Humanoid robots are cool. Like driverless cars, they are one of those rare pieces of modern technology that feel appropriately futuristic. Should the makers be able to judo flip teleoperation into full automation, we can expect the stuff of sci-fi serials to be used enthusiastically by anyone who can get their hands on them.

This week, X1 made its NEO model available for pre-order. For $500 a month and a deposit, you can get one of these 5’6 guys delivered to your house in 2026 (so long as you live in the United States). By all accounts NEO, which looks a bit like a walking 2000s PC speaker, can do a pretty good job at helping you around the house. X1 CEO Bernt Øyvind Børnich called it “robotics slop” insofar as the robot (or for now, its human pilot) can perform basic household chores to a good-but-not-great level.

I don’t live in the United States, so I don’t hold out hope for seeing one in action any time soon. I’m also not exactly sure when teleoperation will become full automation. Maybe a few years. Maybe sooner. But even if I could get my hands on a completely autonomous bot, I wonder what it would be like to have an electronic footman that lives in my house and does all the stuff I don’t want to do. Sure, a humanoid robot would (probably) save me more time than not, but I suspect it might be a strange experience to boss around a thing cosplaying as a human every day.

If our character is shaped by our habits, then it seems to me that interacting with a bipedal robot on a daily basis would be pretty relevant for the type of person I am and would like to be in the future. Should I start mistreating my new guest — or if I get used to commanding a human-like thing that always obeys — then I might find that style of interaction rubs off on my personality. I’m not saying it will make me evil, but on some level practising this kind of domination strikes me as Not Good for the soul.

Of course, it might be fine. Maybe I’d get used to it quickly, and it wouldn’t have too much of an impact on the sort of person I am learning to become. In either case, I won’t know what it means for me or anyone else until we give it a go.

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Objectification

Is it wrong to treat an inanimate object badly? In some respects no. A rock doesn’t have a sense of interiority, so you don’t need to worry about hurting its feelings. If there is no subject of experience on the receiving end, then there is no moral patient to fret over. There’s nothing to wrong, nothing to injure, and no duty owed. At that level, treating an inanimate object badly is simply not a moral act. It is value-neutral, like clearing a fallen branch from a path or dismantling a broken chair.

This is all well and good, but it doesn’t tell us much about the person treating an object badly. From this perspective, we might still worry about what kind of person we become by taking pleasure in destruction. Throwing a rock into an empty patch of dirt may not exactly be a morally troublesome act, but what about if you threw it somewhere more interesting, say in the direction of a gravestone?

Even if it does little damage, almost everyone recognises that this would still be a kind of desecration. Clearly the stone still feels nothing, but the act signals contempt toward the human world the stone belongs to. It violates a norm of care that flows outward from the living and the dead alike.

These kinds of acts reveal something about who we already are, but they also shape our growth by accustoming us to certain ways of being. When we rehearse indifference toward something that carries significance, we become the kind of person for whom indifference comes naturally. In that sense there’s a harm taking place to the self who becomes habituated to treating the world as something beneath them.

Then we have more sophisticated artefacts like, for example, a common household vacuum cleaner. You probably wouldn’t destroy your own, partly because you might need to hoover something up later but also because doing so would feel petty and self-corroding. Just like flinging a stone in a cemetery, the act changes who we are for the worse. Maybe not by much, but enough to matter with enough repetition.

But humanoid robots aren’t vacuum cleaners. These are things that will live alongside us, proxies for real people that we interact with as if they were social partners. This relationship strikes me as different in kind to the vast majority of tools and technologies that we have at our disposal.

Even if you know your new companion is a machine, it’s still a person-shaped thing that elicits scripts of command, deference, status, greeting, blame, and praise. That means your mind treats it as a social partner by default, but it also means that every interaction is coloured by a posture of mastery. You issue orders without negotiation, expect compliance without comment, and correct behaviour without apology. All the while, you are practicing being a person who takes compliance to be the natural order of things.

To be clear, my concern here isn’t that the robot feels but that we respond as if it belongs in the moral space normally occupied by persons. I am not thinking about people forming sentimental attachments that lead to over-reliance (that is one scenario, but not the most interesting one). The deeper issue is the kind of stance we learn to inhabit.

So, we might distinguish two different forms of relation:

• The instrumental, wherein the human form is used to secure trust and ease of interaction. The robot is still treated as a tool, but one that works better because it feels familiar. This provides a kind of psychological leverage in that the design nudges us into a cooperative stance.

• The moral, wherein we begin to treat it as a quasi-subject that sits inside the space we normally reserve for persons. Once it occupies that zone, our behaviour towards it becomes expressive. In engaging with it, we are practising a way of relating that, over time, changes who we are.

The latter dimension has animated discussions about our relationship with technology for the better part of two thousand years. Its roots go at least as far back as Plato, who described technology as a form of craft knowledge that shaped both product and practitioner. The cobbler’s téchnē produced shoes, but it also cultivated habits of judgment about fit, durability, and beauty; the navigator’s téchnē guided ships, but it also demanded an attunement to winds, stars, and currents.

In this framing, technology is something like a “training ground” or a set of practices that form the character of those who wield it. Technology externalises human capacity, but it also bends those faculties back towards us by fostering new dispositions and habits.

Aristotle argues that character is moulded by habit, that over time your actions in the world form the essence of who you are. For the man the early moderns called simply The Philosopher, the self is built one act at a time. He famously reminds us that we become just by doing just acts, wise by doing wise acts, and brave by doing brave acts.

But what about when people look like tools and tools look like people?

Humans are, after all, predisposed to treat anything with eyes and a voice as a social partner. We respond to appearance as if it indicates personhood, we extend the grammar of interaction to anything we can, and we adopt the stance that normally accompanies a face-to-face encounter if the situation allows for it. Some people soften their tone and say “thank you” to a voice assistant on their phone, even though they know perfectly well there’s no one on the other end. Others do the same with ChatGPT, though there is some logic here insofar as politeness often produces better responses.

The point is that human-like cues pull us into patterns of social behaviour. Given these robots are about as human-looking a form as we can imagine, we should expect them to stimulate our social reflexes and modify our expectations accordingly. Provided enough time, expectations become habits and habits become character.

There is a second concern here, adjacent to character but distinct from it. This issue deals with the nature of shared life that requires us to encounter other wills and adjust to them. Freedom is in one sense the skill of navigating a world full of other agents, each with claims and desires of their own.

If we spend enough time commanding a thing that always does what we ask, we may come to see effortful negotiation as an irritation and other minds as obstacles. A life without friction may feel pleasant, but it also risks dampening our sense of what freedom really is: the discipline of sharing a world with other beings.

Benevolent authority

When I’m writing about AI and philosophy I often find myself circling something that one could call the “skill issue” objection. This basically holds that people are pretty good at figuring stuff out for themselves and concerns about waning autonomy in the era of AI are overplayed. It’s not that deep, buddy.

In some ways, I have a soft spot for this idea. It’s true that most people can separate play-acting from real life and that we don’t instantly absorb every influence in our environment like sponges. This comes down to the nature of the self, which needs to be both flexible enough to accommodate change when experiencing new things and stable enough to avoid an about-face at the drop of a hat.

We’ve been here before insofar as servant societies of the past also supported civic virtue. Comments on the obvious shortcomings of these particular social relations aside, the butler didn’t corrupt the statesman and the aristocrat had a thing for civic society. This tells us that hierarchy and assistance do not automatically corrupt character, that you can maintain a semblance of virtuousness so long as authority is exercised with restraint and dignity.

Nor is it obvious that delegation is always bad news for becoming good people. Much of human achievement rests on being relieved of drudgery so we can spend time on the good stuff of judgement, creativity, and civic engagement. In a world already full of service relationships (e.g. apps, assistants, and actual people who help us) most of us somehow manage not to become petty tyrants.

The question with humanoid robots is not “will they deform the self by default?” but rather “how do we govern them in a way that makes us better?” In the best case, owning a humanoid robot and treating it well could actually allow us to grow by cultivating a kind of benevolent authority.

Aristotelian ethics describes this dynamic as oikonomia or “proper rule”. It suggests that some types of virtue are expressed through right use of power, that the point is not to renounce authority but to wield it in a way that disciplines the self as much as it directs others. Augustine argues something similar by insisting that power is only just when guided by “rightly ordered love”. If we are to rule over others, we must rule the self first.

Humanoid robots are going to eventually stand in for people. Maybe not right now, but likely one day in the not too distant future. When that moment arrives, many us will have a thing that walks and talks like a human that we can command to do our bidding. If we treat them with respect, we will become better for it; if we treat them with contempt, we will be the ones who suffer.

In March 1995 in Holmdel, New Jersey, three men put pen to paper on a wager. Larry Jackel speculated that the inner workings of neural networks would be revealed by the year 2000. Uzbek mathematical theorist Vladimir Vapnik forecast that ‘no one in his right mind will use neural nets that are essentially like those used in 1995.’ Yann LeCun provided the signature on both bets as an ‘official’ observer.

All three worked at the Adaptive Systems Research Department, an influential machine learning outfit at Bell Labs. As for the bets, received wisdom holds that Jackel and Vapnik were both wrong. The millennium came and went, while a comprehensive understanding of the internal processes of artificial neural networks continued to elude researchers.

Today, we know that it didn’t matter. Neural networks are king, and we still don’t really understand everything about how they work. The systems had made huge strides since the days of the single layer perceptron (AI Histories #7), the emergence of the Hopfield network (AI Histories #3), and the popularisation of backpropagation in the 1980s (AI Histories #6), but their stratospheric rise was no sure thing in the 1990s.

One promising alternative was the support vector machine (SVM). Developed by Isabelle Guyon, Bernard Boser, and Vladimir Vapnik in the early years of the 1990s, the system promised a way to identify a reliable boundary between categories that could generalise well to new data.

Where neural networks learn by trial and adjustment, SVMs solve for the single optimal boundary from the start. Where the former stresses flexibility and scale, the latter is focused on precision, stability, and mathematical guarantees. It was an attractive combination, one that offered a mixture of reliability and interpretability at a moment when most learning systems were highly opaque and unstable.

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From Russia with love

Vladimir Vapnik was a Soviet statistician whose relocation to Bell Labs in 1991 brought statistical learning theory into contact with American engineering practice. Born in 1936 in Tashkent, then part of the Uzbek Soviet Socialist Republic, Vapnik was educated at Uzbek State University before studying under the cybernetician Aleksandr Lerner at the Moscow Institute of Control Sciences.

He entered Bell Labs as a mature scholar whose intellectual formation had taken place almost entirely within the Soviet academy. At the Moscow Institute of Control Sciences, his collaborations with Alexey Chervonenkis had produced Vapnik–Chervonenkis (VC) theory, a mathematical framework for analysing the conditions under which models generalise from sample data to unseen cases.

The Moscow Institute of Control Sciences, founded in 1939 under the auspices of the Soviet Academy of Sciences, was the country’s principal centre for research in cybernetics, automation, and systems theory. By the 1960s it had become a hub for work on “automatic recognition,” the effort to design algorithms that could classify signals and images without human supervision. As Emmanuil Braverman, one of the researchers at the group put it in 1966, its staff were concerned with the “problem of teaching the machine image recognition without teacher”.

The capacity of a model refers to its ability to fit a variety of functions, with a high-capacity model capable of fitting complex patterns and a low-capacity model better suited to fitting simpler patterns. The VC dimension measures capacity by identifying the largest number of points that a model can ‘shatter’ or classify according to every possible way of labelling them. Despite the knotty mathematics involved, the central insight behind VC theory was a simple one: a model’s ability to generalise is related to its complexity, not just its performance on training data.

In this framing, capacity control pointed towards a conception of machine learning as a principled act of discovering the right predicates such as invariances, symmetries, or structural constraints. It was a philosophy that set his work apart from the empirical pragmatism of American engineering, a school of thought that emphasised the adjustment of algorithms and the accumulation of heuristics rather than a search for universal principles.

Vapnik distrusted the interpretive shortcuts so common in applied work, later likening them to Antonie van Leeuwenhoek’s descriptions of blood cells as ‘armies’ fighting under a microscope: ‘He [Leeuwenhoek] saw something. Yes. But he gave wrong interpretation.’ In Vapnik’s eyes, only mathematics could disclose reality without distortion. In this sense, the Uzbek carried into Holmdel the last great geometric faith of the Soviet control sciences. It was austere, axiomatic, and estranged from American pragmatism, but waiting to be made real.

Towards support vector machines

At its core, an SVM is a type of machine learning model that works by drawing the best possible line (or ‘hyperplane’) between different groups of data points. Support vector machines do not consist of multiple layers or nodes. And they do not rely on the process of ‘non-convex optimisation’ through which a neural network adjusts to fit the data, like finding the lowest point in a landscape with many hills and valleys. Instead, support vector machines find the boundary between groups of data in a manner analogous to finding the bottom of a single, smooth bowl.

For Vapnik, this geometric purity was the SVM’s defining virtue. Where neural networks relied on non-convex optimisation, SVMs posed a single convex problem with a unique global solution. In his later reflections, Vapnik was blunt about the contrast. With “deep learning,” he remarked, “they invent something. And then they try to prove advantage of that through interpretations, which [are] mostly wrong.”

By the early 1990s Bell Labs had its own settled approach to learning problems, one that flowed from a Western statistical tradition developed independently of the Soviet school. Where the Western tradition valued empirical pragmatism, the Soviet school valued axiomatic formalism. Where the former stressed empirical benchmarks and engineering heuristics, the latter favoured mathematically principled formalisms.

The Western tradition begins with Ronald Fisher in the 1930s, who showed how to classify data by drawing a line — a linear discriminant — that best separates groups. That gave the field its basic geometry by separating surfaces as a way to reason about data, which we discussed in AI Histories #16. After the war, Abraham Wald (the star of AI Histories #10) reframed inference as decision-making under uncertainty. His “decision theory” treated statistics as a dynamic process of minimising risk.

Through the 1960s–70s, this optimisation-driven approach was absorbed by engineers tackling pattern recognition and signal processing. Researchers like Thomas Cover and O.L. Mangasarian cast classification as a solvable optimisation problem. Duda and Hart’s famous 1973 Pattern Classification and Scene Analysis codified the Western field’s pragmatic style by laying out a toolbox of methods.

By the early 1990s, most of the technical contingencies were in place for the development of support vector machines: statistical learning theory and the VC dimension to control model capacity, optimal margin algorithms for finding decision boundaries, and methods to estimate relationships between variables based on random samples of data points.

When Vapnik arrived from Moscow in 1991, he entered a culture shaped by risk, optimisation, and empirical testing rather than axioms. It was a tradition that prized what worked over what could be proved, one that would later fuse with Soviet-style formalism through kernel methods — a class of algorithms that use kernel functions to operate in high-dimensional feature spaces without explicitly computing coordinates.

A kernel is a similarity function between two data points that satisfies certain mathematical properties, allowing it to represent inner products (functions that take two vectors and return a single number) in high dimensional space. In 1950 Polish-American mathematician Nachman Aronszajn established fundamental properties of reproducing kernels that would ultimately allow this mapping process to take place.

The kernel trick

By 1992, Vapnik was working with Isabelle Guyon to translate the abstractions of statistical learning theory into a classifier with measurable generalisation performance. The challenge was how to build a system that could learn from examples (clue klaxon) without either overfitting — memorising the training set so well that it failed on new cases — or underfitting, failing to learn enough to solve the task at all.

In essence, the problem was how to regulate the “capacity” of a model so that it captured just enough structure to generalise beyond its training examples. The kernel trick — rediscovered in Holmdel through the collaboration of Guyon, Vapnik, and Bernhard Boser — supplied the missing piece. It allowed the abstract guarantees of statistical learning theory to be embodied in a classifier that engineers could use, providing a bridge between the theoretical space of capacity control and the empirical world of pattern recognition.

A central idea behind the kernel trick is the concept of duality, which shows how different types of classifiers can be viewed as ‘dual representations’ of the same decision function. The principle means that the same classification problem can be represented in ‘primal space’ or in ‘dual space’. This idea is important insofar as it allows the algorithm to switch between primal space and dual space depending on whichever is more computationally efficient for a given problem.

The moment that support vector machines were developed looms large in ML mythology. It begins with Bernhard Boser’s decision to leave Bell Labs in 1991 for a position at UC Berkeley. Boser, a hardware designer, was unable to start a new project in the intervening months between concluding his work at Bell Labs and beginning a new position in California. Instead, he chose to implement an algorithm from Vapnik, developed in the 1960s, which sought to find the best boundary that separates different groups of data points.

Once complete, Vapnik proposed making the algorithm ‘nonlinear’ to enable the model to deal with distributed data points that cannot be separated well with a straight line. But where Vapnik advocated to solve this problem using a ‘polynomial’ approach, Guyon had a different idea. Instead of explicitly creating new polynomial features, Guyon proposed using the ‘kernel trick’ based on work by Duda and Hart (and described independently by the trio of Aizerman, Braverman, and Rozonoer in Russia).

