On AI and Personhood: Pagans Will Be Pagans

  

Wesley J. Smith, a senior fellow at the Discovery Institute's Center on Human Exceptionalism, did a thorough job the other day of taking down the pronouncements of an Anthropic expert tasked with understanding why Anthropic AI systems like Claude behave the way they do.  A New York Times report on a day-long seminar given by Christopher Olah inspired Smith to write a long list of things Claude can't do, in contrast to Olah, who spent all day making the case that Claude should be considered as a person, not just a machine.

 

Olah described tracking Claude's "emotional vectors" and showed transcripts of conversations with one AI model which appeared to be having a nervous breakdown.  He speculated that just as humans sometimes benefit from the act of confession, maybe Claude might find the Catholic sacrament useful as well.  A rabbi who was present said that Olah and his colleagues appeared to believe that Claude models have the moral standing of a human being, and should be accorded the same rights and respect. If that is the case, the rabbi concluded, then what Anthropic is doing with their models amounts to slavery. 

 

Olah's response to this accusation was not recorded. But it doesn't matter, because Olah and his fellow AI creators are following a well-worn path that stretches back to antiquity:  the path of pagans worshipping the god they created. 

 

Smith is exactly right when he pointed out in his column that Claude is not alive, has no intrinsic value, is not conscious, does not think, cannot "feel," cannot suffer, can be damaged but not harmed, and cannot sin.  But if he thinks that well-thought-out evidence-based philosophical assertions of the non-personal status of Claude and its ilk are going to cause scales to fall from the AI community's eyes and make them say, "We see now!  It's just a machine!" he better have another think coming.  Pagans who invest a lot of work in their creations aren't easily dissuaded by some philosophical musings.  Sometimes the only thing that will convince them is power:  a power greater than their god.

 

In the Old Testament book of 1 Samuel, Chapter 5 recounts how the pagan Philistines captured the Hebrews' Ark of the Covenant and set it in their temple devoted to the pagan god Dagon.  The first night, the statue of Dagon fell over face down in front of the Ark.  After the Philistines set him up again, the next morning they found him face downward again, this time with his head and hands cut off.  After that, they decided that maybe the Ark wasn't such a good interior decoration after all, and passed it around to their friends the Ashdodites and Ekronites, to whom a plague of death and tumors happened.  While these incidents probably didn't lead to any conversions to Judaism, it did teach the pagans a lesson that the God of Israel was not to be messed with.

 

The Jewish prophet Isaiah had some choice words to say about a man who cuts down a tree and burns some of it to bake bread.  Then he turns around and uses the rest of the wood to make himself an idol:  "Half of it he burns in the fire . . . 'Aha, I am warm, I have seen the fire!'  And the rest of it he makes into a god, his idol; and falls down to it and worships it; he prays to it and says, 'Deliver me, for thou art my god!'"

 

We are living at a time when old philosophical arguments and ancient pagan ways are emerging into the headlines and the living rooms of millions.  When you ask your Siri device to play jazz, what are you talking to?  The line between use and worship is not always easy to distinguish.  Everyone has the urge to worship something, and it's only natural that people who spend their working lives developing cutting-edge AI models are going to be impressed by what the work of their hands can do.  The urge to anthropomorphize AI, to spend time and emotional investment in it like it was another human being, and even to argue for its legal rights, are all understandable. 

But like all pagan worship of false gods, such moves end sooner or later in disaster, both personal and institutional.

 

I have a friend—a real flesh-and-blood person—who will listen to a news report of some complicated jam that such people have gotten themselves into, and quite often the first thing he'll say is, "Those folks need Jesus."  And he's right. 

 

We can philosophize all day and quote every philosopher from Aristotle to Roger Scruton in trying to convince AI worshippers of the fact that machines aren't people, and all the "can'ts" Wesley Smith lists are true.  But most of the AI crowd would first have to take two semesters of philosophy before they could understand what we are talking about, and even then they'd probably find some way to disagree. 

 

The followers of the Hebrew God knew what was going on with the Ark and the pagans.  Followers of Jesus know what is going on with AI worshippers.  Our first task is not to fall into the trap of idol worship ourselves.  A Martian observing a random sample of one hundred U. S. citizens on a typical day would conclude that the little oblong objects they spend so much time staring at must be something either religious or necessary to survival, or both.  Christians should be different.

 

And the next task is to live lives and do acts that convince nonbelievers of the power of God.  That's not much in evidence today, I have to admit, though there are exceptions, especially in cultures that are not saturated with modernity.  Christians don't have to absolutely reject AI and all its works, which is in any case practically impossible.  (I used Google's AI feature to look up the Old Testament references for this blog.)  But we can resist making AI an idol ourselves, and live as lights in a dark world that warms itself with half its electricity and creates AI idols with the other half.  Some things never change. 

 

Sources:  Wesley J. Smith's article "AI 'Claude' Can Never Be a Person" appeared on the National Review website at https://www.nationalreview.com/corner/ai-claude-can-never-be-a-person/.  The story of the Philistines and the Ark appears in 1 Samuel 5:1-5.  The selection from Isaiah is Isaiah 44:15-17 (Revised Standard Version). 

AI Use in Academia: Is Everything Not Forbidden Compulsory?

  

In T. H. White's imaginative fantasy of the youth of King Arthur, The Once and Future King, the magician Merlin transforms Arthur into a variety of animals in order to teach him the virtues he will need in human society.  One of the most chilling transformations comes about when Merlin launches him into an ant colony.  Suddenly Arthur can hear the constant drone of instructions from on high, and read the ever-present propaganda posters that tell him how regimented the lives of ants are.  One particular sign encapsulates the essence of totalitarianism that leaves no room for free will:  "Everything Not Forbidden Is Compulsory."

