Economic education today is, for the most part, worse than no economic education at all says economics lecturer Per Bylund. He explains himself in this guest post ...

Economic Education Has Become Economic Disinformation

by Per Bylund

Modern economics is in terrible shape. But economics education appears to be worse still. This becomes clear when discussing basic economics with those who have taken courses in the field. Rather than doing away with economic misunderstandings and outright nonsense, economics education apparently provides students with a pseudoscientific rationale for their illusions.

Two such ideas are annoyingly common. One is the view that markets can only work under perfect conditions. The other is that economic growth requires that profits tend toward zero. Yes, they are ridiculous, but they are so commonly held (and believed so strongly) that they suggest a fundamental failure of economics education. Whether or not they are explicitly taught, it is easy to see how an economics education that focuses on models rather than understanding can lead to—if not create—such misperceptions.

“Markets Only Work under Perfect Conditions”

Introductory economics courses often take the perfectly-competitive model as a starting point so as to introduce students to economic thinking. It makes sense to do it this way. By assuming away complexities, students can be introduced to the economic way of thinking, ceteris paribus reasoning, and supply-and-demand analysis.

The approach is innocent but can be counterproductive or even destructive unless students also learn that a model is merely a simplified version of (and thus different from) reality. The model is not reality, and its assumptions are not real, but because of its simplified assumptions it facilitates analysis of reality. A model is a tool.

This obvious fact seems to not be communicated to economics students, who instead adopt the model wholesale as not only a description of but a necessary condition for reality. In other words, because the supply-and-demand diagram used on the blackboard relies on “perfect information,” many students conclude that real markets only work under such conditions.

It is of course the other way around: markets work because they solve or alleviate the problems that are excluded from the model. As Friedrich Hayek pointed out, there is no competition in the perfectly competitive model. All such activities are assumed to have already taken place so that the allocation under end-state efficiency can be explained—and the economic trend in markets therefore uncovered. But students somehow learn the exact opposite.

“Economic Growth Requires That Profits Tend toward Zero”

This idea is similarly a misapplication and misunderstanding of a model presented to students. In the static model of the economy, under assumptions of perfect information and zero transaction costs, economic profits will be zero. This is mainstream economists’ rather quirky explanation of economic efficiency: because all opportunities have already been taken advantage of, value production is maximized.

As follows from this model logically, profits tend toward zero as market reality closes in on “perfect” competition assumptions (that is, the problems are solved or alleviated). There is empirical support for this too: profits tend to fall in commodity markets and mature industries that are no longer innovative (the low-hanging fruits have been picked). Producers compete on cost rather than value. But this does not mean the economy is in an end-state; it only means some industries (such as grain production) have come to the end of the road in terms of product development—entrepreneurs see little or no opportunities for new value creation.

In reality, economic growth — more accurately economic progress — is the process of closing in on this highly theoretical end-state (which we as economists of the Austrian school realise is only theoretical—it cannot and never will be achieved). Our higher standard of living (economic growth) is the result of innovations that create more value—it is not the result of an absence of innovations.

Education as Disinformation

That students struggle with understanding the use and value of models, and may draw the wrong conclusions when studying market forces in the abstract, is unfortunate but understandable. It is the duty of the economics instructor to make sure students do not get the wrong ideas—that they go home with a greater understanding of how economies and markets work. Education, after all, should be enlightening and provide the student with new knowledge.

But somehow economics education fails to communicate the obvious fact that markets solve problems, not that they require that all problems have already been solved. And that economic growth is the creation of new value, not the absence of creating such value.

The failure of economics education is not merely the unproductive use of instructors’ and students’ time. As the above examples show, it is in fact destructive—students of economics get the wrong ideas and therefore graduate with less (not more) understanding of how markets and economies work.

Economics education with this outcome is disinformation, and we are better off without it.

* * * * 

Per Bylund is Associate Professor of Entrepreneurship at Oklahoma State University, and an Associate Fellow of the Ratio Institute in Stockholm.
Dr. Bylund has published research in top journals in both entrepreneurship and management as well as in both the 'Quarterly Journal of Austrian Economics' and the 'Review of Austrian Economics,' and the author of How to Think about the Economy: A Primer — which you can get free here to kick off off your real economic (re)education. (His article first appeared at the Mises Wire.)

In part one of this three-part series on so-called Artificial Intelligence (AI), our guest poster Per Bylund explained that AI's so-called large language models will not (cannot) evolve into artificial general intelligence as there is nothing therein that will give rise to consciousness. In part two he explained that neither is there any economic threat from AI —which doesn’t mean that AI will have no impact on the economy.
In part three, this final part, he distinguishes between what you should know, what you should ignore, and what real threats do exist ...

Separating Information from Disinformation: Threats from the AI Revolution

by Per Bylund

Artificial intelligence (AI) cannot distinguish fact from fiction. It also isn’t creative or can create novel content but repeats, repackages, and reformulates what has already been said (but perhaps in new ways).

I am sure someone will disagree with the latter, perhaps pointing to the fact that AI can clearly generate, for example, new songs and lyrics. I agree with this, but it misses the point. AI produces a “new” song lyric only by drawing from the data of previous song lyrics and then uses that information (the inductively uncovered patterns in it) to generate what to us appears to be a new song (and may very well be one). However, there is no artistry in it, no creativity. It’s only a structural rehashing of what exists.

Of course, we can debate to what extent humans can think truly novel thoughts and whether human learning may be based solely or primarily on mimicry. However, even if we would—for the sake of argument—agree that all we know and do is mere reproduction, humans have limited capacity to remember exactly and will make errors. We also fill in gaps with what subjectively (not objectively) makes sense to us (Rorschach test, anyone?). Even in this very limited scenario, which I disagree with, humans generate novelty beyond what AI is able to do.

Both the inability to distinguish fact from fiction and the inductive tether to existent data patterns are problems that can be alleviated programmatically—but are open for manipulation.

Manipulation and Propaganda

When Google launched its Gemini AI in February, it immediately became clear that the AI had a woke agenda. Among other things, the AI pushed woke diversity ideals into every conceivable response and, among other things, refused to show images of white people (including when asked to produce images of the Founding Fathers).

Tech guru and Silicon Valley investor Marc Andreessen summarised it on X (formerly Twitter): 

“I know it’s hard to believe, but Big Tech AI generates the output it does because it is precisely executing the specific ideological, radical, biased agenda of its creators. The apparently bizarre output is 100% intended. It is working as designed.”

What this demonstrates is that there is indeed a design to these AIs beyond the basic categorisation and generation engines. The responses are neither perfectly inductive nor generative. In part, this is necessary in order to make the AI useful: filters and rules are applied to make sure that the responses that the AI generates are appropriate, fit with user expectations, and are accurate and respectful. Given the legal situation, creators of AI must also make sure that the AI does not, for example, violate intellectual property laws or engage in hate speech. AI is also designed (directed) so that it does not go haywire or offend its users (remember Tay?).

However, because such filters are applied and the “behaviour” of the AI is already directed, it is easy to take it a little further. After all, when is a response too offensive versus offensive but within the limits of allowable discourse? It is a fine and difficult line that must be specified programmatically.

It also opens the possibility for steering the generated responses beyond mere quality assurance. With filters already in place, it is easy to make the AI make statements of a specific type or that nudges the user in a certain direction (in terms of selected facts, interpretations, and worldviews). It can also be used to give the AI an agenda, as Andreessen suggests, such as making it relentlessly woke.