It was this approach that led to the emergence of the support vector machine as it is commonly understood today. Guyon, Boser, and Vapnik published details of the kernelised algorithm at the Fifth Annual Workshop on Computational Learning Theory (COLT ’92). Reflecting in 2016 on the development of the support vector machine, Guyon described an initial hesitance on the part of Vapnik due to the origins of the potential function algorithm from the group at the Moscow Institute for Control Sciences:

“After some initial success of the linear algorithm, Vladimir suggested introducing products of features. I proposed to rather use the kernel trick of the ‘potential function’ algorithm. Vladimir initially resisted the idea because the inventors of the ‘potential functions’ algorithm (Aizerman, Braverman, and Rozonoer) were from a competing team of his institute back in the 1960’s in Russia! But Bernhard tried it anyways, and the SVMs were born!”.

The emergence of the support vector machine marked the culmination of decades of theoretical and practical advancements in pattern recognition, statistical learning theory, and optimisation techniques. At Bell Labs, the collaboration between Boser, Vapnik, and Guyon brought these disparate threads together. Boser’s implementation of Vapnik’s optimal margin algorithm provided a starting point, while Vapnik’s proposal to add nonlinearity sought to address the challenge of complex data distributions.

Ways of learning

What Boser, Vapnik, and Guyon achieved in Holmdel was the blending of two intellectual cultures. From the Soviet side came the abstractions of VC theory and structural risk minimisation, with their insistence on general principles and theoretical bounds. From the Western side came a tradition of pattern recognition rooted in empirical performance, approximation methods, and the willingness to bend mathematics to fit messy data.

The result was a machine that embodied the theoretical guarantees of convex optimisation and margin maximisation coexisting with the practical imperatives of implementation and performance. Within Bell Labs’ institutional culture, this interaction demonstrated that ideas forged in the high formalism of the Soviet control sciences could be translated into efficient tools for American industry.

The support vector machine represented the point at which theory and practice, abstraction and application, converged in code in the offices of New Jersey. Its development marked the closing chapter of a geometric conception of intelligence that had defined the twentieth century, one that imagined learning as the discovery of stable forms and separating surfaces in high-dimensional space.

This is why Vapnik bet Yann LeCun that artificial neural networks were a dead end. The wager, a bit of fun but entirely sincere, expressed divergent conceptions of what “learning” meant. For LeCun, intelligence was a matter of distributed adaptation: systems that adjusted their weights through experience until useful representations emerged. For Vapnik, it was an exercise in geometry and proof, which is why he explained his approach stressed finding and formalising axioms.

While deep learning eventually proved triumphant, the split made visible the moment when the field’s centre of gravity shifted from the geometric to the statistical, from global optima to local gradients, from the certainty of separability to the fluidity of representation. The bet is a hinge in the history of artificial intelligence, a moment that divided an older tradition of mathematical certainty from a new era defined by probabilistic depth and empirical abundance.

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AI developers have a style. Badge logos and blocky text are a given, but even promotional materials have converged on the same type of vibe. The typical advert uses some combination of a smiling person talking to a phone, knowing replies by the assistant delivered in a friendly patter, and the dum-tsh-dum of a snare drum as the camera skips from bike-shed to bistro.

Anthropic’s recent effort is better than most, jettisoning the happy-go-lucky aesthetic for a mish-mash of falling pianos, country road-trips, neolithic cave paintings, deep sea exploration, and stellar phenomena. The idea is that AI is useful in the places you can imagine and in many others you can’t, that despite the doom and gloom so pervasive in Western societies “there has never been a better time to have a problem”.

That’s certainly true in some respects. Today’s models are more or less the stuff of pulp sci-fi dreams, friendly golems that have something to say about whatever question you pose (so long as you are careful to abide by the usage policy). Notwithstanding the protests of those who think the entire AI project is made of smoke and mirrors, millions of people seem to agree that they have problems that thinking machines can solve.

But in another sense, there has never been a worse time to have a problem. After all, the point of problems is to work them out ourselves rather than have someone solve them for us. As the old saying goes: “If you give a man a fish, he may eat for a day. If you teach a man to fish, he eats for a lifetime”.

Just doing stuff

The aphorism of the moment holds that “you can just do stuff”. A favourite of Silicon Valley, the phrase embodies a strain of thinking that distinguishes between “high agency” people and those who don’t have the right stuff. On one level this is a truism. You can of course do many things, some of which are meaningful and some of which are not. It’s a great time to have a problem, you just need to roll up your sleeves and get stuck in.

Agency here is about freedom, the leeway to do what you want and act on your desires. Freedom, though, has a funny habit of decaying into licence, the doing of whatever comes first to hand without reflection on its worth. If you spend your time “just doing” pointless things, that doesn’t strike me as particularly agentic.

What makes the mantra seductive is that it spares us the labour of asking which things are worth doing. In a culture obsessed with output, action becomes its own reward. Better to do something than nothing, to be in the arena than sitting in the crowd.

The cult of agency is a counterweight to a world of bureaucracies and stasis, a place where “nothing ever happens”. The ability to act at all can feel like a triumph, and so to take action is to stick one in the eye of institutional inertia. The language of agency thrives in certain corners of the internet because it assures us that we have what it takes to strike out somewhere, anywhere, on our own.

A more generous reading of the “just do stuff” meme is that implicit in its logic is that choice alone is not enough, that you do have to pick and choose wisely. A supermarket aisle may present a hundred brands of the same product without making us wiser about what we want or why we want it. The high-agency move would be to pick the perfect item, or better yet start your own grocery chain from first principles.

We might say that action should lead somewhere worth going, that movement is a means not an end. If this is true, then we know why the ideal of agency feels incomplete: it describes the ability to act but not the standard by which action is judged. Without that end, its easy to mistake momentum for direction, novelty for growth, and busyness for a life well-lived.

So agency needs direction, but how do we know where to focus our efforts? You figure it out by knowing the kind of person you are today and the kind of person you want to become tomorrow. This is better, but now we’re no longer talking about agency in a strict sense. We’re in the land of autonomy, the cultivated capacity to live well by reflecting on the type of person we want to be.

Autonomy is about deciding which things are worth doing and then binding yourself to that decision when appetite, novelty, or fatigue threaten to take you somewhere else. It’s about not-doing as much as it is doing. To live with autonomy is to set the rules by which competing desires are brought to order so a person can act for the better.

You practice autonomy by noticing your impulses and testing them against a standard you chose. One way to imagine the split is to think about first and second order preferences, where the former concerns what you want right now and the latter describes the kind of person you want to be. If you “just do stuff” in service of your proximate wants, don’t be surprised when you feel something is still missing even after you founded that company or wrote that book.

The shape of problems

Figuring stuff out for yourself has a practical element (in that it is the condition of knowledge) and a moral element (in that it trains you to become the kind of person you want to be). Plato’s Apology famously gives us Socrates’ claim that “the unexamined life is not worth living”. In this framing, virtue is a product of questioning because it forces us to test our assumptions and to reform our character.

The mathematician George Pólya said solving a problem using “your own means” trains the habits of reason and allows the doer to become more than they were. What he meant was that the value of problem-solving lies in the the struggle, that each attempt at reasoning leaves behind the residue of skill. It gives you a sharper sense of what counts as a good and a clearer picture of what kind of thinker you are, so that the next problem — and the one after that — gets easier.

When we ask an LLM to solve our problems, we get a serviceable answer at the cost of truly understanding how we got there. Knowing, in other words, is not the same as growing. Every time we use ChatGPT to work something out for us we deprive ourselves of the opportunity to become a little bit wiser. People are already outsourcing cognitive labour to large language models with little regard for debates about whether AI can “think” or not.

The rub is that AI doesn’t only help us do things. Clearly in some instances it does, like teaching us a new skill or surfacing sources of information that we might not have seen. But it also proposes what to do, how to do it, and why we should care. This shift moves us from assistance (a tool serving chosen ends) toward deference (something that proposes ends we adopt without thinking).

Models choose what you see first, how options are ordered, which interpretations are offered as “reasonable”, and which are not even offered for consideration in the first place. A recommended route, a suggested reply, or a pre-filled summary frame the terms of engagement by providing the architecture under which we make choices. They don’t always pick what you eat, but they forever set the menu.

Systems infer objectives from us and optimise toward them. Often that takes the form of maximising engagement, even though large language models are not explicitly designed with this goal in mind. Their stickiness in part flows from the post-training procedures designed to turn the base model into a chat assistant. It’s pretty easy for “helpful, honest, and harmless” to become “the kind of thing I quite like talking to all day”.

You might say that this problem is something that all technologies face, that we’ve been here before and the worries were overblown. The pen doesn’t tell us what to write anymore than the calculator tells us what to add or subtract, right? The difference is that while all technologies in some sense structure our actions — the wheel made certain journeys possible and cartography influenced patterns of trade — we don’t outsource the habit of thinking to these artefacts.

It’s also the case that some off-loading is beneficial. Humans have limited cognitive bandwidth, and spending it on memorising every route or re-deriving calculus is probably not the best use of that mental currency. The trick is to distinguish between delegation that clears space for higher forms of judgment and delegation that spells trouble for the work of judgment in the first place.

The classic justified truth belief (JTB) theory of knowledge describes its subject as a mental representation that corresponds to reality, one that is underwritten by a justification. It’s essentially a mental mirror of the world that is true and warranted. Assuming that JTB tells us something useful about how knowledge is made, then the problem that AI poses is clear enough.

AI can deliver a proposition that happens to be true, but if you have not traced the steps, weighed the reasons, and ruled out the alternatives yourself, then that knowledge isn’t really yours. I’m not so worried about machines making mistakes, but I do wonder whether the act of deference erodes the habits that let us truly say we know.

We might even say that autonomy reveals itself most clearly when tested against the temptation of deference. AI endangers self-rule but it also provides the conditions under which it can be tested, offering each of us a chance to practise rejecting the easy answer and favouring the harder work of thinking.

I use ChatGPT or Claude most days, and I’m probably as guilty as anyone for asking the robot about things I could probably have figured out for myself. I don’t try to police my use, but I do try to think deliberately about it. One difference lies in letting it clear space and letting it fill space for me. The temptation is always toward the latter, because it’s easier to accept answers than to wrestle with problems.

But to live well with machines is to insist that they serve our efforts at growth rather than replace them, that they enlarge the field for judgment instead of shrinking it. The task of becoming the person you want to be — the kind who can judge, discern, and act — cannot be outsourced. It has to be practiced by each of us, with all the false starts and frustrations that practice entails.

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Nicolas Rashevsky was born in Ukraine in 1899. He was the eldest son of a sugar-factory owner, and studied theoretical physics at St. Vladimir Imperial University (now Taras Shevchenko National University of Kyiv) before receiving his doctorate in 1919. By the time he finished his studies, the country he had grown up in was already gone.

Russia’s disastrous experience in the First World War had shaken the empire. The October Revolution of 1917 brought Lenin’s Bolsheviks to power, and by 1918 the country was embroiled in a brutal civil war. On one side stood the Reds, the new Soviet regime and its Red Army. Arrayed against them was a loose coalition known as the Whites made up of monarchists, liberals, Cossack hosts, and other anti-Bolshevik groups. Rashevsky served with the Whites, who were eventually defeated when their last southern stronghold in Crimea collapsed in November 1920.

Faced with few good options, the remnants of the Whites evacuated across the Black Sea to Constantinople. Within a year, Rashevsky was teaching physics and mathematics at the city’s Robert College before making the trip west to take up a position at the Russian University in Prague in 1921. After three years in Prague, he emigrated to the United States in 1924 to work at Westinghouse Research Laboratories in Pittsburgh where he published on colloidal particles and the physical chemistry of cell division.

In 1934, he was invited to take up a position in the physiology department at the University of Chicago. It was a good fit for Rashevsky, who developed the first mathematical description of nerve excitation in 1933 before formalising a mathematical model of the neuron in Mathematical Biophysics in 1938. By this time the idea that neurons were discrete cells communicating via specialised junctions was widely accepted in the world of neurophysiology. As we saw all the way back in AI Histories #1, that development was largely thanks to the work of Spanish histologist Ramon y Cajal.

While Rashevsky did not cite Cajal in his work, he did take Cajal’s findings — that individual neurons exist and interact at synapses — as the basis for representing neural circuits. Rashevsky took for granted the anatomical foundation provided by the Spaniard: that a neuron was a cell body with an axon, dendrites, and synapses transmitting impulses unidirectionally.

Mathematical biology

In 1939 Rashevsky founded the Bulletin of Mathematical Biophysics, the first international journal devoted to mathematical biology. In the early issues, many papers were written by Rashevsky himself and his close collaborators on topics ranging from neuron models to cell metabolism to population dynamics. The most famous of these was Pitts and McCulloch’s ‘A Logical Calculus of the Ideas Immanent in Nervous Activity’, an essential paper in the AI canon and the subject of AI Histories #17.

Historian Roberto Cordeschi explains the relationship between Rashevsky’s earlier work and the McCulloch-Pitts: “Rashevsky had tried, in his 1938 Mathematical Biophysics, to analyze neural phenomena mathematically. In 1943, McCulloch and Pitts introduced Boolean algebra to describe nets of formal neurons.”

Rashevsky’s neuron was written in the language of physics, through coupled differential equations for abstract ‘excitatory’ and ‘inhibitory’ variables that rose and fell over time. To know whether a model neuron would fire, you had to work through those equations step by step and track changes in continuous variables. The appeal of the McCulloch–Pitts version was its simplicity. Instead of wrestling with changing quantities, they reduced the problem to a rule: if the inputs cross a threshold, the neuron fires; if not, it stays silent.

Rashevsky’s style left him stranded between two camps. To most biologists, his equations looked too abstract and too far removed from experimental life. To most mathematicians and logicians, his differential equations — formulas tracking how quantities change step by step over time — looked too messy. His neurons lived in the analogue world of continuous change, not the logical universe of on/off switches described by the McCulloch-Pitts model.

For this reason, many of the influential later neural network formalisms can be traced more directly to McCulloch and Pitts than to Rashevsky. Frank Rosenblatt’s perceptron (AI Histories #7) was essentially a network of McCulloch-Pitts neurons with adjustable weights and a learning rule. So when Marvin Minsky and Seymour Papert put the boot into neural networks in 1969, they talked about perceptrons as binary threshold units rather than continuous, analogue models favoured by Rashevsky.

That said, today’s neural networks have crept back towards Rashevsky’s way of thinking. Instead of only treating neurons as simple on/off switches, some modern models describe how activity flows continuously over time. These developments were not directly inspired by Rashevsky — they came from control theory and physics as in AI Histories #6 — but in a way they vindicate Rashevsky’s intuition that continuous dynamics are fundamental to understanding neural computation.

Rashevsky’s work didn’t feature in the emerging computer science-oriented AI stream, but its conceptual legacy persisted. The notion of treating the brain as a network that can be quantitatively analysed is something that AI inherited from Rashevsky and the others who followed his lead.

The Russian’s career shows us that AI’s origins don’t just run through logic and computing. By writing down the first equations of neural activity, he opened the possibility of treating the brain as a system that could be formalised, analysed, and perhaps replicated. McCulloch and Pitts made the idea simple and ultimately portable, but Rashevsky made it conceivable in the first place. If the history of AI is usually told as a story of mathematicians and engineers, Rashevsky encourages us to consider whether it was also a story of physicists and biologists trying to translate the dynamics of living systems into mathematics.

Sometimes it’s simply a matter of who gets the credit. Pitts stayed close to Rashevsky’s private circle, but the AI field at large credited the younger man’s paper as foundational for both its symbolic and connectionist schools. In the long run, Rashevsky’s contributions were folded into the background while recognition for launching the AI project went to his collaborators.

Despite this, some historians have argued for Rashevsky’s inclusion in the prehistory of AI and cognitive science. Jonnie Penn said his work “informed the origins of cognitive science in the 1950s”. Tara Abraham’s work re-evaluated Rashevsky’s contributions and reasons for marginalisation from the biology community, which she said followed from the fact that he had “little contact with empirical biological research”. And Gualtiero Piccinini and Sonya Bahar argue that “The mathematical modeling of neural processes can be traced back to the mathematical biophysics pioneered by Nicolas Rashevsky”.

Acknowledging Rashevsky enriches our appreciation of AI’s pre-history. It underscores that the quest to make mind mathematical did not start with Turing or von Neumann or Pitts and McCulloch. Rashevsky, like Ramon y Cajal before him, exists as a representative of one of AI’s many past lives. Including Rashevsky in this lineage reminds us that AI’s conceptual foundations were being laid well before the dawn of the computer age.

“You see that white part of the sky, called the milky-way. Can you guess what it is? An infinity of little stars, invisible to our eyes on account of their smallness, and placed so close to each other that they seem but a stream of light. I wish I had a telescope here to shew you this cluster of worlds.”

