Let’s return to the example of you and Harvey, your uber-talented acquaintance. You’ll each have an area of specialization, but on what basis? Harvey has his pick of very lucrative and stimulating opportunities. You, however, are limited to a less dazzling array of prospects. There might be some overlap, and hard work or luck can make up for large differences, but chances are you’ll specialize in something that requires less talent than Harvey. You might wind up in the same profession, but Harvey will be a star.
Where will you end up? The answer is you and Harvey will find your respective areas of specialization based on comparative advantages, not absolute advantages. Relative opportunity cost is the key here, or its inverse: how much do you expect to gain from a certain area of specialization relative to the rewards you must forego.
For example, Harvey doesn’t sacrifice much by shunning less challenging areas of specialization. That is, he faces a low opportunity cost, while his chosen area offers great rewards for his talent.
You, on the other hand, might not have much to gain in Harvey’s line of work, if you can get it. You might be a flop if you do! Realistically, you forego very little if you instead pursue more achievable success in a less daunting area. You’ll be better off choosing an option for which your relative gains are highest, or said differently, where your relative opportunity cost is low.
A Quick Illustration
If you’re unwilling to slog through a simple numerical example, skip this section and the graph below. The graph was produced the old fashioned way: by a human being with a pencil, paper, ruler, and smart phone camera.
Here goes: Harvey can produce up to 100 units of X per period or 100 units of Y, or some linear combination of the two. Harvey’s opportunity costs are constant along this tradeoff between X and Y because it’s a straight line. It costs him one unit of Y output to produce every additional unit of X, and vice versa.
You, on the other hand, cannot produce X or Y as well as Harvey in an absolute sense. At most, you can produce up to 50 units of X per period, 20 units of Y, or some combination of the two along your own constant cost (straight line) tradeoff. You sacrifice 5/2 = 2.5 units of X to produce each unit of Y, so Harvey has the lower opportunity cost and a comparative advantage for Y. But it only costs you 2/5 = 0.4 units of Y to produce each additional unit of X, so you have a comparative advantage over Harvey in X production.
Reciprocal Advantages
In the end, you and Harvey specialize in the respective areas for which each has their lowest relative opportunity cost and a comparative advantage. If he has a comparative advantage in one area of production, and unless your respective tradeoffs have identical slopes (unlikely), the reciprocal nature of opportunity costs dictates that you have a comparative advantage in the other area of production.
Obviously, Harvey’s formidable absolute advantage over you in everything doesn’t impinge on these choices. In the real world, of course, comparative advantages play out across many dimensions of output, but the principle is the same. And once we specialize, we can trade with one another to mutual advantage.
No Such Thing As a Free AGI Robot
That brings us back to AGI and AGI robots. Like Harvey, they might well have an absolute advantage in every area of specialization, or they can learn quickly to achieve such an advantage, but that doesn’t mean they should do everything!
Just as in times preceding earlier technological breakthroughs, we cannot even imagine the types of jobs that will dominate the human and AGI work forces in the future. We already see complementarity between humans and AGI in many applications. AGI makes those workers much more productive, which leads to higher wages.
However, substitution of AGIs for human labor is a dominant theme of the many AGI “harm” narratives. In fact, substitution is already a reality in many occupations, like coding, and substitution is likely to broaden and intensify as the marriage of AGI and robotics gains speed. But that will occur only in industries for which the relative opportunity costs of AGIs, including all of the ancillary resources needed to produce them, are favorable. Among other things, AGI will require a gigantic expansion in energy production and infrastructure, which necessitates a massive exploitation of resources. Relative opportunity costs in the use of these resources will not always favor the dominance of AGIs in production. Like Harvey, AGIs and their ancillary resources cannot do everything because they cannot have comparative advantages without reciprocal comparative disadvantages.