 

Over the past year I have observed how artificial-intelligence propaganda, if you want to call it that, has pervaded the workplace and, after the usual delay, has washed up on the shores of academia.  At the beginning of the fall semester, just before classes began, I received an invitation to a two-day AI seminar in which a new faculty member would be conducting exercises in how to create a website with AI, how to use it in research and administrative tasks, and more.  Besides invitations from our school director, I received an email directly from the person conducting the seminar the day before it began.  I'm sure he did a good job with the seminar, although I wasn't able to attend it due to some prior travel I'd planned.  But my confidence in the project was not increased when I noted that in the email from the seminar leader, he had misspelled the word "tomorrow."  Either he didn't take his own advice and skipped letting AI check his emails, or the AI system thought he was trying to be funny.

 

It is becoming increasingly clear that anybody in a business that could be described in George Gilder's phrase as symbolic manipulation—and that definitely includes academics—will soon be effectively forced to use AI for any substantial workflow processes.  The reason is simple.  If people who use AI begin producing at a rate that equals what three or four or ten people can do unaided by AI, those who refrain from boosting their output with AI will begin to look like slackers.  If competition is a factor in keeping one's job, as it always is for untenured faculty and continues in some form after tenure, using AI in a way that boosts your output will soon be necessary.

 

I'm in a peculiar position in that a year ago, I began what is called phased retirement, in which I work half-time for three years with an option to continue after that.  The one course I teach involves a hands-on lab course.  Until they bring me an AI robot that can help students figure out what is wrong with a circuit they built, I don't see how AI is going to be of much direct help in the lab. 

 

I'm not an AI Luddite. A few months ago I bought a subscription to Claude and got it to do a few things.  It was an interesting experience, and probably saved me some time compared to how long the tasks would have taken without it.  But there's a learning curve involved in writing effective AI prompts, and I would probably have to use it every day to get good enough at it to be able to say my net productivity had increased.  From what I understand, Claude is now free to faculty members on request, so I'll probably let my subscription lapse.  But I'm still not sure what I'll do with it.

 

The application of the ant-colony slogan to this situation is fairly obvious.  The much-abused concept of academic freedom includes the right (privilege?) of deciding what methods and approaches I will use in teaching my class.  If I am strongly urged, through peer pressure and cheerleading emails, to use AI in teaching, my freedom isn't breached, exactly, but neither is it enhanced.  Nobody has ordered me to start using AI, so my university is very far from an ant colony, although an observer during the break between classes might be forgiven for likening the campus to what happens when you kick over an ant bed. 

 

I'm old enough to remember the transition from the secretarial pool where you took your hand-written report to be typed up, to the word processor programs we were given on our new computer terminals and told to use for reports from now on.  There was some grumbling among the older staff, and I remember one professor who agreed to start using the word processor, but insisted on retaining his secretary to deal with all his emails.  Both he and the secretary have presumably retired by now.  It was an annoying transition for a few years, and produced a lot of badly spelled and composed memos before spell-check programs were added.  But now, personal secretaries are like personal jets, reserved only for members of the very highest reaches of an organization, and everybody else makes do with their own hands on the keyboard.

 

Besides, even without meaning to use AI, I've started using it by simply doing the same kinds of things I've done for a while, such as consulting Google.  It now defaults to AI mode, and you have to positively turn it off if you don't want the AI summary as a response to your inquiry.  As an example, I was pretty sure that the forbidden-compulsory quote was from The Once and Future King, but I asked Google to make sure, as I don't have a copy of the book handy.  Google's AI Overview not only confirmed me in my suspicion, but quoted the exact passage, and appended an explanation of the context, "a world completely stripped of individual free thought and personal freedom." 

 

So if I get a poll in a few months—probably AI-generated—asking me how I'm using AI in my teaching, research, or service, I'll be able to check a little box saying in effect, "Yes, Master, I'm using AI."  But not necessarily because I want to, or because I think it makes me better at what I do.  Simply because as it's no longer forbidden, it's well on the way to becoming compulsory.

 

Sources:  T. H. White's The Once and Future King was published in 1958. 

 

NOTE:  Next week, instead of the usual blog, I will publish a reader's poll.  I currently receive virtually no feedback of any form from publishing this blog.  This poll will be your opportunity to (a) let me know somebody reads the blog and (b) give me suggestions for improvement.  So don't be surprised, as you have been warned.

Aristotle, Aquinas, and AI

What have a Greek philosopher who lived about 2300 years ago, a Dominican friar, theologian, and philosopher who died in 1274 A. D., and artificial intelligence (AI) got to do with each other?  Sounds like the beginning of a bad joke.  What I have to say is no joke, however, because if we don't heed the wisdom of these two parties, which has been almost totally forgotten, we are liable to make some mistakes shortly that will be exceedingly costly in terms of missed opportunities, misdirected hopes, and maybe even lives.

 

The particular wisdom I speak of is known by the technical term of scholastic metaphysics.  It was, and still is, a way of viewing the universe, and it dominated the West for over a thousand years.  The Scientific Revolution vigorously uprooted it and threw it in the trash pile, to oversimplify a very complicated history.  And now it is almost completely forgotten except by a few lucky people such as yourself, if you keep reading.

 

The aspect of scholastic metaphysics I wish to explain concerns the nature of things.  Here's an example, taken from an article by journalist Mary Harrington in the June/July issue of First Things.  Take a dog, any dog.  It may be black, white, spotted, or bald.  But none of those attributes change the fact that it's a dog.  The scholastic metaphysical term for the dogness of a dog is its substantial form, also known as essence.  The aspects of a thing that can change without changing its substantial form are called accidents.  That's a weird use of the word, I know, but I'm not going to argue with Aristotle, who came up with these ideas, and Aquinas, who put them in terms compatible with Christianity.  The substantial form of a thing is its organizing principle.  The accidents are characteristics that can change without altering the substantial form.