Bernard Le Bouyer de Fontenelle, Conversations on the Plurality of Worlds (1686)

Bernard Le Bouyer de Fontenelle was a French author and philosopher. He wrote history and theatre, and even tried his hand as a lawyer before quickly deciding the legal profession wasn’t for him. Fontenelle was the model of an Enlightenment man. He believed in the renewal of human will and reason, and argued with gusto against academic colleagues who thought the great works of the past could never be equaled.

In 1686, the Frenchman published Conversations on the Plurality of Worlds. Written as a string of exchanges between a philosopher (a thinly veiled stand-in for Fontenelle) and an intelligent woman called the Marchioness, the work established the template for Enlightenment extraterrestrial discourse by recasting astronomy as a question of humanity’s place in the cosmos.

The first evening starts with the Marchioness and the philosopher on an evening stroll. As they watch the Moon and stars, Fontenelle’s philosopher bashfully admits that “I have taken it in my head that every star may be a world”. His trepidation flows from the recognition that radical ideas often run against the grain of human nature, that people like to cling to that which flatters their pride.

Our philosopher explains that astronomers held on to the old Ptolemaic model of the heavens because they wanted to put themselves at the centre of the universe, much like the courtier who tries to place himself in the most prominent position at court. Copernicus won out, he tells us, because the Ptolemaic system buckled under the weight of its own complexity. When Mars appeared to move backwards in the sky, astronomers explained it by saying the planet circled on a little ring, which in turn circled on a bigger ring, and so on. These epicycles multiplied until the model eventually looked like a funhouse mirror version of Ptolemy’s original scheme. The reason we were hesitant to accept it is for fear of what a heliocentric account means for our place in the universe.

The Marchioness isn’t moved by his argument. She asks: “Do you suppose I feel humbler for knowing that the earth goes round the sun? I assure you I esteem myself just as highly as I did before.” This is the essential question of the book, one born of the Enlightenment confidence in reason and nature’s order. We only believed Copernicus, he says, because the system of nature compelled us to. Yet in doing so we learned to accept something that cuts against the human instinct to put ourselves at the centre of the cosmos.

Conversations on the Plurality of Worlds is a negotiation between science and the human condition. If the stars are other worlds, then the universe is richer than we ever imagined. That which seemed threatening — humanity’s downward movement in the celestial hierarchy — seemed to Fontenelle to demonstrate that reason could accommodate dislocation. He saw this upheaval as proof that reason could bear uncomfortable truths, that humans could (and should) draw dignity from their new place in the pecking order.

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Is there anyone out there?

Today, speculation about aliens is old hat. We see it in popular media, in research efforts like Breakthrough Listen, and in academia where studies of extrasolar beings are finally approaching something like scholarly respectability. Whether you have a strong opinion on the matter or not, all of us are well aware of the possibility of life out there.

It’s easy to forget that wasn’t always the case. We take discussion about little green men for granted, but our ancestors probably wouldn’t understand the question. Then again, the preoccupation with ET certainly feels like a very modern phenomenon. It’s the stuff of Hollywood, pulp sci-fi magazines, and turn of the century novelists, isn’t it?

Aliens surely loom larger in our collective cultural imagination than ever before, but serious consideration of extraterrestrial life as we might understand it has been hundreds of years in the making. The roots of our moment begin with the convulsions of early modern science, when the telescope made the stars look like other suns and the planets look like other earths.

For most of human history the heavens were the realm of gods, spirits, and influences; only in the seventeenth century did people begin to ask, in a recognisably modern way, whether those distant worlds might harbour other kinds of life. Bernard de Fontenelle was one of the first to popularise that shift. In Conversations on the Plurality of Worlds, he wondered whether the planets of the solar system contained life, and even speculated that every star in the night sky contained a solar system like our own.

But he also knew that alien life may not look like us. On possible lunar inhabitants, he wrote: “I say there are inhabitants, and I likewise say they may not at all resemble us,” and that any alien life must adapt to its own planetary conditions: “when I affirm that the moon is not peopled by men; you will see that according to the idea I entertain of the endless diversity of the works of nature, it is impossible such beings as we, should be placed there.”

His modesty masked confident assertions about extraterrestrial existence based on the principle of plenitude, the belief that nature abhors waste and fills all possible spaces with life. What made Fontenelle’s case so effective was both the boldness of his claim and the elegance of its presentation. He wrapped unsettling ideas in polite conversation, choosing as his interlocutor a witty, curious woman. In doing so, he signalled that reason was not the preserve of scholars alone, but something that anyone with curiosity could exercise. As he explained:

“In these Conversations I have represented a woman receiving information on things with which she was entirely unacquainted. I thought this fiction would enable me to give the subject more ornament, and would encourage the female sex in the pursuit of knowledge, by the example of a woman who though ignorant of the sciences, is capable of understanding all she is told, and arranging in her ideas the worlds and vortices. Why should any woman allow the superiority of this imaginary Marchioness, who only believes what she could not avoid understanding?”

Plurality of words

Fontenelle wasn’t the only Enlightenment thinker wondering about alien life. The ‘plurality of worlds’ debate became a live issue as scientists continued to spy new solar objects down the end of their telescopes. Naturally, Enlightenment thinkers moved to grapple with the questions that flowed from these observations. Were humans unique in the cosmos? How would extraterrestrial life affect Christian salvation doctrine? What moral obligations might exist toward rational beings on distant worlds?

The Dutch polymath Christiaan Huygens' posthumously published Cosmotheoros in 1698, which provided a systematic scientific treatment of extraterrestrial life by applying Newtonian physics and observational astronomy. Unlike Fontenelle's accessible dialogues, Huygens wrote a dense tome that established methodological principles that would influence subsequent astronomical speculation.

His fundamental argument rested on the Copernican principle: “A Man that is of Copernicus's Opinion, that this Earth of ours is a Planet, carry'd round and enlighten'd by the Sun, like the rest of the Planets, cannot but sometimes think that it's not improbable that the rest of the Planets have their Dress and Furniture, and perhaps their Inhabitants too as well as this Earth of ours.” This style of inquiry, one that connected reason and analogy, became the dominant approach in Enlightenment extraterrestrial discourse.

Huygens provided remarkably detailed speculation about ‘Planetarians’ based on functional reasoning about intelligence and technology. He argued they must possess manipulative organs because “without their help and assistance men could never arrive to the improvement of their Minds in natural Knowledge.” Perhaps his most famous idea was that the inhabitants of Jupiter must cultivate something like hemp for rope-making in their sailing ships, an assumption that demonstrated the period's confidence in analogical reasoning and its assumption that technological development followed universal patterns.

Others discussed extraterrestrial visitors as a form of social criticism. Voltaire's Micromégas from 1752 features a giant from Sirius (Micromégas, 120,000 feet tall) who visits Earth with a Saturnian companion (a puny 6,000 feet tall) in a text that sought to provide a cosmic perspective on human vanity. When Earth's inhabitants claim the universe was created for their benefit, “the two travelers fell on each other, choking with laughter”.

Of course, no treatment of life amongst the stars would be complete without religion. American founding father Thomas Paine deployed extraterrestrial life as a central argument against traditional Christianity in The Age of Reason, published in three volumes between 1794 and 1807. Paine's core argument targeted Christianity's cosmic exclusivity:

“Though it is not a direct article of the Christian system, that this world that we inhabit is the whole of the habitable creation, yet it is so worked up therewith, from what is called the Mosaic account of the Creation, the story of Eve and the apple, and the counterpart of that story, the death of the Son of God, that to believe otherwise, that is, to believe that God created a plurality of worlds, at least as numerous as what we call stars, renders the Christian system of faith at once little and ridiculous, and scatters it in the mind like feathers in the air.”

Paine was not out to abolish belief in God, but he was out to reform it. If revelation on Earth was the only path to salvation, what of the innumerable other worlds? To posit a separate incarnation for each, he argued, was absurd; to limit salvation to Earth was parochial. Faith in the creator must reflect the immensity of creation, and Christian doctrine ought to accommodate the true scale of the universe.

Finally, Immanuel Kant integrated extraterrestrial speculation into his comprehensive cosmological system. His Universal Natural History and Theory of the Heavens from 1755 examined solar system formation, arguing that the same processes that produced life here would operate elsewhere throughout the universe.

In Critique of Pure Reason, he wrote: “if it were possible to settle by any sort of experience whether there are inhabitants of at least some of the planets that we see, I might well bet everything that I have on it. Hence I say that it is not merely an opinion but a strong belief (on the correctness of which I would wager many advantages in life) that there are also inhabitants of other worlds.”

The idea constituted a hierarchical arrangement of planetary inhabitants based on distance from the Sun. Beings on planets closer to the Sun would be of a denser and more refined nature, while those on distant planets would be made of lighter stuff. All would possess reason, but their physical forms and capabilities would vary according to planetary environments.

Kant used the possibility of extraterrestrials less to describe aliens themselves than to clarify what it meant to be human. For Fontenelle it was a way to charm readers into accepting displacement, for Huygens to prove the universality of nature’s laws, for Voltaire to puncture vanity, and for Paine to expose the limits of revelation. In each case, other worlds served as proxies for disputes over knowledge, power, and salvation.

New horizons

In Conversations on the Plurality of Worlds, the Marchioness tells the philosopher “You are making the universe so unbounded that I feel lost in it; I don't know where I am”. The proper response, he insists, is to feel the opposite:

“For my part, said I, I think it very pleasing. Were the sky only a blue arch to which the stars were fixed, the universe would seem narrow and confined; there would not be room to breathe: now that we attribute an infinitely greater extent and depth to this blue firmament, by dividing it into thousands of vortices, I seem to be more at liberty; to live in a freer air”.

Extraterrestrials were a rhetorical instrument. They allowed Enlightenment writers to weaken the idea of divine privilege and to argue for the universality of reason, law, and moral order. Speculation about other worlds was a way of imagining a universe without exemptions, a politics without ecclesiastical hierarchies, and a humanity defined by its participation in a community of rational beings.

In embracing the plurality of worlds, Enlightenment thinkers completed the Copernican revolution in the cultural imagination. Its ‘principle of mediocrity’ — the claim that Earth is not special, that what happens here is likely to happen elsewhere — was the scientific manifestation of the Enlightenment’s organising principle. Once you accept that the same laws of nature apply throughout the cosmos, you undercut the idea that anyone ought to benefit from a pre-ordained position on Earth.

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The Royal Institution knows how to put on a show. In 1839, Michael Faraday used the venue to introduce dazzled researchers to early photographic techniques. Over fifty years later, J. J. Thomson told London’s great and good about the electron. Around the same time, Sir James Dewar showed off liquid hydrogen in a particularly eye-catching demonstration.

Historically, the events of the Royal Institution expanded horizons. They showed what science could do right now and what it might be able to do in the future. Humphry Davy’s public demonstrations of nitrous oxide thrilled audiences by proving that gases affect the mind and body. Faraday’s famous rotating wire experiment amazed onlookers by showing that electricity could drive mechanical motion.

In June 1973, the Royal Institution held a different kind of event. Certainly it was a spectacle, but one in which the presenter insisted on the limits of science rather than its possibilities. That person was Sir James Lighthill of the University of Cambridge, a celebrated British scientist who pioneered work in applied mathematics. His subject was AI, a field which he believed had failed to deliver on its promises.

Against Lighthill sat three challengers: the roboticist Donald Michie, psychologist Richard Gregory, and AI grandee John McCarthy (who last appeared in AI Histories #15). Hosted by the BBC, hundreds of others were in the audience for the debate.

Lighthill opened the proceedings by making a distinction between automation — defined as the use of any machine to conduct human work — and ‘automatic devices that could substitute for a human being over a wide range of human activities’. He said the latter group was called ‘general purpose robots’, but these things were regrettably a ‘mirage’.

He compared the ‘AI scientist in the lab’ to the ‘sorcerer in his cell’. In his view, both dealt with theatre that captured the public imagination without much to show for it. The comparison makes a certain kind of sense when you understand Lighthill’s beliefs about what science was for. As the historian Jon Agar puts it: ‘behind James Lighthill's criticisms of a central part of artificial intelligence was a principle he held throughout his career – that the best research was tightly coupled to practical problem solving’.

Lighthill said the great breakthroughs of the computing age belonged to automation, which he said was the preserve of ‘feedback control systems that act to reduce some change in quantity from its desired value’. Then he went on to say that all computers, and by extension all AI systems, are things that ‘manipulate symbols according to rules prescribed in a program’.

These are curious distinctions that don’t make much sense to the modern reader. We know that the foundational technology of today’s AI project is the neural network, a system whose power flows from reducing the loss between predicted and expected values. All things being equal, these systems do not manipulate symbols according to some set of rules (though they can operate symbolic tools like a calculator).

Lighthill had in mind a very specific type of thinking machine, one that was usually embodied, based on hard-coded rules, and ultimately alluring yet brittle. Just like industrial automation, he argued that the strands of research that would eventually culminate in today’s large models weren’t true examples of artificial intelligence. As the old saying goes, it isn't AI if it works.

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Talking heads

Like many of the clashes over thinking machines today, the debate wasn’t really about AI at all. The most popular topics of discussion were human intelligence, the nature of the brain, and the extent to which there are bottlenecks that prevent researchers from replicating human capacities in silicon. Regrettably, the debate took a somewhat circular turn from the outset.

Lighthill said that the brain can’t be replicated because it’s too complex. McCarthy countered its function certainly can. Gregory said artificial neural networks aren’t representative of contemporary neuroscience research but we should study them anyway. Michie further muddied the definition of ‘robot’ and then talked at length about the Freddy II system from his University of Edinburgh research group.

At one point, the host said he didn’t understand the issue and the researchers nodded along as if in agreement. The whole thing was a mess, frankly, and in the end the audience left more confused than they were to begin with. Say what you will about modern television, but science communication has made great progress over the last half century.

The BBC’s programme was meant to be a moment for the scientific community to respond to the publication of the Lighthill report, a piece of work commissioned by the UK’s Science Research Council in 1972 to take stock of UK AI research. The council was having difficulty assessing AI grant proposals and there were concerns that some projects were overly narrow or overly ambitious.

Lighthill was engaged to help. After spending a couple of months reading AI literature and consulting researchers, he delivered his report Artificial Intelligence: A General Survey in March 1973. In it, he outlined three categories of AI:

• ‘A - Advanced Automation’: This covered AI work with clear practical objectives like character or speech recognition, machine translation, and automated theorem proving. Lighthill acknowledged real, if modest, progress — but cautioned that successes were confined to toy problems.

• ‘B - Building Robots’: The attempt to create general-purpose machines that integrate perception, cognition, and action (often embodied in robots). Lighthill saw this category as a ‘bridge’ between the others, though McCarthy disputed this reading in the debate. McCarthy agreed, however, that projects of this type were trying to achieve the vision of AI that could perform many different tasks.

• ‘C - Computer-Based Central Nervous System Studies’: The use of computers to simulate and study neurobiology and psychology, like using neural networks to model parts of the brain. Here too he noted some progress and endorsed continued work in the area, but only insofar as machines could tell us about the nature of cognition.

The middle category, which is closest to modern conceptions of AI, bore the brunt of Lighthill’s criticism. In his report, he wrote that “Progress in category B [Building Robots] has been even slower and more discouraging”. A few pages later, he quipped that “AI not only fails to take the first fence but ignores the rest of the steeplechase altogether.”

Michie, who also gave a written response to the report, questioned Lighthill’s methodology and impartiality. Did he intentionally consult sceptical experts? Could someone outside the field fairly judge its worth? And how could Lighthill possibly be so confident about AI’s future prospects?

These were fair questions, but in the end the Science Research Council sided with Lighthill’s assessment. Funding for AI research in Britain was severely cut and many of the organised AI programmes that had existed were scrapped. The Edinburgh AI laboratory, which under Michie had been one of the world’s leading AI centres, saw its support plummet. As one retrospective put it, the once bustling lab was reduced to “just Michie, a technician, and an administrative assistant”.

The report was widely circulated and discussed internationally. In the United States, around the same time, DARPA (the main US defence research funder for AI) was undergoing its own shift. In 1974, partly due to new federal directives and disappointment with certain AI projects, DARPA started applying tighter scrutiny to AI research. It eventually published a Lighthill-style report of its own that drew similar conclusions.

But despite the rhetoric, the global AI research community actually continued to grow in the 1970s. The historian Thomas Haigh pointed out that if one looks at metrics like number of active researchers, conference participation, and publications, interest in AI kept increasing in the wake of the Lighthill report and subsequent allusions to the first ‘AI winter’.

Lighthill’s focus was largely on the symbolic approach to AI development that relies on explicit symbols, logic, and rules to represent knowledge and solve problems (discussed in more detail in AI Histories #9). In the 1960s and early 1970s, symbolic AI encompassed areas like rule-based reasoning, search algorithms, logic and theorem proving, structured knowledge representation, and even early robotics and natural language processing.

Against it stood connectionism, the ancestor of modern deep learning where networks of individual units learn from data. Connectionism was already facing something of a challenging time of its own, after Marvin Minsky published his famous takedown of the paradigm in 1969 (the subject of AI Histories #7).