Super-Abundance vs. Scarcity
Some might insist that AGIs will lead to such great prosperity that humans will no longer need to work. All of our material wants will be met in a new age of super-abundance. Despite the foregoing, that might suggest to some that AGIs will do everything! But here I make another claim: our future demands on resources will not be satisfied by whatever abundance AGIs make possible. We will still want to do more, whether we choose to construct fusion reactors, megastructures in space (like Dyson spheres or ring worlds), terraform Mars, undertake interstellar travel, perfect asteroid defense, battle disease, extend longevity, or improve our lives in ways now imagined or unimagined.
As a result, scarcity will remain a major force. To that extent, resources will have competing uses, they will face opportunity costs, and they will have comparative advantages vis a vis alternative uses to which they can be put. Scarcity is a reality that governs opportunity costs, and that means humans will always have roles to play in production.
Concluding Remarks
I wrote about human comparative advantages once before, about seven years ago. I think I was groping along the right path. The only other article I’ve seen to explicitly mention a comparative advantage of human labor vs. AGIs in the correct context is by Andrew Mayne in the most recent issue of Reason Magazine. It’s almost a passing reference, but it deserves more because it is foundational.
Harvey Specter shouldn’t occupy his scarce time performing tasks that compromise his ability to deliver his most rewarding services. Likewise, before long it will become apparent that highly productive AGI assets, and the resources required to build and operate them, should not be tied up in activities that humans can perform at lesser sacrifice. That’s a long way of saying that humans will still have productive roles to play, even when AGI achieves an absolute advantage in everything. Some of the roles played by humans will be complimentary to AGIs in production, but human labor will also be valuable as a substitute for AGI assets in other applications. As long as AGI assets have any comparative advantages, humans will have reciprocal comparative advantages as well.
Recent advances in artificial intelligence (AI) are giving hope to advocates of central economic planning. Perhaps, they think, the so-called “knowledge problem” (KP) can be overcome, making society’s reliance on decentralized market forces “unnecessary”. The KP is the barrier faced by planners in collecting and using information to direct resources to their most valued uses. KP is at the heart of the so-called “socialist calculation debate”, but it applies also to the failures of right-wing industrial policies and protectionism.
Apart from raw political motives, run-of-the-mill government incompetence, and poor incentives, the KP is an insurmountable obstacle to successful state planning, as emphasized by Friedrich Hayek and many others. In contrast, market forces are capable of spontaneously harnessing all sources of information on preferences, incentives, resources, as well as existing and emergent technologies in allocating resources efficiently. In addition, the positive sum nature of mutually beneficial exchange makes the market by far the greatest force for voluntary social cooperation known to mankind.
Nevertheless, the hope kindled by AI is that planners would be on an equal footing with markets and allow them to intervene in ways that would be “optimal” for society. This technocratic dream has been astir for years along with advances in computer technology and machine learning. I guess it’s nice that at least a few students of central planning understood the dilemma all along, but as explained below, their hopes for AI are terribly misplaced. AI will never allow planners to allocate resources in ways that exceed or even approximate the efficiency of the market mechanism’s “invisible hand”.
Michael Munger recently described the basic misunderstanding about the information or “data” that markets use to solve the KP. Markets do not rely on a given set of prices, quantities, and production relationships. They do not take any of those as givens with respect to the evolution of transactions, consumption, production, investment, or search activity. Instead, markets generate this data based on unobservable and co-evolving factors such as the shape of preferences across goods, services, and time; perceptions of risk and its cost; the full breadth of technologies; shifting resource availabilities; expectations; locations; perceived transaction costs; and entrepreneurial energy. Most of these factors are “tacit knowledge” that no central database will ever contain.
At each moment, dispersed forces are applied by individual actions in the marketplace. The market essentially solves for the optimal set of transactions subject to all of those factors. These continuously derived solutions are embodied in data on prices, quantities, and production relationships. Opportunity costs and incentives are both an outcome of market processes as well as driving forces, so that they shape the transactional footprint. And then those trades are complete. Attempts to impose the same set of data upon new transactions in some repeated fashion, freezing the observable components of incentives and other requirements, would prevent the market from responding to changing conditions.