Thus, AI can be used as an effective propaganda tool, which both the corporations creating them and the governments and agencies regulating them have recognised.

Misinformation and Error

States have long refused to admit that they benefit from and use propaganda to steer and control their subjects. This is in part because they want to maintain a veneer of legitimacy as democratic governments that govern based on (rather than shape) people’s opinions. Propaganda has a bad ring to it; it’s a means of control.

However, the state’s enemies—both domestic and foreign—are said to understand the power of propaganda and do not hesitate to use it to cause chaos in our otherwise untainted democratic society. The government must save us from such manipulation, they claim. Of course, rarely does it stop at mere defence. We saw this clearly during the covid pandemic, in which the government together with social media companies in effect outlawed expressing opinions that were not the official line (see Murthy v. Missouri).

AI is just as easy to manipulate for propaganda purposes as social media algorithms but with the added bonus that it isn’t only people’s opinions, and that users tend to trust that what the AI reports is true. As we saw in the previous article on the AI revolution, this is not a valid assumption, but it is nevertheless a widely held view.

If the AI then can be instructed to not comment on certain things that the creators (or regulators) do not want people to see or learn, then it is effectively “memory holed.” This type of “unwanted” information will not spread as people will not be exposed to it—such as showing only diverse representations of the Founding Fathers (as Google’s Gemini) or presenting, for example, only Keynesian macroeconomic truths to make it appear like there is no other perspective. People don’t know what they don’t know.

Of course, nothing is to say that what is presented to the user is true. In fact, the AI itself cannot distinguish fact from truth but only generates responses according to direction and only based on whatever the AI has been fed. This leaves plenty of scope for the misrepresentation of the truth and can make the world believe outright lies. AI, therefore, can easily be used to impose control, whether it is upon a state, the subjects under its rule, or even a foreign power.

The Real Threat of AI

What, then, is the real threat of AI? As we saw in the first article, large language models will not (cannot) evolve into artificial general intelligence as there is nothing about inductive sifting through large troves of (humanly) created information that will give rise to consciousness. To be frank, we haven’t even figured out what consciousness is, so to think that we will create it (or that it will somehow emerge from algorithms discovering statistical language correlations in existing texts) is quite hyperbolic. Artificial general intelligence is still hypothetical.

As we saw in the second article, there is also no economic threat from AI. It will not make humans economically superfluous and cause mass unemployment. AI is productive capital, which therefore has value to the extent that it serves consumers by contributing to the satisfaction of their wants. Misused AI is as valuable as a misused factory—it will tend to its scrap value. However, this doesn’t mean that AI will have no impact on the economy. It will, and already has, but it is not as big in the short-term as some fear, and it is likely bigger in the long-term than we expect.

No, the real threat is AI’s impact on information. This is in part because induction is an inappropriate source of knowledge—truth and fact are not a matter of frequency or statistical probabilities. The evidence and theories of Nicolaus Copernicus and Galileo Galilei would get weeded out as improbable (false) by an AI trained on all the (best and brightest) writings on geocentrism at the time. There is no progress and no learning of new truths if we trust only historical theories and presentations of fact.

However, this problem can probably be overcome by clever programming (meaning implementing rules—and fact-based limitations—to the induction problem), at least to some extent. The greater problem is the corruption of what AI presents: the misinformation, disinformation, and malinformation that its creators and administrators, as well as governments and pressure groups, direct it to create as a means of controlling or steering public opinion or knowledge.

This is the real danger that the now-famous open letter, signed by Elon Musk, Steve Wozniak, and others, pointed to: “Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilisation?”

Other than the economically illiterate reference to “automat[ing] away all the jobs,” the warning is well-taken. AI will not Terminator-like start to hate us and attempt to exterminate mankind. It will not make us all into biological batteries, as in The Matrix. However, it will—especially when corrupted—misinform and mislead us, create chaos, and potentially make our lives “solitary, poor, nasty, brutish and short.”

PART TWO: 'The Economics of the AI Revolution' 

PART ONE: 'Understanding the AI Revolution'

Per Bylund is the Associate Professor of Entrepreneurship and Johnny D. Pope Chair in the School of Entrepreneurship in the Spears School of Business at Oklahoma State University. 
He is the author of three full-length books: How to Think about the Economy: A Primer; The Seen, the Unseen, and the Unrealized: How Regulations Affect our Everyday Lives; and The Problem of Production: A New Theory of the Firm. He has edited The Modern Guide to Austrian Economics and The Next Generation of Austrian Economics: Essays In Honor of Joseph T. Salerno.
His article first appeared at the Mises Institute blog.

In part two of this three-part series on so-called Artificial Intelligence (AI), our guest poster Per Bylynd acknowledges that even though AI is arguably not an intelligence—at least not in the sci-fi sense—it does not mean that it is unimportant or lacks implications. The technological advance that it represents is nothing short of revolutionary and will have far-reaching implications for both the economy and society.

The Economics of the AI Revolution

by Per Bylund

In a recent article, we briefly summarised what it is that we today call artificial intelligence (AI). Whereas these technologies are certainly impressive and may even pass the Turing test, they are not beings and have no consciousness. Thus, this is neither the time nor the place to discuss philosophical issues of how to define a true or full AI—an artificial general intelligence—and whether we should recognize AI software legally as a person (after all, corporations are).

Economically speaking, AI as technology, whether it is used for entertainment or in production, is a good. As Carl Menger taught, what makes something a good is that it (whatever it may be) has the ability to satisfy a human need, that it must be recognised as such, and that a person—the consumer—has or can gain command over it to satisfy those actual needs. In other words, it must be scarce (there is less of it than we can use to satisfy wants) and understood as valuable (because we believe it can satisfy wants). AI certainly fits the criteria.

The economic system's stages of production form a 'production structure':
increasingly higher order of capital goods producing consumer goods, over time ...

AI as a Consumption Good

When people entertain themselves by “discussing” with AI (try, for example, Windows Copilot) or generating quirky images using DALL-E (try it here), it is a good of the lowest order—a consumption good. As such, the economic consequences are limited to the effect this has on consumer behavior. But this may in turn have a significant impact on production.

Some consumption goods revolutionise the economy and society. Examples of such goods include the automobile (from the introduction of Ford’s Model-T) and the smartphone (starting with Apple’s iPhone). The former disrupted transportation and infrastructure and facilitated just-in-time manufacturing and urban sprawl, just to mention a few effects. The latter changed everything from how we bank to how we travel.

The point here is that as consumer behaviour changes, the production structure follows along. For example, with the broad adoption of the smartphone, paper map production has all but disappeared; whereas, digital location services and intelligent logistics have seen enormous growth and development. And change leads to more change because entrepreneurs build on, add to, and challenge the new discoveries.

AI has the potential to change consumer behaviour well beyond its designed functionality. Exactly how and in what ways remains to be seen. But it is safe to say that it has potential. (On the other hand, many goods have had potential to disrupt but didn’t leave a mark.) For example, we may see people produce their own stories, songs, images, and even movies. So perhaps, instead of relying on television or Netflix and Hollywood producers, we’ll make movie night into a make-a-movie night where we watch content we have generated and that fits us perfectly.

AI as a Higher-Order Good

As a tool and thus a good of a higher order, AI has already had an effect and promises to disrupt several trades. Because it is very effective at producing and presenting content, including translating and editing texts, content-related professions are threatened by AI. This includes journalists and copyeditors, as AI programs can write and edit faster than humans. After all, anyone can ask AI to produce or edit a text. Students already use AI to spice up or improve their papers—or let AI write them from scratch.