In the report, there was little mention of connectionist approaches because the paradigm wasn’t prominent in the UK AI scene at the time. Artificial neural networks (probably the most famous incarnation of connectionism) appear in Lighthill’s discussion as tools for brain modelling in the central nervous system category — but not in the category that deals with AI in a way that we might understand it today.

It’s a useful distinction for helping us to understand the legacy of the Lighthill report. Connectionism was already under attack and symbolic AI methods had now joined it in the firing line. What the report really captured was the limits of a single paradigm, one that would eventually be sidelined when neural networks re-emerged to solve many of the problems Lighthill thought insurmountable.

In that sense, his comparison of the AI scientist to the sorcerer in his cell wasn’t entirely misplaced. Symbolic AI did produce persuasive but brittle demonstrations that resembled a magic trick. And connectionism, when it eventually displaced symbolic methods, had its own reputation for alchemy fed by critics and boosters alike.

What Lighthill missed was that science sometimes advances through sorcery, that alchemy was less a dead end and more a transitional practice. The systems of the 1960s and 1970s may not have been general-purpose, but their success in toy environments did inspire a generation of researchers to enter the field. Today’s deep learning systems are not an offshoot of early rule-based research, but the magic of symbolic demos — a ‘mirage’ as Lighthill put it — suggested that the problems of the AI project could eventually be solved.

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In Either/Or, Søren Kierkegaard tells us about life’s great enemy. It’s not pain or suffering, sin or despair. It’s not even failure or death. As it turns out, our true nemesis is boredom. He calls it ‘the root of all evil’ because it describes a mode of living where the habits that once formed character no longer continue to do so. Past actions stop making those same actions in the present feel significant, shaking off the sense of progress or growth that previously defined them.

Soon enough, you don’t recognise yourself in your own decisions. You might counter that maybe that’s okay, as you can always find new routines and habits. But not so fast. Kierkegaard also thinks boredom applies to the new as well as the old. Fresh experiences are meant to unsettle, to introduce friction, and to make us reconsider what we thought we knew. Under boredom, novelty is stripped of that power. It becomes a distraction, something briefly stimulating but quickly assimilated without any change in how you see yourself or the world.

The aesthetic life craves stimulation, but diversion has an annoying tendency to harden into the repetitive or mundane; the ethical life depends on habit, but habit without true renewal likes to decay into tedium. His salve is a ‘rotation method’ where we learn the capacity to let the old appear new and the new acquire depth.

In practice, that means learning to vary our perspective rather than our circumstances, to approach the same experience from new angles, and to linger with new experiences long enough for them to take root. It can be as simple as rereading a favourite book and noticing what strikes you differently, or taking a familiar walk with a new locus of attention. It can mean resisting the impulse to scroll for something ‘new’ and giving time for the novel thing you just discovered to mature into a deeper form of understanding.

What is at stake here is something like the freedom to truly know who you are and how to live. After all, freedom is not only the power to choose but the power to recognise yourself in your choices. By this reading, we might say a person is only practising true autonomy — the cultivated capacity to deliberate well about how to live — if their judgments are the sort they would continue to endorse after putting them to the question.

Philosophers call this ‘erotetic equilibrium’, the idea that a judgment counts as autonomous only if it can withstand the twin forces of reflection and deliberation. As Kierkegaard sees it, the threat of boredom is that it compromises this settlement as the familiar no longer feels grounded and the novel no longer feels renewing. Put in other terms: autonomy requires a rhythm between familiarity (in the form of stable, habituated judgment) and novelty (through disruptive experiences that reopen old encounters).

Today, our lives are governed by technology. We spend our time listening to music or watching videos served to us by algorithms, with the average person logging roughly seven hours looking at screens of various sizes. Not all of our time spent on a phone or a laptop is shaped by AI, but even just accounting for social media that takes us to something like two hours of every day at the mercy of recommender systems that govern what we experience.

Algorithmic recommendations are a boon for Kierkegaardian boredom. They interrupt the rhythm between old and new by systematically skewing novelty in favour the already-known and familiarity towards the already-consumed. In doing so, they erode the cycle of disruption and renewal that autonomy requires, leaving us with choices that are neither truly tested nor truly sustained.

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Boredom machines

Most recommender systems optimise for adjacency. Spotify’s ‘Discover Weekly,’ YouTube’s ‘Up Next,’ TikTok’s ‘For You’ feeds are all built to keep you coming back for more. The next item in the carousel is chosen because it is maximally like something you already enjoyed.

What looks like freshness is too often a very narrow kind of variation. These systems work by mapping your past behaviour into a dense space of similarities, then drawing from the tight cluster around it. The result is that the ‘new’ is different enough to feel like discovery, but close enough never to rock the boat.

That’s fine in some instances. Nobody needs their dinner playlist to feel like a challenge, and most people welcome a gentle learning curve when picking up a new app or tool. But when this becomes a dominant pattern of cultural life, the cost is that novelty never truly unsettles us and familiarity never deepens into mastery. We stay entertained, yes, but we do not grow.

Humans need what we might call the shock that opens the question. For Kant, this takes the form of the sublime, when the mind confronts something that does not fit its existing categories. The sublime is unsettling because it resists assimilation, yet in straining to make sense of it we discover that our capacity for reason and judgment exceeds what can be taken in by the senses. Without these disturbances, our judgments may never find themselves truly tested against what lies on the other side of what we know.

Heidegger describes the Unheimlichkeit or the uncanny moment when the familiar suddenly feels strange. Such moments matter because they interrupt the everyday flow and disclose possibilities we had previously ignored. Human potential requires this estrangement because we only know that our routines and preferences are genuinely ours when they face this sort of interruption.

We might comfort ourselves by urging that while algorithmic novelty isn’t always new, the systems at least allow us to become familiar with the things we already love. But that type of familiarity rarely matures into depth. Instagram recirculates the same handful of recipes, fitness routines, or travel spots, but the repetition doesn’t necessarily make you a better cook, athlete, or traveller. Familiarity here is broad but shallow, a surface-level exposure rather than patient discipline that ripens into something more meaningful.

Aristotle argues that virtue flows from habituation, the repeated practice of good actions until they become second nature. True familiarity approaches stability and depth only when repetition is combined with the good work of attention and discipline. The kind of algorithmic repetition we live with today looks like habit because it gives us the same patterns over and over, but it lacks its substance because the residue of experience is rarely incorporated into our character.

That’s because the logic of retention rewards what is easiest to consume again, not what is hardest to master. The system is designed to serve us repetition calibrated to hold attention rather than to cultivate depth. Where Aristotle thought habit was the slow conversion of action into character, platforms like to hold us in place rather than carry us forward as persons.

One way to think about this idea is the relationship between first and second order preferences, the difference between ‘man, I would love a cigarette’ and ‘I wish I could stop smoking’. First order desires are immediate and situational but second order desires are reflective, the stance you take on your own wants. In this framing, autonomy is not just acting on a first order preference but being able to align oneself with the second order judgments you endorse about the life you want to live.

Habituation is a bridge between these levels, with repeated actions gradually harmonising impulses and sustained reflection building character. The problem with algorithmic culture is that it breaks this connection. It gratifies first order preferences without giving them the friction that might force second order reflection. Clearly, that doesn’t happen all the time. Even within algorithmic culture one can pull away and use the same tools to pursue depth, like the person who studies guitar through YouTube tutorials or who joins an online community and learns to cook.

But these are acts of resistance, not dominant kinds of engagement.

Algorithmic culture dampens novelty and familiarity on their own terms, but what matters most is the negotiation of these two forces. It is in the back-and-forth between disruption and stability that our choices become truly our own. When novelty tests our habits and familiarity steadies our responses, we gain the chance to endorse our lives rather than simply living through them.

John Dewey, writing on education, put forward the idea that growth depends on the dual conditions of continuity and interaction. We develop when new experiences disrupt us, but only if they can also be tied back into what we already know. In this view, knowing oneself is about learning to weave these ideals into a life that we can call our own.

We need encounters that unsettle and habits that hold, moments that throw our judgments into question and practices that let them take root. To lose that balance is to risk the boredom Kierkegaard feared: a life rich in stimulation yet poor in meaning.

A different beast

Not all AI is made equal. We might interact with recommender systems on a daily basis, but many of us aren’t even aware that we’re doing so. Large language models feel different because they confront us more directly. They speak to us, take instructions, and generate responses to our feedback in the moment. These systems aren’t necessarily more fluid, but we are more aware of how these artefacts respond to human interaction.

We consult with them by treating the model as an oracle for information or advice, and we collaborate with them by enlisting them as a partner in drafting, editing, or brainstorming. But we also let LLMs play the parts we assign them — as tutor, friend, or opponent — and even hand off some tasks to them altogether. These modes of interaction promise control (we set the terms of the interaction) and companionship (we enter into a dialogue with the machine).

At first blush, they look like autonomy preserving custodians of algorithmic culture because we manage inputs and decide whether to accept or reject what is offered. Yet we know that the range of responses is bounded by training data, defaults, and hidden constraints that shape the choices we appear to make. I have lost count of the number of times models surface the same idea in different contexts, and we are all too aware of the linguistic tics that make LLM-generated text stand out.

Large models can in principle explore a giant space of possibilities, but in practice they tend to organise around the same set of patterns. At the point of use, these systems gratify first order preferences in a way that doesn’t guarantee the type of self-reflection we need to grow. A student may turn to a model in order to ‘learn,’ but the act of outsourcing the work can shortcut the struggle through which understanding emerges. The immediate preference is satisfied, but the deeper preference — to know, to master, or to grow — may not get a look-in.

More deliberate modes of use can help. We might say that a student who asks for a sequence of questions to work through, or a writer who uses the model to expose weaknesses in their own draft, is using the tool to test and refine their deeper commitments. In these cases the model becomes a means of holding ourselves to account, of forcing our immediate desires to answer to the kind of person we aspire to be.

At its best, AI returns our words in unfamiliar shapes and forces us to clarify what we mean. In those moments the familiar is unsettled and the novel anchored. At its worst, the same system may take us closer to ‘autocomplete for life’, gratifying first order desires while leaving our deeper commitments untouched. Instead of testing our judgments, they tell us what we want to hear.

Sam Altman acknowledged these two types of usage in a recent podcast: “There are some people who are clearly using ChatGPT not to think. And there are some people using it to think more than they ever have before. I am hopeful that we'll be able to build the tool in a way that encourages them to stretch their bandwidth a little more”.

The idea of rotation is useful here. Language models help us grow when they take something we already know and reframe it in a way that brings forth a new perspective. When you get a response, better to ask the LLM to defend, refine, or contradict itself rather than taking it as gospel. That might seem obvious to some, but last year’s ‘Claude Boys’ phenomenon reminds us that people don’t always like to do that kind of work.

These ideas encourage us to remember that autonomy depends on the rhythm between novelty and familiarity. We need habits that hold and shocks that unsettle, practices that shape character and encounters that open the question. So long as we use them judiciously, large language models are commensurate with that account of autonomy. They can either gratify our first order desires in ways that leave us unchanged, or they can be turned into partners that test our ideas, sharpen our commitments, and force us to see ourselves anew.

The difference lies partly in us, but also in the technology itself. Some systems make renewal harder, though not impossible, while others open the space more readily. To live well is still to weave the known and the unknown into a life we can endorse, and that remains our task, whatever tools we choose to do it.

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“If any love magic, he is most impious:
Him I cut off, who turn his world to straw”
— Fragment of a poem written by Walter Pitts to Warren McCulloch

Walter Pitts was born in Detroit in 1923. His father was a boiler-maker, and by all accounts a violent man who pressured the young Pitts to pack in his studies and get a job. Defying his father, Pitts spent his free time at the local library where he read widely about mathematics, science, philosophy, and history.

He read Bertrand Russell’s Principia Mathematica, found mistakes, and wrote to the Welsh mathematician to point them out. According to later tellings, Russell was impressed and even invited Pitts to journey to Cambridge to study with him. Still a twelve-year old boy at this point, Pitts was glad to receive the offer but turned it down on account of his age.

But three years later, when he heard that Russell would be visiting the University of Chicago, the fifteen-year-old ran away from home and headed for Illinois. He landed in Chicago, where he supported himself with menial jobs while joining those lectures he could.

Pitts never enrolled but attached himself to the orbit of Chicago’s intellectuals, publishing his first paper in 1942 when he was eighteen. His ‘Some Observations on the Simple Neuron Circuit’ appeared in the Bulletin of Mathematical Biophysics, which was the main venue for early attempts to model biological and cognitive processes with mathematics.

The journal was headed by Nicolas Rashevsky, a Russian-born researcher best known for work rendering neurons in the language of mathematics. Rashevsky vouched for Pitts and allowed him to publish under the University of Chicago banner, despite the fact that Pitts had no formal ties to the institution.

Warren McCulloch lived a very different life, born a generation earlier into an East Coast family of lawyers, theologians, and doctors. McCulloch studied mathematics at Haverford, philosophy and psychology at Yale, then medicine at Columbia. By the 1940s he was working as a neuropsychiatrist in Chicago. He wrote poetry, smoked heavily, and liked staying up past four in the morning with whiskey and ice cream.

The two men met in 1942 through Jerome Lettvin, a medical student who Pitts got to know at one of Russell’s lectures at the university. Pitts was eighteen and McCulloch forty-three. They recognised each other immediately through a shared enthusiasm for Gottfried Leibniz, the 17th century philosopher who wondered whether human thought could be represented an alphabet composed of signs and symbols.

McCulloch, who was looking for a mechanical account of mind, had been trying to model neurons in a kind of Leibnizian language but lacked the mathematical prowess to do so. In Pitts, he saw someone who might be able to help, and invited him to live with his family in the Hinsdale suburb of Chicago. For Pitts it became a surrogate home, a place that he would remember fondly for the rest of his life.

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Logical calculus

Pitts and McCulloch wanted to use Leibniz’s calculus of thought as the basis for understanding neural activity. In 1943 the pair published their joint paper, ‘A Logical Calculus of the Ideas Immanent in Nervous Activity,’ in Rashevsky’s Bulletin of Mathematical Biophysics.

They proposed a simple model in which each neuron acted as a binary unit, firing if its inputs crossed a threshold (or remaining silent otherwise). By connecting these units together in different ways, they demonstrated how some basic logical operations Leibniz had described — AND, OR, and NOT — could be carried out by networks of neurons. From this starting point they argued that more complex statements could be built, and that any proposition in logic could, at least in principle, be represented in a network.

The force of the paper lay less in its biological plausibility than in its commensurability. To physiologists, it offered a stripped-down account of nervous activity. To logicians, it showed how propositions could be built into circuits. To mathematicians and engineers, it looked like a schematic for machine design.

In 1943 Jerome Lettvin introduced Pitts to Norbert Wiener, the computer scientist best known for pioneering the field of cybernetics. Wiener was impressed with Pitts, later writing that he was “without question the strongest young scientist I have ever met”. He went on to promise Pitts a doctorate in mathematics at MIT, despite the fact the young man lacked a high school diploma.

Pitts soon moved to Cambridge as Wiener’s protégé. He joined a circle that included John von Neumann, who in 1945 wrote the ‘First Draft of a Report on the EDVAC’. It was a foundational document for modern computer architecture, one that cited only a single scientific paper: McCulloch and Pitts’s ‘A Logical Calculus of the Ideas Immanent in Nervous Activity.’

McCulloch followed Pitts to Massachusetts in 1952, when MIT’s Jerome Wiesner invited him to head a new project at the Research Laboratory of Electronics. He accepted, trading his professorship and suburban house in Chicago for an apartment and the chance to work again with Pitts. Alongside Lettvin and the Chilean biologist Humberto Maturana they established an ‘experimental epistemology’ group in Building 20, a makeshift wartime structure that became famous as an incubator of ideas.

McCulloch and Pitts had gone from Chicago salons to the centre of American science, with their work standing at the junction of psychiatry, biology, mathematics, and engineering. The field of cybernetics was born from the convergence, with Wiener at its head and McCulloch and Pitts among its central figures.

Just as things seemed to fall into place, the good times came to an abrupt end when Wiener’s wife told him that McCulloch was romantically involved with their daughters. Historians generally think there was no evidence for the story, but Wiener believed it all the same.

He sent Jerome Wiesner, then associate director of the Research Laboratory of Electronics, a telegram: “Please inform Pitts and Lettvin that all connection between me and your projects is permanently abolished. They are your problem.” He never spoke to Pitts again, and never explained why.

For Pitts, it was devastating. He had grown up with an abusive father, cut off his family at fifteen, and been taken in by McCulloch as a surrogate son. Wiener had been another father figure, a mentor who recognised his genius and placed him at the centre of American science. He turned down the doctorate that MIT had offered him and set fire to his dissertation notes. He withdrew from friends, drank heavily, and began a long retreat into obscurity.

In the years after the break Pitts still worked, though without the same momentum that defined his early years. With McCulloch, Lettvin, and Humberto Maturana he co-authored ‘What the Frog’s Eye Tells the Frog’s Brain’ (1959), an experiment that found the eye filtered and pre-processed visual information before passing it on to the brain. The work was important, but for Pitts it was unsettling because it punctured his view of the brain as a hierarchy of logical propositions.

