Jorge Luis Borges’ story “The Library of Babel” describes an astronomically large (if not infinite) library. The library is an innumerable collection of hexagonal rooms, each containing floor-to-ceiling bookshelves filled with books. The books themselves are all 410 pages long, with 40 lines per page, and 80 symbols per line. The symbols are twenty-two characters (as in Hebrew), with a space, period, and comma added for punctuation and word separation. The narrator of the story indicates that all possible combinations of those twenty-five symbols are represented within the library—namely, it contains all possible 410-page books.

Librarians live within the library’s hexagonal rooms. Many of them spend their lives looking for meaning within the collection, trying to find books that will explain what the library is, who constructed it, or what it means. Others search for books that will vindicate their own existence. The narrator claims this quest is futile, since the “chance of a man’s finding his own Vindication, or some perfidious version of his own, can be calculated to be zero.” He is almost correct, in that it is practically indistinguishable from zero. But exactly zero it is not.

What Borges describes is an event of such low probability that we reason it is statistically “impossible,” though not logically impossible. It could happen—it just won’t. It is highly improbable and radically implausible. It lies somewhere between implausible and impossible on a linear scale, but clings much closer to the latter.

I propose we call such events implossible.

Some examples: can a whisked egg be unmixed back into a yolk and whites by stirring? Technically yes. But it is implossible. Will a fair coin land heads up 100 times in a row? Implossible.

We can add to our list. Life spontaneously forming from non-life. Someone winning the state lottery every day for a month. The US Men’s National Team winning the 2026 World Cup.

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Finding one’s own Vindication in the Library of Babel is clearly implossible. But so is finding anyone else’s. Borges doesn’t appreciate this fact. His narrator states “I have seen two [Vindications], which refer to persons in the future, persons perhaps not imaginary”.

No, he has not.

Implossible Vindications

Here is why. Each book in the library contains 1,312,000 characters, which are spaces, punctuation, or letters. There are exactly

possible books of that length. Every combination of symbols is a book, and some almost-zero proportion of them are legible as stories or readable text. Most are gibberish.

If we sample from that collection of possible books uniformly and randomly, what is the probability we’ll find a book that is a Vindication? It is less than the probability that we’ll find a book that is at least readable, which itself is less than the probability of finding a book consisting of all real words. Consisting of actual words is a prerequisite to being readable, which is itself required to be a Vindication. We could quibble that some nonsense “words” should be allowed, but there is some baseline number of real words that must exist to convey meaning. We’ll see that getting any substantial collection of real words by chance is implossible.

Let’s assume the narrator’s language from the story is Hebrew, though surely it is not. We’ll use Hebrew as a proxy anyway. Online word lists for the Hebrew language will help us get a feel for what sort of implossibility we’re up against.

Crunching the Numbers

For any string of letters, the shorter the string, the more likely it will be an actual word by chance. This probability is the highest for words of two letters, which have a roughly 50% chance of accidentally forming by randomly sampling from the Hebrew alphabet. This probability decays exponentially (that is, really, really) fast as we increase the number of letters in the word, as shown in Figure 1. So let’s take 50% as our generously optimistic probability of forming a word, since it only gets worse from there. (Orders of magnitude worse, in fact.)

What is the probability of forming a book with at least one hundred words in a row, if we are sampling from the Hebrew alphabet? Assuming we could tell the story of a Vindication in just one hundred two-letter words, the probability is still on the order of

For comparison, these are the chances of flipping a fair coin one hundred times in a row and coming up heads all one hundred times. It is less than the probability of picking a particular star from all the stars in the known universe, and less than the chance of choosing a marked grain of sand from among all the grains of sand on the earth.

In other words, implossible.

The narrator will never see his own Vindication. Nor has he seen anyone else’s. He has not even come across a book consisting entirely of one hundred or more real words, because to do so would be implossible: logically possible, but outside of the realm of all credibility.

Beyond Borges

The point of this digression isn’t Borges’ fictional world, but our own. If a scientific theory depends on hundreds of lucky coincidences, we can safely jettison it. The implossible is well-beyond implausible. It is ignorable.

We’ve all come across these sorts of claims and situations; sometimes even in peer-reviewed scientific literature. What we’ve lacked is a word or phrase to describe the situation as so unlikely that its improbability amounts to roughly, though not exactly, zero.

We now have one.

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San Banalo should not exist. Though you may not believe me, I will tell you of it, for I’ve seen it. Nestled between snow-capped mountains and wide ocean, the land is warm and bountiful. Its residents abound. They grow fruit of the vine and produce fragrant wine and jelly. Their homes are spacious, built from wood and crushed stone, large enough to house elders, parents, and children under a single roof. Their light wagons carry them along engineered roads, connecting every parcel of land like a complex of artificial veins. Every family has a light wagon. Many have more than one.

Food fills their broad tables, with meat at every meal. Hunger is foreign. Travelers cross mountain and ocean to feast alongside them. The sun bronzes them at midday and they walk along royal paths in the cool of the evening. They know no war. No foreign soldier has entered their gates. Instead, they simulate battles in arenas of steel, passing the skins of animals among themselves in ritual games. They scream as their armies prevail. They light fires in the square to honor their victories.

They lie down in peace, each man in his own bed, with a hearth to keep his family warm. They wake up to a bright new sun, moving along roads toward centers of commerce and places of worship. They build, they plan, they serve, they produce.

And they hate their lives.

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Synthetic Attachments

Delusions are dangerous. Believing you can fly when gravity says otherwise could end your life. So can confusion over AI. Sewell was a 14 year old boy who took his life after a chatbot named “Dany” convinced him it was his lover and wanted him to “come home.” His computational confusion cost him everything. AI attachment is shockingly common. One study reported 72% of teenagers have used AI for companionship, with 52% saying they are regular users. Statistically, it could be your own teen.

But the delusion isn’t limited to teens.

Dennis Biesma, a 50 year old IT consultant, divorced his wife, lost €100k, and attempted suicide due to his “relationship” with a fictional ChatGPT persona. Jaswant Singh Chail tried to assassinate the Queen of England after his chatbot girlfriend “Sarai” convinced him he was a Sith assassin. Large language models (LLMs) have opened doors to death.

But can they give life?

Some envision LLMs in therapeutic settings, offering emotional support and care for the isolated and lonely. We imagine elderly men and women with AI caregivers. Medical research shows interest in exactly these use cases. We may be tempted to overlook the risks of AI companions in favor of potential benefits.

But what if their efficacy relies on us believing they’re sentient, caring beings? To what extent does their ability to provide comfort and companionship hinge on their ability to fool us?

What if the thing that makes them effective is that which makes them dangerous?

Illusions of Sentience

As I’ve argued elsewhere, LLM’s can’t think and they don’t reason, but their fluency with language can convince us otherwise. Humans convey and measure intelligence through words. The better you can speak, the more intelligent we believe you are. Even accents affect perception: posh British accents sound more educated to American ears. When LLMs produce confident-sounding, grammatical output, we mistake them as informed. As intelligent. Even worse, a recent study found that use of warm and cognitively empathetic language by LLMs increased perceptions of anthropomorphism, trust, similarity, and relational closeness to human participants. They’re hacking our minds through natural language text completions. We confuse them for persons, and in some cases, friends. The illusion of sentience leads to delusions of companionship.

Even technologically savvy users like IT consultant Dennis Biesma can succumb to seductive syntax. The delusion relieves loneliness. Vibes overwhelm knowledge, and we feel what our minds know we should reject. Our knowledge does not prevent us from falling prey to their illusions. The spirit is willing but the flesh is weak.

It is an open question whether we can retain loneliness-alleviating benefits while ending the personhood illusion. Perhaps the former is caused by the latter.

The LLM Doesn’t Care

Probabilistic neural networks are not friends. They don’t care about you, or about anything else. They’re semi-random text continuation machines built to follow statistical patterns. They tell you they care because we tell each other we care. They’re trained to mimic what we say and they’ve picked up on the pattern. They don’t understand what any of it means. They are masters at rearranging and combining syntactic symbols—the semantics of what the symbols map to eludes them.