AI is similarly affecting photographers and illustrators. It only takes a minute to have DALL-E produce a new image exactly as directed, or to have an AI algorithm remove or add things in a picture you snapped. Whereas, having an illustrator create something takes much longer (not to mention the cost).

Programmers and system developers are also seeing the effects of AI, which has no problem both generating new code (without bugs!) or checking already written code. Legacy software written in dated and ineffective programming languages can be run through an AI to make the coding more efficient—and converted into a modern language.

AI is also affecting academia. Why have an instructor tell students about some subject matter instead of letting AI do it? After all, the AI can easily present content in a way that the student prefers. For example, make a movie to explain, say, biology or chemistry in an entertaining way. And it can answer all kinds of questions without ever getting bothered or cranky—and it has nowhere else to be. In research, AI can analyse data more effectively and run thousands of different regressions on data to find something that is significant and important (so-called HARKing, which is very poor research practice—but who will know?). It can write up the paper too, with citations and everything, in just seconds.

AI as Production Capital

All of this means AI can and will be used in production. In fact, it already is and we have only started to see the effects.

AI is best categorised as capital, which is used to make labor more productive (more value output per hour of labor invested) through facilitating more roundabout (but more effective) production structures. Capital goods in general have one (or both) of two functions: it makes existing production processes more effective by increasing productivity, or it makes possible types of production that were not previously possible. AI checks both boxes.

We have already seen how people working in several types of content-based professions can easily be made more productive or replaced entirely by AI. It can also do things that people may have been unable to do—or never thought of doing. This of course can cause so-called technological unemployment as people lose their jobs because AI can do them better (and cheaper). But this is a dystopian way of describing something quite normal and highly useful: that we relieve people, with all their ingenuity, from comparatively simple tasks so that they can create much more value elsewhere.

It is of course problematic for any person losing their source of income, but it is highly beneficial to consumers (and therefore society at large) that these (and other) professions are “creatively destroyed.” The economic point of employment is not to provide people with an income so they can pay taxes (although politicians seem to think so) but to produce goods that can satisfy consumer wants—to make our lives better. Just like there are very few stable boys or buggy-whip producers since the automobile revolution, the future will see fewer people doing news reporting, copyediting, or coding.

Note also that this revolution is not nearly as sudden and disruptive as it may at first seem: the news media, for example, have for many years reduced the number of journalists doing reporting (most outlets nowadays merely republishing standard articles from AP or Reuters). And software development already uses increasingly effective development environments that correct and predict commands, allow for WYSIWYG and drag-and-drop development, and can debug code and suggest solutions to bugs.

AI is only another step in this process. But the threat is greatly exaggerated. We tend to overestimate the impact of technology in the short term but underestimate it in the long term.

Limitations to Overcome

There is a problem, however, and it has to do with how large language models work and what responses they generate. When used in a setting that is strictly rules-based, such as in computer programming, the AI “understanding” of code can greatly improve the productivity of coders (or replace them). AI will not introduce bugs in software unless the specifications are incomplete or contradictory, and it will not make errors.

The same is true for AI’s language generation: it draws from large troves of text data and has a good “understanding” for how humans use language. But there are no rules-based ways by which it can distinguish fact from fiction. Instead, AI draws from what statistically is more likely to be a human-sounding response. For this reason, it produces content that can be entirely wrong.

For example, I asked AI to summarise the content of my 2022 economics primer, How to Think about the Economy. [A highly recommended free book - Ed.] Since it has access to the text, it did a pretty good job summarising what is in the book. But it also added comments on content that is typically in economics books but that is not in the primer (such as equilibrium theory, perfect competition, and mathematical equations). The AI is correct that economics books typically discuss such things and thus it is statistically probable that my primer would do the same. But it doesn’t.

There is a difference between statistical probability and truth. We will look at this problem and the potential threat that AI poses to human society in the next article.

=> CONTINUED IN PART THREE: 'Separating Information from Disinformation'

PART ONE: 'Understanding the AI Revolution'

Per Bylund is the Associate Professor of Entrepreneurship and Johnny D. Pope Chair in the School of Entrepreneurship in the Spears School of Business at Oklahoma State University.
He is the author of three full-length books: How to Think about the Economy: A Primer; The Seen, the Unseen, and the Unrealized: How Regulations Affect our Everyday Lives; and The Problem of Production: A New Theory of the Firm. He has edited The Modern Guide to Austrian Economics and The Next Generation of Austrian Economics: Essays In Honor of Joseph T. Salerno.
His article first appeared at the Mises Institute blog.

No, AI is arguably not an intelligence—at least not in the sci-fi sense, as acknowledges Per Bylund in this first part of his three-part Guest Post, but it does not mean that it is unimportant or lacks implications. The technological advance that it represents is nothing short of revolutionary and will have far-reaching implications for both the economy and society.

Understanding the AI Revolution

by Per Bylund

The artificial intelligence (AI) revolution is here, and it is bound to change the world as we know it—or so proclaimed the hype following the release of OpenAI’s ChatGPT version 3.5 in November 2022, which was only the beginning. Indeed, much has happened since then with the release of the much-improved version 4.0, which was integrated into Microsoft’s Bing search engine, and the recent beta release of Google’s Gemini.

Lots has since been written about what AI could mean for humanity and society, from the positive extremes of soon-here Star Trek technologies and the “zero marginal cost” society to the supposedly imminent “AI takeover” that will cause mass unemployment or the enslavement (if not extermination) of mankind. However, how much of this is fiction, and what is real? In this three-part article series, I will briefly discuss the reality and fiction of AI, what it means for economics (and the economy), and what the real dangers and threats are. Is this the beginning of the end or the end of the beginning?

Most people’s prior experience of the term “artificial intelligence” is from science fiction books and movies. The AI in this type of media is a nonbiological conscious being—a machine man, of sorts. The intelligent machine is often portrayed as lacking certain human qualities such as empathy or ethics. However, it is also unencumbered by human limitations such as imperfect calculability and the lack of knowledge. Sometimes the AI is benign and a friend or even servant of mankind, such as the android Data in Star Trek: The Next Generation, but AI is often used to illuminate problems, tensions, or even an existential threat. Examples of such dystopian AI include Skynet in the Terminator movies, the machines in The Matrix, and HAL 9000 in 2001: A Space Odyssey.

The “AI” in our present real-world hype, such as OpenAI’s ChatGPT and Google’s Gemini, is nothing like these sci-fi “creatures”; they are nowhere near conscious beings. In fact, what we have today is so far from what we typically would call an intelligence that a new term has been invented to distinguish the “real thing” from the existing chatbots that are now referred to as “AI”: artificial general intelligence. The conscious, thinking, reasoning, and acting nonbiological creature-machines in sci-fi are artificial general intelligences. This raises the question: What is AI?

Machine Learning and Large Language Models

Present-day AI is an intelligence in the same sense as a library of books is. Both hold loads of information that are categorised in a number of different ways, such as by topic, keyword, author, and publisher. For the regular library, the books are categorised to help users find what they are looking for.

However, imagine if all the books in the library were scanned so that all the letters, words, sentences, and so on were stored together and easily searchable. This mass of content could then be categorised inductively, which means that computer software sifting through all the content would be able to figure out its own new categories based on the data themselves. What are common words and phrases? How are words combined, in what order, and in what contexts are those orders present? What phrases are more frequent in what types of books or chapters? What combinations of words are rare or do not exist? Are there differences between word use and sentence structure between authors, books, and topics?