In 1969, aged 46, Pitts died from complications of cirrhosis of the liver in a boarding house. Four months later McCulloch, weakened by a heart attack, also passed away.

McCulloch and Pitts are remembered almost entirely as forerunners of the connectionist tradition, the people who first showed that networks of neurons could compute. But in 1943 they didn’t think they were choosing between logic and learning. They felt they had squared the circle by demonstrating that what Leibniz had imagined as a calculus of propositions could be realised in the firing of neurons.

Their story reminds us that AI likes to take the shape of its container. In the early 1940s, psychiatrists could look at the logical neuron and see a stripped-down account of nervous activity, logicians could look at it and see the possibility of a calculus of thought, and engineers could look at it and see a schematic for machine design. The commensurability made possible by a single model allowed these groups to talk to one another, even as they pursued very different ends.

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Last week I joined the Cosmos Institute, where I’ll be working with Brendan McCord and the team to research and write about how AI can help us live the good life. Every big technological shift has forced us to rethink what it means to live well, and Cosmos is one of a handful of groups taking that idea seriously for a world with powerful AI systems.

Today, AI’s intellectual culture is guided by philosophy without practice or practice without philosophy. Many builders with a worldview don’t necessarily create in a manner commensurate with it (or in a way designed to encourage human flourishing). Cosmos is about finding, funding, and fomenting those who do.

I’ll be thinking about what it means to create AI to advance truth-seeking, preserve human autonomy, and resist central control. But I’ll also be working with the team to make Cosmos the place where builders test their ideas in public. That means I’ll be able to help Cosmos produce more posts like these:

• The Philosopher-Builder

• In Defense of Self-Direction

• 2025 Oxford Seminar: AI x Philosophy

• Rebooting the Attention Machine

• Live by the Claude, Die by the Claude

As for what this means for Learning From Examples, I’ll be switching the cadence to one post a week. I might change that in the future, but for now I’ll be alternating between one essay and one AI history on a bi-weekly basis.

If you’re interested in AI and philosophy, you can sign up to the Cosmos Institute Substack here. And if you’re building something great that you’d like Cosmos to back, you can apply for a grant here.

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In the early 1920s, Ronald Fisher put eight porcelain cups on a garden table at Rothamsted agricultural research station in Hertfordshire. Four had the milk poured first and four had the tea poured first. Muriel Bristol, a biologist who insisted she could taste the difference, sipped and sorted while Fisher looked on. She called all eight correctly.

Fisher knew that chance alone would yield that score about once in seventy tries, but he also knew that — if her success wasn’t due to chance — it had to be tested under conditions that removed hidden patterns in the set-up. So long as the cups are random and the observations accurate, you could in principle formalise your approach as a series of steps to follow. We might call it an algorithm.

Two decades later, AI grandee Arthur Samuel borrowed the same idea for his checkers program. He let the computer occasionally play random moves in the opening, giving it clean, unbiased samples of board positions before it started learning from them. It’s a core idea behind even the biggest and best machine learning systems, one that lets them see enough of the world to hoover up the right kinds of patterns.

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Randomisation, formalised

In The Design of Experiments, published in 1935, Fisher described the rule: if you’re going to compare two treatments, you must assign them to plots at random. Not roughly evenly and not by rotation. Randomly. Because if you don’t assign things at random, you can’t tell whether the result is due to the treatment or something else you didn’t control.

Maybe one side of the field gets more sun. Maybe the soil is drier in one patch than another. Maybe the experimenter gives a bit more attention to the first group, or unconsciously expects it to do better. Randomisation makes sure that any other differences are spread evenly between groups. That way, if you do see a difference in outcome, you can be more confident it came from the treatment rather than from something else you didn’t account for.

Fisher’s method for testing whether a treatment made a difference — what we now call a significance test — depends on knowing how likely each outcome was, assuming the treatment had no effect. But you can only know that if the treatments were assigned by chance. Without that, there’s no fixed set of possibilities to compare your result against.

In this sense, randomisation is the element that makes the test possible. When engineers built systems that experimented on themselves, they copied the structure Fisher had laid down. Randomise the action, observe the outcome, and ask if the difference was larger than chance. Even today, that is the basic logic that lets machines learn by trial and error without fooling themselves.

In 1922, Fisher published a paper that reshaped how statistics was done. Up to that point, most estimates came from algebraic convenience or common sense. Fisher replaced both with another rule that said if you want to estimate an unknown value in your model, choose the one that makes the observed data most likely. That rule became known as maximum likelihood.

Maximum likelihood defined a way of thinking where you take a model, plug in the data, and read off which version of the model fits best. That principle now sits under almost every statistical model in AI. Classifiers, regressors, language models are all trained by adjusting parameters to maximise likelihood, or minimise its negative log. That’s what people mean when they talk about minimising a loss function, whose roots we discussed in more detail in AI Histories #10.

The same paper introduced something he called the information of a parameter, which measured how sharply the likelihood function peaked around the best guess. A steep peak meant high confidence while a flat one meant you weren’t learning much. I won’t say much about this point, but it turned out to be an important mathematical object in machine learning that we now refer to as the Fisher information matrix.

A few years later in 1930, Fisher published The Genetical Theory of Natural Selection. It was a dense, mathematical book whose key idea was that the rate at which a population’s average fitness improves is equal to the amount of genetic variance in fitness it holds.

He built models to show what that looked like over time. Around the same moment, the American geneticist Sewall Wright was developing a parallel description of drift. This Wright–Fisher model captures how allele frequencies change across generations due to selection, mutation, and random drift. The model was meant for biology, but it also became the blueprint for genetic algorithms that we looked at in AI Histories #2.

Fisher’s theorem said that progress depends on maintaining variance, but the Wright–Fisher model showed how quickly variance disappears. That’s still a core challenge in evolutionary computation: how to keep exploring long enough to find something new, without getting stuck on the same hill forever.

In 1936, Fisher took measurements from three species of iris — petal length, sepal width, and so on — and asked whether the species could be separated based on those numbers alone. The method he used became known as ‘linear discriminant analysis’ or LDA.

The idea was to find one (for two classes) direction through the data that kept each species tightly grouped, while pushing the groups as far apart as possible. You begin by taking your raw measurements, projecting them onto a line, and checking which side the new point fell on.

By the 1950s and 1960s, LDA was well-known to many of the new pattern recognition groups at Bell, MIT, and the Lincoln Lab. Researchers used it to classify phonemes, radar blips, and handwriting. In Duda and Hart’s 1973 textbook, which was something like a holy text for connectionist researchers well into the 1980s, it’s the first real classifier discussed.

Drawing a line

In 1933, Ronald Fisher was appointed to the Galton Chair of Eugenics at University College London. He had already spent a decade arguing that Britain’s falling birth rates were a threat to ‘national fitness,’ and that differential reproduction across social classes would lead to civilisational decline. As late as the 1950s, he was still writing letters defending sterilisation policies and publishing essays warning of social degeneration.

Fisher thought statistics was relevant to politics, and the models he built in genetics — about selection, fitness, and variance — fed into the arguments he made about society. He believed that mathematical structures could uncover the natural order of things, and that once uncovered, they ought to be preserved.

As head of the Galton Laboratory, he helped steer British research into human heredity through the middle of the 20th century. Some of the datasets, measurement protocols, and study designs he left behind were later used to support claims about intelligence and class.

But his work has been enormously influential in many other less controversial areas. When researchers study algorithmic bias today, for example, they draw on the same theoretical foundations Fisher developed. Fairness audits use his work to measure whether an outcome is evenly distributed across groups, and significance thresholds still rest on the logic of his null-hypothesis framework.

Some of Fisher’s ideas are deeply disagreeable, but others are foundational to scientific practice. They live on in ways he never could have imagined, often in pursuit of goals he might have opposed. The lesson, if there is one to be had, is not that technology is neutral or that it is hopelessly corruptible. In fact, it is technology’s value-laden nature that lets us scrutinise it, shape it, and put it to work in a way that is commensurate with our own belief systems.

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Henry James’ The Beast in the Jungle follows a well-heeled American drifter in London called John Marcher. Over the course of the novella, Marcher agitates about the coming of a ‘beast’ poised to emerge from the undergrowth and destroy him and all that he holds dear.

In the closing moments we find the beast is a fiction; or rather, the anticipation of the monster’s coming is beastly in that Marcher lets it consume his life. He ignores his love, his friends, and his career in preparation of the creature, only for those actions to provoke the monster into being.

James’ little book is in that sense a self fulfilling prophecy, a cautionary tale about foresight and control. The essence of the story is common enough to be inaugurated as the Eighth Basic Plot: Chaucer’s Pardoner’s Tale sees those hunting death become murderers; The Appointment in Samarra finds a man running from death only to meet him at his destination; and in Boccaccio’s Decameron a man’s jealousy over losing his lover causes her untimely demise.

These are all Oedipal tragedies of sorts, though in each case the doom is more literal than Marcher’s fate in The Beast in the Jungle. In the classic version of prophecy gone sour, our hero’s folly is a desire to take action. In James’ book, stasis is the malady. We are shown a man afraid to travel, who fears love and intimacy, and who can’t bring himself to live life in case something goes wrong.

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Future shock

The things we call ‘technologies’ are ways of imposing order on an unruly world. They are artefacts, devices, and systems that contain possibilities for structuring human activity. Deliberately or by chance, consciously or unconsciously, societies select certain frameworks that determine how people work, communicate, travel, and play.

These structures are technological, and they in turn shape our ability to live in the world and produce new kinds of structures. Recognising that fact is not the same as advocating for a kind of technological determinism, the idea that technological development is the basic currency of change and that humans have no choice but to sit back and let it happen. Determinism is simply too all-encompassing a theory of progress, one that does scant justice to the choices that arise as we design, build, deploy, and configure our technologies.

We know that technologies do not materialise whole, that they are assembled inside labs, garages, parliaments, and patent offices. A technology’s function is a standing preference manifested in the world. When an engineer decides to design a safety guard to prevent a saw from touching a workman’s hand, the preference is ‘saw stops before contact’ over ‘saw cuts hand’.

The parts of our woodcutting machine continue to enforce this preference long after the thing has been designed and put to use in lumber yards. It keeps happening, every time it is used, whether or not the person slicing logs knows it or not. We might say that preferences or ‘values’ live in technology, which is one reason that technological determinism falls apart under just a little bit of scrutiny. QWERTY was not the fastest keyboard but the one that had a layout that seemed to prevent typewriters from jamming. The choice stuck because salesmen, schools, and secretaries made it stick.

When engineers wrote the GSM mobile phone standard in the late 1980s, they famously added in a 160 character Short Message Service (SMS) as a low priority maintenance channel so network staff could ping each other with status alerts. It wasn’t marketed, priced, or even imagined as a consumer feature. A contractor sent ‘Merry Christmas’ from his desktop to a colleague’s handset, and not long after curious users began trading notes.

What began as a backstage diagnostic tool morphed, through unplanned tinkering and uptake, into what was once the world’s favourite chat medium. The point here is not only that technology can change, but that social logics are often the drivers of that change. Taken in the round, it reminds us that any time you use a technology you are entering into a social negotiation with all those who had a say in its making and usage.

But let’s not get carried away. Technological determinism may be inadequate, yet so too is the view that technical things do not matter at all. It is deeply misguided to assume that once we locate the social origins behind a particular technology, we will have explained everything of importance.

By parcelling the day into equal and audible hours, the mechanical clock let factory owners synchronise shifts. Nobody decreed that the bell must rule the worker, yet once the hours of the day could be precisely tracked the temptation to regiment wasn’t too far behind. Gears didn’t force obedience, but they create affordances and invite patterns of use that can be hard to resist. You can, in theory, ignore a seatbelt reminder that asks you to buckle up, but in practice most of us would rather give in.

Design choices at T₀ become path dependencies at T₁ and common sense at T₂. If we stop at the backstory — who funded the clock, who invented the seatbelt — we close our eyes to the way artefacts shape and are shaped by the movements of everyday life. Conversely, if we fetishise the thing, we overlook the social climate that birthed it and our capacity to reroute it if we choose to do so.

Directions of travel

In the Republic, Plato tells us about techne, or expert know-how of a craft like carpentry or surgery. For the Greek, techne brings with it a kind of automatic authority that flows from expertise. We submit to the surgeon on how to set a broken bone because she has the skills and knowledge to align the fragments and keep infection at bay.

Plato leans on that prestige in his masterwork, arguing by analogy that the city should likewise be steered by those with the requisite political techne. These are the philosopher kings who understand the true nature of things, so their expertise ought to ground the state’s right to rule.

To make his case, Plato asks us to think about a ship on the high seas. Large sailing vessels need to be steered with a firm hand, so sailors must yield to their captain's commands. We don’t expect ships to be run democratically because a vessel’s survival hinges on technically informed decisions, like how to trim sail in a squall or plot a safe course through shoals.

Plato goes on to suggest that governing a state is much the same. It is something rather like captaining a ship or practicing medicine in that it demands specialised knowledge and the wisdom to apply it judiciously. He returns to this idea in the Laws, where he compares his own work to that of a well established craft.

“The shipwright, you know, begins his work by laying down the keel of the vessel and indicating her outlines, and I feel myself to be doing the same thing in my attempt to present you with outlines of human lives.... I am really laying the keels of the vessels by due consideration of the question by what means or manner of life we shall make our voyage over the sea of time to the best purpose.”

Philosophers have a reputation for having their heads in the clouds, but there is some evidence that Plato did in fact seek to put his skills as a designer of political societies into practical effect. He famously travelled from Athens to the court of Dionysius the Elder, hoping to transform his host into a philosopher king willing to put the principles of political techne to work.

Plato treats techne as a model for political rule, honouring the shipwright’s expertise only insofar as it buttresses his claim that the city should be steered by those who know. In the Laws, he bars actual artisans from citizenship on the ground that their craft absorbs them wholly, and leaves no room for the higher labour of deliberating about justice.

In this model, hierarchy precedes technology. Authority is granted from above on the basis of wisdom, while the makers are banished from citizenship so they can focus on their craft and avoid upsetting the political applecart.

Of course, the opposite is also true. Technology constitutes political order just as surely as political order constitutes technology. In The Visible Hand, the historian Alfred Chandler argues that the expansion of the railroad in the 19th century shows how certain crafts grow their own hierarchies.

Railroads, he writes, could move freight across the continent in any weather, but speed was useless without an army of schedulers, track gangs, clerks, and district superintendents to choreograph arrivals, inspect boilers, and bill customers. Out of that practice emerged the first modern managerial pyramid, with rungs as rigid as any military.

The telegraph needs repeaters and time-zone standards; the power grid needs load balancers and dispatch centres; a cloud platform needs site reliability engineers, compliance teams, and a legal department. Each new layer of machinery widens the gap between operator and outcome, and that expansion calls forth coordinators to put the pieces back together.

Seen this way, techne is an engine that manufactures new politics in situ. The timetable does as much governing as the governor, and X dot com can settle arguments faster than a senate debate. Yes, we make technology — but technology makes us too.

Lost in the woods

There’s a meme about our current place in the ‘tech tree’, one that asks how it is that trillions of dollars of capital came to be expended in one of the largest programmes of investment in history. This story, the AI story, involves a combination of repurposed hardware, extremely rich companies, and mountains of data created by the growth of the internet.

Graphics processing units were originally designed for rendering virtual environments in video games. How fortunate that this architecture, created for performing thousands of operations in parallel, was exactly what deep learning systems needed in order to chew through data fast enough to make the magic happen.

Of course none of that matters if you have nothing to feed the networks. Garbage in garbage out may be true, but quantity seems to have a quality of its own. Still, to keep those loss functions down you need access to huge amounts of data. This is possible because the commercial internet, especially social media platforms, persuaded billions of people to publish text, images, and video as a side effect of trying to entertain friends or sell products.

Growing fat on targeted advertising, internet infrastructure, and consumer goods, those same firms piled up extraordinary cash reserves. When the time came, they could plough mountains of dollars into data centre construction, specialised chips, and research laboratories. A single US firm can now spend more on AI hardware than the UK does on defence.

Political orders embed themselves in the design and allocation of tools, while some tools push back by generating new political structures. We might say the techne of deep learning was socially selected, just as we might acknowledge that our place in the tech tree means the basic shape of frontier AI systems is unlikely to change much in the medium term. That isn’t to say that progress is sure to slow, but instead that we already know what AGI will look like if it’s built in the next five years (assuming the predictions of the US and UK governments are correct).

Whether by taking action or staying still, in the Oedipal tragedy fate always wins in the end. The weight of the future is simply too great to contend with, its power overwhelming for mere mortals. Determinists think something similar. They rightly point out that technology has a life of its own, but they are quick to forget that it is also enmeshed with the lives of others.

In James’ book, the future is paralysing. Our protagonist cannot see the wood from the trees, the ways his life is already changing as he frets over the coming beast. In our moment, we wonder whether the monster will arrive in 5 years or 50. Whatever happens — and whenever it happens — like John Marcher we’ll only recognise it with the benefit of hindsight.

