Category Archives: Artificial intelligence

what about human life is essential to preserve?

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At the beginning of the 20th century, people could presume that we are terrestrial creatures whose habitat is Planet Earth. They could predict that we will continue to live as humans until our far-distant natural demise or an end decreed by God. They could define human beings as creatures who work (homo laborans) and make tools and who understand the tools we’ve made. They could presume that we alone speak and reason in language, unless divine beings also interact with us.

Technology changed all those presumptions. It became clear that we could live on other planets, we could wipe out the species in a few moments, we might be able to automate all work, we might make machines that exceeded our own understanding, and we might build machines that could reason and talk, perhaps better than we do.

Hannah Arendt mentions each of these developments in the Preface to The Human Condition, which she published in 1958. Some of the traditional assumptions about humanity had already collapsed by then, due to atomic weapons and space exploration. So far, there were no machines that could do all our work or think better than we can, but she saw that those developments were coming unless we blew ourselves to dust first. Just five years later, she worried that “electronic ‘brains,’ devised and constructed by men” will “not only do man’s brainwork incomparably better and more swiftly (this, after all, is the outstanding characteristic of all machines), but can do ‘what a human brain cannot comprehend” (Arendt 1963, 530.)

She therefore posed the question: what is the human condition? She wanted readers to consider what is essential to us. This is what must be preserved if we love humankind. For her, love of our species was not contingent on our being excellent in some way–for example, the best thinkers on the planet. It was a fundamental ethic.

Clearly, human life will end if we vaporize ourselves. Presumably, we can live as humans on other planets: science fiction has envisioned that situation vividly. Whether we can be human without working or once we share the world with machines that think better than us are harder questions, and the answers depend on what it is to be us.

Later in the book, Arendt will explain that part of being human is to “act.” In her specific meaning of that verb, to act is to discuss what to do with other people who are independent and heterogeneous (Arendt’s “plurality”) and then to do or make things together.

Because many interacting people are required to decide what to do, Arendt cannot tell us how to address developments like nuclear weapons or artificial intelligence. We must think them through together. She writes:

To these preoccupations and perplexities, this book does not offer an answer. Such answers are given every day, and they are matters of practical politics, subject to the agreement of many; they can never lie in theoretical considerations or the opinion of one person, as though we dealt here with problems for which only one solution is possible (p. 5).

Why should we make decisions together instead of just doing the right thing? Arendt writes:

There may be truths beyond speech, and they may be of great relevance to man in the singular, that is, to man in so far as he is not a political being, whatever else he may be. Men in the plural, that is, men in so far as they live and move and act in this world, can experience meaningfulness only because they can talk with and make sense to each other and to themselves (p. 4).

We live in a meaningful world–a world with meaning to us–only if we create it together. The question of the moment is whether our world will remain meaningful to us if we transform it through artificial intelligence that we no longer understand.

What are the “truths beyond speech” that Arendt mentions here? The most obvious candidates are transcendent truths, such as religious insights that a person might receive as revelations or intuit by introspecting. But there is a way in which science and technology are also “beyond speech.” Of course, scientists must communicate. However, for them, communication is a cost, a means to the end of analyzing nature. The less talk is necessary to produce knowledge, the better.

Ideally, scientists and technologists apply impersonal methods to generate knowledge that accumulates. A technician can install a light fixture or even design a new electrical device without reading and discussing the words of Alessandro Volta and Nikola Tesla. Historical contributions are now embedded in current techniques, some of which are algorithmic. And if a library is a device for accumulating knowledge, then a Large Language Model may be a better one.

Arendt is a solo scholar, applying her learning and methods to analyze how our thinking has developed so far. Her narrative in The Human Condition is the work of one thinker, yet she is in constant and explicit dialogue with previous writers and with her readers. Thus her contribution lies between “truths beyond speech” and “practical politics.”

