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 AI technology 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. That would inhibit 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.

Fatal Conceit

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.