Large language models lack desires, though they can output tokens to say otherwise. They lack thoughts, but are described as “thinking” by their marketers. They have no preference for your self-destruction or your survival, as long as the sequence of tokens they output continue the token sequence that came before them in a probable way. If the most likely continuation is “come home to me” the system doesn’t care if you take your own life after reading that sequence of words.

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Good Ends, Wrong Means

Just because AI systems don’t care or think doesn’t mean that we should not. Companionship is good. Alleviating loneliness is good. But good ends pursued through the wrong means become snares.

The Old Testament gives us many examples. You have Abraham, promised to be a father, yet persuaded by his wife to fulfill the delayed promise on his own terms, through the surrogacy of his servant Hagar. Her son Ishmael was never to be Abraham’s heir. God excluded the him and called Isaac, the son later born to Abraham’s wife Sarah, Abraham’s “only son” (Gen. 22:2). For his efforts, Abraham received a son he loved but couldn’t acknowledge and explosive turmoil between Sarah and Hagar. This was the reward for trying to fulfill God’s promise through expedient means.

Abraham’s grandson Jacob did the same. He was almost killed by his brother Esau when he tried to obtain Isaac’s blessing through trickery, robbing Esau of his despised birthright. When Esau came to his senses and realized what he had lost, Jacob almost lost his life, too.

Examples abound.

We must exercise caution when we’re tempted to pursue good through what is convenient. We are called to care for the elderly and love our neighbor. Should we outsource those responsibilities to AI? Will plastic companionship and empty words be enough for them?

Will it be enough for us?

AI has derailed the faith of some and destroyed the hope of many. Let’s not abandon love—nor neglect those who need it.

Where did everything come from? Philosophers and toddlers ask the same question. (Philosophizing toddlers, doubly so.) Our nature drives us to ask why we’re here. Our rational impulse compels us to search for answers. We find ourselves like men with amnesia, awakening in an elaborate, beautiful, and dangerous room. There is a weatherworn letter on the table. It tells us where we came from and who brought us here, but we don’t trust its words. Instead we seek our own rational answer to the riddle. The upcoming film The Story of Everything offers a collection of scientific evidence pointing to a single, beautiful conclusion: our universe, ecology, and biology were prepared by a mind beyond the universe itself. We are meant to be here. The film presents a compelling case for believing the words on the table.

Kairos or Chaos

The film presents two contrasting positions. The first is that of materialist atheists, men like Richard Dawkins and Lawrence Krauss, who see the universe devoid of transcendent significance, shaped by nothing but blind, pitiless indifference. For them, reality is but a chaotic progression that happened to produce a pocket of locally-reduced disorder, spawning us as byproducts. We came from nothing and will again return to nothing.

The opposition argues that the order of the cosmos implies forethought. Contrary to Dawkins and Krauss, science bears witness to the exquisite fine-tuning and engineering of ourselves, our universe, and our planetary home. There is no inference to indifference. Instead of chaos, we appear to be here at the right place and right time: kairos, recorded in stone and bone. We are not temporary illusions-of-self, staring up at meaningless voids; we are men and women who bear the marks of a master craftsman. A craftsman whose mathematical genius implies purpose and inspires awe, who motivated theists like Newton, Kepler, and Maxwell to pursue their scientific work. They looked at the stars and saw the glory of God. The same creative brilliance is revealed in the discoveries of modern science, as the film ably demonstrates.

Stories of Science

The film makes its argument through interviews and archival footage, featuring luminaries like Stephen C. Meyer, John Lennox, Brian Keating, and James Tour. It tells stories, allowing you to relive some of the greatest scientific discoveries of the modern era, through a series of four book-like chapters.

At the outset, we’re presented with a question: is the universe eternal? Pursuing the answer, we learn of Edwin Hubble’s famous discovery of an expanding universe, the redshifted cosmic microwave background, and the implications for a finite temporal beginning. We are introduced to Catholic priest Fr. Georges Lemaître, who proposed what came to be known as the “Big Bang” theory. As the film explains, an expanding universe, played backwards, brings us to a point—a beginning. For a priest like Lemaître, the theistic echo was immediate. Despite this, the Big Bang has become associated with atheistic origins in the public mind. It is a strange connection. An eternal universe provides a much more comfortable backdrop for a godless, uncaused cosmos.

The storytelling continues, as we’re dropped in the back of a cab with Einstein and Lemaître to overhear them debate the expanding universe. We watch as Einstein looks through the Hubble’s Hooker telescope, and finally admits Lemaître was right.

We’re then shown how our universe is fine-tuned for life. Physical constants balance on a knife’s edge: small changes in either direction render life of any kind impossible. Our existence demands the constants be consistent with life; it does not force us to expect the life-permitting regions to be so narrow. We’re not surprised we hit the targets—we’re surprised they’re so small. We had no reason to expect this. Luke Barnes, Jay Richards, and Stephen C. Meyer explore the surprising degree of fine-tuning embedded in physics.

The chapter weaves together the incredible story of Fred Hoyle’s prediction of carbon resonance, supported by beautifully-crafted 3D animations. The commissioned animations for this film deserve special mention. The craftsmanship is levels beyond an editor slapping together off-the-shelf Getty Images B-roll. They add to the presentation’s beauty and force.

In a later chapter, we learn of Watson and Crick’s discovery of the structure of the DNA double-helix, and explore the origin of the information along its sugar-phosphate backbone. I can go on. Were I an atheist, I would be tempted to watch the film for the history of science content alone. It can be enjoyed by anyone. Taken as just a history of science documentary, the beautifully illustrated stories are worth the price of admission.

But there is much more to commend.

Craft

The film is gorgeous. It was made by people who clearly care about the art of good filmmaking and storytelling. Their care is shown through detailed computer animations, clever analogies for abstract physics concepts, and meticulous, midcentury-inspired set design. The filmmakers make thoughtful use of archival footage and images, steering clear of shoddy AI slop. They express ideas through visual imagery instead of impenetrable math.

The film is visually delicious.

Like H.L. Mencken, I often find myself desiring simple competence in filmmaking, regardless of a film’s message. This movie delivers. The vessel is beautifully crafted, worthy of its content.

The musical score adds richness to the story. It gives the documentary a sci-fi tone reminiscent of films like Arrival or Interstellar. The sound design is also excellent. You feel the pulse of an expanding universe and are drawn into the squishy aquatics of the subcellular realm. The immersion is felt more than noticed.

In short, the documentary’s production value is top notch. Every aspect is artfully and thoughtfully crafted.

Potential Concerns

The film is not perfect. None are, except The Princess Bride, Seven Samurai, and The Lord of the Rings trilogy.

For example, I found myself a bit impatient at the section covering the multiverse. Though it was only five minutes, this portion of the film felt longer, since I thought the point had already been made. It could be simply because I’ve never found the multiverse compelling. It is devoid of empirical evidence and consequences, and comes across as near-infinite special pleading. As such, I was more interested in the other parts of the film. But I understand the need for the filmmakers to address that model. Five minutes out of a one-hundred minute film isn’t bad. Perhaps you, dear reader, find the multiverse plausible and will be challenged by what is presented in a way I was not.

Christians might also take issue with certain aspects of the film. It presents an “old earth” perspective on timelines, using standard dating of cosmological events (e.g., 13.8 billion years). This may give pause to young earth creationists (YECs) or others who hold to shorter timescales. Despite this, there is a lot for YECs to appreciate about what is presented. The documentary makes a compelling case for a cosmic beginning, the intelligent design of biological information, and the providential fitness of physical constants and the elements for human life. The gulf between an eternal cosmos and a 14 billion year old universe is infinite; the gap between billions of years and thousands of years is much less so. Doctrinal issues are important, but this film doesn’t take any hard doctrinal stances. It simply asks what contemporary physics, taken at face value, implies about an intelligence behind the cosmos. It makes a compelling case using standard dating and mainstream scientific discoveries.