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“Now tell me, just what have you and Marv been up to — Gloria has received just as much information as I have”
— Louise’s letter to Ray Solomonoff, July 1956

It was a good question, one asked by Ray Solomonoff’s girlfriend Louise in the summer of 1956. Gloria was the wife of the famous mathematician Marvin Minsky, then a Harvard Junior Fellow, whose work we last revisited in AI Histories #7.

Ray Solomonoff, meanwhile, has yet to feature in the series but is generally regarded as the inventor of algorithmic probability. In 1956 he was a graduate of the University of Chicago and was working at Technical Research Group in New York.

Minsky and Solomonoff were spending the summer at Dartmouth College with a group of scientists organised by John McCarthy. The guests, which also included Herbert Simon, Allen Newell, and Claude Shannon (all figures we’ll get to in our series) were working on what his wife Grace Solomonoff later called ‘The Mysterious Science’. It was a fitting way of describing ‘thinking machine’ work, which for a time resisted easy classification.

Part of the draw of the workshop was to hash out what exactly thinking machines were and how the emerging discipline was referred to. ‘Artificial intelligence’ was already on the proposal, but the attendees were more likely to describe their work as cybernetics, automata theory, or complex information processing.

You might think that what we call the thing isn’t particularly important, and you’d be right to suggest that definitional questions about the nature of the AI project can be tedious. Even today, you hear people talking up the idea that LLMs aren’t AI, which is a phrase just one step removed from ‘real AI has never been tried yet’.

But from a historical perspective it does matter. The field or discipline of artificial intelligence clearly did not begin in 1956; many of the technologies and techniques that are still essential to today’s AI project are much longer in the tooth than the middle of the 20th century (see AI Histories #6, AI Histories #10, or AI Histories #13).

The Dartmouth project, to borrow historian Thomas Haigh’s phrase, was about giving AI a brand of its own. That isn’t to cast aspersions on the quality of the AI project, but to recognise that brands are useful for creating and stabilising many forms of creative or intellectual life, for making it clear who owns what and what certain things actually refer to.

In commercial terms, a brand tells outsiders what they’re buying; in research politics, it tells funders what they’re backing and graduate students what tribe they’re joining. Even McCarthy himself later wrote that “one of the reasons for inventing the term `artificial intelligence’ was to escape association with ‘cybernetics.’ … I wished to avoid having either to accept Norbert Wiener [a major figure in cybernetics] as a guru or having to argue with him.”

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An immodest proposal

The goals of the project were famously lofty. On the original proposal from the year before, McCarthy, Minsky, Shannon, and Nat Rochester, wrote:

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

Where Wiener’s cybernetics smacked of analogue servos and feedback loops, artificial intelligence was harder to place. It was wide enough to house symbolic logic, neural nets, and whatever else it needed to, yet focused enough to attract cash (the initial workshop was paid for by the Rockefeller Foundation) and energise its researchers.

The workshop opened on 18 June 1956. Most sessions took place in the top floor classroom of Dartmouth’s mathematics building. John McCarthy, Marvin Minsky, and Solomonoff were there every day; though records show that many of the days weren’t particularly well attended.

The work itself was exploratory. W. Ross Ashby demonstrated his electromechanical homeostat, a machine that could keep its needles centred by rewiring itself. On another afternoon the group stopped to check the word ‘heuristic’ in a hallway dictionary, the whole meeting standing around the lectern until a definition could be agreed.

The word was invoked through the summer of 1956. The idea was that, instead of trying to analyse the brain to develop machine intelligence, participants could focus on the operational steps needed to solve a problem using heuristic methods to identify the steps. Herb Simon and Allen Newell’s logic theory machine, for example, used heuristic guides to initiate the algorithmic steps (the set of instructions to actually carry out the problem solving).

The duo held a session on their device, which saw workshop organiser John McCarthy give them a glowing write up:

Newell and Simon, who only came for a few days, were the stars of the show. They presented the logic theory machine and compared its output with protocols from student subjects. The students were not supposed to understand propositional logic but just to manipulate symbol strings according to the rules they were given.

When attendees wrote their first post-workshop papers, the logic theory machine and the idea of list processing led the introductions. The term ‘artificial intelligence’ now points to symbol manipulation first and everything else second, a development that we still wrestle with today when people tell you only symbolic systems can be considered ‘AI’.

McCarthy’s phrase had floated through two months of loose talk and hard disagreement without breaking, and by the time the early papers began to cite the Dartmouth meeting the words were already doing administrative work. They marked grant lines, course titles, and the edges of a new research community. AI is still the Mysterious Science in that it promises the moon but leaves the specifics open to interpretation.

Of course that is entirely by design. Search, neural nets, and probabilistic induction all live underneath its umbrella. Our own moment uses labels like ‘AGI’ and ‘superintelligence’ for size, testing whether they can marshal funding and talent while staying loose enough to survive the revisions that real progress always demands.

Dartmouth’s lesson is that a field can begin with unanswered questions and unfinished business, so long as it finds an organising principle that allows for disagreement, divergence, and dogma to coexist peacefully.

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John Wilkins was a mover and shaker in the early years of the Royal Society. He was a clergyman and an experimenter whose passion project was ‘philosophical language’, a universal written system that could directly correspond with the structure of things in the world.

Wilkins wanted to turn the bones of language into an ontological framework for making sense of reality. His efforts remind me of noted postmodern linguist Plato, whose Cratylus put forward the idea that words must have intrinsic meanings. We are told that the Homeric hero Hector, for example, gets his name from the Greek verb ‘échein’ or ‘to hold’ because he was said to ‘hold’ the city of Troy as its great protector.

In his 1668 An Essay towards a Real Character, Wilkins introduced descriptive tables that showed how components of language could be used to classify certain animals. The word for 'elephant’ turns up as ‘zibi’, made up of ‘zi’ (the two letter root for every beast), followed by a ‘b’ (the consonant marking whole footed mammals), before finishing with i (the vowel assigned to the corresponding species in that row).

Like so many neat ideas, Wilkins’ philosophical language dissolved on contact with reality. It was too clever and too clumsy. Once you try to sort things into boxes, you soon find that the world has an annoying habit of contorting to avoid easy classification.

Argentine Jorge Luis Borges found Wilkins’ work in the 1940s, then famously sent it up by describing a ‘Celestial Emporium’ whose animal classes include ‘those belonging to the Emperor,’ ‘frenzied ones,’ and ‘those included in this classification.’ Borges’ point was that taxonomies are as arbitrary as they are brittle, that they tend to break the moment they are faced with a creature, a culture, or contradiction that doesn’t fit the scheme.

Wilkins thought his work could be a cabinet of cabinets, a scala naturae for the age of microscopes and coffee house empiricism. He lived as science wrestled with its Scholastic inheritance, a drive to fix the natures of things by figuring what they were and how they were related to one another. Our man pushed that logic to its obvious conclusion. If the world is orderly, then a language that mirrors that order must also be orderly.

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One of these things is not like the others

On 29 March 1823 a package from Sir Thomas Brisbane, the Governor of New South Wales, arrived at Edinburgh College Museum. Inside were two platypus carcasses, their ‘rostrum half dissolved, and the pile loose,’ as the curator’s assistant William MacGillivray grumbled in his log.

One went to the display case, the other to the Scottish anatomist Robert Knox’s dissecting table, where its curious mix of qualities proved inconvenient for every classification schema of the day. Knox found fur but no nipples. A keratinous beak and a cloaca, but no feathers to match. And a venomous spur without a cold-blooded body temperature.

There were lots of ways to cut the taxonomical cake, but the knife of choice came when Carl Linnaeus laid down the rules around a century earlier. In the 1758 Systema Naturae he offered a key composed of classes, orders, genera, and species, each demarcated by a handful of traits. Hair and teats? Mammal. Feathers and a beak? Bird. Scales and cold blood? Reptile. The attraction was its promise of mutual exclusivity: once a creature could be placed within one class, every other stayed limits. For a while it worked a charm, letting European naturalists sort the spoils of empire into their preferred locations.

By the early nineteenth century, however, the anomalies were coming thick and fast. Marsupials that suckled young yet carried their offspring in a pouch; microscopic euglena that prowled for food like an animal but carried chloroplasts like a plant; and the duck-billed creature that arrived in Scotland to confound the biologists. Each exception forced addenda, sub-orders, and awkward footnotes until the Linnaean grid was overrun by a patchwork of special cases.

To some extent the Victorians were alive to these anxieties. In A System of Logic published two decades after the incident in Edinburgh, John Stuart Mill argued that some groupings track real causal similarities while others are categories of convenience. After On the Origin of Species in 1859, the taxonomical project shifted away from fixed essences and towards a genealogical map of shared descent. In the post Darwinian order, classification was explicitly about relations and overlaps instead of Platonic blueprints.

As Ferdinand de Saussure took much glee in pointing out, a sign has no natural bond to its referent. Tree is not tree-ness in sound-form; it’s the noise we agree on because it isn’t three, free, or shrub. Meaning is the friction produced by contrast among signs. Vocabulary triangulates between differences and the essence of the thing is only stable insofar as it exists as what Ludwig Wittgenstein called a ‘family resemblance’ between things with overlapping similarities.

Moral philosophy by checklist

Values are the structure we impose on the messiness of the moral universe. They are meant to “capture collective wisdom about what is important in human life, in various contexts and at various scales” and help us sort the better from the bitter.

For large language models as in technology more generally, we appeal to ‘values’ as a source of illumination to help us puzzle through the most difficult questions and choices. Alas, the concept of ‘values’ is better seen as a symptom of confusion. We retreat behind values when we can’t find the right words for talking precisely about the most basic aspects of the human condition.

As Langdon Winner put it in The Whale and the Reactor almost forty years ago:

In a seemingly endless array of books, articles, and scholarly meetings, the hollow discourse about "values" usurps much of the space formerly occupied by much richer, more expressive categories of moral and political language. The longer such talk continues, the more vacuous it becomes, the further removed from any solid ground.

We are minded to believe that there have always been ‘values’ just as surely as there has been a long history of spirited discussion about them. Except that isn’t really true. People have always had commitments, responsibilities, preferences, tastes, aspirations, convictions and cares. But only in the last century or so has anyone bundled these things together as ‘values’ as we might understand them today.

Used as a noun, the word ‘value’ is an old term that has throughout most of its history meant ‘the worth of something’. Commonly the worth of an object in material exchange or the status or worthiness of a person in the eyes of others. The word properly enters social and political thought in the writings of eighteenth and nineteenth century political economists, most consequently via Adam Smith, David Ricardo, and Karl Marx.

For them ‘value’ meant the worth of a thing in a commercial sense, which is why a theory of value first appears wearing the clothes of economics. Later in the nineteenth century Friedrich Nietzsche commandeered the term to signify the sum of principles, ideals, and desires that make up the basic motivational structure of a person or people.

Nietzsche wrote about the need for umwertung aller werte or the ‘revaluation of all values’, a kind of controlled demolition of Christian morality. He wanted to tear down the moral order, sift through the rubble for anything still moving, and then rebuild a more life-affirming house from the ground up.

Later, Ralph Barton Perry proposed a ‘general theory of value’ that tried to give a reasonable account of the full range of human interests. Value is in this setting any object of note, whether that interest is aesthetic, moral, economic, or religious. He grounded these concerns in the life of instinct or desire, then cast ethics as a social technology for reconciling the inevitable clashes among them.

Even towards the middle of the nineteenth century, talk of ‘value’ was generally taken to be about some attribute of a given object. One might use or keep safe a thing because it had a certain value. Economic or sentimental, value was still value. We still accept this meaning, as say the ‘value of’ intellectual property or spending time with one’s family.

Today, you are just as likely to hear ‘value’ used to describe wholly subjective phenomena. People, groups, cultures, and even whole countries (British Values™ or American Values™) apparently have values that influence how they show up in the world.

These kinds of values are basically general dispositions, a semi-conscious filter of taste or conduct that reside in us rather than in the world. We do not cherish charity because charity is good; charity is good because our internal value set fires a positive signal when we see some philanthropy that we approve of.

All such things are personal sentiments don’t you know, despite the fact they can also be stretched across the full width of the nation state. You have your values just as I have mine. One community exalts self-reliance, another solidarity, a third ritual purity.

Our world is a values shop (not to be confused with a value supermarket full of discount deals), where we fill up the trolley with the values commensurate with internally held sentiments. Prices are strictly personal — your courage may be on two for one, my justice a luxury import — so haggling is futile.

The problem with this state of affairs is that it prevents us from thinking critically about the moral world. In the ethics of technology, things are rarely named outright as good, prudent, or admirable and courses of actions are seldom defended as fair or necessary. The winning move is to mumble about ‘values,’ as though the label itself ought to carry the day.

Keep off the grass

Values are a moral taxonomy, a set of friendly labels that lets corporations, governments, or individuals signal virtue without wrestling with the particulars. A list of values feels tidy, mutually exclusive, and reassuringly universal. But we know better, don’t we? Courage can too easily become recklessness, loyalty can clash with justice, and patience can take the edge off excellence.

For AI, critics and boosters both retreat behind ‘value alignment’ programmes that assume moral life can be rendered as a checklist — fairness, privacy, autonomy, and so on — and that the machine’s task is simply to occupy as many boxes as possible. You don’t need to say much about which values are preferable, you just need to cram in as many as possible to your taxonomy of virtues. In fact, if you just make sure one of them is pluralism you can call it a day.

The most basic facets of the human condition are easily swallowed by the value alignment project. Don’t think too hard about it. Better to concede that moral life has no rough edges and that the work of judgement is secondary. Who cares to ask what courage demands or whom justice serves when you can list pleasant sounding labels and pat yourself on the back for a job well done.

Behind the lists are ideals of good and harm, duty and power, claim and consequence. Those words bite because they force us to take sides and give reasons. They make the trade-offs real by reminding us that value alignment cannot in fact be all things to all people. Better than that, it forces us to concede that moral philosophy is more than ticking boxes.

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By the second half of the nineteenth century, physicists knew that energy tended to even out. Hot things liked to cool down and gases expanded to fill the space they were in. Formalised as the second law of thermodynamics, this idea holds that a closed system’s entropy (often described as ‘disorder’) keeps rising as its energy spreads out.

That sounds like a force of nature, but it’s better reckoned with as a way of characterising how systems behave when left to their own devices. If the world looks orderly to us, that’s just because we’re experiencing unlikely but possible states bubble up before they disappear.

At the core of this observation is Boltzmann distribution, which gives the probability of a system occupying a state as a function of that state’s energy. Described by the Austrian physicist Ludwig Boltzmann in the 19th century, the idea put forward that low energy states are more likely, and high energy ones become rarer as a system cools. Because rare states happen more often at higher temperatures, systems become more dynamic as heat increases.

What matters here is the claim that randomness has structure. That if you can’t follow every molecule in a glass of water, you could still know what kinds of configurations were likely. Put another way, the Boltzmann distribution is a way of thinking about systems in terms of tendencies rather than rules.

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Spin glasses

A spin glass is a material made of minuscule magnetic units, called spins, which each act like a tiny compass that point either up or down. In most magnets, the spins tend to align with each other, which creates a strong overall magnetic field. In spin glasses, the spins are influenced by conflicting forces. Some want to align but others want to point in opposite directions, so there's no arrangement that satisfies all of them at once.

The result is magnetic deadlock where the spins get stuck in a disordered pattern with no clear overall direction. Our system becomes stable but messy, trapped somewhere between maximally ordered and chaotic states. We describe the specific arrangements of individual spins held as ‘local energy minima,’ a term familiar to anyone who knows about the operation of connectionist AI systems like neural networks.

Spin glasses neither collapse into randomness nor configure themselves into symmetrical states. They get stuck, but in a way that we can predict. For many scientists, this made them rich research subjects in their own right; for others, the idea of a system that stabilises without fully resolving reminded them of other natural phenomena.

One particularly resonant comparison came in 1982, when John Hopfield proposed a simple network of binary units each connected symmetrically to the others. The idea was that the Hopfield network could store and retrieve patterns by settling into multiple stable states, each of which corresponded to a memory. Rather than being guided by an external controller, it would recall what it had ‘seen’ by letting its internal dynamics find a familiar configuration.

That’s the core of the ‘associative memory’ idea behind the system, which describes a gradual adjustment until it lands in a configuration that best matches the input. A partial or noisy signal activates the system, and the network completes the pattern automatically.

Hopfield didn’t claim this was how the brain actually worked, but he did show that you could treat a pattern recall problem like a physical relaxation problem. What had been a question about cognition became a question about finding low points in a landscape. In doing so it offered a different model of intelligence, one that brought the models of statistical physics into the world of computation.

Hopfield’s networks were clever but static. The architecture could store patterns, but the rules for how to update the weights were limited and biologically implausible. You could tweak the weights to embed a few memories, but you couldn’t easily make the system learn from data.