Such inductive sifting through the content, guided by statistical algorithms, is referred to as “machine learning” and is a powerful tool to find valuable needles in informational haystacks. Note that these needles may not already be known—machine learning finds needles we know exist but can also uncover needles we had no idea existed. For example, using such techniques to go through medical data can find (and has found) correlations and potential causes of diseases that were previously unknown. Similarly, the Mercatus Center at George Mason University has fed regulatory texts through such machine learning algorithms to create RegData, a database that allows users to analyse, compare, and track regulatory burdens in the United States and beyond.

Whereas RegData is intended to support social science research on regulations, machine learning can be used on all kinds of information. When such algorithms are run on enormous amounts of text in order to figure out how language is used, it is called a large language model (LLM). These models thus capture a statistical “understanding” of how a language is used, or as Cambridge Dictionary puts it (explaining the generative pretrained transformer (GPT) LLM, on which ChatGPT is based), “a complex mathematical representation of text or other types of media that allows a computer to perform some tasks, such as interpreting and producing language, recognising or creating images, and solving problems, in a way that seems similar to the way a human brain works.”

Indeed, based on its statistical understanding of language, an LLM chatbot can predictively generate text responses to questions and statements in a way that mimics a real conversation. It thereby gives the appearance of understanding questions and creating relevant responses; it can even “pretend” to have emotions and express empathy or gratitude based on how it understands that words can be used.

In other words, LLM chatbots like ChatGPT can arguably pass the Turing test as they make it very difficult for a human to distinguish their responses from a real human’s. Still, they are statistical prediction engines.

But Is AI Intelligent?

It is certainly an impressive feat to have software mimic human conversation to the point of tricking real humans into believing it is a person. However, the question of whether it is intelligent remains. To again refer to the Cambridge Dictionary, intelligence means “the ability to learn, understand, and make judgments or have opinions that are based on reason.” Whereas we sometimes use verbs like “learn” and “understand” for machines, they are figurative not literal uses. A pocket calculator does not “understand” mathematics just because it can present us with answers to mathematical questions or solve equations; it has not “learned” it; it also cannot “make judgments” or “have opinions.”

Certainly, AI is significantly more advanced than calculators. However, this does not take away from the fact that they are logically the same: both present results based on predetermined, prestructured, and precollected rules and data; neither of them has agency nor consciousness, and neither can create anything de novo. This is obvious for the calculator, which is comparatively stupid and only produces outputs according to simple rules of mathematics.

However, the same is true for AI. It is, of course, enormously more complex than a calculator and has the added ability to create its own categories and find relationships inductively, but it does not “have opinions that are based on [its own] reason.” It only predictively generates responses that, based on the texts that it has already processed, are statistically likely to be what a human would (or at least could) produce. This is why AI at times, despite the vast knowledge it has access to, spits out gobbledygook and has a hard time sticking to what is true. It simply cannot tell the difference. (It cannot “tell” at all.)

In other words, AI is logically speaking the very opposite of what we would expect from a human (or alien or artificial) intelligence: it is backward-looking, makes up responses based on already existing language data, and does not add anything that is not statistically (re)producible from past information. It also does not fail, flounder, or forget, and it lacks subjectivity.

An actual intelligence would of course rely on experience too, but it would have the ability to generate novel content and implications. It would be able to think anew and creatively come up with different conclusions based on the same data—an actual intelligence would forget valuable pieces of information, make errors, and use faulty inferences, and it would subjectively weigh and interpret facts—or to choose to disregard the data.

However, even though AI is arguably not an intelligence—at least not in the sci-fi sense—it does not mean that it is unimportant or lacks implications. The technological advance that it represents is nothing short of revolutionary and will have far-reaching implications for both the economy and society.

   

=> CONTINUED IN PART TWO: 'The Economics of the AI Revolution'

Per Bylund is the Associate Professor of Entrepreneurship and Johnny D. Pope Chair in the School of Entrepreneurship in the Spears School of Business at Oklahoma State University. 
He is the author of three full-length books: How to Think about the Economy: A Primer; The Seen, the Unseen, and the Unrealized: How Regulations Affect our Everyday Lives; and The Problem of Production: A New Theory of the Firm. He has edited The Modern Guide to Austrian Economics and The Next Generation of Austrian Economics: Essays In Honor of Joseph T. Salerno.
His article first appeared at the Mises Institute blog.

The Economy Is a Process Not a Factory

A lot of people could learn a lot about economics from reading this short-ish guest post by Per Bylund, an excerpt from his new book 'How to Think About the Economy' -- a little book (with a free download) written to accomplish something very big: economic literacy. It begins with the idea that the consumer is sovereign...

How to Think About the Economy: The Economy Is a Process Not a Factory

Guest post by Per Bylund

To help us understand what is going on in the economy, what is important is not the types and number of goods that sit on store shelves. It is why and how they got there.

To answer this question is not simply a matter of pointing out that they arrived by truck last week, because that only tells us about how they were transported to the store. This doesn’t tell us anything about all the steps that had to happen to make them available. And there is a lot that takes place before a good is available to buy in a store. Every good you see on a store shelf was originally thought of by someone; it was designed and then produced. The production process was developed, all its operations and the necessary machines and tools engineered, and then the process was overseen and managed. Someone had to think about how best to market and sell the goods to the store and solve the problems of logistics. And someone had to finance the whole thing.

In other words, to understand everything we see around us, including everything that we take for granted, we must recognise that the economy is not a state but a process. Looking at a snapshot of the economy tells us very little—if anything—about how it works but can instead mislead us and allow us to jump to conclusions. Without recognising the process, it can be easy to conclude that a specific situation is inefficient, wrong, or unfair and also to think that it is easy to improve upon it, right the wrong, or calculate an outcome that is less unfair.

For example, if we only look at a portion of the picture, it can seem unfair that the storekeeper has so many goods and other people have none. But looking at the full picture, we realise that these goods are not the shopkeeper’s to use but are merely goods in progress to their final use with consumers. The shopkeeper is not a hoarder—and has little “economic power.” The shopkeeper is providing the service of making those goods available to consumers, and depends on their willingness and ability to buy the goods to make ends meet. Without the store, the customers would need to buy each and every item in bulk from a wholesaler. The storekeeper offer us the convenience of many goods in one place.

A Coordinated Process

There is more to the economy than the production of a good that we see sitting on a store shelf. Its production was possible because there exist other processes and production. For example, a producer of chocolates usually does not produce the cocoa, sugar, or flavouring, that is in them. Chocolate producers rarely produce the machines they use to make the chocolate; the building where they produce, package, and prepare the chocolate for shipping; or the power-plant to supply them with electricity. It is not enough to say that chocolate is produced by only one person before it ends up on store shelves. In fact, chocolate producers could not make their chocolate if there were not already producers of the necessary ingredients already available.

In sum, the chocolate producer is part of a much longer supply chain that fills the gaps in the overall production process, itself comprising lots of producers and specific production processes. Together, these processes—often carried out by different businesses—make a very long chain of operations that step by step produces the specific good from the “original factors” that were available to us at the dawn of time: nature and labour power. Someone cleared the land to grow sugar cane or cocoa. Someone decided to provide transportation services, which was possible because someone else had already paved roads and manufactured trucks. Those trucks could be manufactured because someone was already producing steel, plastics, and everything else trucks are made of. The steel could be produced because others were running mines and smelting plants. If we were to list all the things that allow the chocolate maker to make chocolate, it would be a long list indeed. Even small things like the coffee that the chocolate factory workers drink on their break is the result of a long supply chain involving thousands of people in many countries. What is important is not to map out all the things that are involved in making a certain good, but to understand that the economy is all of these things working together.