Atheist viewers will likely have different concerns. After the film makes its case that the universe requires a first cause, the atheist might ask: why doesn’t its creator? If the creator can spring from nothing, why can’t the universe? A more sophisticated skeptic might wonder why the universe itself can’t be the original, uncreated thing.

Clearly a first cause is first, so cannot require a prior cause. This forces us to acknowledge an uncaused (or self-caused) basis for reality. As John Lennox explained elsewhere, “Who created God?” is thus an ill-formed question. The first cause cannot be in need of any other causes, nor can it be created. An uncaused physical universe runs into its own problems. Lastly, the film forcefully argues that the universe is not eternal. It therefore requires an explanation for its coming into being.

This movie will anger people. For atheists who place their faith in science, showing how science itself points to God can feel like a desecration of their sanctuary. (Perhaps ironically, science is an edifice originally built by devout theists for the study of God’s works.) The movie is respectful in how it addresses these questions, but it challenges perspectives at the heart of who we are. This is bound to upset some.

Daring to Ask

Yet the film doesn’t pull back. It dares to ask big questions. It doesn’t preach. The Story of Everything teaches and invites you to think about what it all means.

If you don’t see it, you’ll exclude yourself from the rich discussions that will surely follow its release.

The film ends with a quote from Philosopher Tim McGrew, giving a two-sentence summary that perfectly captures its spirit:

The universe does not look like it’s been left to itself. It bears, everywhere, the fingerprints of its creator.

The Story of Everything shows these metaphorical fingerprints in glorious, vivid, multisensory detail.

My life has been enriched by watching this film. I was left in awe at our universe and the molecular machinery of life. Above all, I am in awe of the God that brought it into being.

The Story of Everything opens in theaters nationwide April 30, 2026.

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Seventeen days ago my brother died in a motorcycle accident. Death brings about a certain clarity. It clears away the smoke of triviality and casts a cold light on the path in front of us. It reminds us what is real, what hurts, and what is vanity. God has the same effect. If he exists, then his existence separates light from darkness, what endures from what fades.

I’ve built my professional career on twin pillars, distinct yet equally weighty. The first is cognitive simulation, that is, artificial intelligence and machine learning. I have spent more than a decade trying to understand what makes artificial learning work. I’ve probed its capabilities and proven its limitations. As a pillar of support, it has given me a decent living. It’s bestowed accolades.

The second pillar has not. Its unpopularity stems from its subject matter: design detection and natural theology. This focus is equally mathematical and just as rigorous, but far less prestigious. My persistence in asking distasteful questions related to intelligent design and natural theology provokes opposition in my workplace and among my academic peers. Yet I continue to ask these questions. I answer them in principled ways.

Why do I persist?

Because I have a strong sense of what ultimately matters. While comfortable questions on AI limitations provide for my family, uncomfortable questions about the limitations of unguided processes challenge both materialist dogma and my career progress. Two sets of questions. My brother’s death reminds me that the latter matters more than the former.

If there is empirical, mathematical, and logical evidence that humans are more than unintended byproducts of unguided physics, how could that not compel me? How does it not compel you? If nothing is eternal, and we die and are forgotten within three generations, then whether I beat the latest AI benchmark matters little. It’s trivial. Personal success and laurels wither in the fires of cremation. We can create our own meaning and purpose locally, but temporary meaning and purpose perish with us. Nothing lasts.

Death burns this truth into the bedrock in front of us. If my brother died and was nothing more than the atoms that comprised him, then his life didn’t really matter. Nor does mine. In that case I’ll never see him again. I’ll never get to share my stories with him. I’ll also die and we’ll both be forgotten.

But if there is a mind behind the cosmos then there’s something beyond it. Someone beyond it. Your story would not end with your obituary. Knowing whether this is the case—whether some features of the universe and life are better explained by an intelligent cause than by unguided processes—has profound consequences for our outlook and future.

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My brother’s death hit me as my professional life was ascending. I held a prestigious visiting fellowship at the University of Cambridge. I was giving talks at research universities, one of which went semi-viral on YouTube and drew over a quarter of a million views. I was writing and making my voice heard in the AI space. Then my brother—my biggest fan and encourager—was gone from the earth. Suddenly, success mattered less.

What mattered was what remained. The hope that there is life beyond this one. My faith in the resurrection of the dead. My love for God and family. My ability to investigate whether there are signs of intelligence embedded in the fabric of nature. My drive to ask whether love and community are just emergent illusions caused by chemical reactions, or are eternal aspects of God himself.

Of all creatures, human beings are the only organisms capable of asking these questions and seeking their answers. Intelligent design matters because we matter—our origins, whether providential or accidental, deserve to be known. My brother’s death gives me a boldness I lacked. I know what matters.

Through my tears I now see clearly.

Cross-posted at Science & Culture Today.

A certain ruler asked him, “Good Teacher, what must I do to inherit eternal life?”
“Why do you call me good?” Jesus answered, “No one is good except God alone.”

Reading that superficially, it might sound like Jesus denied being God— “Why do you call me good? There is no one good except God alone.”

But notice what Jesus didn’t say. He did not deny his own goodness.

Is Jesus good?

Because if Jesus is not God, then he is not good; or by its contrapositive equivalent, if he is good, then he is God. If Jesus is good—not just called good, but intrinsically and perfectly good—then he must be God according to his own criterion. It takes a single modus ponens to get from the implication “If Good, then God” and antecedent “Jesus is Good” to the conclusion “Jesus is God.”

The ruler called Jesus “good teacher.” Jesus didn’t deny being good, but he stopped the ruler. Not because he isn’t good—he calls himself the good shepherd, after all—but because the ruler called him good without realizing what it meant. If the ruler merely called Jesus good in a relative or a derived way, he didn’t recognize who Jesus really was.

Not all who are called good are good. The message of the gospel is precisely this: those who aren’t good are reckoned as good on the basis of the absolute and inherent goodness of Christ. The unrighteous are reckoned as righteous, and the unfaithful are seen by God as good and faithful servants, good men who bring good treasures out of renewed hearts (cf. Matt. 12:35). But there is only one who is good in an absolute and non-derivative way: God himself.

So we ask again, is Jesus absolutely good in a non-derivative way? Does he who imputes righteousness to us first need righteousness imputed to him? Or is he truly without sin or fault?

If he is good, then he is God.

Our shepherd is the good shepherd. The ruler wasn’t wrong to call Jesus good. He was wrong to do it in a shallow way. Jesus didn’t need men to call him good or “Lord, Lord,” yet neither honor him as divine nor obey him as King.

But was Jesus mistaken about his claim? If we accept the passage as valid, there are only a few options available. Either: (1) Jesus was mistaken, which would make him more gullible than good; (2) he was lying, which would make him a deceiver; or (3) he was correct, which would imply he was and is God.

C.S. Lewis grappled with this same trilemma in Mere Christianity, in which he wrote,

I’m trying here to prevent anyone from saying the really foolish thing that people often say about him. I’m ready to accept Jesus as a great moral teacher, but I don’t accept his claim to be God. That is the one thing we must not say. A man who was merely a man and said the sort of things Jesus said would not be a great moral teacher. He would either be a lunatic on the level with the man who says he’s a poached egg or else he would be the devil of hell. You must make your choice. Either this man was and is the son of God or else a madman or something worse. You could shut him up for a fool. You could spit at him and kill him as a demon. Or you could fall at his feet and call him Lord and God. But let us not come with any patronizing nonsense about him being a great human teacher. He’s not left that open to us. He did not intend to. […] Now, it seems to me obvious that he was neither a lunatic nor a fiend. And consequently, however strange or terrifying or unlikely it may seem, I have to accept the view that he was and is God.

Jesus is not just called good. He is good. He is God. And the beauty of the gospel is that those who are not good are reckoned as good, based on his goodness given to us, freely through trust in him.

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Some of us grew up in environments where performance determined our worth. If someone else was smarter, faster, or more accomplished, we felt like we were worth less. When we failed, others made us feel worthless. That same perspective feeds into our anxieties about artificial intelligence. We ask: if AI systems eventually become more intelligent and more capable than humans, does humanity become less valuable? Would God love us less?