Thus, the KP facing planners isn’t really about “calculating” anything. Rather, it’s the impossibility of matching or replicating the market’s capacity to generate these data and solutions. There will never be an AI with sufficient power to match the efficiency of the market mechanism because it’s not a matter of mere “calculation”. The necessary inputs are never fully unobservable and, in any case, are unknown until transactions actually take place such that prices and quantities can be recorded.
In my 2020post “Central Planning With AI Will Still Suck”, I reviewed a paper by Jesús Fernández-Villaverde (JFV), who was skeptical of AI’s powers to achieve better outcomes via planning than under market forces. His critique of the “planner position” anticipated the distinction highlighted by Munger between “market data” and the market’s continuous generation of transactions and their observable footprints.
JFV emphasized three reasons for the ultimate failure of AI-enabled planning: impossible data requirements; the endogeneity of expectations and behavior; and the knowledge problem. Again, the discovery and collection of “data” is a major obstacle to effective planning. If that were the only difficulty, then planners would have a mere “calculation” problem. This shouldn’t be conflated with the broader KP. That is, observable “data” is a narrow category relative the arrays of unobservables and the simultaneous generation of inputs and outcomes that takes place in markets. And these solutions are found by market processes subject to an array of largely unobservable constraints.
An interesting obstacle to AI planning cited by JFV is the endogeneity of expectations. It too can be considered part of the KP. From my 2020 post:
“Policy Change Often Makes the Past Irrelevant: Planning algorithms are subject to the so-called Lucas Critique, a well known principle in macroeconomics named after Nobel Prize winner Robert Lucas. The idea is that policy decisions based on observed behavior will change expectations, prompting responses that differ from the earlier observations under the former policy regime. … If [machine learning] is used to “plan” certain outcomes desired by some authority, based on past relationships and transactions, the Lucas Critique implies that things are unlikely to go as planned.”
Again, note that central planning and attempts at “calculation” are not solely in the province of socialist governance. They are also required by protectionist or industrial policies supported at times by either end of the political spectrum. Don Boudreaux offers this wisdom on the point:
“People on the political right typically assume that support for socialist interventions comes uniquely from people on the political left, but this assumption is mistaken. While conservative interventionists don’t call themselves “socialists,” many of their proposed interventions – for example, industrial policy – are indeed socialist interventions. These interventions are socialist because, in their attempts to improve the overall performance of the economy, proponents of these interventions advocate that market-directed allocations of resources be replaced with allocations carried out by government diktat.”
The hope that non-market planning can be made highly efficient via AI is a fantasy. In addition to substituting the arbitrary preferences of planners and politicians for those of private agents, the multiplicity of forces bearing on individual decisions will always be inaccessible to AIs. Many of these factors are deeply embedded within individual minds, and often in varying ways. That is why the knowledge problem emphasized by Hayek is much deeper than any sort of “calculation problem” fit for exploitation via computer power.
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Note: The image at the top of this post is attributed by Bing to the CATO Institute-sponsored website Libertarianism.org and an article that appeared there in 2013, though that piece, by Jason Kuznicki, no longer seems to feature that image.
Artificial intelligence (AI) has become a very hot topic with incredible recent advances in AI performance. It’s very promising technology, and the expectations shown in thechart above illustrate what would be a profound economic impact. Like many new technologies, however, many find it threatening and are reacting with great alarm, There’s a movement within the tech industry itself, partly motivated by competitive self-interest, calling for a “pause”, or a six-month moratorium on certain development activities. Politicians in Washington are beginning to clamor for legislation that would subject AI to regulation. However, neither a voluntary pause nor regulatory action are likely to be successful. In fact, either would likely do more harm than good.
