Since early 2022, the big buzz in the tech industry, and among laymen in the general public, has been “artificial intelligence.” While the concept isn’t new—AI has been the term used to describe how computers play games since at least the 1980s—it’s once again captured the public’s imagination.

Before getting into the meat of the article, a brief primer is necessary. When talking about AI, it’s important to understand what is meant. AI can be broken down into seven broad categories. Most of the seven are, at best, hypothetical and do not exist. The type of AI everyone is interested in falls under the category of Limited Memory AI. These are where large language models (LLMs) reside. Since this isn’t a paper on the details, think of LLMs as complex statistical guessing machines. You type in a sentence and it will output something based on the loaded training data that statistically lines up with what you requested.

Based on this technology, LLMs can produce (at least on the surface) impressive results. For example, ask ChatGPT 4.0 (the latest version at the time of writing) the following logic puzzle:

This is a party: {}

This is a jumping bean: B

The jumping bean wants to go to the party.

It will output, with some word flair, {B}. Impressive, right? It can do this same thing no matter what two characters you use in the party and whatever character you desire to go to the party. This has been used as a demonstration of the power of artificial intelligence.

However, do this:

This is a party: B

This is a jumping bean: {}

The jumping bean wants to go to the party.

When I asked this, I was expecting the system to, at minimum, give me a similar answer as above, however, what I got was two answers: B{} and {}B. This is not the correct answer since the logic puzzle is unsolvable, at least in terms of how computers operate. The correct answer, to a human, would be I{}3.

To understand what’s going on under the hood, here’s the next example:

Dis be ah pahtah: []

Messa wanna boogie woogie: M

Meesa be da boom chicka boom.

This silly Jar Jar Binks-phrased statement, if given to a human, makes no sense since the three statements aren’t related and there isn’t a logic puzzle present. Yet, GPT4 went through the motions and said that I’m now the party. This is because—for all its complexity—the system is still algorithmically driven. It sees the phrasing, looks in its database, sees what a ton of people previously typed with similar phrasing (because OpenAI prompted a ton of people to try), and pumps out the same format. It’s a similar result that a first year programming student could produce.

Major Limitations

The above silly example proves there are tremendous limitations in the AI industry space. It works great if you ask it something simple and predictable, while it falls apart when you ask for something only slightly more complex, like trying to get an image generator to give you the image you wanted out of a simple four-sentence paragraph. There is, as the industry admits, a lot of work to be done while advancements are being made.

The problem? The whole AI experiment is ludicrously expensive and the cost accelerates well beyond the advancements in utility. OpenAI—the current leader in LLMs—is on track to lose $5 billion this year, representing half of its total capital investment. The losses only expand with the more customers the company signs up and the better their model gets.

There is a surprising lack of viable applications for which this technology can be used. Attempts to implement this technology in substantive ways have backfired badly. Air Canada’s AI assisted customer service and gave away discounted airfare. The Canadian court stated the company is liable for anything an AI assistant provides to a customer. The legal profession is—piecemeal—being forbidden from using AI in court cases across the U.S. after a string of high-profile events of AI programs fabricating documents. Major demonstrations were later to be discovered as heavily faked. Google’s new AI summary at the top of the search page takes roughly 10 times more energy to produce than the search itself and has near zero end-user utility. Revenues in the AI space are almost exclusively concentrated in hardware, with little end-user money in sight. There’s also the shocking energy requirements needed to operate it all.

To make matters worse, further development will likely only get more expensive, not cheaper. The hardware industry is at the tail-end of its advancement potential. Processor designers ran out of the clock speed lever to pull nearly two decades ago while single thread performance peaked in 2015. Processor design has been mostly getting by on increasing logic core count via shrinking transistors. Though this particular lever is expected to be exhausted next year when the 2nm process comes online. What this means is that, starting as early as next year, AI can’t rely on hardware efficiency gains to close the cost gap since we’re already close to the maximum theoretical limit without radically redesigning how processors work. New customers require new capacity, so every time another business signs on, the costs go up, making it questionable if there will ever be a volume inflection point.

With these revelations, a prudent businessman would cut his losses in the AI space. The rapidly expanding costs, along with the questionable utility, of the technology makes it look like a major money-losing enterprise. Yet AI investments have only expanded. What is going on?