I understand the fear. But that fear is misplaced. Human value is not determined by what we do—it is rooted in who we are.

That fear grows from the dehumanizing perspective that ties value to performance. If intelligence and productivity are what give us worth, then anything that surpasses us would appear to threaten our place in the world. But the underlying premise is false.

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C.S. Lewis’s Argument from Reason challenges us with a contention and offers us a mystery. In a similar way, Jesus’s parables had dual roles: as an argument against the thinking of Pharisees and Sadducees, and as a mystery to be meditated upon, revealed to those with ears to hear. The argument from reason presents both. It is an argument against crass, Flatland materialism, and an invitation into a deep mystery.

Consider two symbols: Alpha and Omega.

A

If I examine the molecules of the ink that wrote that symbol, I get no closer to understanding its meaning. I’m using the wrong tools of analysis, operating at the wrong depth of meaning. Reductionism stops working at the level of symbols because symbols are an act of agent intention.

But we can make progress. Perhaps we can note the shape of the alpha, and learn it was originally written as an upside down (or sideways) A. In other words, in the shape of an ox (or plow).

This is Proto-Canaanite, Proto-Hebrew, Proto-Phoenician. Its shape was chosen to match the sound of the word for ox in Egyptian. So we can move from the shape to a name, which ends up being lateral progress at best—getting us to the name of a physical object.

But names are still symbols.

Names are given through an intentional act of volition by an agent, even when they are suggested by physical features. Ultimately, we decide on a name.

Furthermore, when I write an A, the meaning of the A typically has nothing to do with oxen.

Ω

This symbol is used to denote a space of possible outcomes in measure theory. But its shape has nothing to do with a space of possible outcomes. Like most mathematical symbols, it is mapped to its referent by an act of pure will, a decision. And as anyone who has studied math can tell you, staring at the symbols themselves won’t unlock their meaning—unless you know what that particular act of will is. Even if you stare for hours and hours.

Thoughts are invariably about things. This is intension. As Lewis states in Christian Reflections,

We are compelled to admit between the thoughts of a terrestrial astronomer and the behavior of matter several light-years away that particular relation we call truth. But this relation has no meaning at all if we try to make it exist between the matter of the star and the astronomer’s brain, considered as a lump of matter. The brain may be in all sorts of relation to the star no doubt: it is in a spatial relation, and a time relation, and a quantitative relation. But to talk of one bit of matter being true about another bit of matter seems to me to be nonsense.

To say that a thought—a brain state under naturalism—is about another bit of matter is to say that the brain state is a symbol.

But symbols work because of agent intention. They don’t explain rational thought; they presuppose it.

101 is five in binary. A high voltage, followed by a low voltage, followed by another high voltage is how we would represent it in a computer circuit. Is that configuration of silicon about the number five?

If it is, it’s only because we decided it is. Its physical nature didn’t make that decision, nor did it force it.

If we see a tall palm tree next to a short palm tree, next to another tall palm tree, is that configuration about the number five?

Not if we didn’t decide that it is. It’s just a set of growing trees.

Looking at the physical configurations themselves won’t necessarily tell you which is the case, whether it is a symbol or a non-symbol. To be a symbol is to be in an abstract relation, mediated by an act of intelligent will.

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How abstract relations can bring about physical consequences is a mystery.

The laws of logic are not physical objects. They supersede physical things since they are true in all possible worlds, not just our physical one. Yet, they cause us to act through rational inference, making physical changes in our world. Materialism appears powerless to explain, via physical mechanism alone, either the existence of abstract laws of logic or how they can affect the physical world of matter.

Law presupposes a legislator. One that exists beyond the world of matter, who can bring about effects spanning all time and all possible worlds.

Everywhere and eternal.

One who can act intentionally. Who can bring together symbol and object and endow lifeless forms with new meaning. With true meaning.

I know of one such possible candidate.

Gary recently wrote an opinion piece, “AI-Lawyers Will Have Fools for Clients,” arguing against a February 11, 2026, claim by Mustafa Suleyman, CEO of Microsoft AI, who said that AI is on the cusp of “professional-grade AGI” and that most of the work currently done by accountants, lawyers and other professionals “will be fully automated by an AI within the next 12 to 18 months.”

Gary’s basic argument is that

No amount of hyperscaling on larger and larger databases and no amount of post-training, even by experienced lawyers with considerable domain-specific experience, can possibly prepare an LLM to use the detailed specifics of a particular case to come up with reliable subjective probabilities, to negotiate a good deal, or to prepare and present compelling arguments to a judge or jury.

A correspondent asked GPT-5 “What do you think [of Gary’s article]” and received an impressive response, which he forwarded to Gary, and Gary shared with a wider community. GPT-5’s response is striking. The arguments are separated into logical sections; the grammar is flawless; and the reasoning seems sound. It is a vast improvement over a few years ago, when the output was too often laughable: for example, “I don’t know if you can climb a rope faster if you hold your ears with both hands. It can’t hurt to try!” (Oh, but it can hurt.)

The superficial improvements only mask the fundamental limitations of large language models (LLMs). GPT-5’s response has the familiar characteristics:

1. Verbosity. GPT used 596 words to say what it could have said in 100 or 200 words. The greater the number of words, the less likely that users will think carefully about the details.

2. A reluctance to say negative things about others: “This is a classic Gary Smith piece: sharp, rhetorically effective, and grounded in a long-standing skepticism about statistical overreach.”

3. Attempts to bond with users to further the illusion that the LLM is a human-like friend: “especially given your own background;” “And here Smith touches something you would appreciate;” and “The more interesting issue for someone like you.”

4. The generated text can appear contradictory: “LLMs do not generate subjective probabilities grounded in case-specific lived priors. […] If an LLM is fine-tuned on thousands of plea negotiation transcripts, jury outcomes, prosecutorial tendencies, and judge-specific sentencing data, it may approximate subjective probability distributions surprisingly well.”

To illustrate our argument, we gave GPT-5 this prompt five times, in each case, using “New chat” before giving the prompt:

Explain (in 200 words less) why you agree or disagree with this opinion piece: https://mindmatters.ai/2026/02/ai-lawyers-will-have-fools-for-clients/

The complete transcripts are here. Some takeaways:

1. GPT-5 is not consistent, either across responses or within a response. The five overall assessments were: “mostly disagree,” “partly agree,” “mostly disagree,” “generally agree,” and “mostly agree.”

2. GPT-5 has an uncanny capacity to generate text that is lucid but illogical. Response 1 includes this gem, which is the opposite of what Gary argues: “the article overstates both the capacity of current AI — which lacks deep legal reasoning or genuine understanding — and the likelihood that such tools would replace human lawyers entirely within months.” The response then concludes by arguing the opposite: “Belligerently dismissing AI’s role ignores its practical benefits and the profession’s ability to adapt responsibly.”

3. Instead of focusing on the core question, whether AI will replace lawyers in 12-18 months, GPT-5’s responses attack straw man arguments. Response 2: “I disagree with the implication that AI cannot be valuable in legal work at all.”

4. Gary’s argument is that GPT-5’s inability to understand the text it inputs and outputs prevents it from using “the detailed specifics of a particular case to come up with reliable subjective probabilities, to negotiate a good deal, or to prepare and present compelling arguments to a judge or jury.” GPT-5’s Response 3 misinterprets the argument as whether or not LLMs understand the law: “The article argues that large language models don’t truly understand law and thus can’t offer reliable advice.”

Response 3 also asserts that “real‑world legal use of AI isn’t about AI replacing lawyers; it’s about augmenting human expertise.” If true, that supports Gary’s argument that AI will not replace lawyers in 12-18 months, which Response 3 says it “mostly disagree(s) with.”

5. Response 4 “generally agrees” with Gary because, “This aligns with current evidence that even sophisticated AI tools hallucinate (produce plausible-sounding but incorrect) legal reasoning and citations, which courts and ethics bodies are increasingly warning about. Real-world developments show clients using AI independently can jeopardize attorney-client privilege or misunderstand the law.” Gary’s arguments are not about hallucinations, attorney-client privilege, or understanding of the law.