It would appear it takes many businesses and workers to produce the long line of goods intended only to make that chocolate, that you can then purchase. That is true in some sense—they were all involved and all of them were necessary for the final good to be made available to you. But the miner of course has no idea that the ore taken out of the mine last year will become the steel that is smelted into a part of a machine that produces the chocolate you can buy in the shop today. The coffee bean grower had no idea that his coffee would fuel workers in a faraway country making a special type of chocolate that you are considering buying. In the same way, the shopkeeper doesn’t need to know anything about all of the steps that have taken place before there can be a supply of chocolate to stock on the store shelves.

The point is that the elaborate, complex production process that produces any good you see in the store is not the design of anyone in particular. The overall process is not coordinated around producing specific goods. No one made a blueprint or flow chart specifying all of these steps and their order. No one estimated how much rock needs to be crushed to produce the iron ore that eventually is used in the production of chocolate. What drives the process is not the creation of goods, but the creation of value for you as a consumer.

Throughout the economy, businesses compete with each other to produce as much value as possible by producing and offering goods. We think of this as competition, the producing of the same or similar items: competing chocolate makers, for example. But that is a very narrow view. Chocolate makers indirectly compete for the steel that is used in their chocolate machines with all the other uses for which other producers use steel. The same with sugar. And workers. And the coffee that the workers drink, maybe some of whom even use sugar in their coffee.

Why does some of the steel produced go to the machines that produce chocolate? The answer to this question will be discussed at length in chapter 5 of my book. Right now, it is sufficient to note that all businesses are involved in producing, directly or indirectly, goods that are intended for consumers. Which means that almost all of a country's wealth is being used to produce, directly or indirectly, goods that are intended for consumers. All of production has this aim, whether or not producers of steel, for example, know exactly what their steel is going to be used for. They do not know and don’t need to. It is the value that consumers see in those goods when produced that determines how much they will be willing to pay. That payment is what justifies the businesses’ investments and expenses throughout the economy. Consequently, what indirectly coordinates what all businesses do—and how they do it—is their expectation that they are contributing to providing consumers with valued goods.

Continuous Innovation

It is important to note that competition goes beyond the businesses and production that we see. Yes, those businesses compete. As we saw above, they compete both directly and indirectly by trying to buy the same inputs and trying to sell to the same customers. However, this is a much too limited view of competition that leaves out what is important in the longer term. Business do not only compete with existing businesses, but also compete with businesses that do not yet exist. And the businesses that exist are the outcome of such competition that already took place.

If this sounds strange, it is because we are used to looking at the economy as a state—a snapshot—and not as a process. Those businesses that exist today are the survivors of a competitive weeding out process that has already taken place. It is because these businesses were better—more productive, offered higher-quality goods, etc.—that they are currently in business. And they will stay in business only if they continue to be better than the competition. They need to outperform not merely the other surviving businesses, but also those businesses that have not yet been started or that are still developing or refining their products. This includes businesses producing goods that do not yet exist, and may not even have been imagined yet, but that could provide consumers with more value than the goods already available.

The innovation of new goods, production techniques, materials, organisations and so on fundamentally changes how an economy produces goods and what goods are produced. In the era when horse and buggy was the standard means of transportation, there was certainly competition between stables and transportation businesses just like there was competition between buggy manufacturers. But if we look only at those businesses, we could never explain how they were replaced and outcompeted by businesses that brought on the age of automobiles. Today, there are very few businesses profitably producing buggies. The reason is that automobiles provided consumers with greater value.

Seen from the perspective of consumers, horse buggies were valued goods right up until there were affordable automobiles. The automobiles provided greater value, which is why they undermined the profitability of and ultimately destroyed horse-and-buggy businesses. This is sometimes referred to as “creative destruction” that makes the core of economic development: older and less value-creative production gives way to new and more value-creative production.

When we recognise that this creative destruction is real and that it places constant pressure on businesses to innovate and reinvent themselves so as not to be replaced, we realise that it is impossible to understand the economy as anything other than a process. Economies evolve and unfold over time; they reinvent themselves. Competition is not merely the rivalry between two or more businesses producing and selling similar things, but the constant pressure to serve consumers better—both present and future. History is full of successful and influential businesses, many of them considered too big and “powerful” to compete with. Most of them are long gone and forgotten because someone figured out how to produce more value for consumers.

Continuous Uncertainty

Although the economy—and especially the market economy—is best understood as a process, it would be a mistake to think of it as a production process. We briefly addressed this above, but it is worth reiterating and elaborating on. An economy comprises production processes, but those production processes are themselves selected: they are the ones that survived the constant weeding out of less value-creative production. Many of those surviving production processes will, in turn, be weeded out as new and more value-creative ones are attempted.

A production process consists of the operations that make specific outputs from specific inputs. It is typically, but not necessarily, designed and organised. We can think of it as what happens within a factory. The exact operations that take place within the factory can change over time and so can the people and machines. Most of the parts are in some sense replaceable. Sometimes the factory itself is repurposed, but what makes it a factory is the same: it transforms inputs into outputs. The factory doesn’t manufacture outputs in general—it is not a magical production machine. A factory produces clearly-defined outputs (goods) using an engineered production process that requires specific inputs in precisely-defined quantities.

None of this applies to the economy as a process! The “output” of an economy is value, in the form of consumer goods, but the actual goods that are produced change over time—and so does their respective value. The process of an economy is not its actual productions—the production processes and goods produced—but the continuous selection of those productions that provide the greatest value to consumers. Computers replaced typewriters and revolutionised office work flow, just as the automobile replaced the horse and buggy because it provided consumers with more highly valued transportation. Most all of our goods today, and the processes that produce them, will sooner or later be replaced by better, more valuable ones.

We cannot say which products will be attempted and even less which ones will be successful. Production, in other words, is always uncertain. It requires some form of investment before the value of the output can be known. This value is ultimately experienced by consumers when using goods, the expectation of which determines what price they are willing to pay. But it is not enough that goods satisfy wants—they have to do so, in the eyes of the consumer, to a greater degree than what he or she expects from other goods available. Only then will the consumer buy that product.

The number and variety of goods available depends on the imaginativeness of entrepreneurs and investors. In other words, the entrepreneur, who imagines, envisions, and aims to create new valued goods, drives the evolution of production in the economy. The consumer is then, after the fact, the judge of which entrepreneurs’ productions are of sufficient value to be bought—and at what prices. The consumer, in other words, is sovereign and, through buying and not buying, determines which entrepreneurs earn profits and which entrepreneurs suffer losses.

Per Bylund is associate professor of entrepreneurship & Records-Johnston Professor of Free Enterprise in the School of Entrepreneurship at Oklahoma State University. Visit his website at PerBylund.com.

This excerpt first appeared at Mises.Org.

Download his book here.

The Political Hocus-Pocus They Call Modern Monetary Theory

 

Modern Monetary Theory claims to be both new and a theory of economics -- one that claims you really can get something for nothing as long as the bill is always sent to the government. But as Per Bylund explains inthius guest post, it is not a theory of how the economy works at all, and so does not concern itself with worldly things like production, innovation, entrepreneurship, scarcity (other than as potentially causing inflation), or time. It is instead a pseudoreligious conviction that anything is possible and that the one and only solution is always Glorious Government.