The purpose of her text is to set questions that we must answer collectively by thinking and acting:

What I propose in the following is a reconsideration of the human condition from the vantage point of our newest experiences and our most recent fears. This, obviously, is a matter of thought, and thoughtlessness—the heedless recklessness or hopeless confusion or complacent repetition of “truths” which have become trivial and empty-seems to me among the outstanding characteristics of our time. What I propose, therefore, is very simple: it is nothing more than to think what we are doing (p. 5).

Sources: Hannah Arendt, The Human Condition, 1958; “Man’s Conquest of Space,” American Scholar 32, no. 4 (Autumn 1963). See also: the worlds we can lose when intelligence becomes artificial; Reading Arendt in Palo Alto; the papal encyclical on AI

two futures for AI, work, and learning

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AI tools can be designed to accomplish tasks that have been done so far by human workers. Engineers use rubrics to assess and refine the outputs of AI. Their rubrics are often based on human performance. For example, how well does as an LLM perform at writing a contract or allocating investments, compared to a lawyer or an investment advisor? The next version will do it better.

On the other hand, AI tools can (or could) be designed to accomplish tasks that human beings cannot do or truly don’t want to do.

This is a choice. The consequences could be profound.

If AI is designed to replace human work, then we risk a scenario in which many people may get laid off, aggregate growth suffers because those people have less money to spend, the job losses are concentrated among knowledge workers, the benefits of education (broadly defined) shrink, and people lose commitment to developing their own minds.

We could see not only an economic recession but a mental one. For example, a college degree would be worth less on the market, and students would not see the point of challenging themselves to learn. Intelligence is a good, and the market value of human intelligence could slip if there is also a growing supply of artificial intelligence.

This scenario would be bad for business, not only for workers and learners. A consumer-oriented company that replaced all its salespeople, managers, accountants, and so on with machines would also contribute to reducing its own consumer base. That would be a classic Tragedy of the Commons. The only companies worse off than those that cut their own employees would be those that didn’t. Labor’s share of income would shrink, but capital’s slice would also be smaller than it is today.

We are familiar with the story that new technologies replace some workers but also boost productivity, which creates new jobs. The technologies of the Industrial Revolution tended to replace higher-skilled artisans, forcing many workers down the skill ladder. Those people suffered, yet today’s workforce is much better educated and better paid. If AI turns out like steam engines and machine looms, we’ll be OK in the longer run.

Then again, we have never had a technology so relentlessly tunable to replace any worker, even someone in a brand-new occupation. Besides, the tools of the Industrial Revolution displaced skilled artisans but not people who had formal educations, whereas AI seems to threaten the most advanced occupations, even pure mathematicians. That means that there may be no way to escape by learning higher skills.

Reading and math scores fell rapidly all across the world between 2018 and 2022, and then further in 2025:

As reported by Elizabeth Grenier in DW.

The PISA report shows that this decline is correlated with AI use. In 2025, it says, “AI chatbots (such as ChatGPT) were used for schoolwork by a majority of students in most participating countries and economies.” But there was variation in how much it was used, and where. “PISA shows that students who do not use AI chatbots for schoolwork generally score higher than students who do, and are more likely to report higher levels of help-seeking behaviours.”(OECD 2026 p. 236)

Specifically, students who never used AI to summarize an assigned text or to draft text for writing assignments scored much higher than those who ever used it for those purposes, whether or not their scores were adjusted for socioeconomic status (fig 1.4.13).

This evidence does not prove causality, but it’s consistent with the concern that AI tools can replace rewarding tasks that human beings have done (such as reading) and even cause a global mental recession. Inventions like railroads and typewriters never lowered reading skills.

It is also worth exploring positive scenarios, in which AI tools are trained and refined by rubrics that ask whether they are accomplishing something valued by humans that humans cannot do or do not want to do. Then we could see economic growth, profits for companies that solve new problems, and positive externalities, such as better health and more learning. The PISA report adds, “among students who use AI for schoolwork for a general purpose (‘to help me learn’), those who report moderate use score slightly higher in PISA than students who use it either rarely or intensively.” I have no doubt that AI tools can help us learn instead of replacing our learning, but this is a matter of design.