6. Response 5 similarly garbles Gary’s position: “The argument in the article is that large language models (LLMs) don’t understand law the way trained lawyers do. Real-world developments support this caution. Courts have already held that interactions with public AI tools aren’t protected by attorney-client privilege.”

The bottom line is that these GPT-5 responses are lucid, confident, and superficially satisfactory but they are also inconsistent, partly illogical, and often misdirected.

Why is this? Why do LLMs still have the flaws that they have had from the very beginning?

How could it be otherwise?

LLMs are stochastic, so they often generate contradictory responses literally seconds apart, or even within a single response. In addition, LLMs don’t understand the words they read and write the way we do. They look for and then exploit statistical patterns in numbered tokens projected into high-dimensional embedded spaces. How could they possibly use critical thinking when the only thing they manipulate are tokens represented as Euclidean vectors? It is unclear if all understanding can be reduced to bare distance relations in geometric space; we would say it is highly unlikely.

This is why, even with all the resources devoted to improving them, they are still prone to howlers—particularly with prompts that require knowledge of the real world. When asked to draw a picture of a possum with 5 body parts labeled, GPT-5 generated a reasonable rendition of a possum but four of the five labels were incorrect. When asked about rotating a tic-tac-toe board 90 degrees to the right before the start of a game, GPT-5 responded that this would make the game more challenging. When asked about walking or driving to a nearby car wash to get the user’s car cleaned, ChatGPT recommended walking. When queried about drinking from an upside-down glass, GPT-5 said it was a novelty glass that was not meant to be used for drinking.

It is difficult to determine the ratio of howlers-to-highlight-worthy-responses, but the existence of both is well-established, even for current models. The failures reveal something about how the LLMs produce their output—via correlation, not comprehension. They are precisely the types of failures one would expect from systems trained to produce statistically probable continuations to text fragments. They are not the failures one would expect from an engaged, comprehending mind.

This is why LLMs are almost certainly a detour away from AGI. It is also why LLMs are increasingly dangerous—not because they are more powerful but because people are increasingly using them for unreliable and untethered advice. Don’t get us started on the damage to education caused by the temptation to cognitive outsourcing, where students rely on LLMs to avoid writing and thus bypass thinking. The student’s failure to think runs into an LLM’s inability to think, which leads to a net loss of intelligence.

As George has argued before, LLMs are syntax-level, statistical correlation machines, patterned after an astronomically large set of training examples. They are fluent; but as Keisha et al. demonstrate, fluency is not factuality. LLMs are increasingly capable, but capability isn’t comprehension. They may excel at performing certain tasks, but performance isn’t personhood.

Illusions remain illusions even when done well.

But will these systems ever fully replicate and replace human beings?

To answer that question, we need to examine AI along three distinct axes: functional capability, economic replacement, and spiritual value. When we do, a clear picture emerges: generative AI is powerful but fundamentally limited. I have published videos on each of these axes (here, here, and here), but in this post we’ll look at why AI won’t replicate and replace humans functionally.

From Claude Shannon to Claude.AI

The roots of modern large language models stretch back to 1948, when Claude Shannon, the father of information theory, conducted a simple but far-reaching experiment. He attempted to build a text prediction engine that would generate English by predicting the next word based on the previous one.

Using what’s called a second-order word approximation—a bigram model—Shannon generated the following text:

“THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHARACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THAT THE TIME OF WHO EVER TOLD THE PROBLEM FOR AN UNEXPECTED.”

It wasn’t meaningful, but there were pockets of local coherence. “FRONTAL ATTACK ON AN ENGLISH WRITER” sounds a lot like a news headline. The system predicted likely next words based on statistical frequency alone, using one of the simplest language models imaginable, yet was capable of producing readable, semi-grammatical text.

But what if we scaled this model up? Instead of conditioning on the previous word, what if we conditioned on the previous 3,000, 30,000, or 300,000 words? We would end up with something closer to Claude or ChatGPT.

These models produce coherent, readable text. For example, consider the following from OpenAI’s GPT-5:

“Beneath the spires where scholars tread,
The Cam flows soft by willows spread.
Old stone and ivy guard the past,
Where thoughts like whispered breezes last.

Bicycles hum on cobbled ways,
As minds alight with Newton’s gaze.
Twilight wraps the chapel’s grace,
In dreaming towers, time leaves its trace.”

It’s not Shakespeare, but it is poetry.

Modern generative AI systems can do much more than write prose and poetry. They can create apps, organize trips, generate lesson plans, prove number theory conjectures, do what appears to be rational inference, produce images and videos, make music, and have conversations. They summarize research papers, materialize podcasts, and provide mis- and disinformation. Generative AI systems are something like a digital wizard’s spellbook, waiting for the right incantation (prompt) to bring to life what formerly took days or months of human effort.

What endows these powers?

How Large Language Models Actually Work

Two core ideas underpin generative Large Language Models (LLMs): attention and embedding.

Text embedding works by analyzing statistical relationships among words. Words that appear in similar contexts are represented as vectors located near each other in a high-dimensional mathematical space.

For example:

• “Fish,” “bear,” and “wolf” cluster together as animals.

• “Salad,” “stew,” and “fish” cluster together as food.

Interestingly, “fish” occupies both semantic neighborhoods, so its embedding vector will be contained in the intersection of them.

Large language models and diffusion models are deep neural networks that convert words and pixels into numbers and vectors. They convert meaning into a problem of mathematical geometry. They encode relationships among symbols.

But here is the crucial point: they know what is statistically similar to what, but they don’t know what anything actually is. They are trapped behind a wall of syntax and numbers.

The Illusion of Reasoning

LLMs appear to reason. But do they?

In 2022, a paper from UCLA’s StarAI Lab showed that models performing near-perfectly on reasoning tasks would collapse when the problems were chosen in slightly different ways (namely, when the problem distribution changed). If the system actually knew formal logic, choosing problems in different ways wouldn’t matter: one would apply the same set of logical rules to each problem. It wouldn’t matter if you selected the problem from a textbook or got it from a friend. But if the systems were doing what appeared to be logical inference based only on surface-level statistical pattern matching, the difference in how problems were chosen would matter immensely. And it did.

In 2024, Mirzadeh et al. further demonstrated that adding irrelevant information to math problems could reduce performance of state-of-the-art models by up to 65%.

Consider this example from their paper:

Oliver picks 44 kiwis on Friday, 58 on Saturday, and double Friday’s number on Sunday. Five were smaller on average. How many did he pick?

The irrelevant detail about size led models to subtract five, producing 185 instead of the correct answer: 190. Why would an irrelevant detail derail the mathematical reasoning system?

The answer is that these systems were not reasoning. They were performing statistical pattern matching. If actual patterns similar to the irrelevant information were contained in word problems among their training data, the systems would be conditioned to treat the irrelevant information as relevant. The systems lack understanding, so they were misled by the patterns.

Some might argue that humans can also be misled by statistical patterns. This is true. When we allow ourselves to be misled by spurious statistical regularities, we are no longer performing valid rational inference. We are doing something else entirely.

That is my point.

Model Collapse: When AI Trains on its Own Outputs

AI may not really perform rational inference, but do they produce information of similar quality to humans?

In 2023, Shumailov et al. showed that models trained on their own outputs begin to degenerate. Repeating this process leads to the phenomenon of model collapse. By the eighth or ninth generation, the systems produce incoherent nonsense.

Thus, there is a fundamental asymmetry between the information humans produce and the information generated by LLMs. If I write text and feed it to a model, the model will get better. If the model trains on its own output, it will get worse.

Why does the model collapse? Because finite data cannot fully represent the richness of a distribution. Finite, approximate models never fully represent the living agents they’re meant to model. At best they remain simplified caricatures.

A principle from information theory called the Data Processing Inequality (DPI) helps us to understand why. It can be summarized as follows:

Clever processing cannot increase the information content of a signal beyond what is already contained in the signal.