 

What about people?  We have an organizing principle called the soul.  No matter what accidental characteristics we have—whether a person is fat, thin, from Teaneck, New Jersey, or in a coma—that substantial form remains. 

 

What about computers, and all the systems that are known by the general term of AI?  AI is, of course, artificial—that is, it is a man-made artifact.  Unlike living things, an artificial object has no substantial form that is prior to what its human makers say it is.  We can cobble together a bunch of transistors, call it a computer, and get it to do certain things for us.  But computers, and the AI systems supported by them, are simply assemblies of accidents.  This makes AI systems fundamentally different in kind from human beings. 

 

What emerges from looking at humans and AI from the viewpoint of scholastic metaphysics is this:  human beings are just a clean different kind of thing from AI systems.  And treating AI systems and humans as though they were basically the same kind of thing, simply piles of atoms arranged differently—which is how modern materialistic science encourages us to think—ignores a vital difference between the two.  Human beings have souls as their organizing principle, and AI systems do not.  No imaginable or unimaginable future progress in AI can change that fact.

 

This viewpoint flies in the face of the transhumanist agenda so popular in Silicon Valley, which looks forward to the day when human beings can transcend their messy, disease-and-death-prone meat-cage origins and live forever in an eternal bit-world of computer-borne bliss that will take over the universe.  (I may have left something out in that description, but I think I got most of it.)  Human beings are the rational animals that result from propagation by other human beings.  Anything you might upload from a human being to a computer is not going to be that human being.  And treating it, or any other AI product, as if it were human is a category error that can lead to heinous consequences.

 

In Harrington's words, trying to teach sand to think is not only futile, but if we start treating the result as though it had a soul, we are in danger of losing our own souls.

 

This may sound like just another anti-technology rant, but it's not.  AI can do truly amazing and useful things, but only if its human users keep in mind what it is and don't try to endow it with quasi-human agency.  A tool should be under the control of its human users at all times.  That control may be loose and barely perceptible sometimes, but it has to be there.  If we begin to bow down to AI entities as though they were thinking humans like us, we will soon regret it.  An automobile provides useful service when under control, but if it gets out of control it becomes a dangerous missile.  The same thing is true of AI.

 

Thinking in terms of scholastic metaphysics seems strange to most people unfamiliar with it, and it takes a while to realize that it's just as useful as it ever was.  I have had a passing acquaintance with it ever since I read a book on Thomistic philosophy in the 1990s.  The thing that drove me to that book was a statement by a former theoretical physicist turned pastor, who said that the Catholic Church's notion of transubstantiation of the bread and wine of the Eucharist into the body and blood of Christ was based on an outmoded theory of matter.  I wanted to know more about this outmoded theory, and it turned out to be scholastic metaphysics. 

 

What modern science did to scholastic metaphysics is a classic case of throwing out the baby with the bathwater.  There was a lot of useless bathwater in Aristotle's physics—rocks having a desire to fall downward and so on—but there was also great wisdom in his metaphysics.  The wisdom of scholastic metaphysics can save us from making lots of mistakes that could be avoided if we just keep in mind that however clever and human-like AI appears, it's really just words written in the sand.  And we wrote the words to start with. 

 

Sources:  Mary Harrington's article "Thomophobia" appeared on pp. 19-27 of the June/July 2026 issue of First Things.  For those wanting to know more, she refers to Edward Feser's book Scholastic Metaphysics (Editiones Scholasticae, 2014, $34 on Amazon).

 

US Fears AI, Uses It Anyway

  

A recent report in National Review summarized opinion polls about what U. S. residents think of artificial intelligence (AI) and how much they are using it.  Paradoxically, the more people use AI, the more they fear it.

 

A poll by NBC News showed that 46% of those queried had a negative opinion of AI versus only 26% positive.  Other polls show that citizens expect mostly or entirely negative effects on society from the widespread use of AI, and believe it will lead to serious job losses.  Over half the Americans polled by Democratic research firm Blue Rose feared that AI will lose them their job or a relative's job. 

 

At the same time, polls asking about AI use show that most people queried have used an AI tool in the past month, and a fourth say they use it every day.  So the old saying "familiarity breeds contempt" may be a guiding principle in how AI is viewed by the general public.

 

In a way, none of this matters.  If a new technology gets widely used and the companies providing it make money, who cares what people think about it?  Another technology that spread rapidly in only a few years, and also had profound effects on society, was television.  In 1950, only 9% of households had a TV, but by 1955 over half did.  And while there may have been a few voices raised in opposition to its growth, I think it's fair to say that the only groups that looked on the spread of TV with disfavor were industries threatened by it:  Hollywood, for instance.  And Hollywood has long ago made peace with the advent of television.  Your average person in the early 1950s was just waiting to see when TV sets got affordable enough to buy, and any negative consequences of TV use were not noted much in public before the 1960s.

 

One difference between the advent of TV and the advent of AI is that TV didn't threaten jobs like AI does.  And one job sector that is already seeing big effects from AI is computer science and computer programming.  The thing about public perception, regardless of whether it's accurate or not, is that it can easily become reality.  I work at a university, and I have heard in the last week that enrollment in computer-science programs is dropping across the board, after years of steady growth.  The reasons for this are not entirely clear, but one factor may well be that students fear spending four or more years getting a degree and then finding that all the entry-level positions are now being done by a few senior people writing AI prompts. 