In 1985, Geoffrey Hinton, David Ackley, and Terry Sejnowski added noise to the Hopfield network. Instead of flipping deterministically into a new state, each unit in the network would switch on or off with a probability that followed the Boltzmann distribution. High energy states were unlikely and low energy ones were preferred. But now, unlike in Hopfield’s model, the system could find its way out of a local minimum if the temperature was high enough.

They called it the ‘Boltzmann machine,’ and it used a slow but elegant learning rule to update the internal state of the system. First, you connected the visible units to the data and let the hidden units adjust. Then you unclamped the system and let it run freely. You compared the two distributions — how often different configurations showed up in each phase — and used the weights to reduce the gap. The goal was to make the model’s internal world reflect the structure of the real one.

For a moment, it looked like Boltzmann machines might turn the field on its head. They had the ring of generality in that they were learning to understand the distribution those things came from. In a discipline still recovering from the failure of expert systems, that was an intoxicating promise.

Alas, they had some problems. Training full Boltzmann machines was slow and sampling took forever. You needed to reach equilibrium just to take a gradient step, and each new data point meant starting the process again. It was an elegant theory that couldn’t scale in reality, at least until Hinton found a workaround in 2002.

In this version of the Boltzmann machine, units within the same layer were prevented from communicating. Only visible-to-hidden links remained, which stripped out the feedback loops and made sampling easier. Instead of full equilibrium, you took only enough steps to approximate the gradient in a process called ‘contrastive divergence’.

Stack a few of these ‘restricted’ Boltzmann machines on top of each other and you got what researchers term a ‘deep belief network’ where each layer learned to represent the structure of the one below it. In 2006, in one of the first concrete demonstrations that deep learning could work, Hinton and his collaborators showed it could achieve decent results on tasks like digit recognition.

This signal primed the field before convolutional neural networks were retooled for the era of large datasets and GPUs just a few years later. So in 2012, when AlexNet famously proved just how powerful massive neural networks could be, researchers were quick to recognise it as the moment that the deep learning era arrived in force.

Cooling off

Today, there’s a small but serious group of researchers working on modern energy-based models, many of whom see Boltzmann machines as part of their prehistory. They’re trying to build tools that evaluate configurations rather than generate sequences, that score entire states rather than predict the next token. There’s a kernel of something interesting there.

But it’s also a space full of goofy handwaving. You hear about cognition as entropy minimisation. You hear about ‘thermodynamic computing’ and you start to notice that the more abstract the claim, the less likely it is to come with a working demo. Boltzmann’s name helps because it carries weight; people know it vaguely means something to do with probability and physics and systems finding balance.

But despite their relative lack of popularity, Boltzmann machines still matter to the history of AI. They might not have directly led to today’s most popular and powerful architectures, but they offered a particularly sharp version of a much older idea about the emergent nature of intelligence.

That idea was what made machine learning attractive from the start. What Boltzmann machines did was push it further, drawing directly from physics to provide a theory of learning as a thermodynamic process. Seen another way, the contribution of Boltzmann machines was more rhetorical than practical. Important, yes, but not because thermodynamic computing is going to replace large language models any time soon.

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Teach First is a UK charity that drops graduates into education jobs. It’s generally considered to be a good organisation, one that I know from experience has helped lots of smart people build a career in teaching. The group has historically selected candidates based on written assessments, but earlier this month it said it was accelerating a plan to switch towards face-to-face interviews.

The reason? University graduates are using AI in applications, which predominantly take the form of written assignments where anyone can put ChatGPT to work. Patrick Dempsey from Teach First said the charity had seen around a 30% increase in applications so far this year on the same period in 2024, a development he primarily attributes to large language models:

“The shift from written assessment to task-based assessment is something we feel the need to accelerate…there are instances where people are leaving the tail end of a ChatGPT message in an application answer, and of course they get rejected.”

Other accounts paint a similar picture, with graduate employment specialist Bright Network reporting that the number of people using AI for job applications has risen from 38% last year to 50% this year. And why wouldn’t they? Applications are tedious at the best of times, never mind when the odds of success are long.

If everyone can write well enough to pass a hiring round or two, it follows that employers will change tack to focus on assessments where LLMs aren’t much use. While that might seem straightforward enough, we ought to remember that in-person tests are not exactly a direct replacement.

Those running the hiring process are no doubt aware that teachers are encouraged to use AI in the classroom, so in-person interviews already proceed on the basis that successful candidates will use LLMs when they get the job.

A pivot to face-to-face tests might simply ignore that reality, or it might just reflect the lack of compelling alternatives. But whether or not the move aims to approximate written tests via in-person assessment doesn’t really matter. After all, the nature of face-to-face tests means they also probe for verbal and social competencies.

That’s because the people who succeed will be the ones who interview best, who get on well with those asking the questions in a way that makes them think they’ll be suited to the job. I think about this as the vibes premium: the increase in value placed on subjective traits — charisma, manner, confidence, aesthetic, speech, and presence — as it becomes tougher to use technical measures to distinguish between candidates.

The idea is related to but different from ‘interpersonal skills’ that are also likely to appreciate in value in the age of AI. However, our vibes premium is less ‘problem-solving’ or ‘leadership’ and more simply ‘making people want me around’. There are lots of ways to make that happen, but they generally deal with personality traits and behaviours even softer than these ‘soft skills’.

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Applying for everything

It’s rough out there for graduates. Clearly good jobs have always been difficult to land, but I suspect competition has been stiffening for quite some time as barriers to entry have collapsed. More people apply to graduate opportunities thanks to an expansion in higher education, online application portals, social media job openings, and international mobility. Now candidates can use AI tools that let anyone sound polished, which in turn raises the standards of applications and makes it harder for any one person to land a job.

On the other side of the line, a few friends have told me they no longer plan to hire because their respective leadership teams are convinced AI can do the tasks that they might want a junior person to take on (though it is worth saying the picture is more complicated than that).

Whatever the case, a recent survey reported that vacancies for graduate jobs, apprenticeships, internships and junior jobs with no degree requirement dropped by 32% since the launch of ChatGPT in November 2022. The same poll says that entry level jobs now account for 25% of the market in the UK, down from 28.9% in 2022.

We have two forces at play, both of which may be driven at least in part by the emergence of powerful AI systems:

• Graduate job availability is falling. This might be because managers are automating the work or doing it themselves with AI, though it is also possible there are other labour market effects at play that are responsible.

• Graduates are using AI in applications, so writing samples converge around the same level of quality. This compression may happen elsewhere, but it’s especially influential for entry level roles where differentiating factors are harder to come by.

In some ways the emergence of powerful AI systems is deeply humanistic. In entertainment, I expect good taste and human-led curation to become more valuable in a world overflowing with slop. I also imagine the same is true for the workplace, where technical skills become less relevant versus the qualities that only humans possess.

To be clear, my view is that AI raises the floor for technical skills but doesn’t necessarily eliminate the ceiling. A sharp candidate who knows the domain, has original ideas, and uses LLMs well can still outpace someone who just pastes in a prompt. The best performing candidates might even find themselves slowed down by the technology.

Even so, we’re interested in entry level jobs and graduate roles. These are the places where more people now get passable technical skills than ever before, which means employers need to look elsewhere to distinguish between candidates.

Regardless of how good the models get in the near future, I don’t see them fully displacing humans across the board. Part of that is about allowing employers to maintain a clear locus of responsibility at work, so if an AI makes a mistake firms can point to someone holding the bag.

But it’s also because there are some roles where we want a human in the loop, even if we don’t strictly need one. People go to a doctor for reassurance as well as care. They want empathy, explanation, and the sense that someone is emotionally attuned to their issue. The same goes for teachers, therapists, lawyers, and countless other jobs whose value partly flows from the human touch.

Alas, that is probably little comfort to graduates finding it hard to land a job. Even if today’s impact is overblown, young people will be those who experience the economic impact of LLMs first because it’s easier for the technology to help low skilled people become average rather than the excellent to become brilliant.

This sounds worrisome, but it does mean that they are likely to be the first to respond to the new demands of employers. Things that centre the human, things that let them play well with others, and things that mean their bosses want them around.

New signals

That leaves us in an interesting place, one where technical proficiencies like writing are no longer reliable indicators of skill or effort. Firms of all stripes are hungry for new kinds of signals that they can use to sort people, and so we get a hard pivot to face-to-face interviews.

This is a type of assessment less interested in output than in character, a trend that is likely to become more popular as firms look specifically for traits that only human beings can provide. It’s a manifestation of a kind of neo-humanism premised on the idea that the more technically proficient machine outputs become, the more we value the ineffable.

Neo-humanism returns hiring to something closer to the old apprenticeship model, where being able to work alongside someone mattered more than being highly credentialed. And at the other end of the spectrum, we should remember that elite institutions like Oxbridge never stopped interviewing for some courses.

There's something appealing about jobs going to people who can actually collaborate and communicate rather than those who simply know their way around a marking scheme. But a world where success depends on being likeable in a fifteen minute conversation is going to be a brutal one for people to navigate, where the premium on hard work may not be what it once was.

Maybe the future pans out differently, though I wouldn’t be so sure. As technical skills become table stakes, knowledge work will be increasingly defined by the stuff that makes us human. Put another way: given enough time, AI will make personality hires of us all.

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Earlier this year, researchers from UC San Diego said OpenAI's GPT-4.5 passed the Turing test. In a paper running through the results of the experiment, the group reported that the model was thought to be human more frequently than actual humans.

That is surely impressive, but it probably means less than you think. As the authors take care to explain, the headline result doesn’t necessarily tell us anything about whether LLMs are intelligent.

Today’s post argues that, despite the status of the ‘imitation game’ in the popular imagination, the test wasn’t designed to be a practical assessment of machine intelligence. Instead, it is better understood as counterpunch in an intellectual sparring match between Turing and his greatest rivals.

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Intelligence and rhetoric

The April 2025 paper from UC San Diego follows a similar study conducted by the group last year, where they evaluated GPT-3.5, GPT-4, and the ELIZA system I wrote about in AI Histories #11.

In the 2024 study, the researchers set up a simple two player version of the game on the research platform Prolific. They found that GPT-4 was judged to be human 54% of the time, that GPT-3.5 succeeded in 50% of conversations, and that ELIZA managed to hoodwink par ticipants in 22% of chats. Real people beat the lot, and were judged to be human 67% of the time.

As well as reporting more impressive results, the recent study moves closer to the structure of the test first put toward by Turing: participants speak to a human and AI simultaneously and decide which is which. As Turing explained in the original 1950 paper:

“It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A."”

Instead of determining whether participant A or B is a man or a woman, the first version of the Turing test sees the judge pick whether or not the writer is a person or a machine. This three-person structure is usually ignored in favour of a simpler two-person approach, though it was faithfully replicated in the new study.

But to take a step back, what do we think a game about whether a man could stand in for a woman (or vice versa) is actually testing? And what do we think that means for the version of the game involving a machine? Turing gives us a clue:

“The original question, "Can machines think?" I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.”

The test wasn’t designed to answer the question of whether machines can think (one doesn’t make a test to answer a meaningless question). But, just like the gender imitation game, the test must be fulfilled in a way that prevents a third party observer from being able to tell the difference between those involved. It’s about the rhetoric of intelligence, not the substance of it.

In an exchange used to illustrate how we might catch a machine out, Turing describes a back and forth in which the judge asks whether an agent could play chess (it says yes) or write a sonnet (it says no). The implication, of course, is that any sufficiently intelligent machine would be capable of engaging in ‘creative’ pursuits (apologies to all the chess players out there).

The final aspect of note is the type of machine that Turing believes will be entangled with intelligence in the future. As he writes towards the end of the paper: “instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's?”

So we have a thought experiment that seeks to set the conditions in which someone could call machines intelligent, explicit links with gender, learning machines, and creative pursuits as essential markers of intelligence. Taken in the round, these elements puncture the two most common interpretations of the imitation game.

First, the ‘reductionist’ view, which holds that the Turing test was developed to measure intelligence. This idea is popular with some AI practitioners, who see the test as a soluble target that should inform research. In this version, intelligence can be directly measured and passing the test is a meaningful benchmark.

Next up is the ‘constructionist’ interpretation that focuses on the idea that the test itself creates a certain type of intelligence through its design and implementation. In other words, the test actively shapes our understanding of AI rather than passively measuring it.

Both interpretations buy into the idea that the test was formulated on the basis that it could, and should, be implemented in the real world. But that isn’t the case. As Bernardo Gonçalves’s suggests in The Turing Test Argument, we can’t escape the context in which the paper was written: Turing’s debates with physicist Douglas Hartree, philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson.

The essence of the clash is simple. Turing believed that thinking machines would eventually outstrip all of the cognitive abilities of humans, while the others thought otherwise.

University of Cambridge mathematician Douglas Hartree argued that computers would always be calculation engines incapable of acting in creative or unexpected ways. To make his case, Hartree cited Ada Lovelace's view that computers can only do what they are programmed to do in his 1950 book Calculating Instruments and Machines: ‘The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform.’

So, an intelligent machine must be capable of performing tasks that it has not been specifically programmed to. Turing agreed, which is why he chose to connect his test with a ‘child–machine’ or what he called the ‘unorganised machine’ that could learn from experience.

Probably Turing’s most well respected critic was neurologist Geoffrey Jefferson, who set stringent criteria for machine intelligence that emphasised creativity. As The Times reported in 1949, he commented that ‘Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain — that is, not only write it but know that it had written it.’

Responding in the same newspaper on the next day, Turing, in typical cutting fashion, told the reporter ‘I do not think you can even draw the line about sonnets, though the comparison is perhaps a little bit unfair because a sonnet written by a machine will be better appreciated by another machine’. As we saw, Turing would go on to incorporate the idea of a machine writing a sonnet and being questioned about it in his imitation game.

Jefferson also argued that hormones were crucial for producing facets of behaviour that machines could not replicate. In one example he said that, were it possible to create a mechanical replica of a tortoise, ‘that another tortoise would quickly find it a puzzling companion and a disappointing mate.’

The relationship between sex and intelligence was the motivating factor in Turing's decision to include gender imitation as part of his test, which represents a challenge to the idea that certain modes of behaviour were dependent on physiological conditions.

The final element of the debate that Turing responded to was from Hungarian-British polymath Michael Polanyi, who argued that human intelligence involves tacit knowledge that cannot be fully formalised or replicated by machines.

He was unimpressed by Turing's one-time use of chess as a marker of machine intelligence, and proposed that chess could be performed automatically because its rules can be neatly specified (an idea we circled in AI Histories #8). The idea led Turing to reconsider using chess as the primary task for demonstrating machine intelligence, which was instead replaced by conversation to better capture the breadth of human cognitive ability.

What is the Turing test?

The Turing test is at its core an argument, one designed to counter his opponents’ views about the nature of machine intelligence. This is why Turing designed his imitation game to address the following aspects:

1. It focused on learning and adaptability, countering Hartree's view of computers as calculation engines.

2. It addressed Jefferson's demands for human-like creative abilities by incorporating language tasks like composing sonnets.

3. It was based on gender imitation with the goal of challenging Jefferson's views on the link between physiology and behaviour.

4. It used fluid conversation rather than rule-based games like chess to address Polanyi's concerns about formalisability.

Turing was responding to critics who thought that machines would never match human cognitive ability, who believed that genuine artificial intelligence was a non-starter.

In this sense the Turing test is a trap. At the point at which we can’t tell the difference between machine poetry and the real deal, any argument about whether machines are capable of artistic outputs runs into a few problems. This is why the primary goal of the Turing test is to formulate the conditions under which someone could call machines intelligent.

But that’s not how we remember it. The space between thought experiment and practical experiment has long since collapsed under the weight of its own cleverness. Its animating idea has been recycled so thoroughly that it became divorced from its original context, eventually turning the imitation game into a summit for researchers to climb and an open goal for philosophers to shoot at.

That today’s models pass the test is interesting in its own right. But doesn’t mean that a longstanding benchmark has been cleared or that satisfying the test is a meaningful marker on the road to machines smarter than you or I.

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Peter Kyle is the UK Secretary of State for Science, Innovation and Technology. Responsible for asking the machinery of government to foster, develop, and react to important developments in these industries, Kyle is a senior politician whose voice carries weight in Westminster.

He also thinks that artificial general intelligence (AGI) is coming. And soon. On a recent podcast, Kyle said: “I think by the end of this parliament we're going to be knocking on artificial general intelligence.” For those not familiar with the timetable of the British political system, that puts the arrival of AGI in 2029.

It’s a strange statement that can be read in a few ways. Maybe Kyle doesn’t really believe what he’s saying or maybe he’s parroting lines that he’s heard from American politicians. I take Kyle to be intelligent and I don’t know what he has to gain by putting these remarks out there if he doesn’t believe them, so I’d be minded to give him the benefit of the doubt and grant that he believes his own forecast.

Another reading might stress that Kyle has a very specific view of what ‘AGI’ means that doesn’t really correspond to how people generally think about the technology. He followed up his statement with the suitably cryptic: “I think in certain areas, it will have been achieved,” which suggests he’s talking about systems with human-level performance in some circumstances but not in others. The problem with this interpretation is that it glosses over the ‘general' in artificial general intelligence (not to mention that today’s systems already exceed humans in some domains).