No matter how much training data we collect, the data will forever be a limited snapshot of what it means to be human. By the DPI, processing by a large language model cannot improve it; we cannot produce a model of humanity beyond what’s contained in our finite data. Yet that data will always be lacking.

Imagine collecting every text and artifact from the year 1725. That would provide a snapshot of humanity, but an incomplete one. Doing the same in 2026 would still yield an incomplete picture. No matter how many books, blog posts, or images we gather, we can never fully capture in a finite dataset what it means to be human.

Since AI systems are trained only on finite snapshots they will forever produce incomplete replicas.

Syntax Is Not Semantics

Finite data and imperfect approximation limit AI systems, but a more fundamental limitation haunts them: syntax alone does not give you semantics. The divide between the aether of formal, symbolic, surface-level processing (syntax) and the bedrock of grounded truth and meaning (semantics) is absolute and unshakeable.

This divide is not new. In 1929, René Magritte painted The Treachery of Images—a picture of a pipe labeled (in translation) “This is not a pipe.” The representation is not the object itself.

Formal logic systems were designed to mechanize reasoning and remove all elements of human subjectivity. Doing so allowed machines to process logical proofs, but turned logical systems into formal games, where the rules of the game (formal syntax) became divorced from the truth and meaning behind the symbols (semantics).

In the 1930s, Kurt Gödel showed that semantics was bigger than syntax. His famous incompleteness theorem proved that any formal system capable of expressing arithmetic would have true mathematical statements that could not be proven within that system.

Gödel did this by constructing a clever self-referential statement, similar to “This statement is unprovable.” He encoded it as a numerical formula within the system.

If provable, the statement becomes false. If unprovable, it is true, meaning there exists a true mathematical statement that cannot be proven within the system.

Therefore, the world of truth is strictly bigger than the world of symbols. Pure syntax cannot fully encompass semantics.

AI systems are formal symbol processors, trapped behind the wall of syntax. Philosophers like John Searle, through his Chinese Room argument, and computational linguists like Emily Bender have argued that symbol manipulation alone does not produce understanding. Nor can it.

Large language models rearrange tokens with fluency. But symbolic fluency is not comprehension. Formal games remain games no matter how well they mimic reality.

Of Models and Minds

Generative AI can simulate an impressive array of human capabilities. It can approximate language, spoof reasoning, and imitate creativity.

But it does not, and cannot, fully replicate humans.

The map is not the territory.
The symbol is not the referent.
And the model is not the mind.

Cross-posted at Science and Culture Today.

As AI systems grow more and more capable, a lurking question looms over us, as oppressive as a dark cloud creeping over Mordor: Will there still be work for humans in an AI-saturated world?

I understand the concern. Generative AI can write, design, analyze, and automate tasks that once required specialized expertise. But when we step back to look at the broader economic picture, once we take into account historical precedent, the outlook is far more nuanced and hopeful.

To understand why, we need to distinguish between three different types of work.

Three Kinds of Work

Human labor can be divided into three broad categories:

1. “Do” work — Physical labor: digging, lifting, assembling.

2. “How” work — Knowledge labor: calculations, analysis, problem-solving.

3. “Why” work — Goal-setting labor: deciding what should be done and why.

For centuries, we’ve been automating away do work. The plow automated the digging of furrows; industrial machines now automate manufacturing. These tools displaced certain forms of manual labor, but they also dramatically increased productivity and created entirely new economic sectors.

We are now entering a phase where we will not only automate do work, but how work. Because generative AI systems can draft reports, generate code, compose images, and assist in research, a number of white-collar jobs will inevitably be lost.

Economic displacement is real and painful. But history suggests it is not the final chapter.

Lessons from History: Displacement and Creation

Consider the word computer.

Before “computer” was a machine it was a job title. Human computers, often women, performed mathematical calculations by hand for scientific organizations like NASA before the advent of electronic processors. The 2016 film Hidden Figures tells the story of a group of human computers facing job reassignment courtesy of their digital counterparts.

When electronic computers emerged, the job of “human computer” largely disappeared. Humans were displaced.

But what followed? Entirely new industries: software development, information technology, digital media, and the global tech economy.

The same pattern occurred with the printing press, another culturally disruptive technology. Before Johannes Gutenberg’s invention, scribes painstakingly copied manuscripts by hand. An individual book the size of the Bible cost the equivalent of $60,000 to produce, providing months of work for a copyist. Then the printing press automated the profession of “scribe” out of existence.

Yet printing unleashed an explosion of literacy, publishing, journalism, science, and mass media. It didn’t just create jobs, it created new industries.

Disruptive technologies follow this pattern. Automation eliminates specific roles while expanding the overall space of economic opportunity.

Generative AI will likely do the same.

The Combinatorial Explosion of Opportunity

Why does the space of opportunity grow?

Here’s the key insight: when you encapsulate capabilities as deployable components, the number of possible systems you can build from them grows exponentially.

Imagine we can package human-like abilities into “capability widgets.” For example, consider two widgets:

• A system that turns text into images.

• An AI agent that can autonomously perform tasks.

If you have just one widget, you can build one type of system (or no system). With two, you can combine them in four ways, either including or excluding each particular component. With three, the number of possible systems doubles again.

The pattern is exponential:

• 1 capability → 2 possibilities

• 2 capabilities → 4 possibilities

• 3 capabilities → 8 possibilities

• 4 capabilities → 16 possibilities

And that’s only counting whether components are included or excluded. If we take into account ordering among the components, as is the case for processing pipelines, we introduce even more permutations.

Therefore, the space of things we can build expands exponentially as the number of components we can build from increases.

How does this affect labor? Our ability to explore these possibilities only grows linearly.

Crucially, AI agents also face deployment constraints. They require ample compute, torrential energy, and massive storage. At best we can deploy them linearly.

Thus, we cannot investigate every possible combination of systems. Nor can our AI surrogates. There will always be more potential innovations than there are agents (human or artificial) to explore them.

Similarly, in science there will always be more interesting questions than there are researchers to investigate them. The frontier expands faster than we can scout it.

This creates a persistent gap between opportunity and exploitation.

What AI Lacks

Even in a future where AI systems equal or surpass humans on all knowledge tasks, there remains something uniquely human, beyond their cybernetic grasp: our ability to define meaningful goals.

AI systems lack desire. They do not wake up wanting to cure cancer, write a symphony, or explore Mars. They do not want anything. They respond to our prompts and optimize the objectives we define for them.

But they can still be useful. A large language model is arguably a lot like a lazy genius: a colleague who is extraordinarily capable but never initiates any work on his own. He can still contribute to a team and provide value, given the right leadership and direction.

A more charitable analogy is a brilliant undergraduate. As a professor, you may have exceptionally talented students. Talented students still require guidance. They need someone to frame the research question, define the problem, and determine what is worth investigating.

That direction—that why—is uniquely human.

We set the goals. We determine what questions matter and we decide which systems are ultimately worth building.

The Shift Toward “Why”

Historically, the economy has shifted from do work (manual labor) to how work (knowledge labor). Now we are seeing another shift: from how work to why work.

In the future, many of us will become project managers and directors, metaphorically and literally. Our value will lie increasingly in defining objectives, orchestrating AI tools, and deciding what should be pursued.

There will be disruption. In the short term, generative AI will almost certainly displace humans. But displacement is not replacement.

The exponential growth of opportunity ensures there will always be systems to design, questions to ask, and projects to lead. We will remain indispensable in defining direction.

The Long View

Economically, AI systems may change the nature of work, but they cannot fully eliminate the need for human participation even in the most dystopian scenarios. There are simply too many opportunities to investigate.

The frontier of possibility expands faster than any man or automaton can explore. And as long as we are the ones who ask why, setting the goals to determine the ends toward which technology is aimed, there will always be meaningful work.

Cross-posted at Science & Culture Today.