 

On the other hand, one of the most enthusiastic proponents of AI I know is an 80-plus professor of biology who has been using ChatGPT in his research for the last year or two.  He says it helps him write papers more clearly and to organize his thoughts, and claims it's the greatest thing that's happened to him research-wise in a long time. 

 

Many of the polls mentioned were commissioned by political interests with a view toward forming policies about AI.  Currently, the Trump administration favors few if any regulations on the technology, and wants to keep states from enacting a patchwork of legislation that would encumber the field.  Historically, this approach has worked well for computer- and network-intensive industries themselves, allowing them to create vast new economies and profit mightily therefrom.  But it has also led to a number of real and lasting problems, ranging from the maleficent effects on politics of social media and the quantified and well-known harms to children and teenagers whose lives are distorted by the use of smartphones. 

 

The crystal ball of predicting how technologies will affect society is always more or less cloudy, and I will not venture to say what the future effects of AI's negative polling will be.  Even if AI were universally detested, it's not clear that Washington could get its act together enough to pass meaningful regulatory legislation, especially when Big Tech and the federal government sometimes seem to blur into each other.  On the state level, if the feds don't stop them, some states may pass laws attempting to regulate AI, but it's a little bit like trying to nail Jell-O to the wall.  When the thing you are trying to regulate is so protean and shape-changing, it's hard to decide what regulations to pass, let alone to figure out if they've been violated.

 

Some of the anxiety the public feels about AI is simply due to the breathtaking speed with which it has advanced and improved.  Arthur C. Clarke's principle that any sufficiently advanced technology is indistinguishable from magic applies here.  Real magic is scary if it happens, and I still feel a kind of queasiness when I type commands into a chat box and the program comes back with "I did this and that."  It's understandable that millions of teenagers use AI chatbots as a substitute friend, and it's also very creepy.

 

Going to extremes, a few people believe AI will engender the end of civilization as we know it.  Other hyper-tech-optimists such as Ray Kurzweil look forward to being uploaded to an eternal cloud and think it will be heaven on earth.  The truth probably lies somewhere in between.  What we can do as individuals is to keep reminding ourselves that AI systems are not human beings, and that human beings are not machines.  But both of those truths may become harder to keep in mind as time goes on. 

 

Sources:  The National Review website carried James Lynch's article "The More Americans Use AI, the More They Fear It" on Mar. 25, 2026 at https://www.nationalreview.com/news/the-more-americans-use-ai-the-more-they-fear-it/. 

Up Close and Personal with AI

  

After writing about AI for years, I still hadn't had what you might call a serious encounter with it in its personalized form.  Anyone who uses Google has probably been offered their "AI summary" before the conventional search results.  I've found these summaries helpful sometimes and not so helpful other times, but I haven't sought them out. 

 

What made me turn the corner was something a friend sent me by a software engineer named Matt Shumer, whose essay "Something Big is Happening" appeared on the Fortune website on Feb. 11.  Shumer's point was that the latest iterations of AI systems are so much more capable than what has gone before that whole swathes of what George Gilder calls "symbolic manipulators"—lawyers, engineers, judges, doctors, architects, you name it—now face a radical choice.  Either embrace AI and by doing so outperform your peers by orders of magnitude, or turn away from it and watch your career flame out.  That's a little exaggerated, but not much.

 

This reinforced something another friend has told me about his own personal use of AI:  that it has benefited his writing and research greatly, acting as a mostly trustworthy assistant to summarize large bodies of literature and help him clarify his thoughts.  The biggest problem this friend has had with it is that it tends to be sycophantic and flatter him excessively.  But he sat down with it one day and told it to refer to him as "the researcher" and itself as the "AI system," instead of "you" and "me," and things got better. 

 

So I decided to opt for a paid version of Anthropic's AI product, which Shumer said was significantly better than the free version, and decided to give it a major task that I would ordinarily give to a grad student, if I had one (funding is very hard to find in my research area).

 

The job involved reading about a thousand rows of data in a big spreadsheet to fill out some yes/no questions about each row.  The data was in the form of comments submitted by various individuals in response to questions.  I gave the AI system examples to follow and what I thought were pretty detailed instructions.

 

All this was in the form of the usual chat format, with me and the AI system taking turns typing into chat boxes.

 

After a misunderstanding in which I thought the system was working on the problem and it thought I hadn't told it to start yet, it got to work and spat out various things like "Ran 6 commands . . . Examine the spreadsheet structure" and so on.

 

It was done in about ten minutes.  Then I spent an hour or so going over its work.

 

I wish I could say I couldn't have done better myself.  But I could have, by a long shot. 

I didn't exhaustively examine all 700 rows of entries that the system produced—that would have taken many hours, about as long as it would take me to just do the job myself.  So I sampled every tenth row for a hundred rows to see how the thing did.

 

In looking at ten rows, I found nine mistakes.  This is not a good average.

 

In the system's defense, this is absolutely the first time I've ever tried anything like this.  I could go back and get a lot more explicit about the rules for answering the yes/no questions about each row, and let it try again.  But in comments online about this particular version of AI (Sonnet 4.6, I think), some people said that you get results faster, but you have to fix problems more often.  That is consistent with my experience.

 

Good things about this exercise include how fast the thing ran, how it basically grasped what I wanted, and how it produced something in only ten minutes.  But speed isn't everything. 

 

Some not-so-good aspects include the errors and a kind of weird fawning or flattery I also noticed.  I'd call it "gushing" when it spontaneously responded "This is a genuinely exciting dataset — ball lightning is one of the most mysterious atmospheric phenomena ever reported!" 

 

I suppose that sort of thing has been cultivated by the AI's keepers, probably to keep the user engaged, or encouraged, or something.  I found myself wishing that instead, they had adopted the mien of Joe Friday in the old Dragnet true-crime series.  Friday was famed for his flat "Just the facts, ma'am" aspect, and that seems more in keeping with a system that supposedly can tackle highly sophisticated and challenging jobs of major import. 