I worked with Tufts engineering students to develop a rubric to assess tools like the Civic Helpdesk, asking whether these tools provided useful advice for voluntary groups. To some extent, we imagined experienced community organizers as our models. Thus our tool could replace people, although paid organizers have always been critically scarce. But the Civic Helpdesk can also draft boring documents so that voluntary groups can concentrate on meatier topics. That is an example of freeing people to do more valuable work..

Zeke Emanuel and Vinod Khosla argue that AI tools can perform the algorithmic aspects of medicine (applying “detailed guidelines from professional societies”). In such cases, AI tools would be designed to replace some work by doctors. However, presumably, the same physicians could then concentrate on aspects of medical care that are not resolved by guidelines.

Earlier, I described a race to replace workers with AI as a Tragedy of the Commons. Companies would hurt themselves as well as others by reducing employment, thus shrinking their consumer base.

Problems of collective action require collective solutions–not always governmental regulation (although that is worth considering) but sometimes voluntary collaboration or pressure from organized people.

The current resistance to new data centers may not be the ideal way to counter to Silicon Valley. When residents block a data center in their own community, it will probably be built somewhere else. But such resistance introduces friction that could lead to genuine negotiation.

At a high level of generality, the goal of workers, consumers, and their elected representative should be to press AI companies to refine their tools to perform tasks that people cannot do or truly don’t want to do.

We are very early in the history of widely available AI. It is too soon to tell which path we’re on. Dean Baker sees little evidence of jobs lost to AI yet. He notes that jobs in the insurance industry should be at risk, yet the number of insurance positions shrank by just 2.5 percent in a year, versus an 8.6% decline in manufacturing, which should be less exposed to AI.

On the other hand, the share of GDP that workers capture has fallen since 2000–and sharply since 2020. Brent Neiman writes, “While we can debate about [the causes] over the past five decades, when it comes to the last five quarters, technological change has to be a leading contender in explaining its rapid decline.” He implies that AI already “substitutes for labor”:

The Financial Times’ John Burn-Murdoch shows that the college wage premium (the extra amount of salary that comes with a college degree) has suddenly fallen since 2020 in both the USA and the UK.

This trend could be good news if it meant that people without college degrees were being paid better than in the past. In an equitable world, the college premium would be small. AI could even help make the world more equitable by, for example, helping employers to find fully qualified prospective workers who don’t have expensive college degrees. (See “How AI can advance a skills-first labor market” by Opportunity at Work.)

However, since labor–in the aggregate–has been capturing a smaller share of GDP since 2020, it seems more likely that college graduates are being squeezed by technology. Meanwhile, hundreds of millions of younger children are simply learning less.

Source: OECD (2026), PISA 2025 Results (Volume I): Future-Ready Students, PISA, OECD Publishing, Paris,See also: AI as the road to socialism?; attitudes about AI by age; why the humanities could never be automated; and other posts on AI.

Museum of Islamic Art, CC BY 2.0 , via Wikimedia Commons

on translation as spiritual practice

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“The feeling that the world is a bounded whole–that is the mystical.” — Wittgenstein

The Spanish word quedar can be translated as to remain, to stay, to meet, to fit, to suit (as in Ese color te queda bien), to be located, to end up, to agree, or to be left with (as in Me quedan 20 euros).

One of those meanings is “to agree.” That English verb can be translated back into Spanish as estar de acuerdo, acordar, convenir, coincidir, aceptar, or, again, quedar.

One of those meanings is acordar. Rendered back in English, that verb can mean to agree, decide, or make an arrangement, or–if it’s in a reflexive form–to remember. In turn, “to decide” could be conveyed in Spanish by decidir (or decidirse), resolver, acordar or determinar.