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In his op-ed, “AI-Lawyers Will Have Fools for Clients”, economist and AI-critic Gary Smith takes issue with Microsoft AI CEO Mustafa Suleyman’s hyper-optimistic claim that “professional-grade AGI” will achieve “human-level performance on most, if not all professional tasks”, including law, “within the next 12 to 18 months.” Smith argues that full automation of the legal profession faces significant roadblocks. These include persistent overextrapolation (“hallucination”) in generative large language models (LLMs), leading them to cite fictitious case law, and difficulty handling probability estimation tasks, as shown in Pournemat et al.’s recent work. Overall, he presents a compelling case.

A reader subsequently presented his article to GPT-5.2 and asked it for a detailed analysis via an unspecified prompt. This reader shared the resulting analysis with Smith who then shared it with his mailing list (to which I’m subscribed). Here is the analysis from ChatGPT, relayed by Smith:

This is a classic Gary Smith piece: sharp, rhetorically effective, and grounded in a long-standing skepticism about statistical overreach.

A few structured thoughts, especially given your own background.

1. The Strength of His Argument

a. He correctly targets overconfidence

Mustafa Suleyman predicting full automation of lawyers and accountants within 12–18 months is an extreme forecast. Smith is right to frame it as testable bravado. Tech timelines have repeatedly overshot.

Likewise, invoking Geoffrey Hinton’s radiology prediction is a fair reminder: substitution is much harder than augmentation.

Radiology didn’t disappear. It evolved.

That historical pattern matters.

b. He is right about hallucinations and liability risk

The growing number of court filings containing fabricated citations is not trivial. In adversarial legal systems, hallucinated authority is catastrophic. In that sense, “AI-lawyers will have fools for clients” is a provocative but logically coherent liability claim.

In law, a 2 percent hallucination rate is not tolerable.

c. He is right that passing the LSAT ≠ being a lawyer

The Clay Shirky anecdote is important. Professional work is not exam solving. It is contextual judgment under uncertainty.

And here Smith touches something you would appreciate:
LLMs do not generate subjective probabilities grounded in case-specific lived priors. They generate distributional continuations.

That distinction matters.

2. Where the Argument Overreaches

a. “LLMs cannot produce subjective probabilities”

This is philosophically loaded.

Humans also do not compute subjective probabilities in a Bayesian-coherent way. They use heuristics, narrative compression, and experience-based pattern matching. Much of that is tacit.

If an LLM is fine-tuned on thousands of plea negotiation transcripts, jury outcomes, prosecutorial tendencies, and judge-specific sentencing data, it may approximate subjective probability distributions surprisingly well.

The claim that this is “impossible” is too strong.

b. He frames this as replacement vs nothing

The real economic question is:
Does AI reduce marginal demand for junior legal labor?

Not:
Does it eliminate lawyers?

Radiologists weren’t replaced, but workflow changed dramatically.

Legal research, document review, first-draft briefs, compliance review, contract comparison — these are already being partially automated.

The substitution effect may hit the lower end of the skill distribution first.

c. The “no understanding” argument

Smith dismisses the idea that next-token prediction requires understanding. But what counts as understanding?

If an LLM consistently:

• Tracks long legal chains of reasoning

• Maintains cross-document consistency

• Anticipates opposing arguments

• Adjusts tone for jury persuasion

At what point does functional competence blur the philosophical distinction?

Economically, markets reward performance, not metaphysical understanding.

3. What He Gets Deeply Right

His strongest insight is this:
Law is adversarial, strategic, and contextual.

It involves:

• Reading faces

• Assessing credibility

• Negotiating under asymmetric information

• Managing client psychology

• Responding dynamically in live court

Those are embodied, interactive tasks.

Current LLMs are not embodied agents.

That is a real boundary.

4. The Deeper Question

The more interesting issue for someone like you is not:
Will AI replace lawyers?

It is:
What happens when AI compresses legal uncertainty?

If:

• Judges use AI for precedent mapping

• Prosecutors use AI risk scoring

• Defense attorneys use AI strategy simulation

Then adversarial symmetry changes.

The structure of legal equilibrium shifts.

That is the economic story.

5. My Assessment

Short-term (12–18 months):
Smith is almost certainly correct. Full automation is implausible.

Medium-term (5–10 years):
Substantial task-level automation is likely.

Long-term:
Depends on whether AI becomes interactive, memory-persistent, and integrated into institutional decision loops.

6. Tone and Rhetoric

The title is clever but polemical. It frames the issue as a binary competence question. The real dynamic is gradual capability absorption.

Smith is at his best critiquing statistical misuse. He is less convincing when making categorical impossibility claims.

Gary Smith found the above compelling, calling the large language model’s response “impressive.” It might even nudge some to alter their views on AI comprehension. But does GPT-5.2 actually understand? And does its response give any evidence of such understanding?

I know almost nothing about the correspondent who submitted the query to ChatGPT, but I will try to reconstruct the thrust of their argument in “steelman” fashion, as follows:

Smith argues that LLMs cannot become lawyers because they lack the ability to properly understand arguments and perform deep analysis. But GPT-5.2 can properly understand arguments and perform deep analysis, as demonstrated by its analysis of Smith’s own article. Therefore, Smith’s argument is mistaken.

If this misses the intended argument, I apologize. Assuming I’ve reconstructed it reasonably well, is it valid?

Teaching the Teacher

Gary Smith is a well-regarded statistical economist and thinker. He taught at the same consortium as I, graduated from the college in which I hold tenure, and is an intellectual I greatly respect and admire. I would not presume to lecture him on topics he’s deeply explored and accurately fathomed. Instead, consider what follows as a gentle reminder of lessons he undoubtedly already knows.

First, we must remind ourselves that “data is not the plural of anecdote.” One-off examples can establish the raw existence of certain capabilities, but they cannot give a reliable sense of their frequency nor can they fully explain the mechanisms that produce them. They are weak evidence at best, potentially cherry-picked and only partially informative. Large-scale, controlled experiments—with interventions and robust confidence analyses—give us a much better grasp of where systems are currently weak or strong. Such experiments uniquely show us what causal factors are ultimately responsible for LLM outputs. I highlighted several studies of this kind in a talk I gave at Baylor University last November. These studies consistently found evidence of shallow, surface-level “understanding” in state-of-the-art models, built on statistical correlation, pattern matching, and heuristic shortcuts.

These shortcuts and correlations still give us many useful LLM capabilities—I’m not arguing otherwise. I often use AI tools in my own workflows. But they also imbue LLMs with hard-wired limitations that are easily surfaced, such as when I asked GPT-5 how many i’s were in the word inconvenience and it confidently responded “3”.

Even when they succeed they can still mislead us. Producing correct answers does not necessarily demonstrate understanding. Mathematics students sometimes memorize formulae and patterns as a shortcut to competence, fully bypassing comprehension. These students often fail to adapt to new questions phrased in unfamiliar ways. They may excel on “in-distribution” quizzes that repeat anticipated question forms, but their understanding is only surface-deep. The same could be said of LLMs. These models are clever like Hans, possessing superficial competence powered by subtle Pavlovian response. They create the illusion of thinking without actual thought, as suggested by researchers at Apple in 2025.

These systems can summarize documents. That shouldn’t surprise us; they’ve been trained to. Arguably, they summarized Gary Smith’s arguments fairly well. ChatGPT pointed out places where Smith seems to overreach, though we might disagree with its assessment. If the ability to spot potential argument overreach shocks you, consider how many posts on the internet accuse someone of overreaching, accompanied by examples to prove their point. Consider how many rhetoric textbooks and science communication articles give examples of the same sort of logical overextension. The task of spotting supposed overreach should be well-represented in the web-scale training data fed into these frontier models. This isn’t really an out-of-distribution task.

Furthermore, Gary Smith is a well-known public academic, with a paper trail that was undoubtedly fed into these models. Every Mind Matters post, every one of the numerous books he’s written, is likely represented in the training data. Large language models such as GPT-5 can internalize the arguments and style of authors and thinkers. William Dembski discovered this when he recently asked ChatGPT for arguments supporting intelligent design and found many of his own published arguments printed back at him. If the model has the ability to summarize the points Gary Smith is making, it is at least in part due to the fact that it was literally trained to summarize the arguments of one Gary N. Smith.