 

But like everybody else who doesn't work for Anthropic or the other four or five leading AI companies, we will simply have to take what we can get and deal with the negative aspects as well as we can.

 

Will I try again?  Probably, but maybe with a different task.  As part of my signup process, Anthropic has been emailing me little suggestions of other things to try:  writing recipes, managing emails, creating content, solving problems, visualizing data, or helping me decide whether to go to Portugal or Spain on vacation (no-brainer for me:  Spain, but I don't have time right now). 

 

I am not especially tempted to try any of these suggestions right yet. But I do admit that if I can get the thing to turn out useful work, it could be worth what I spent on it.  I paid for a year's subscription in advance, perhaps not the wisest thing to do, but I'm the type of person who is motivated to get his money's worth, and spending the money in advance may get me engaged when nothing else would.

 

I see that Anthropic just had a dustup with the Pentagon, which banned its use within the armed forces as punishment for uncooperation, or something.  Now that we are apparently in a war with Iran, the leaders of Anthropic may feel glad that their product isn't part of the war.  But not all battles are fought with bombs and bullets, and I have a feeling that the greatest battles involving AI are yet to come.

 

Sources:  The essay by Matt Shumer, who runs an AI applications company, appeared on Feb. 11, 2026 at https://fortune.com/2026/02/11/something-big-is-happening-ai-february-2020-moment-matt-shumer/. 

Can We Afford to Power AI?

 

That is the question posed by Stephen Witt's article in this week's New Yorker, "Information Overload."  Witt, a biographer of Nvidia founder Jensen Huang, has toured several of the giant data centers operated by CoreWeave, the leading independent data-center operator in the U. S.  He brings from these visits and interviews some news that may affect everyone in the country, whether or not you think you use artificial-intelligence (AI) services:  the need to power the growing number of data centers may send electric-power costs through the roof.

 

Witt gives a fascinating inside view of what actually goes on in the highly secure, anonymous-looking giant buildings that house the Nvidia equipment used by virtually all large-scale AI firms.  Inside are rack after rack of "nodes," each node holding four water-cooled graphics-processing units (GPUs), which are the workhorse silicon engines of current AI operations.  Thousands of these nodes are housed in each data center, and each runs at top speed, emphasizing performance over energy conservation. 

 

Gigawatts of power are used both in the training phase of AI implementation, which feeds on gigabytes of raw data (books, images, etc.) to develop the weights used by AI networks to perform "inferences," which basically means answering queries.  Both training and inference use power, although training appears to demand more intense bursts of energy. 

 

The global economy has taken a headlong plunge into AI, making Nvidia the first company worldwide to surpass $5 trillion in market capitalization.  (Just in case anybody's thinking about a government takeover of Nvidia, $5 trillion would pay off only about one-eighth of the U. S. government's debt, but it's still a lot of money.)  With about 90% of the market share for GPU chips, Nvidia is in a classic monopoly position, and significant competitors are not yet on the horizon.

 

Simple rules of supply and demand tell us that the other commodity needed by data centers, namely electric power, will also rise in price unless a lot of new suppliers rush into the market.  Unfortunately, increasing the supply of electricity overnight is well-nigh impossible. 

 

The U. S. electric-utility industry is coming off a period when demand was increasing slowly, if at all.  Because both generation and transmission systems take years to plan and build, the industry was expecting only gradual increases in demand for both, and planned accordingly.  But only in the last couple of years has it become clear that data centers are like the hungry baby bird of the utility industry:  mouths always open demanding more. 

 

This is putting a severe strain on the existing power grid, and promises to get worse if the rate of data-center construction keeps up its current frenetic pace.  Witt cites an analysis by Bloomberg showing that wholesale electricity costs near data centers have about doubled in the last five years.  And at some point, the power simply won't be available no matter what companies like CoreWeave are willing to pay.  If that happens, the data centers may move offshore to more hospitable climes where power is cheaper, such as China.  As China's power still comes largely from coal, that would bode no good for the climate.

 

Witt compares the current data-center boom to the U. S. railroad-building boom of the nineteenth century, which consumed even more of the country's GNP than data centers are doing now.  That boom resulted in overbuilding and a crash, and there are signs that something similar may be in the offing with AI.  Something that can't go on forever must eventually stop, and besides limitations in power production, another limit that may be even harder to overcome is the finite amount of data available for training.  Witt says there are concerns that in less than a decade, AI developers could use up the entire "usable supply of human text."  Of course, once that's done, the AI systems can still deal with it in more sophisticated ways.  But lawyers are starting to go to work suing AI developers for copyright infringement.  Recently, one AI developer named Anthropic paid $1.5 billion to a class-action lawsuit group of publishers whose material was used without permission.  That is a drop in the bucket of trillions that are sloshing around in the AI business, but once the lawyers get in their stride, the copyright-infringement leak in the bucket might get bigger.

 

The overall picture is of a disruptive new technology wildly boosted by sheep-like business leaders to the point of stressing numerous more traditional sectors, and causing indirect distress to electricity consumers, namely everybody else.  Witt cites Jevons's Paradox in this connection, which says increasing the efficiency with which a resource is used can cause it to be used even more.  A good example is the use of electricity for lighting.  When only expensive one-use batteries were available to power noisy, inefficient arc lamps, electric lighting was confined to the special-effects department of the theatrical world.  But when Edison and others developed both efficient generators and more efficient incandescent lamps, the highly price-sensitive market embraced electric lighting, which underwent a boom comparable in some ways to the current AI boom. 