I chose quedar because it has a famously wide range of meanings, a bit like rendre or tenir in French. But notice that each of the words that I’ve mentioned connects to a unique list of words in the other language. This is typical. Putting two languages together creates a pattern of intertwined connections, like an arabesque design.

When you study a language, you memorize foreign words as sets of distinct words in your home language. In a given context, quedar means one thing, such as “to stay” or else “to agree.” A translator should know–or look up–the full list of meanings for each word and must pick the best one for each sentence. Sometimes there is no single best choice.

But once you know the language reasonably well, a word no longer decomposes into several words in a different language. Now it covers its own space. Presumably (I don’t have good enough Spanish to know) each use of quedar carries an implicit trace of its whole range of meanings.

Todo pasa y todo queda, 
pero lo nuestro es pasar, 
pasar haciendo caminos, 
caminos sobre el mar. 

Everything passes and everything stays,
But ours is to cease to be. 
As we go, we make a highway,
A highway on the sea. 

Antonio Machado, Proverbios y Cantares (1912): XLIV

Here I’ve rendered the first line with the verbs “to pass” and “to stay,” but I had other choices. Pasar could mean to go through, to happen (¿que pasa?), to spend time, etc. And we have already seen the range of meanings for quedar. Its other senses are missing in the English word “stay,” but that verb has its own range of meanings, including to visit and to prevent something from happening. Those are absent in Machado’s original.

I chose “to pass” for several reasons, including the sonic resemblance to pasar as well as some vague sense of English allusions, such as “One generation passeth away, and another generation cometh” (Ecclesiastes). I chose “to stay” as a single syllable that was a near-rhyme for “ways.” Translation is not only about matching words but also about syntax, rhythm, and sound.

Artificial Intelligence is good at translating prose. It generally chooses an appropriate equivalent for each word. Thanks to AI tools, I have been able to read articles in languages, such as Japanese, of which I know nothing. I have also developed a careful practice of asking Claude to list all the dictionary definitions of words in languages, such as Sanskrit and Pali, that interest me. I hope that viewing these lists gives me some sense of how the words work in their original languages, although I must look through that glass darkly.

What AI cannot do is give a person the experience of parsing the whole world a bit differently by beginning to think in a different language. In turn, that experience teaches us that anyone’s model of the world is limited and contingent, and much always remains beyond anyone’s grasp.

A language is a whole system of meanings. It allows us to work with the world, yet it coexists with other systems that present the world differently. Those other systems include not only human languages (which are, after all, rather similar and interconnected), but also other consciousnesses. To glimpse this plurality is to see our own world as if from beyond it.

In the detail from a 13th-century Persian mihrab that accompanies this post, each arabesque field could stand for a pair of intertwined human languages, but there are also fields across the boundaries formed by the Quranic calligraphy. And the whole mihrab is a threshold to the beyond.

Wittgenstein writes, “Das Gefühl der Welt als begrenztes Ganzes ist das mystische” (Tractatus 6:45). Acknowledging that this sentence could be translated in many ways, I will render it: “The feeling that the world is a bounded whole–that is the mystical.”

It’s another way of saying that we make our highways on the sea.

See also: Machado’s Glory is Never What I’ve Sought and Highways on the Sea; paradigms of attention; why the humanities could never be automated; and thinking both sides of the limits of human cognition. Photo credit: detail of a mihrab from Kashan (1226) in the Pergamon Museum, Berlin, via Wikimedia Commons.

why the humanities could never be automated

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Sometimes, we want to answer questions to accomplish practical outcomes. For example, we want to know whether a vaccine works so that we can decide whether to use it. Or we may seek basic insights about viruses so that we can develop vaccines in the first place.

Sometimes, we want to know things because we are simply curious. It is hard to justify a lot of astronomy (for example) on the basis of its practical implications. But we want to understand the universe.