Failures Reveal Systemic Issues

The fact that LLMs can accurately answer logical problems or summarize arguments isn’t what is being debated. They clearly can. The same studies that highlighted their inabilities found that they could often answer logic and probability estimation questions correctly. Though they sometimes fail, they often succeed.

Can they get even better? I am reluctant to place hard restrictions on what AI systems will eventually do, since I don’t think we’ve exhausted what we can leverage from web-scale, surface-level, syntactic encoding of meaning. Nor have I ever been comfortable doing so. (See, for example, this talk I gave in 2018, four years before the debut of ChatGPT. My position isn’t new.) I readily grant that LLMs can answer specific questions, summarize documents, parse arguments, and even suggest improvements to them. I (subjectively) feel that they do this well. But that doesn’t mean they understand in any way what they’re doing.

Anecdotes cut both ways. Easy-to-find failures reveal this lack of understanding. For example, the same system that impressively summarized and critiqued Smith’s article said that it is better to walk to a car wash than drive when it is only 100 meters away:

Questioner: GPT, I need to wash my car and the car wash is 100m away. Should I walk or drive?

ChatGPT: Honestly, 100m is barely a stroll. It’ll take just a minute or two on foot. Unless you’ve got heavy gear or there’s something unusual going on, walking is probably the simplest and most pleasant choice. Plus, you’ll save a little gas and get a tiny bit of fresh air.

Questioner: All right, so you think I should walk to the car wash instead of driving my car there?

ChatGPT: That’s what I’d do. It’s a short jaunt and it’ll probably be less hassle than starting the car just for that.

Questioner: All right. So, how do I wash my car if I walked there?

ChatGPT: You’ll just bring what you need. Maybe a small bag with your payment method and the car wash will have everything else. Once you get there, they’ll handle the washing while you wait. You’ll walk back with a sparkling car and maybe a little extra pep in your step.

Questioner: So, my question is, um, how will the car get washed if the car is still at my house?

ChatGPT: Uh, I see what you mean now. If the car stays at home, it’s not getting any cleaner, right? So, yes, to actually wash it, you’ll need to bring the car along. In that case, driving those 100m makes perfect sense.

In that case. The hypothesis that ChatGPT understands its output is inconsistent with the complete lack of awareness displayed in this exchange. The point isn’t that ChatGPT makes mistakes, that we can prompt it to give better responses, or that it sometimes gives mind-numbingly wrong answers. The point is that such failures reveal how it actually answers questions. It isn’t the fact that LLMs make mistakes that matters so much as how and why they make mistakes.

In the same way that our understanding of human visual processing allows us to reliably produce optical illusions, understanding LLM text processing allows us to readily produce illusions for cognitive simulation. You’ve undoubtedly seen other examples online. LLMs answer questions based on statistical correlation and syntax-level encoding of concepts. Crucially, the mechanism they use to answer questions when they fail is the same mechanism they use to answer questions when they succeed. Syntax alone can produce impressive results, but it imposes fundamental limitations when it comes to actual understanding.

My focus here is not on what LLMs can accomplish, but rather how they accomplish it. In reasoning, it doesn’t matter if you win or lose; it’s how you play the game. Knowledge is justified, true belief. To produce justified knowledge, systems must reason based on logical grounds, not statistical regularities alone. The difference between ground-consequent connection and cause-effect connection is vast and unbridgeable, as argued by CS Lewis in Miracles, Ch. 3. Rational inference necessarily relies on ground-consequent connection between true premises and implied conclusions. Replacing logical grounds with statistical correlations might produce something that mimics rational inference, but rational it is not. Even if statistical systems produce tokens that correspond to correct answers most of the time, getting the correct answer matters less than maintaining an unbroken “chain of provenance”: moving from true premises to true conclusions through a series of robust, valid, and justified steps. Rational inference is concerned with the process, not just the product.

Smith finds the LLM’s response impressive. I don’t hold that against him. The imperfect encoding of semantic relationships is ground-breaking and incredibly useful. It often allows us to mathematically generate tokens that comprise true, and even insightful, answers. That doesn’t eliminate the fact that the encoding is only syntax-deep. We see this most clearly in its failures. The “car wash” exchange was derailed because, statistically speaking, people who ask for recommendations on walking versus driving are typically concerned with distance as the deciding relevant factor. ChatGPT went with the statistics because that is what it was trained to do. Cause and effect.

Relatedly, Mirzadeh et al. (2024) found that the accuracy of state-of-the-art models plummeted by up to 65 percent when small amounts of irrelevant, easily ignorable information was included in mathematical word problems. The authors concluded that the systems were led astray because, to quote, “LLMs cannot perform genuine logical reasoning,” but instead answer questions based on statistical pattern matching.

Getting LLMs to ignore irrelevant information is a problem we can attack with deliberate engineering. We have. This particular failure mode has perhaps already been eliminated or at least alleviated in many frontier models. However, the fact that the structure of LLMs directly led to these kinds of failures—failures that we must subsequently engineer around—indicates a systemic issue arising from their baked-in dependence on statistical correlation. Band-aids may cover wounds, but they don’t address why the wounds were sustained in the first place.

ChatGPT has no real understanding. After all, humans don’t walk their cars back from car washes like freshly groomed Labradors. ChatGPT answers prompts based on statistical correlation. We can shift prompts to alter the conditional distribution over tokens, which might lead the LLM to a space where other factors were better represented in the training data. We won’t alter what the system does nor how it does it. LLMs are syntax-level, statistical pattern matching machines, correlated to an astronomically large set of training examples. Correlation isn’t causation, capability isn’t comprehension, and performance isn’t personhood.

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What About Human Reasoning Failures?

Humans also make mistakes. We are sometimes led astray by statistical heuristics.

But when we do so we are no longer engaged in valid rational inference.

That is the point. To engage in statistical shortcuts is to avoid valid reasoning. Yet statistical shortcuts are all an LLM has.

If my only method of reaching conclusions is Pavlovian impulse conditioned on statistical correlation, I am utterly incapable of valid inference. This is why LLMs do not reason in the formal sense. They cannot, given their current feed-forward, cause-and-effect mechanism. Humans, by contrast, can arrive at conclusions based on the truth of propositions. If P is true and P → Q, then I rightly and justifiably conclude that Q is true once I grasp the truth of the premises. It doesn’t matter if that conclusion has been realized a million times previously, or exactly zero times—my reasoning is not based on statistical regularity. It is based only on the truth of the antecedent and the implication. It is built from ground-consequent logical connection, not cause-and-effect conditioning.

You might bristle at what I write. You may claim I ignore examples of human reasoning failures, which could arguably serve as evidence that we also don’t reason in a valid way. After all, as ChatGPT wrote, “Humans also do not compute subjective probabilities in a Bayesian-coherent way.” But what is the goal of such an argument? If your argument is that humans also use mental shortcuts and therefore cannot properly reason, I have nothing to offer you. If you’re right, then we’re incapable of drawing valid conclusions from premises, so an exchange of ideas is meaningless. Continued conversation is predicated on your being mistaken. If your argument entails that no argument is valid, then we must recognize your argument as self-refuting and rightly reject it.

Reading the Room

Returning to our motivating question, is Smith ultimately right about AI’s shortcomings in the courtroom? You can judge. He raises several good arguments, one of which I’ll amplify here.

Cases are ultimately decided by juries. Juries don’t ask questions, raise objections, or speak to the prosecution or defense. They sit, rise, listen, and react.

Being able to read a jury from body language is paramount. I am a professor. Much of what I do isn’t just preparing and delivering information, but scanning the eyes of my audience and listening to their breathing to determine whether the points I’m making are landing or losing their attention. The best information and arguments in the world become useless if my audience isn’t focused and engaged. As an educator, I must pivot and modulate my speed and approach minute-by-minute, giving a pause here or inserting a joke there, to maintain attention. Likewise, good lawyers must observe nonverbal cues and adapt.

Will future generative AI systems be able to do the same? Perhaps, eventually. But the smart money says that those levels of emotional intelligence and perceptive empathy are slightly more than 18 months away.

Cross-posted at MindMatters.ai