 

Booms always overshoot to some degree, and we don't yet know what overbuilding or saturation looks like in AI development.  The market for AI is so new that pricing structures are still uncertain, and many firms are operating at a loss in an attempt to gain market share.  That can't go on forever either, and so five years from now we will see a very different picture in the AI world than the one we see now. 

 

Whether it will be one of only modest electric-price increases and a stable stock of data centers, or a continuing boom in some out-of-the-way energy-rich and regulation-poor country remains to be seen.  Independent of the morality and social influences of AI, the sheer size of the hardware footprint needed and its insatiable demand for fresh human-generated information may place natural limits on it.  After the novelty wears off, AI may be like a new guest at a party who has three good jokes, but after that can't say anything that anybody wants to listen to.  We will just have to wait and see.

 

Sources:  Stephen Witt's article "Information Overload" appeared on pp. 20-25 of the Nov. 3, 2025 issue of The New Yorker.  I also referred to an article from the University of Wisconsin at https://ls.wisc.edu/news/the-hidden-cost-of-ai and Wikipedia articles on Nvidia and Jevons Paradox.

Asking the Wrong Question About Artificial General Intelligence

 

An article in the October issue of IEEE Spectrum investigates the status of artificial-intelligence IQ tests, and speculates on when and whether we will see the arrival of so-called artificial general intelligence (AGI), which author Matthew Hutson says is "AI technology that can match the abilities of humans at most tasks."  But mostly unasked in the article is an even more basic question:  what do we mean by human intelligence? 

 

To be fair, Hutson has done a good job of surveying several popular benchmark tests for AI intelligence.  One of the more popular tests is something called the Abstraction and Reasoning Corpus (ARC for short), whose developer François Chollet has made something of a go-to standard, as charts in the article show the scores of over a dozen different AI programs on various versions of Chollets tests.  Engineers like numbers, and standardizing a test is a good thing as long as the test measures what you want to know.  But does ARC do that?

 

The version of the ARC test described in the article consists largely of coming up with patterns of colored figures that follow rules abstracted from examples.  Human beings can score higher than AI systems on these tests, although the systems are improving.  But it's an open question as to whether abstracting patterns from geometric shapes has a lot to do with being generally intelligent.

 

Chollet feels that his test measures the ability to acquire new abilities easily, which he thinks is the prime measure of intelligence.  Whether it actually does that is debatable, but it looks like ARC is to the AI world what the old Stanford-Binet IQ test is for people.  That IQ test was developed over a century ago and is now in its fifth edition.

 

Hutson comes close to the problem when he admits that "notions of intelligence vary across place and time."  The Stanford-Binet test is mainly used to identify people who don't fit in well with the public-school system, which is mainly designed to produce worker bees for the modern economy.  As the modern economy is shifting all the time, what counts as intelligence does too. 

And even if we could perfectly track these shifts, the admittedly infinite array of "tasks" that people perform present an almost insurmountable problem to anyone who wants not only to define, but to evaluate something that could justifiably be called artificial general intelligence.

 

Geoffrey Hinton, a recent Nobel Prize winner in AI, is quoted in the article as saying that if an AI robot could successfully do household plumbing, that would be a milestone in AGI, and he thinks it's still about ten years off.  I hope I'm around in ten years to check this prediction, which I personally feel is optimistic.  For one thing, humanoid robots will have to get a lot cheaper before people even consider using one to fix a toilet.

 

All these approaches to AGI ignore a distinction in the field of human psychology which was first pointed out by Aristotle.  The distinction has been described in various ways, but the most succinct is to differentiate between perceptual thought and conceptual thought. 

 

Perceptual thought, which humans share with other animals and machines, consists in perceiving, remembering, imagining, and making associations among perceptions and memories, broadly speaking.  Inanimate material objects like computers can display perceptual thought, and in crawling the Internet for raw material by which to answer queries, all AI chatbots and similar systems use perceptual thought, which ultimately has to do with concrete individual things.

 

On the other hand, conceptual thought involves the consideration of universals:  freedom, for example, or the color blue, or triangularity as a property of a geometric figure, as opposed to considering any individual triangle.  There are good reasons to believe that no strictly material system (and this includes all AI) can engage in truly conceptual thought.  With suitable programming by humans, a computing system may provide a good simulation of conceptual thought, as a movie provides a good simulation of human beings walking around and even engaging in conceptual thought.  But a movie is just a sequence of images and sounds, and can't respond to its environment in an intelligent way.

 

Neither can an AI program engage in conceptual thought, although by finding examples of such thought in its training, it can provide a convincing simulation of it.  While having a robot do plumbing is all very well, the real goal sought by those who want to achieve AGI is human-likeness in every significant respect.  And a human incapable of conceptual thought would at the least be considered severely disabled, though still worthy of respect as a member of the human community.

 

The vital and provable distinction between perceptual and conceptual thought has been all but forgotten by AI researchers and the wider culture.  But if we ignore it, and allow AI to take over more and more tasks formerly done by humans, we will surround ourselves with concept-free entities.  This will be dangerous. 

 

A good example of a powerful concept-free entity is a tiger.  If you walk into the cage of a hungry tiger, all it sees in you is a specific perception:  here's dinner.  There is no reasoning over abstractions with a tiger, just a power struggle in which the human has a distinct disadvantage.

 

Aristotle restricted the term "intellect" to mean that part of the human mind capable of dealing with concepts.  It is what distinguishes us from the other animals, and from every AI system as well.  Try as they might, AI researchers will not be able to develop anything that can entertain concepts.  And attempts to replace humans in jobs where concepts are important, such as almost any occupation that involves dealing with humans as one ethical being to another, can easily turn into the kind of hungry-tiger encounter that humans generally lose.  Anyone who has struggled with an AI-powered phone-answering system to gain the privilege of talking with an actual human being will know what I mean.