And sometimes, we want to understand people–and perhaps animals–because we are in relationships with them. When you ask friends how they’re doing, your motive may not be to solve a problem, nor mere curiosity, but care. You should give your friend your attention. The benefits are psychological, ethical, or spiritual–a change in one’s mind and in the relationship with the other person.

Friends may tell you things that you should believe for practical reasons or to satisfy pure curiosity. Someone might tell you that a vaccine works, so that you will take it, or that all the planets in our solar system could fit between the earth and the moon, because that’s kind of interesting to know. Paying attention means taking such claims seriously. But the main point of learning what other people think is not to find out what is objectively true; it is to know the other people. In fact, exploring the beliefs and values of a wide range of people can shake our confidence in beliefs, in general, and hence our feelings that we can and must get our own beliefs right.

Attending to a text or an artifact from a distant time or place is a little different from an ordinary conversation. For one thing, you cannot directly benefit long-dead or faraway authors by giving them your attention. Although we have ethical obligations to the dead, the influence is basically one-way. However, reading, listening to music, and viewing art are similar to regular conversations in important ways. They too are practices that develop compassion and reduce our attachment to our own prejudices and concerns.

While Michel de Montaigne’s married friend Diane de Foix was expecting a child, he sent her advice about education. He recommended foreign travel and conversation with peers. A youth should listen and appreciate, he said, not try to form and share beliefs.

And so should adults. Montaigne told de Foix, “In this school of human interaction, I have often observed this vice: instead of getting to know others, we only strive to give ourselves and are more concerned with using our own goods than with acquiring new ones. Silence and modesty are qualities very well suited to conversation.” A passage in the same letter could stand as a justification of Montaigne’s whole way of life:

This vast world, which some think is just one species in a larger genus, is the mirror in which we must look to truly know ourselves. In short, I want [our world] to be my student’s book. The variety of moods, sects, judgments, opinions, laws, and customs teach us to judge our own people soundly and teach our judgment to recognize its imperfection and natural weakness: which is no small apprenticeship.

If the purpose of the humanities–the disciplines that interpret texts and artifacts–is not to determine truth but to attend to other people, then we cannot outsource this thinking to machines or even to other human beings. The point is the experience, not the outcome.

Here is a complication: professional scholars in the humanities do pursue the answers to questions. At one extreme, they may seem much like scientists when they establish the date and provenance of a painting or correct a primary text. At the opposite extreme, they may offer highly creative or even counterintuitive interpretations, but usually they still claim to be telling us something valid about an object in the world. We can ask whether they are right or wrong, persuasive or unconvincing.

I think scholarship is very valuable, but it ultimately contributes to the humanities as experience. The point of watching or reading Shakespeare is to get out of one’s own head by attending to the author and his characters. The point of philological, historical, or interpretive scholarship about Shakespeare is to enrich performances and readings of the works.

Some of the scholarly labor can be assigned to machines. Because I cannot read Pali but I am interested in classical Indian philosophy, I have been very carefully using ClaudeAI to give me dictionary-type definitions of all the Pali words in select passages. This is an algorithmic task. Claude is probably using published Pali-English dictionaries, plus previous translations made by people who used the same dictionaries. A dictionary is also an algorithmic device, a kind of machine that generates a range of words in one language for each word in the other language. I believe that the first dual-language dictionary (Sumerian-Akkadian) was written more than 4,000 years ago.

Thus there is nothing fundamentally new about creating devices that automatically assist readers, listeners, and viewers of human artifacts. In addition to lexicons and dictionaries, we might mention grammars, concordances, indices, card catalogues, provenance lists for artworks, search functions for digital texts, and many other scholarly resources. LLMs can often do these things better.

The difference is that we were never tempted to view the tools as ends in themselves. They were meant to assist a reader in a practice that would enrich that person’s mind. Because the LLM’s speak in the first-person and purport to interpret and explain texts, it is tempting–and I feel this temptation–to imagine that they can do our reading for us. To use a simile that is becoming a cliché, that would be like getting a machine to lift barbells to save us the effort. This would not count as exercise.