Leaps and Bounds
The pace of advance in AI has been breathtaking. From ChatGPT 3.5 to ChatGPT 4, in a matter of just a few months, the tool went from relatively poor performance on tests like professional and graduate entrance exams (e.g., bar exams, LSAT, GRE) to very high scores. Using these tools can be a rather startling experience, as I learned for myself recently when I allowed one to write the first draft of a post. (Despite my initial surprise, my experience with ChatGPT 3.5 was somewhat underwhelming after careful review, but I’ve seen more impressive results with ChatGPT 4). They seem to know so much and produce it almost instantly, though it’s true they sometimes “hallucinate”, reflect bias, or invent sources, so thorough review is a must.
Nevertheless, AIs can write essays and computer code, solve complex problems, create or interpret images, sounds and music, simulate speech, diagnose illnesses, render investment advice, and many other things. They can create subroutines to help themselves solve problems. And they can replicate!
As a gauge of the effectiveness of models like ChatGPT, consider that today AI is helping promote “over-employment”. That is, there are a number of ambitious individuals who, working from home, are holding down several different jobs with the help of AI models. In fact, some of these folks say AIs are doing 80% of their work. They are the best “assistants” one could possibly hire, according to a man who has four different jobs.
Economist Bryan Caplan is an inveterate skeptic of almost all claims that smack of hyperbole, and he’s won a series of bets he’s solicited against others willing to take sides in support of such claims. However, Caplan thinks he’s probably lost his bet on the speed of progress on AI development. Needless to say, it has far exceeded his expectations.
Naturally, the rapid progress has rattled lots of people, including many experts in the AI field. Already, we’re witnessing the emergence of “agency” on the part of AI Learning Language Models (LLMs), or so called “agentic” behavior. Here’s an interesting thread on agentic AI behavior. Certain models are capable of teaching themselves in pursuit of a specified goal, gathering new information and recursively optimizing their performance toward that goal. Continued gains may lead to an AI model having artificial generative intelligence (AGI), a superhuman level of intelligence that would go beyond acting upon an initial set of instructions. Some believe this will occur suddenly, which is often described as the “foom” event.
Team Uh-Oh
Concern about where this will lead runs so deep that a letter was recently signed by thousands of tech industry employees, AI experts, and other interested parties calling for a six-month worldwide pause in AI development activity so that safety protocols can be developed. One prominent researcher in machine intelligence, Eliezer Yudkowsky, goes much further: he believes that avoiding human extinction requires immediate worldwide limits on resources dedicated to AI development. Is this a severely overwrought application of the precautionary principle? That’s a matter I’ll consider at greater length below, but like Caplan, I’m congenitally skeptical of claims of impending doom, whether from the mouth of Yudkowsky, Greta Thunberg, Paul Ehrlich, or Nassim Taleb.
As I mentioned at the top, I suspect competition among AI developers played a role in motivating some of the signatories of the “AI pause” letter, and some of the non-signatories as well. Robin Hanson points out that Sam Altman, the CEO of OpenAI, did not sign the letter. OpenAI (controlled by a nonprofit foundation) owns ChatGPT and is the current leader in rolling out AI tools to the public. ChatGPT 4 can be used with the Microsoft search engine Bing, and Microsoft’s Bill Gates also did not sign the letter. Meanwhile, Google was caught flat-footed by the ChatGPT rollout, and its CEO signed. Elon Musk (who signed) wants to jump in with his own AI development: TruthGPT. Of course, the pause letter stirred up a number of members of Congress, which I suspect was the real intent. It’s reasonable to view the letter as a means of leveling the competitive landscape. Thus, it looks something like a classic rent-seeking maneuver, buttressed by the inevitable calls for regulation of AIs. However, I certainly don’t doubt that a number of signatories did so out of a sincere belief that the risks of AI must be dealt with before further development takes place.
The vast dimensions of the supposed AI “threat” may have some libertarians questioning their unequivocal opposition to public intervention. If so, they might just as well fear the potential that AI already holds for manipulation and control by central authorities in concert with their tech and media industry proxies. But realistically, broad compliance with any precautionary agreement between countries or institutions, should one ever be reached, is pretty unlikely. On that basis, a “scout’s honor” temporary moratorium or set of permanent restrictions might be comparable to something like the Paris Climate Accord. China and a few other nations are unlikely to honor the agreement, and we really won’t know whether they’re going along with it except for any traceable artifacts their models might leave in their wake. So we’ll have to hope that safeguards can be identified and implemented broadly.