 

ARC may become the default IQ test for new AI prototypes vying for the title of AGI.  But the concept symbolized by the acronym AGI is itself incomprehensible by AI.  As long as there are humans left, we will be the ones awarding the titles, not the AI bots.  But only if they let us.

 

Sources:  Matthew Hutson's article "Can We Build a Better IQ Test for AI?" appears on pp. 34-39 of the October 2025 issue of IEEE Spectrum.  I also referred to the Stanford Encyclopedia of Philosophy article on Aristotle.  For a more detailed argument about why AI cannot perform conceptual thought, see the article in AI & Society "Artificial Intelligence and Its Natural Limits," by Karl Stephan and Gyula Klima, pp. 9-18, vol. 36 (2021).

What Managers Think About Replacing Workers With AI

 

It's hard to look at a website, talk to anybody in business, or read a magazine for very long these days without encountering something about artificial intelligence (AI).  One of the biggest concerns for the average symbolic-manipulator employee, in George Gilder's phrase, is whether AI will replace you in your job.  Examples of symbolic manipulators are software developers, accountants, writers, and to some degree sales people and counselors.  Hospital nurses and construction workers, on the other hand, are not symbolic manipulators, at least not most of the time. 

 

A company called Trio realized that even if AI was available to replace a lot of these folks, somebody would have to decide to do it.  And that somebody would be middle-level managers, for the most part.  So last month, they performed an online survey of about 3000 U. S. managers in all 50 states to find out their attitudes toward replacing their employees with AI.  And the results are illuminating.

 

First of all, when broken down by state, there are wide variations in how enthusiastic managers are in replacing flesh-and-blood workers with AI software.  Openness to doing this varies from a high of 67% in Maine to a low of 8% in Idaho.  Both being fairly rural states, the difference is hard to account for except by cultural factors.  If I had to guess, I'd say finding good, reliable workers is more of a challenge in Maine, and so that may be one reason why managers in our most northeastern state would rather skip the hassle of hiring people and go straight to an AI program.

 

When all states were lumped together, the top reason that managers would replace workers with AI turns out to be pressure from upper management or shareholders, at 36%.  Presumably this was one of a list of "choose-one" options handed to the survey respondents.  The next most favored reason was for productivity gains (31%) and then cost savings (27%).  This pressure from above makes me think that a sheep-like mentality which now and then manifests itself in the boardroom may be why we are hearing so much about AI replacing workers.  No CEO wants to be left behind in a stampede to the next great thing, even if the thing turns out to be not so great.

 

What is perhaps most disturbing to engineers about the survey results is the kinds of jobs that managers see most ripe for replacement by AI.  "Technical roles like coding and design" (sounds like engineering to me) were perceived as most replaceable at 33%, while the least replaceable jobs were seen to be sales (11%) and "creative work" at 15%.  There are a lot of sales jobs that could pretty easily be replaced by good AI software, but evidently managers still believe in that personal touch that good salespeople can bring to the task.  Whereas, engineers and programmers, who are always carried as overhead on budgets, don't have as direct a connection between sales and their salaries as salespeople do. 

 

Independent of the question about whether a given job can actually be done better by AI than by a human, this survey looks at those who would be making the immediate decision to do so.  Of course, the options presented to those surveyed were simplified ones.  The fact is that rather than making a simple choice between AI and a human in a given job, what seems to be happening is that almost anybody in the symbolic-manipulation business is adopting some form of AI almost by default—some deliberately and enthusiastically, others (like myself) reluctantly and only if it can't be avoided. 

 

These large workplace shifts tend to be hard to discern over the short term, because they happen gradually.  Take the development of computer-aided design (CAD) software as an example.  My late father-in-law never obtained a four-year college degree, yet in the 1950s he got a good job as a civil engineer and worked for the Texas Highway Department.  If you'd visited him shortly after he went to work there, he would have been sitting at a drafting table in a huge room full of guys (all guys) sitting at drafting tables, churning out drawings that were turned into blueprints for the construction crews working on the new interstate-highway system.

 

Visit that same office today (the building is still there), and you'll see fewer engineers than those old drafting rooms held, and they'll be sitting at computers.  The computer can't design anything by itself, and while the engineer is in some sense in charge of the process, the amount of sheer dogwork handled by the computer far exceeds the mental effort put forth by the engineer, who now does the work of ten or fifteen (or more) of the old drafting-room people.  And the field of civil engineering didn't collapse:  my school (Texas State University) started a new civil-engineering program a few years ago, and we have no problem placing our graduates.

 

The advent of AI, which has actually been going on for a decade at least and isn't as sudden as news reports make it sound, will probably be like the advent of CAD, only more so.  It's easy to forget that the computers need us as much as we need the computers.  Take away the largely-human-produced Internet from ChatGPT, and you'd have a lot of useless server farms on your hands. 

 

There are clearly dangers, of course, if we get too lazy and allow AI to make decisions that should remain in human hands, or minds.  And there are sectors where AI has already done serious damage, such as the harm AI-fueled social media has done to the psychological health of children and teenagers.  But we're not letting the pied piper of AI march away with all our kids.  Schools all across the U. S. are starting to ban smartphone use during classes, and parents are wising up to how harmful too-early use of smartphones can be to young people. 

 

Even if all managers were dying to replace their staff with AI as fast as they could, the software simply isn't available yet.  At the present time, AI has the looks of a fad, indicated by the survey's showing that pressure from upper management is the biggest reason bosses are considering it.  So it's no time to panic, but keep your wits about you and be ready to deal with AI in your job, assuming you still have one.