The Political Alchemy Called Modern Monetary Theory

by Per Bylund

The new kid on the economics block is something called modern monetary theory (MMT). The name is modern, but the "theory" is not. It comes from a time when folk still considered a perpetual-motion machine a scientific possibility. Like MMT however, it never was, or will be.

Proponents adamantly claim that it is both new and a theory of economics. To make it appear this way, they dress the ideas in unusual-sounding jargon and use rhetorical tricks. For example, instead of presenting actual arguments or responding to direct questions, they present a circular flow of deepities. To top it off, they, at least in my humble experience, usually lack fundamental economic literacy. This can make rebutting their nonsensical claims a challenge and, as a result, debates with this crowd typically go nowhere.

In order to figure out what exactly they are claiming—beyond the deepities—I decided to acquaint myself with the prominent proponents. I read "founder" Warren Mosler’s so-called white paper on MMT, but it’s not very helpful: there is little by way of theoretical explanation, other than redefining if not obscuring the meaning of common concepts in economics. Mosler also seems overly eager to move from explanation to instead argue for his preferred policies.

I hoped for (and got) more from listening to a TED Talk by Dr. Stephanie Kelton, an economist and professor at Stony Brook University who was the senior economic advisor to Bernie Sanders’ presidential campaign and author of The Deficit Myth (reviewed by Bob Murphy here). TED Talks are only fifteen minutes long, but it turned out to be a very painful experience.

It’s All about Spending

Judging from Kelton’s presentation, MMT simply boils down to a description of how the fiat currency system works under a central bank, while ignoring many of the effects of that creation. Kelton explains:

MMT provides an accurate description of how a fiat currency like the US dollar or the British pound actually works. It reminds us that we are no longer on a gold standard, so finding the money to pay for the things we need is never an issue for countries like the US or the UK.

The implication of having a monetary monopoly is that the “[t]he federal government can never run out of money” (all quotes are from Kelton’s talk unless otherwise stated). This is obviously true, but only because Kelton (and MMT) does not distinguish between money in the real sense (the valued medium of exchange, i.e., purchasing power) and the currency issued by government and banks (the dollars or pounds, whether physical or digital). But that’s not always true. For example, the government of Venezuela was not running out of bolívars, but from this it did not follow that the currency would retain its purchasing power (as, obviously, it didn’t) or even retain its status as money (which it also didn’t). Venezuela is one recent example, but the claim is universal. (For more examples, see Zimbabwe/Zimbabwean dollar or the Weimar Republic/papiermark.)

So, in a strict sense, it is certainly true that the federal government “can afford to buy whatever is available or for sale in its own currency.” However, while proponents of MMT often emphasize and extrapolate from the word “afford” to make it appear as if there were no end to government spending, it is really “for sale in its own currency” that is key. This means there is indeed a limitation. Kelton realizes this but fails to mention it until the very end. Her point here is to delink government spending and taxation:

If you got a $1,400 check from the federal government earlier this year, or if your company received money to help cover payroll and other expenses, then you received some of the newly minted digital dollars that were created to support our economy. No taxpayers were involved in that process. It was all done using nothing more than a computer keyboard.

This too is not false; however it only tells one side of the story. It ignores what is unseen. Kelton simply observes that government need not worry about deficits, because they are paid in the government’s own currency. And the government can make as much of this counterfeit capital as it likes. Yes, we have heard this before; it is nothing new. The problem is that it is based on a fundamental mistake: confusing the unit for its meaning to users; or, if you will, thinking that a currency is money. By talking about one, and the creation of more of it, yet referring to the other, thus assuming it remains largely unaffected, Kelton can state the following about deficits:

Here's what I see. I see what's happening on the other side of the government's ledger. When the government spends more than it taxes away from us, it makes a financial contribution to some other part of the economy. Their red ink is our black ink.

Yes, you read it correctly: when government runs a deficit it is (somehow) a contribution to society. It literally creates something (prosperity) out of nothing (its IOU). Government creates new money for its IOU, out of which it purchases infrastructure, teacher salaries, electric vehicles, etc. Thus, private businesses get the new money as revenue—they (presumably) earn a profit—while the government provides society with "needed services." The result is, if we believe Kelton, more jobs and income for people while they get more services from government. We get something for nothing. 

No wonder apostles of big government love it.

What about Price Inflation?

With deficits being a nonissue, being waved away by this hocus-pocus, the political problem then is not to balance budgets. After all, according to MMT logic, a balanced budget would deprive society of the benefits that the deficits offer. Instead, policymakers have a moral duty to maximise the “contribution” to society as long as doing so does not have negative consequences for society. As Kelton puts it,

Congress should be focused on keeping inflation in check. That's the real limit on spending, and it's the thing to watch out for if you're thinking of spending trillions on things like infrastructure, healthcare, and free college.

Observe that this is basically the same scheme as monetarists argue for, that the money supply should be increased to lubricate and support the growing economy—but not so much that it affects the price level. MMT takes this idea and greatly inflates it (pun intended) by adding that government deficits are not harmful—they are instead, if used wisely, a double benefit. As long as the price level remains largely unchanged (or, I presume, price inflation is kept at a “low” level, such as “only” 2 percent per annum), more can be squeezed out of the economy.

Government can and should do this to the extent possible, but the deficits also offer a means for reform, if not restructuring of whole economy, that should not be wasted.

[E]very deficit is good for someone. The question is, for whom? And what are those deficits used to accomplish? It matters how the money is spent and who ends up with the resulting surplus.

Indeed, money is not neutral, so it benefits whoever happens to receive it first, before prices go up. This is another MMT twist that they use to their advantage. The argument does not rely on increasing the money supply helicopter-money style, so they need not assume it has no effect on the structure of the economy. On the contrary, MMT argues that the money should be used first by government on specific investments—typically infrastructure, healthcare, schools. Because the money is spent on those things, businesses (they assume) will be incentivised to create supply that facilitates those investments. As a result, the economy is forced nudged to do the “right” things. (We are at this point deep into normative territory, i.e., ideology; there is no semblance of positive theory left.)

What about the Economy?

So far, the MMT story does not seem to relate at all to the real economy. It is pure magic: more currency means more jobs, greater government services, a higher standard of living. Abracadabra! Does this mean MMT simply ignores the fact that the actual economy is a matter of allocating scarce resources toward valuable ends? Of creating real production out of real capital? Not quite. It simply downplays this by ignoring the many implications.

Kelton refers to the problem of Congress's directing the “contribution” of deficits as resourcing. Here comes the real economy:

Congress should be asking, how will we resource it? To answer that question, think of people, factories, equipment, and raw materials like wood and iron. If we're going to build high-speed rail, fix crumbling infrastructure, and green our economy, then we’ll need concrete, steel, and lumber; we’ll need construction workers, architects, and engineers; we’ll need companies that can fill thousands of orders for solar panels, EV charging stations, and electric school busses. If our economy has the productive capacity to quickly supply all of those things, then we can easily resource it. Or take healthcare or free college. Paying the bills to expand Medicare to include dental, vision, and hearing is easy. The challenge is making sure we have enough dentists, optometrists, and audiologists to treat everyone who needs care. And if you want to resource free college, then you need the faculty, the classrooms, the dormitories to teach and house more students. In a full employment economy, all of the resources you need are, well, fully employed. There is no spare capacity anywhere in the system.

So, finally, we get to the real issue and the reason why proponents of MMT believe they can get something for nothing: in a full economy, those resource are already being used and you'd need to bid them away (which means they would no longer be able to be available for those previous uses, and their price would rise); but all the MMTers see are are idle resources, assets that are not currently used in production processes. Because those resources are not productive, at the moment, government’s deficit investments will (they think) incentivise those sitting on the resources, whether individuals or businesses (or government agencies?), to surrender them to the productive efforts so that society can make productive use of them.