Likewise, efforts to regulate by individual nations are likely to fail, and for similar reasons. One cannot count on other powers to enforce the same kinds of rules, or any rules at all. Putting our faith in that kind of cooperation with countries who are otherwise hostile is a prescription for ceding them an advantage in AI development and deployment. Regulation of the evolution of AI will likely fail. As Robert Louis Stevenson once wrote, “Thus paternal laws are made, thus they are evaded”. And if it “succeeds, it will leave us with a technology that will fall short of its potential to benefit consumers and society at large. That, unfortunately, is usually the nature of state intrusion into a process of innovation, especially when devised by a cadre of politicians with little expertise in the area.
Again, according to experts like Yudkowsky, AGI would pose serious risks. He thinks the AI Pause letter falls far short of what’s needed. For this reason, there’s been much discussion of somehow achieving an alignment between the interests of humanity and the objectives of AIs. Here is a good discussion by Seth Herd on the LessWrong blog about the difficulties of alignment issues.
Some experts feel that alignment is an impossibility, and that there are ways to “live and thrive” with unalignment (and see here). Alignment might also be achieved through incentives for AIs. Those are all hopeful opinions. Others insist that these models still have a long way to go before they become a serious threat. More on that below. Of course, the models do have their shortcomings, and current models get easily off-track into indeterminacy when attempting to optimize toward an objective.
But there’s an obvious question that hasn’t been answered in full: what exactly are all these risks?As Tyler Cowen has said, it appears that no one has comprehensively catalogued the risks or specified precise mechanisms through which those risks would present. In fact, AGI is such a conundrum that it might be impossible to know precisely what threats we’ll face. But even now, with deployment of AIs still in its infancy, it’s easy to see a few transition problems on the horizon.
White Collar Wipeout
Job losses seem like a rather mundane outcome relative to extinction. Those losses might come quickly, particularly among white collar workers like programmers, attorneys, accountants, and a variety of administrative staffers. According to a survey of 1,000 businesses conducted in February:
“Forty-eight percent of companies have replaced workers with ChatGPT since it became available in November of last year. … When asked if ChatGPT will lead to any workers being laid off by the end of 2023, 33% of business leaders say ‘definitely,’ while 26% say ‘probably.’ … Within 5 years, 63% of business leaders say ChatGPT will ‘definitely’ (32%) or ‘probably’ (31%) lead to workers being laid off.”
A rapid rate of adoption could well lead to widespread unemployment and even social upheaval. For perspective, that implies a much more rapid rate of technological diffusion than we’ve ever witnessed, so this outcome is viewed with skepticism in some quarters. But in fact, the early adoption phase of AI models is proceeding rather quickly. You can use ChatGPT 4 easily enough on the Bing platform right now!
Contrary to the doomsayers, AI will not just enhance human productivity. Like all new technologies, it will lead to opportunities for human actors that are as yet unforeseen. AI is likely to identify better ways for humans to do many things, or do wonderful things that are now unimagined. At a minimum, however, the transition will be disruptive for a large number of workers, and it will take some time for new opportunities and roles for humans to come to fruition.
Robin Hanson has a unique proposal for meeting the kind of challenge faced by white collar workers vulnerable to displacement by AI, or for blue collar workers who are vulnerable to displacement by robots (the deployment of which has been hastened by minimum wage and living wage activism). This treatment of Hanson’s idea will be inadequate, but he suggests a kind of insurance or contract sold to both workers and investors by owners of assets likely to be insensitive to AI risks. The underlying assets are paid out to workers if automation causes some defined aggregate level of job loss. Otherwise, the assets are paid out to investors taking the other side of the bet. Workers could buy these contracts themselves, or employers could do so on their workers’ behalf. The prices of the contracts would be determined by a market assessment of the probability of the defined job loss “event”. Governmental units could buy the assets for their citizens, for that matter. The “worker contracts” would be cheap if the probability of the job-loss event is low. Sounds far-fetched, but perhaps the idea is itself an entrepreneurial opportunity for creative players in the financial industry.