 

Sources:  The summarized results of the Trio survey can be seen at https://trio.dev/managers-are-ready-to-replace-employees-with-ai/.  The San Marcos Daily Record of Oct. 10, 2025 carried a story on the survey on p. 8, which is how I found out about it, from an old-fashioned piece of paper.  But then I went online. 

Waymo 80% Safer than Human Drivers: Why Not Switch?

 

According to Waymo, the Google division that operates self-driving robotaxis in several U. S. cities, their vehicles are involved in 80% fewer injury-causing crashes than human-driven cars.  This was a surprise to me, as it may also have been to Kelsey Piper, who writes in a substack called The Argument that if we want to reduce the number of traffic fatalities in the U. S by 80%, all we have to do is switch to Waymos.

 

At the current rate of nearly 40,000 U. S. traffic fatalities a year, that would be saving 31,000 lives a year.  But she cites a poll conducted by The Argument which found overall that only about 28% of respondents favored allowing self-driving cars in one's town or city, and 41% favored a ban, as Boston and other cities have considered doing.

 

Piper speculates that a general distaste for AI-powered things may be behind what looks like irrational opposition to self-driving cars.  Some Waymos have even been attacked by modern-day Luddites.  As she puts it, ". . . you can't vandalize ChatGPT, so anti-AI sentiment finds its expression in harrassing Waymos."

 

She realizes that anything like a wholesale move to self-driving cars would cause massive disruption to our present transportation system.  She also acknowledges the tendency of government to make optional things mandatory.  Right now, Waymo is one private company offering a specific service in a few carefully chosen markets such as Atlanta, Austin, San Francisco, and Phoenix.  With the possible exception of Atlanta, these are all locations with plenty of sunshine and relatively few days of inclement weather.  And the service in Atlanta only commenced last June, so Waymos in that city have not gone through a typical Atlanta winter, which almost always includes an ice storm, as I know from living there a couple of years.  Ice may present serious challenges to Waymo's algorithms.

 

So there are practical limitations to the areas that Waymo can operate.  As one of the commenters on Piper's article mentions, Waymo has somewhat cherry-picked markets in which it can compete while maintaining its very good safety record.  Even if Boston decided to allow Waymos, I expect a long development period would delay its deployment as the company figured out how to deal with snow, ice, and slush on the former cowpaths that are now Boston streets.  At least the Waymo cars probably wouldn't get lost as much as I did whenever I drove to Boston when I lived in New England, which was, well, pretty much every time I went there. 

 

But there's those 31,000 lives.  As Piper points out, that's more lives than are lost every year to homicides.  Wouldn't it be nice if we could eliminate all homicides?  Well, numerically, changing to self-driving cars would do that. 

 

The psychology of why we tolerate such a lot of annual deaths to traffic accidents is interesting.  People don't always act toward various risks in reasonable ways.  The classic example is driving to the airport to fly somewhere.  Unless you live within walking distance of the airport, you're going to drive or ride.  And unless you take a Waymo there, you will ride in a human-operated car.  While lots of people are afraid of flying much more than they are afraid of driving, the chances of dying in a plane crash are much lower than the chances of dying in a car wreck on the way to or from the airport. 

 

I suspect that we have gotten so used to the 40,000 or so traffic deaths every year with some variation on the notion that it always happens to someone else, and if I were in the same situation that somebody else died in, I would have been clever enough to save myself.  These are pure rationalizations, but the alternative is to experience a little squirt of adrenaline every time we buckle the seat belt and pull out of the garage. 

 

Five or ten years ago, there was a lot of hype about how every new car would be self-driving within a few years.  That obviously hasn't happened, for a variety of reasons.  One factor is the expense per vehicle.  Waymo doesn't advertise how much each of their vehicles cost, but various estimates say it's probably on the order of $160,000 to $300,000.  This puts them in the super-luxury class, and explains why they aren't deployed in areas that will not generate pretty good revenue.  Even with the carefully-chosen markets that Waymo has selected, it appears that they are not making a profit yet, which means the whole thing is still an elaborate experiment oriented toward some future situation that hasn't materialized.

 

Still, I will admit that if I could have a self-driving car that was absolutely trustworthy, a Level-5 one according to the SAE autonomous-vehicle rating system, one you could read or sleep in while the machine takes care of all driving tasks, and not pay a whole lot more for it than I'm paying now, I would at least consider it.  But in my relatively small town, which would not provide enough revenue for Waymo under its present operating circumstances, that's not going to happen.

 

Now and then on my trips to Austin, I see a Waymo eerily coasting along with nobody inside, and once I saw two in a row.  I will admit to having a flash of mischief when I saw one the first time, and wondering what would happen if you pulled in front of it and slammed on the brakes suddenly.  Probably nothing bad.  Fortunately, I was a passenger in the car I was in, not the driver, and so the experiment was never performed.

 

Unless something radical happens in the areas of AI, sensors, or legislation regarding the right to drive your own vehicle, it looks like Waymo and similar autonomous vehicle companies may not spread their wares much farther than they've already done.  And that's too bad for the 40,000 or so people who die on roadways each year.  Some ideas look good in theory, but when you start examining everything that would have to change to put them into practice, it just falls apart.  And converting our fleet of vehicles to nearly 100% self-driving looks like another one of those nice ideas that has had an unfortunate encounter with reality.

 

Sources:  A note in the online magazine The Dispatch referred me to Kelsey Piper's article "Please let the robots have this one," at https://www.theargumentmag.com/p/please-let-the-robots-have-this-one.  I also referred to a Reddit item on the estimated cost of Waymo vehicles at https://www.reddit.com/r/SelfDrivingCars/comments/1g8vv7o/where_did_the_whole_talk_about_the_cost_of_waymo/, and the Wikipedia article "Waymo."