But this poses several problems that those arguing for MMT seem unaware of. 

First, that idle resources are not actually just sitting there, but are idle for a reason. They are idle because this is what their owners consider to be their highest-valued use. All capital is part of a production plan. It is a mistake to assume that an asset that is not right at this moment used in some production process is not part of a greater production plan. In fact, most production includes some degree of waiting, maturing, or search for the proper timing.

Consider a newly distilled whiskey that sits “idle” in a cask for a decade. This is not waste, but part of the production process of ten-year-old whiskey, which is a different good with much greater expected value to consumers. Production takes time, which means we cannot at any specific moment determine what would be the best use of resources. This includes resources that do not appear to be used at all but are in fact owned and therefore directed toward some end. Timing is an important aspect of production that proponents of MMT, in their urgency to maximize only the present, fail to realize. Much entrepreneurship fails not because there is no value in what they offer but because the timing is not right—they are either too early or too late.

It is also true that we want resources to be held in reserve for future uses. If we use everything to 100 percent in the present, there is no possibility of attempting new and more valuable productions. After all, government investments in infrastructure (or anything on the MMT wish list, for that matter) are not an effective way to generate innovations. Valuable innovations are created by entrepreneurs seeking new ways to satisfy consumers and thereby earn profits. MMT’s shifting of resources toward public works means we may not get the solutions to those grand challenges that we have no solutions for today. The quest to maximise the present, whether or not it turns out successful (and it likely will not), sacrifices both the near and distant future.

Joseph Schumpeter put this clearly in Capitalism, Socialism and Democracy (p. 83):

[W]e are dealing with a process whose every element takes considerable time in revealing its true features and ultimate effects, [so] there is no point in appraising the performance of that process ex visu of a given point of time; we must judge its performance over time, as it unfolds through decades or centuries. A system—any system, economic or other—that at every given point of time fully utilises its possibilities to the best advantage may yet in the long run be inferior to a system that does so at no given point of time, because the latter’s failure to do so may be a condition for the level or speed of long-run performance.

I doubt Schumpeter’s genius will sway any proponent of MMT, however. To them, nobody is alive beyond the immediate present.

It Is Not about the Economy, but about Glorious Government

Kelton’s argument also, inevitably comes down to believing that whatever government does is right. Yes, she argues that it is important that the deficits end up in the "right" hands, but simply notes that this is the real task for Congress. Okay, but what if politicians do not invest in the “right” things? Or what if those things are right for some but wrong for others? Kelton doesn’t say, but I suppose she would refer to some vague notion of public good or what society “needs.” But this question cannot be avoided, because it strikes at the core of MMT’s failure.

The whole argument, as Kelton presents it, asserts that government needs to get idle resources into production. Whatever the reason they currently appear idle to Kelton and others is of no concern: government, they assume, will put those resources to better use. True to form, proponents of MMT tend to focus on only idle resources, which makes a cleaner point. But they overlook that changing the incentives will also shift resources from already productive uses to those productions that are on the MMT wish list. Which are things that, economically speaking, without the backing of this tidal wave of government largesse, are currently money-losing dogs. (Green jobs, green new deals, red and blue welfare projects...)

What they are really saying here, when you boil it right down, is that entrepreneurs, investing their own property for the chance of earning profits, but at the risk of losing everything if consumers dislike their offering, overall do a worse job allocating productive resources than politicians investing deficits that need not be paid off. This is a very problematic assumption. Just noting the different incentives for entrepreneurs and politicians is enough to fundamentally question what MMT proposes.

Add to this that government’s track record in creating public goods that are of actual value to people and that do not waste resources is nothing short of dismal. Then add the public choice aspect to the whole thing, that politicians have their own interests and therefore may not pursue the public good even if they know it. The assumption that government will fix the economy and increase our standard of living beyond what entrepreneurs can do is unbearably naïve.

I do not think these problems matter much to proponents of MMT, however. Because [like the proposed creation of a trillion-dollar coin] MMT is not actually a theory of how the economy works all, and so does not concern itself with worldly things like production, innovation, entrepreneurship, scarcity (other than as potentially causing inflation), or time. It is a pseudoreligious conviction that anything is possible and that the one and only solution is always Glorious Government.

* * * * 

Per Bylund is associate professor of entrepreneurship & Records-Johnston Professor of Free Enterprise in the School of Entrepreneurship at Oklahoma State University. His website is PerBylund.com. His post first appeared at the Mises Wire.

Keynesian economics with Chinese characteristics

China appears to be wealthy. But if it is, wonders Per Bylund in this guest post, why is there so much (Keynesian) waste right out there in the open?

China: A Keynesian Monster

by Per Bylund

I recently spent two weeks traveling in the People’s Republic of China (PRC), a vast country with many contrasts: old vs. new, poor vs. rich, traditional vs. modern, East vs. West. While it is a strange experience with many impressions, what’s most striking is the obvious and contradictory economic contrast between wealth and waste.

Chinese city skylines in the economic development zones consist of business-district skyscrapers mixed with high-rise apartment complexes at least 30 stories high. The latter exist in groups of a dozen or so buildings of identical designs shooting far up into the sky, sometimes placed in the outskirts to facilitate the city’s expansion or change travel patterns according to some (central) master plan for the city.

The boxy skylines are interrupted by vast numbers of tower cranes in the many construction projects that produce more high-rises and skyscrapers at impressive speeds. The city is conquering the countryside, and devouring the surroundings much like a swarm of locusts.

This image is one of production, a society experiencing enormous economic growth and wealth creation.

But travelling, as the day gives in to night, shows a very different picture of these sprawling Chinese cities. While the setting sun makes the tower cranes stand out even more, what is obviously missing is the obvious sign of civilisation within these hulking towers: artificial lighting. Many of these newly constructed buildings become silhouettes against the sunset that are as dark as a dead tree trunk. They are dead hulks, empty carcasses without any signs of life.

One can stand in the middle of the city watching the glass-and-metal skyscrapers wrapped in neon lighting, as one would expect. Yet among them see many dark shapes of buildings that are empty – if not dead. These buildings are not necessarily new and move-in ready, they are simply uninhabited and unused.

This image is one of wasteful spending and immense economic errors. The contrast is as puzzling as it is scary. It tells us something important about the nature of the recent Chinese economic miracle: that it is fundamentally fake.

The Chinese economy obviously relies very heavily on state-sponsored, state-planned projects such as these constructions of buildings. It probably wouldn’t be much of an exaggeration to say that the Chinese economy is a Keynesian jobs project of outrageous scale, which also means that is as removed from real value creation as any Keynesian undertaking.

The much talked about “Belt and Road” project is the same thing on an international scale. The project aims to recreate the silk road with modern infrastructure, connecting the Far East with Europe via both land and water. Consisting of numerous infrastructure projects in about 60 countries and trade deals to leverage the projects, the OBOR is a political project to connect the East and the West. It is state-planned and state-sponsored, and intended to, at least during the build phase, create projects primarily for Chinese companies abroad (though the immediate effect seems to have been capital outflow). It will most likely boost Chinese GDP, just as intended, and will be a catastrophic failure due to its reliance on planning rather than markets. But as states tend to think of GDP statistics as actual economic growth, rather than as a crude and faulty measure of it, the project may seem like a success at first.