The threat of job losses to AI has also given new energy to advocates of widespread adoption of universal basic income payments by government. Hanson’s solution is far preferable to government dependence, but perhaps the state could serve as an enabler or conduit through which workers could acquire AI and non-AI capital.
Human Capital
Current incarnations of AI are not just a threat to employment. One might add the prospect that heavy reliance on AI could undermine the future education and critical thinking skills of the general population. Essentially allowing machines to do all the thinking, research, and planning won’t inure to the cognitive strength of the human race, especially over several generations. Already people suffer from an inability to perform what were once considered basic life skills, to say nothing of tasks that were fundamental to survival in the not too distant past. In other words, AI could exaggerate a process of “dumbing down” the populace, a rather undesirable prospect.
Fraud and Privacy
AI is responsible for still more disruptions already taking place, in particular violations of privacy, security, and trust. For example, a company called Clearview AI has scraped 30 billion photos from social media and used them to create what its CEO proudly calls a “perpetual police lineup”, which it has provided for the convenience of law enforcement and security agencies.
AI is also a threat to encryption in securing data and systems. Conceivably, AI could be of value in perpetrating identity theft and other kinds of fraud, but it can also be of value in preventing them. AI is also a potential source of misleading information. It is often biased, reflecting specific portions of the on-line terrain upon which it is trained, including skewed model weights applied to information reflecting particular points of view. Furthermore, misinformation can be spread by AIs via “synthetic media” and the propagation of “fake news”. These are fairly clear and present threats of social, economic, and political manipulation. They are all foreseeable dangers posed by AI in the hands of bad actors, and I would include certain nudge-happy and politically-motivated players in that last category.
The Sky-Already-Fell Crowd
Certain ethicists with extensive experience in AI have condemned the signatories of the “Pause Letter” for a focus on “longtermism”, or risks as yet hypothetical, rather than the dangers and wrongs attributable to AIs that are already extant:TechCrunch quotes a rebuke penned by some of these dissenting ethicists to supporters of the “Pause Letter”:
“‘Those hypothetical risks are the focus of a dangerous ideology called longtermism that ignores the actual harms resulting from the deployment of AI systems today,’ they wrote, citing worker exploitation, data theft, synthetic media that props up existing power structures and the further concentration of those power structures in fewer hands.”
So these ethicists bemoan AI’s presumed contribution to the strength and concentration of “existing power structures”. In that, I detect just a whiff of distaste for private initiative and private rewards, or perhaps against the sovereign power of states to allow a laissez faire approach to AI development (or to actively sponsor it). I have trouble taking this “rebuke” too seriously, but it will be fruitless in any case. Some form of cooperation between AI developers on safety protocols might be well advised, but competing interests also serve as a check on bad actors, and it could bring us better solutions as other dilemmas posed by AI reveal themselves.
ImaginingAI Catastrophes
What are the more consequential (and completely hypothetical) risks feared by the “pausers” and “stoppers”. Some might have to do with the possibility of widespread social upheaval and ultimately mayhem caused by some of the “mundane” risks described above. But the most noteworthy warnings are existential: the end of the human race! How might this occur when AGI is something confined to computers? Just how does the supposed destructive power of AGIs get “outside the box”? It must do so either by tricking us into doing something stupid, hacking into dangerous systems (including AI weapons systems or other robotics), and/or through the direction and assistance of bad human actors. Perhaps all three!