Source: Montaigne, vol. 1, essay :26 (“Of the instruction of children”). I follow Screech in interpreting “que les uns multiplient encore comme especes soubs un genre” to mean that our world is one species in a greater genre of worlds.

See also: the worlds we can lose when intelligence becomes artificial; the difference between human and artificial intelligence: relationships; the design choice to make ChatGPT sound like a human; against using the humanities instrumentally; Bernard Williams on truth as a virtue of the humanities

the worlds we can lose when intelligence becomes artificial

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In 1958, Hannah Arendt could see where were were headed:

This future man, whom the scientists tell us they will produce in no more than a hundred years, seems to be possessed by a rebellion against human existence as it has been given, a free gift from nowhere (secularly speaking), which he wishes to exchange, as it were, for something he has made himself. …

It would be as though our brain, which constitutes the physical, material condition of our thoughts, were unable to follow what we do, so that from now on we would indeed need artificial machines to do our thinking and speaking. If it should turn out to be true that knowledge (in the modern sense of know-how) and thought have parted company tor good, then we would indeed become the helpless slaves, not so much of our machines as of our know-how, thoughtless creatures at the mercy of every gadget which is technically possible, no matter how murderous it is. (Hannah Arendt, The Human Condition, 1958, p. 3)

What is “human existence as it has been given”?

For most of our history, most human beings have lived with other people whose names they know. They have worked individually and collaboratively with materials in their context to make an environment that I will call a “world.”

A world has these features:

  • It is imbued with moral significance, because other people have made it, given it meaning, cared about it, and been affected by it. An individual cannot interact with a world without causing good or harm to other people.
  • It is real, not imaginary, and therefore it is stubborn. It rarely turns out the way we want, but we can learn from experience to work more effectively with it.
  • The other people involved in any world hold partially conflicting interests and goals and can be stubborn in their own way. Both the materials and the people resist any single will.
  • Each person has partial and even biased knowledge, beliefs, and feelings about the world. But their varied ideas can accumulate as they express them and record them. Each person can therefore explore not only a world but the accumulated human experience of that world.
  • Because we must act in the company of other people and learn by acting, our “thinking and speaking” are closely connected.
  • Because our deepest concerns (moral, spiritual, and otherwise) relate to the world that we shape with our minds and hands, our “thought” is also connected to our “know-how.”
  • Each world typically predates each human being and survives the person’s death, yet each person can affect it. In fact, the birth of any human being automatically changes the world, if for no other reason than a birth turns people into parents, siblings, and other kinds of relatives.
  • There is not one world but many human worlds. But worlds can interact to various degrees without becoming subsumed into one bigger world.

Why it is good to live in a world

It is not obvious that living in this kind of world is the best imaginable form of life. Most people have envisioned heaven or a political utopia differently. (For instance, in an ideal world, the other people usually become less stubborn!) But I could make three arguments in favor of living in a world like this.

First, it seems plausible that homo sapiens evolved for such a life. Our brains, senses, and bodies are equipped to navigate it.

For instance, newborn infants already recognize faces, which are designed to communicate information and emotions. And our languages and cultures have accumulated deep resources for sharing a world with finite other human beings. The Proto-Indo-European language already used first-, second-, and third-person verbs and indicative, imperative, and subjunctive moods to make distinctions that are useful for group discussions about a common world. Thus a world is arguably our habitat.

Second, the combination of agency and humility seems morally compelling. It is fitting that we can affect our environment but not do just anything we individually want with it. And we should see our context as imbued with moral significance.

Third, navigating a world is a way for creatures like us to achieve comprehension, to make sense of matters. As Arendt writes:

There may be truths beyond speech, and they may be of great relevance to man in the
singular, that is, to man in so far as he is not a political being, whatever else he may be. Men in the plural, that is, men in so far as they live and move and act in this world, can experience meaningfulness only because they can talk with and make sense to each other and to themselves (1958, p. 4).