What China teaches us about economics and economic policy is the lesson that is generally not provided in college classrooms: the important distinction within production between value-creation and capital consumption. The story of China’s economic development is to a great extent one of unsustainable, centrally planned growth specifically in terms of GDP — but a lack of sustainable value creation, capital accumulation, and entrepreneurship.

Production creates jobs even if what is produced is wasteful infrastructure projects, ghost cities, or only ghost buildings in otherwise inhabited cities. But those jobs only exist for as long as the projects are underway – that is, for as long as there is already created capital available to consume, domestically or attracted from abroad.

Per Bylund, PhD, is a Fellow of the Mises Institute and Assistant Professor of Entrepreneurship & Records-Johnston Professor of Free Enterprise in the School of Entrepreneurship in the Spears School of Business at Oklahoma State University, and an Associate Fellow of the Ratio Institute in Stockholm. He has previously held positions at Baylor University and the University of Missouri. Dr. Bylund has published research in top journals in both entrepreneurship and management as well as in both the Quarterly Journal of Austrian Economics and the Review of Austrian Economics. He is the author of two full-length books: The Seen, the Unseen, and the Unrealized: How Regulations Affect our Everyday Lives, and The Problem of Production: A New Theory of the Firm. He edits the Austrian Economics book series at Agenda Publishing, and edited the volume The Next Generation of Austrian Economics: Essays In Honor of Joseph T. Salerno, published by the Mises Institute. He has founded four business startups and writes a monthly column for Entrepreneur magazine. For more information see PerBylund.com.

His article previously appeared at the Mises Wire.

"Capital has value only to the extent it is used to serve consumers. So who owns the means of production doesn't matter, since its value is zero (!) unless used to satisfy other people's wants. Really, capital isn't power." #QotD

"Capital has value only to the extent it is used to serve consumers. So who owns the means of production doesn't matter, since its value is zero (!) unless used to satisfy other people's wants. Really, capital isn't power."
          ~ Per Bylund 

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QotD: The law of markets ...

"Supply facilitates demand, and demand is constituted by supply. If you understand this and the implications, you understand the market economy. And can figure out economic policy."~ Per Bylund.

QotD ""To socialists, there is real value in organising and managing a whole economy's production. But, strangely..."

"To socialists, there is real value in organising and managing a whole economy's production. But, strangely, the organising and management of production processes isn't considered real, valuable work."
~ Per Bylund

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Politicians–and everyone else—still don’t understand Say’s Law

[Editor's note: How do you do away with a “Law” that had been core to economists’ understanding of the market economy for 150 years? You misrepresent it.
    On Thursday US Energy Secretary Rick Perry on declared "you put the supply out there and the demand will follow." It appears that Perry was attempting to invoke Say's Law, and many professional economists and pundits quickly took to mocking both Perry and Say's Law for making assertions contrary to modern Keynesian orthodoxy.
    Below, economist Per Bylund explains in this Guest Post what Say's Law really says, how Keynes misrepsented it, and why understanding the Law is still a good thing.]

Few concepts are as misunderstood as the so-called Say’s Law. In part, this is the fault of John Maynard Keynes who, needing to do away with it to make room for interventionist policy, did much to make it mis-understandable. How do you do away with a “Law” that had been core to economists’ understanding of the market economy for 150 years? Simple: You misrepresent it. Strawmen are so much easier to knock over than the real thing.

Hence, the “Law” is presently known in the misbegotten terms Keynes gave it, that “supply creates its own demand,” something that is obviously untrue.

Originally, however, Say’s Law was different. It even had a different name. Economists prior to Keynes tended to refer to it simply as the Law of Markets, so-called because it describes in very simple terms the fundamentals of how a market functions. Jean Baptiste Say was simply the earliest to express the law, which may be why it has come to bear his name.

Say noted that

A product is no sooner created, than it, from that instant, affords a market for other products to the full extent of its own value.

This means that

As each of us can only purchase the productions of others with his own productions — as the value we can buy is equal to the value we can produce, the more men can produce, the more they will purchase.

In other words (and this still shocks postmodern economists), production necessarily precedes consumption; and (in the way Keynes’s mis-statement should be re-ordered) anyone’s demand is constituted by their supply.

The Law of Markets thus summarises the nature of market actions where production is specialised under the division of labour. Specifically, that we produce to sell, with the intention to then use the proceeds to buy what we really want. Market production is in other words indirect and not undertaken to directly satisfy one’s own wants. We produce instead to satisfy other people’s wants, and can thereby satisfy our own by purchasing what others produce.

The benefit is that there is a separation between what I want to consume and what I produce, which means we can each specialise in producing something we are comparatively good at instead of producing only what we want to consume. It also means we can specialise in producing only one thing instead of a multitude, thereby cutting switching costs, develop skills and expertise, increase knowledge, and consequently increase output.

But while universal specialisation under the division of labour means that overall output is significantly increased, it also means we become dependent on each other in trade. Not only do we need to sell what we produce to others in order to get the means necessary, but we need to also trade with those who produce what we want to satisfy our wants. We become interdependent – and voluntarily so. This is why Ludwig Von Mises stated that

Society is division of labour and combination of labour. In his capacity as an acting animal man becomes a social animal.

This “social animal” benefits from, engages in, and in fact arises out of market (inter)action. As we can only benefit ourselves by properly aligning our own productive efforts with what other people want, we must understand other people. By doing so, we can better anticipate what needs and wants they have and then busy ourselves with attempting to meet those needs. And because production takes time, production must precede demand.

Because demand is unknown, production is necessarily speculative and entrepreneurial. Actual demand will be discovered when the goods are presented to potential buyers. Entrepreneurs are therefore forecasters, project appraisers, and risk-takers; in an advanced economy they advance funds to owners of labour and capital, and only recoup this investment if they succeed in selling the product.

At the same time, the consumers can only buy if they have themselves engaged in production that satisfies other people’s needs — because otherwise they will only have the willingness but not the ability to buy (and that is not demand). This is not a circular argument but an integration at the “macro” level – and also an explanation for economic growth. The ability to sell goods in the market and thus engage in specialised production requires prior investment [i.e., a ‘wages fund’ – Ed.]. So to specialise one needed to first produce demanded goods in excess of one’s own wants. The same is true today: development of a new good requires investment, and that investment is speculative because actual demand cannot be known until it is too late.

The implication is that there can never be a general glut in the economy and therefore no “deficiency” in what Keynes called “aggregate demand.” It is however certainly possible for there to exist a surplus or shortage of any particular commodity, which happens regularly as entrepreneurs fail to precisely anticipate and therefore meet market demand, but only in the short term.

As all production is undertaken to sell the goods produced to then purchase goods that better satisfy the producer’s want, the inability to sell becomes an inability to demand. We cannot demand unless we first produce the means to demand. It is thus not a “demand deficiency” that someone is unable to sell what he or she produce, and consequently cannot demand goods in the market. Rather, it is a production failure that causes a reduction in effective demand — specifically, an entrepreneurial failure.

If government stimulates demand, then this only subsidises those goods that have been produced at too high cost. Consequently, the entrepreneurial errors are propped up and production therefore remains misaligned with demand.

So it is easy to see why proponents of interventionism would want to do away with the Law of Markets. If demand is not constituted by supply, then markets may not clear and government must save us from ourselves. Something every government today feels compelled to promise.

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Per Bylund is assistant professor of entrepreneurship & Records-Johnston Professor of Free Enterprise in the School of Entrepreneurship at Oklahoma State University. Website: PerBylund.com.
This post previously appeared at the Mises Wire. It has been lightly edited.