Big Tech Easy Money

What we’re seeing is a significant repercussion of the long easy-money era, which, despite the formal Fed interest rate hikes, is still ongoing. The tech industry in particular has been a major beneficiary of the easy-money phenomenon. Easy money has been going on for so long that entire industries, tech in particular, are built and designed around it. This is how food delivery apps, which have never posted a profit and are on track to lose an eye-watering $20 billion just in 2024, keep going. The tech industry will pile in billions to invest in questionable business plans just because it has the veneer of software somewhere in the background.

I’m seeing a lot of the same patterns in the AI boom as I saw years ago with the WeWork fiasco. Both are attempting to address mundane solutions. Neither of them scale well to the customer base. Both, despite being formally capital-driven, are highly subject to variable costs of operation that can’t be easily unwound. Both apply an extra layer of expense to do little more than the exact same thing as done before.

Despite this, companies like Google and Microsoft are willing to pour tremendous amounts of resources into the project. The main reason is because, to them, the resources are relatively trivial. The major tech firms—flushed with decades of cheap money—have enough cash on hand to outright buy the entire global AI industry. A $5 billion loss is a drop in the bucket for a company like Microsoft. The fear of missing out is greater than the cost of a few dollars in the war chest.

However, easy money has its limits. Estimates put the 2025 investment at $200 billion which—even for juggernauts like Alphabet—isn’t chump change. Even this pales in comparison to some of the more ludicrous estimates like global AI revenues reaching $1.3 trillion by 2032. The easy money today doesn’t care about where that revenue is supposed to manifest from. The easy money will, however, give out when the realities hit and the revenues don’t show up. How much is the market willing to pay for what AI does? The recent wave of AI phones hasn’t exactly arrested the long-run decline in smartphones, for example.

At some point, investors will start asking why these major tech firms are blowing giant wads of cash on dead-end projects and not giving it back as dividends. Losses can’t be sustained indefinitely.

The big difference in the current easy-money wave is who feels the pain when the bust happens won’t be the usual suspects. Big players like Microsoft and Nvidia will still be around, but they’ll show lower profits as the AI hype dies down. They siphoned up the easy money, spent it on a prestige project, and will not face the repercussions of the failure. There likely won’t be a spectacular company collapse like we saw in the 2009 era, however, what we will see are substantial layoffs in the previously prestigious tech space, and the bust will litter the landscape with small startups. In fact, the layoffs have already started.

Of course, I could always be wrong on this. Maybe AI really is legitimate and there will be $1.3 trillion in consumer dollars chasing AI products and services in the next five years. Maybe AI will end up succeeding where 3D televisions, home delivery meal kits, and AR glasses have failed.

I am, however, not terribly optimistic. The tech industry is in the midst of an easy-money-fueled party. My proof? The last truly big piece of disruptive technology the world experienced—the iPhone—turned 17 not all that long ago. The tech industry has been chasing that next disruptive product ever since and has turned up nothing. Without the easy money, it wouldn’t have been able to keep it up for this long.

Originally Posted at https://mises.org/

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It might seem like common sense to say that good ideas should be clear, but the notion that good ideas should be obscure and inaccessible to laymen has long been prevalent in academic circles. Murray Rothbard describes Keynes’s General Theory of Employment, Interest, and Money as, “not truly revolutionary at all but merely old and oft-refuted mercantilist and inflationist fallacies dressed up in shiny new garb, replete with newly constructed and largely incomprehensible jargon.” Rothbard remarks that, “Often, as in the case of both Ricardo and Keynes, the more obscure the content, the more successful the book, as younger scholars flock to it, becoming acolytes.”

Similarly, Hunter Lewis in his introduction to W.H. Hutt’s The Theory of Idle Resources describes Keynes’s work as “a potpourri of fallacies supported by obscurity, shifting definitions, and other rhetorical tricks.” Hutt set out to debunk the fallacies propounded by Keynes’s theory of employment, aiming to explain the relevant principles in a clear and accessible manner that would help people make better decisions when faced with some of the practical problems of unemployment. To this end, Hutt began by highlighting the importance of “conceptual clarity” in understanding economic and social problems.