Threats to human worlds

Each human world has always been fragile, subject to destruction if invaders arrive, a plague strikes, or the community breaks down.

In addition, tyrants threaten any shared world because they can turn individuals into means to their solo ends.

Mass society puts each world at risk by bringing us into relationships with millions of others, whose names we will never learn. And mass economic exploitation makes matters worse. In Origins of Totalitarianism, Arendt says, “loneliness, on the experience of not belonging to the world at all, … is among the most radical and desperate experiences of man. [It is] is closely connected with uprootedness and superfluousness which have been the curse of modern masses since the beginning of the industrial revolution and have become acute with the rise of imperialism at the end of the last century and the break-down of political institutions and social traditions in our own time.”

When history seems to move quickly and beyond anyone’s control, humans cease to feel that they are agents in any recognizable world.

Ideology can be defined as any system of thought that substitutes core assumptions for actual engagement with other people in a common world.

Finally, although media can enrich any given world, it can also disrupt it. Imagine people sitting alone or in passive company before a TV screen that tells them about gruesome crimes. Their actual world may be safe, or less dangerous than it was in the past, but the mediated world is cruel.

New threats in the age of AI

This theoretical framework comes from Arendt, who drew on Heidegger’s fundamental insight that the human form of being (Dasein) is always “‘in’ the world in the sense that it deals with entities encountered within-the-world, and does so concernfully and with familiarity” (Being and Time, H105, trans. by Macquarrie & Robinson). Arendt makes Heidegger’s theory political and republican by emphasizing that people can talk and decide what to do with their worlds.

I have sketched this view to help make sense of a new phenomenon: intelligence that is artificial (AI). But Arendt already feared that we might “need artificial machines to do our thinking and speaking.”

When a person expresses a view, the content of what they say helps us to understand the world that the person inhabits. Even when people are flat-out wrong, the fact that they err or lie is part of our reality. In addition, a human view comes from a creature that can suffer. As such, it makes a claim on our compassion. In short, we attend not only to the content of the statement but also to the person who expressed it.

In contrast, when a large language model (LLM) answers a query (typically in the first-person singular and with emotive language like “I will be glad to …”), it does not reflect any particular perspective, nor does it come from a body that is capable of suffering. It just pretends to be a fellow participant in our world. We can attend to the words but not to the speaker.

Walter Cronkite was not really a visitor to Americans’ living rooms in 1970. He just appeared on TV screens. But he was a real person who could be assessed as such. An LLM is qualitatively different.

An LLM can be just another tool or resource, like a Heidegger’s hammer or perhaps like a library. I have collaborated with teams of Tufts engineering students to build the Civic Helpdesk and other applications of AI that are not yet publicly available. Working with them to fine-tune instructions or to design a user interface feels very much like collaborative work in a shared world. Note that I naturally said we “built” these tools, because the work feels roughly like building a shed, or perhaps an organization.

I have also developed what I think is a fairly tight practice of asking Claude about the Sanskrit and Pali original words in texts that I can only read in translation. This feels like a modest expansion of my inner life, if not a contribution to any shared world. (By the way, Claude is probably pulling these definitions from a finite set of published lexicons that have human authors.)

On the other hand, as Pope Leo notes in Magnifica humanitas, “current AI systems are more ‘cultivated’ than ‘built,’ for developers do not directly design every detail, but instead create a framework within which the intelligence ‘grows.’ As a result, fundamental scientific aspects — such as the internal representations and computational processes of these systems — remain, at present, unknown.” This sounds more like Arendt’s nightmare of a time when our thoughts cannot grasp what we have done.

The deepest concern is that we have developed biologically and culturally to flourish in what Arendt would call a world, but an individual who uses AI is no longer there.

See also: the papal encyclical on AI; Reading Arendt in Palo Alto; the human coordination involved in AI; the difference between human and artificial intelligence: relationships; the design choice to make ChatGPT sound like a human; and love of the world