The first question is this: why would an AGI do anything so destructive? No matter how much we might like to anthropomorphize an “intelligent” machine, it would still be a machine. It really wouldn’t like or dislike humanity. What it would do, however, is act on its objectives. It would seek to optimize a series of objective functions toward achieving a goal or a set of goals it is given. Hence the role for bad actors. Let’s face it, there are suicidal people who might like nothing more than to take the whole world with them.
Otherwise, if humanity happens to be an obstruction to solving an AGI’s objective, then we’d have a very big problem. Humanity could be an aid to solving an AGI’s optimization problem in ways that are dangerous. As Yudkowsky says, we might represent mere “atoms it could use somewhere else.” And if an autonomous AGI were capable of setting it’s own objectives, without alignment, the danger would be greatly magnified. An example might be the goal of reducing carbon emissions to pre-industrial levels. How aggressively would an AGI act in pursuit of that goal? Would killing most humans contribute to the achievement of that goal?
Here’s one that might seem far-fetched, but the imagination runs wild: some individuals might be so taken with the power of vastly intelligent AGI as to make it an object of worship. Such an “AGI God” might be able to convert a sufficient number of human disciples to perpetrate deadly mischief on its behalf. Metaphorically speaking, the disciples might be persuaded to deliver poison kool-aid worldwide before gulping it down themselves in a Jim Jones style mass suicide. Or perhaps the devoted will survive to live in a new world mono-theocracy. Of course, these human disciples would be able to assist the “AGI God” in any number of destructive ways. And when brain-wave translation comes to fruition, they better watch out. Only the truly devoted will survive.
An AGI would be able to create the illusion of emergency, such as a nuclear launch by an adversary nation. In fact, two or many adversary nations might each be fooled into taking actions that would assure mutual destruction and a nuclear winter. If safeguards such as human intermediaries were required to authorize strikes, it might still be possible for an AGI to fool those humans. And there is no guarantee that all parties to such a manufactured conflict could be counted upon to have adequate safeguards, even if some did.
Yudkowsky offers at least one fairly concrete example of existential AGI risk:
“A sufficiently intelligent AI won’t stay confined to computers for long. In today’s world you can email DNA strings to laboratories that will produce proteins on demand, allowing an AI initially confined to the internet to build artificial life forms or bootstrap straight to postbiological molecular manufacturing.”
There are many types of physical infrastructure or systems that an AGI could conceivably compromise, especially with the aid of machinery like robots or drones to which it could pass instructions. Safeguards at nuclear power plants could be disabled before steps to trigger melt down. Water systems, rivers, and bodies of water could be poisoned. The same is true of food sources, or even the air we breathe. In any case, complete social disarray might lead to a situation in which food supply chains become completely dysfunctional. So, a super-intelligence could probably devise plenty of “imaginative” ways to rid the earth of human beings.
Back To Earth
Is all this concern overblown? Many think so. Bryan Caplan now has a $500 bet with Eliezer Yudkowsky that AI will not exterminate the human race by 2030. He’s already paid Yudkowsky, who will pay him $1,000 if we survive. Robin Hanson says “Most AI Fear Is Future Fear”, and I’m inclined to agree with that assessment. In a way, I’m inclined to view the AI doomsters as highly sophisticated, change-fearing Luddites, but Luddites nevertheless.
Ben Hayum is very concerned about the dangers of AI, butwriting at LessWrong, he recognizes some real technical barriers that must be overcome for recursive optimization to be successful. He also notes that the big AI developers are all highly focused on safety. Nevertheless, he says it might not take long before independent users are able to bootstrap their own plug-ins or modules on top of AI models to successfully optimize without running off the rails. Depending on the specified goals, he thinks that will be a scary development.
James Pethokoukis raises a point that hasn’t had enough recognition: successful innovations are usually dependent on other enablers, such as appropriate infrastructure and process adaptations. What this means is that AI, while making spectacular progress thus far, won’t have a tremendous impact on productivity for at least several years, nor will it pose a truly existential threat. The lag in the response of productivity growth would also limit the destructive potential of AGI in the near term, since installation of the “social plant” that a destructive AGI would require will take time. This also buys time for attempting to solve the AI alignment problem.