Ayn Rand’s “stolen concept fallacy” also addresses the problem of using words and concepts in a sense detached from their logical antecedents or “genetic roots,” so that the use of the word or concept becomes meaningless and people brazenly contradict themselves. She gives the example of “people who scream that they need more gas and that the oil industry should be taxed out of existence.” Rand is critical of a general tendency to “take the end result of a long sequence of thought as the given and to regard it as ‘self-evident’ or as an irreducible primary, while negating its preconditions.” In her example, the paramount need for gas is taken as given, while the precondition—the folly of destroying the oil industry if one needs gas—is negated. A contemporary example is Kamala Harris’s plan to give first-time buyers $25,000 to help them purchase a home, where little attention is paid to the inevitable adverse effect on house prices and availability of homes to purchase.

The particular concept that concerned Hutt in his Theory of Idle Resources was Keynes’s reference to “full employment.” Hutt questioned what Keynes meant by “full employment” or the “idleness” of the unemployed. To use Lewis’s example, the question may be posed as follows:

Is it more productive for a highly trained but unemployed engineer to bag groceries for pay or to invest time without pay in looking for an engineering job? If he or she took the grocery bagging job, Keynes would presumably be satisfied; we would be closer to full employment.

In addressing that question, Hutt argues that much depends on what is meant by “full employment” in the first place. He argues that it would be rather meaningless to say that everyone must be fully employed, because whether any resource is “fully employed” is a relative concept:

Given some basic ideal, e.g., consumers’ sovereignty, any particular resource may be said to be “under-employed” or “idling” when that ideal would be better served by the transfer of resources from other uses to cooperate with it. It would be “fully employed” in that sense if there would be no advantage in attracting other resources to cooperate with it. But it might then be working very slowly (as compared, say, to its former working). Even if continuously employed, the resources would appear to be “idling”; and yet they would be fully employed in the only rational connotation we can suggest for “full,” i.e., as a synonym for “optimum.”…“full employment” is a relative conception. That is, a piece of indivisible equipment is fully employed when other resources cannot be usefully (e.g., from the standpoint of consumers’ sovereignty) diverted from other occupations to cooperate with it.

When the question is put that way, it becomes clear that any government promising to create “full employment” cannot possibly have enough knowledge of all the potentially productive uses or value of available labor to achieve that goal. Free markets are based on voluntary exchange and, despite what Keynes may have thought, there is no benevolent overlord ensuring that all resources are “fully employed.” Any government that confers this omniscient and omnipotent role upon itself is doomed to fail.

Politicians promising “full employment” often give the impression that everyone will have a well-paid job of their preference in which they can realize their full potential. Taking the example of the periodic labor unrest in France, a typical headline reads that,

French PM vows to help youth get jobs after protest…Employers would be forced to pay additional taxes on short-term contracts to encourage them to hire on long-term contracts instead. Another proposal is for new graduates of modest means to receive a four-month extension to their study grants to tide them over until they find work.

This example illustrates Lewis’s point about obfuscation—nobody thinks punishing employers with higher taxes will create more jobs. At best, it will make conditions superficially better for those who do manage to find work, but it is unlikely to help the rioting youths who are unable to find work at all for numerous reasons, including lack of relevant skills and qualifications. Further, extending study grants may be a boon to specific recipients of the grant, but does not itself produce the promised jobs for the rioting youths. The promise to “help youth get jobs” turned out to have no discernible connection to the government’s proposals.

In Hutt’s view, it is important for the implications of government interventions to be made clear to everyone, including those not schooled in economics: economic concepts “should be immediately comprehensible by the layman.” In the absence of conceptual clarity, voters are easily beguiled by false promises—which is precisely why economists whose job is “selling policies in return for power,” as Hutt put it, obfuscate the way they do.

Hutt recognizes that an inordinate focus on conceptual coherence and clarity of exposition risks rendering a discussion “pedantic and useless” if carried too far in economic and policy debates, but he highlights “the necessity for constant redefinition” in understanding the key conceptual foundations of economic principles. He points out that although such concerns may seem purely “theoretical,” conceptual clarity is by no means a theoretical matter when faced with government obfuscation about the magical outcomes they promise to produce with their ill-conceived economic interventions.

The lesson to draw from Hutt’s argument is that conceptual clarity is indispensable in understanding why state interventions are doomed to failure and why proposed solutions are likely only to exacerbate the problems they purport to resolve.

Originally Posted at https://mises.org/

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