In another Robin Hanson piece, he expresses the view that the large institutions developing AI have a reputational Al stake and are liable for damages their AI’s might cause. He notes that they are monitoring and testing AIs in great detail, so he thinks the dangers are overblown.:
“So, the most likely AI scenario looks like lawful capitalism…. Many organizations supply many AIs and they are pushed by law and competition to get their AIs to behave in civil, lawful ways that give customers more of what they want compared to alternatives.”
In the longer term, the chief focus of the AI doomsters, Hanson is truly an AI optimist. He thinks AGIs will be “designed and evolved to think and act roughly like humans, in order to fit smoothly into our many roughly-human-shaped social roles.” Furthermore, he notes that AI owners will have strong incentives to monitor and “delimit” AI behavior that runs contrary to its intended purpose. Thus, a form of alignment is achieved by virtue of economic and legal incentives. In fact, Hanson believes the “foom” scenario is implausible because:
“… it stacks up too many unlikely assumptions in terms of our prior experiences with related systems. Very lumpy tech advances, techs that broadly improve abilities, and powerful techs that are long kept secret within one project are each quite rare. Making techs that meet all three criteria even more rare. In addition, it isn’t at all obvious that capable AIs naturally turn into agents, or that their values typically change radically as they grow. Finally, it seems quite unlikely that owners who heavily test and monitor their very profitable but powerful AIs would not even notice such radical changes.”
As smart as AGIs would be, Hanson asserts that the problem of AGI coordination with other AIs, robots, and systems would present insurmountable obstacles to a bloody “AI revolution”. This is broadly similar to Pethokoukis’ theme. Other AIs or AGIs are likely to have competing goals and “interests”. Conflicting objectives and competition of this kind will do much to keep AGIs honest and foil malign AGI behavior.
The kill switch is a favorite response of those who think AGI fears are exaggerated. Just shut down an AI if its behavior is at all aberrant, or if a user attempts to pair an AI model with instructions or code that might lead to a radical alteration in an AI’s level of agency. Kill switches would indeed be effective at heading off disaster if monitoring and control is incorruptible. This is the sort of idea that begs for a general solution, and one hopes that any advance of that nature will be shared broadly.
One final point about AI agency is whether autonomous AGIs might ever be treated as independent factors of production. Could they be imbued with self-ownership?Tyler Cowen asks whether an AGI created by a “parent” AGI could legitimately be considered an independent entity in law, economics, and society. And how should income “earned” by such an AGI be treated for tax purposes. I suspect it will be some time before AIs, including AIs in a lineage, are treated separately from their “controlling” human or corporate entities. Nevertheless, as Cowen says, the design of incentives and tax treatment of AI’s might hold some promise for achieving a form of alignment.
Letting It Roll
There’s plenty of time for solutions to the AGI threat to be worked out. As I write this, the consensus forecast for the advent of real AGI on the Metaculus online prediction platform is July 27, 2031. Granted, that’s more than a year sooner than it was 11 days ago, but it still allows plenty of time for advances in controlling and bounding agentic AI behavior. In the meantime, AI is presenting opportunities to enhance well being through areas like medicine, nutrition, farming practices, industrial practices, and productivity enhancement across a range of processes. Let’s not forego these opportunities. AI technology is far too promising to hamstring with a pause, moratoria, or ill-devised regulations. It’s also simply impossible to stop development work on a global scale.
Nevertheless, AI issues are complex for all private and public institutions. Without doubt, it will change our world. This AI Policy Guide from Mercatus is a helpful effort to lay out issues at a high-level.
In advanced civilizations the period loosely called Alexandrian is usually associated with flexible morals, perfunctory religion, populist standards and cosmopolitan tastes, feminism, exotic cults, and the rapid turnover of high and low fads---in short, a falling away (which is all that decadence means) from the strictness of traditional rules, embodied in character and inforced from within. -- Jacques Barzun