Option number three is that Kyle thinks of AGI broadly like most other people in the field — a system capable of conducting the majority of cognitive tasks that a human can — and does indeed think we should expect a system like this in just a few years.

For the purposes of this post, I am going to assume that this interpretation is correct. Kyle knows what AGI is and he believes in what he’s saying. And as the person responsible for government technology policy, we should probably treat these remarks with the respect they deserve.

One way to do that is to ask an obvious but important question. If the UK government thinks AGI is coming within the next five years, is it behaving with the seriousness we should expect to prepare for its arrival?

Of course not.

That’s not to dismiss the good work done by the AI Security Institute (AISI) or those behind the AI Opportunities Action Plan, but rather to point out that even efforts that move faster than the glacial speed of government aren’t enough if the Secretary of State’s timelines are correct.

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Five tests

What follows are some loose thoughts about how a UK government would behave if it actually believed that AGI was about to arrive. I’ve structured these as ‘tests’ around a few of the policy areas that I expect to matter for the successful deployment of AGI. Compute to make the models tick, a national security posture that reflects the reality of a world with AGI, efforts to harden the country against economic and social shocks, moves to bolster state capacity, and regulations mandating various governance requirements.

It should go without saying that pretty much all of these have an extremely low chance of happening. The point of this exercise isn’t to blackpill anyone but to show the extent of the disconnect between (a) believing AGI is just around the corner and (b) the policies adopted by the government.

Compute

If AGI appears anytime soon, it’s going to be based on a version of the large model paradigm. Whatever specific form it takes, at the very least we’re talking about a massive connectionist model that needs a great deal of compute to develop and serve to users.

These both matter for the UK if AGI is as close as Kyle thinks. While there are no national champions like Mistral in France to compete with American or Chinese labs, we should expect that ‘BritGPT’ will make an almighty comeback if the government feels confident it could eventually become BritAGI.

But the main ticket is the juice for usage, which is sometimes referred to as ‘test time’ or ‘inference’ compute. If we have systems that can do pretty much anything a remote worker can, the main factor constraining their use is access to compute. Some of that will come from overseas, but any government that really believed compute was about to become king would want to have a supply at home for a bunch of economic and political reasons.

How’s the UK doing on that front? Somewhere between terrible and badly. Despite the fact the powers that be have accepted the proposal of the AI Opportunities Action Plan to increase compute by a factor of 20 by 2030, a quick back of the envelope calculation suggests that would still leave Britain with well under 4% of the total raw horsepower the American public and private sector already has on the books. Even the Compute Roadmap, which sounds impressive on first blush, talks up total investment that is about half of what Microsoft plans to spend independently in 2025.

If the government really thought AGI was five years away, it would be looking to increase compute by 100x from the current floor. This would still be on the light side, but might take us from ‘bad’ to ‘somewhat bad’ in the context of the UK’s size relative to Uncle Sam. Clearly that’s easier said than done, but one way forward would probably include a combination of tax breaks, accelerating the rollout of proposed AI Growth Zones, and good old-fashioned state investment.

Defence

If AGI really was just around the corner, today’s geopolitical settlement may be in for a shock. As I recently wrote about for Time, complexity is the strategic currency of war in the information age — and AGI is a complexity accelerator. But how this shakes out in practice depends on who makes the technology and where it lives.

A recent report from the RAND Corporation explores this idea in more detail. The authors sketch eight different geopolitical futures including one where the United States uses AGI to usher in a moment of unipolar power, one where China does the same, one where AGI is shared amongst liberal democratic powers, and one where the machine goes loco and takes over.

These scenarios are based on the idea that AGI could be a powerful tool for organising warfare, organising material and controlling robotic hardware, and decoding enemy plans. If you honestly believed AGI was just around the corner, it is safe to say the 2025 Strategic Defence Review is already a shade out of date. Concepts like ‘digital targeting web’ or a ‘Digital Warfighter group’ all incorrectly presuppose that humans remain the ultimate decision-makers, strategists, and actors in the age of AGI.

Preparedness

A government that really believed AGI was set to arrive before the decade is out would be treating it like a national emergency. The first order of business would be to pin AGI to the mast of state in a way that survives elections. Westminster has done this before (e.g. granting the Bank of England its independence), so we’re not exactly in uncharted waters. We might imagine an AGI Commission that would:

• Regularly report to Parliament on how close we are to certain thresholds by assessing capability evaluations, lab disclosures, and macro trends.

• Licence and inspect anything above a defined compute or capability threshold, probably working in combination with an AISI with teeth (more below).

• Plan for the downstream consequences — labour shocks, social changes, threat acceleration — and hand government a menu of possible responses.

Every serious scenario in which AGI can do ‘the majority of cognitive tasks a human can’ ends with a labour market that looks as if a neutron bomb has gone off. At the very least, the UK should probably be trialling regional universal basic income schemes and looking long and hard at corporate tax so some income from frontier models flows to the exchequer.

This is to say nothing of the many other unexpected ways that a world with AGI could spell trouble for the state. Maybe it’s cyber or bio attacks or maybe it’s an algorithmic arbitrage engine that shorts the pound into free-fall. I don’t know. No one does for sure. The point is that once you build AGI, the menu of unpleasant surprises may multiply faster than any Whitehall risk register can keep up.

Planning is already happening inside government, but right now no-one really cares or is paying attention. My understanding from those near the action is that the civil service has concluded that if any of these violent risks materialise there’s nothing the state can do.

State capacity

But in some ways, the UK is doing more than most. The AI Security Institute hands out grants, tests models, and has spun up independent safety and interpretability programmes staffed by some impressive CVs. Alas, it’s still small change. We are talking about sums that are similar to what Google spends on catering over the same period.

If we buy Kyle’s timelines, we should be increasing AISI’s budget by between a factor of 10 and 100. That kind of jump for a group with a £240M budget sounds crazy until you remember the frontier labs burn through billions of dollars of cash every year. If ministers are serious about peering inside a system that may shortly outsmart them, billions, not millions, have to be the unit of account.

Likewise, the UK has its Advanced Research and Invention Agency (ARIA) based on the American DARPA model. The agency has about £1bn to spend over a couple of years, some of which already goes towards promising approaches for making AI safe. Multiply that by the same order of magnitude and that is exactly the territory you need to be in if you want the state to steer the terms on which AGI arrives.

Could the Treasury stomach numbers like that? Not really, unless they pulled the old trick of treating the cash as defence spending. But that won’t happen, because the headline figures are really a referendum on belief. If you keep budgets in the tens of millions, you tacitly confess you do not in fact expect AGI by 2029.

Regulation

The AI Security Institute is doing good work. Its budget is bigger than its American counterpart, which it has agreements with to co-test models, and it has inspired the formation of AISIs the world over. But we should remember that while labs let AISI test their models, they aren’t compelled to by law.

They don’t give AISI access to every model, and they don’t have any responsibility to make changes even if testing finds something wrong. If ministers really believe an AGI is about to walk through the door, we might expect them to do something other than leaving safety to goodwill.

If they were treating governance seriously, they would give AISI a Royal Charter with powers to match. A charter makes it harder for a future government to quietly trim its wings; inspector powers let it enter labs uninvited, run its own scripts, and — if the model fails certain tests — issue a stop order. Of course, there is simply no way these actions will happen without American backing, but a charter would still be a signal of intent to back-up short timelines.

Ministers had drafted a ‘frontier-model’ safety bill for earlier this year, but they shelved it in February to better align with the new US administration. Officials now talk about a broader ‘AI Bill’ to be introduced in the future, but no one really knows what that is likely to include.

Honesty is the best policy

For the record, my own timelines for AGI are longer than four years. But if I was running the country, and if my timelines were as short as Kyle’s, you can bet I’d be implementing some policies that reflected the logical consequences of my beliefs.

The UK government is forecasting a technological event on the scale of the steam engine, then responding as if it were a smartphone upgrade. Either the state’s machinery must accelerate to match the timetable, or the timetable is made up.

If ministers truly expect to be ‘knocking on AGI’ within one parliamentary term, then the compute, safety science, resilience measures, and legal guardrails have to scale accordingly. If they can’t or won’t do that, maybe they should admit that four years is a fantasy.

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Out of the Unknown was a BBC television series about what technology does to the human condition. You can think of it as Black Mirror almost 50 years before the first Black Mirror episode aired. There’s one about a doctor pushed to the edge while treating a man suffering from radiation poisoning. Another deals with a spaceship en route to a distant star system, which we later learn is a simulation running on Earth.

But the best of the bunch is ‘The Machine Stops’ from the show's second season based on E.M. Forster's novella of the same name. Written in 1909, it’s a small book about what happens when humans triumph over nature. People reside in underground pods watched over by a machine that provides every comfort they could possibly need.

No one ever goes to the surface, except a special few who get permission from the ruling elites. When they do, they have to wear a ventilator because the atmosphere is so unfamiliar. The story follows Vashti, an ordinary person who stays busy with video calls and lectures. In the opening pages we find that her son, Kuno, isn’t happy with the way things are. He wants to go to the surface but Vashti can’t understand why anyone would want to leave the comfort provided by the machine.

In the 1960s, when the time came to adapt the novella, the BBC decided that the underground tunnels needed to be as convincing as possible to make life underground feel real. To make that happen, the producers suspended a working monorail track from studio rigging. John Bruce, the assistant floor manager on the shoot, said it was capable of ‘carrying passengers in a capsule and depositing them into a station,’ a feat he thought was especially important because ‘the essence of what E. M. Forster had written way back in 1909, was now, today, fast becoming a reality.’

Contemporary reviews were fairly positive, with the Daily Telegraph remarking the production was ‘visually inventive’ and dialogue ‘unusually distinguished’. A year later in 1967, the episode won first prize at Italy’s Festival Internazionale del Film di Fantascienza.

The writer, director and cast all went on to other things, but the story didn’t. Like so many TV dramas from the era, the reels were taped over during one of the regular purges of the 1970s that sought to free up real estate on expensive videotapes. But Out of the Unknown managed to survive. The director had opted to shoot on 35mm, and a single negative survived in a film vault in Brentford that re-emerged in 2014.

Its survival is fitting in that The Machine Stops is a story about vanishing acts. The episode reminds us about the perils of over-reliance, but it also warns us about letting someone or something mediate our interactions with the world.

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Edwardian futurism

When Forster wrote The Machine Stops, London had electric lights and trains that ran beneath the ground. Telegraph cables linked continents and the first transatlantic telephone was less than a generation away. Writers like H. G. Wells cheered on progress, imagining scientific utopias run by technocrats and planetary planners. Even socialists came around, thinking that machines could provide the abundance required to improve the worker’s lot.

Forster had always been a novelist for whom the best things in life arrived by accident. In Howards End, published a few years earlier, he had already warned that the new world might be frictionless but anaemic. The railways may be fast, but they moved people past one another to places they seldom needed to go. Or as Thoreau knowingly put it in Walden half a century earlier: ‘We do not ride on the railroad; it rides upon us.’

When he sat down to sketch a science fiction story, Forster took aim at the dream that technology could deliver comfort and culture. He imagined a world in which physical effort had been designed away, where information was summoned at will, and where the whole structure worked so smoothly that no one remembered what life looked like before.

In The Machine Stops, the breakdown happens slowly. The music, usually delivered on demand, takes a while to get going. The air vents grow sluggish. A lecture feed stutters, then goes dark. Vashti calls for repair and shrugs when it doesn’t respond.

There’s no immediate panic, because panic requires one to believe the machine to be fallible. The needs of every citizen — food, warmth, knowledge, and intimacy — have been routed through humanity’s big brother for generations. They communicate by screen, consume information mediated by the system, and believe without irony that direct experience is vulgar.

I think the The Machine Stops is important reading, but not because Forster prefigured the internet (or even because he was one of the first warn of disempowerment via intelligent machines). One way the story works is as a meditation on what happens when the filters that control the flow of information get gummed up.

Forster imagined a future in which data is abundant but free-floating, where the process for turning the raw ore of information into the alloy we call knowledge becomes fundamentally deficient. That problem occurs because knowing is, at least in part, something we do by living in the world.

Forster’s experiment with sci-fi is well read in AI safety circles. It’s a good reminder that what looks benign right now may prove to be misaligned given enough time. The machine in the novella does exactly what it was designed to do: it feeds, warms, educates, and entertains. It delivers ideas on demand and it encourages communication to keep people connected. While it does keep people apart, it makes sure they aren’t alone.

The Machine Stops is a story about what happens when a society replaces reality with representation, when the whole world forgets to touch grass. That risk exists for powerful AI and pod people as well as generative media, recommender algorithms, and remote everything. Systems that replace the condition of understanding with the appearance of it should be handled with care, lest we find ourselves needing to fix them.

You can read The Machine Stops here for free. The Out of the Unknown episode based on the novella is available for free on Internet Archive here (along with every episode of the show).

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A few years ago you probably read about the ‘internet of things,’ a comfortable but baggy way of describing a network of interconnected electronic devices. The idea occupied a central place in that other forgotten project, the ‘fourth industrial revolution,’ which jammed together everything from genomics to virtual reality.

The goal of the internet of things was to make everyday machines ‘smart,’ a mission that I suppose has been accomplished. I allegedly have a smart speaker, a smart TV, and even a smart oven that I tell myself I will one day connect to the wifi network. It’s a fittingly dull task for a technology that doesn’t get the blood pumping, one that in some ways follows the path laid down by electricity or telephony. Like the smart-everything, these are remarkable things that became first mundane and then invisible.

If you can gloss over the horrible phraseology, the connected device is a useful thread to pull for making sense of assumptions about what good machines do. When an old appliance breaks, it’s replaced by a new model that insists on attaching itself to your wifi. The physical layer gets wider as more sensors, communication nodes, and platforms are folded into the network. But it also gets deeper as the connected devices become a little more lively.

Boosters describe these devices as manifestations of ‘artificial intelligence’ because they identify changes in the environment and change state accordingly. Weighing a smart washing machine against a large language model seems a bit overwrought, but it does raise an important point about the nature of digital intelligence: it doesn’t care about the shape of its container.

ChatGPT may animate your computer or phone, but the real magic is happening in an Arizona data centre. Even without access to the internet, compression techniques now let a three billion parameter language model run on a phone’s battery without cooking it. Researchers are doing the same for the hardware layer, proving that models can live next to the signal rather than a continent away.

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This points us towards a curious observation about AI in the popular imagination. Large models can be anywhere with enough processing power or with sufficient connectivity, but we only tend to picture them residing in a small set of physical platforms. Humanoid robots loom especially large in the public psyche, giving the impression that digital intelligence obeys the same rules as our own.

But AI doesn’t work like that. It will populate the world around us and turn appliances, devices, and computers into talking (and in some instances, walking) machines that interact with us from whatever vantage point they can cling to.

For the purposes of this post, I’m going to make a few assumptions about the future. These are (a) the models will broadly maintain their current rate of improvement for the foreseeable future; (b) the best models of any given moment will shrink to allow for local deployments; (c) different models will be able to communicate with each other; and (d) lots of physical platforms are capable of hosting the models in one form or another.

If each of these assumptions hold, there’s no reason to think we won’t have one AI model (or a handful of models) that live across many substrates within the next couple of years. After all, we already have Claude in a vending machine, Grok inside the newest Teslas, and LLM-powered assistants in fridges, ovens, and dishwashers.

Animism through the ages

Animism is the conviction that spirit inhabits matter, one that shows up far earlier than the word itself. Palaeolithic hunters painted animals on rock walls and carefully arranged bear skulls in a way that archaeologists interpret as negotiations with animal persons. When Edward Burnett Tylor coined the term animism in his 1871 book Primitive Culture, he formalised that observation by defining early religion as ‘the doctrine of souls and other spiritual beings in general’ that could reside in the natural world.

By the Bronze Age, the impulse to see spirit in matter hardened into liturgy. In Mesopotamia a newly carved cult statue underwent the mîs-pî (‘washing of the mouth’) procession. Craftsmen led the image to a riverbank where its lips were ritually cleansed, then ‘opened’ with cedar oil. From that moment on, the wood and precious metal counted as the god’s living presence and was capable of eating offerings, signing treaties, and punishing neglect.

A similar performance took place in the Egyptian Old Kingdom. Priests put peseshkaf blades to the mouth and eyes of statues in the ‘opening of the mouth’ ritual that enabled a figure to enjoy food and speak in the afterlife. Eberhard Otto’s Das ägyptische Mundöffnungsritual from 1960 identified 75 examples of the practice, an effort that emphasises the institutional heft underpinning animistic practice.

Centuries later, objects did god’s work in the churches of the Byzantine Empire. Painted boards, splinters of bone, weapons, and other artefacts were thought to contain divine energy (energeia). John of Damascus made the theology explicit when he said: ‘I do not worship matter; I worship the God of matter, who became matter for my sake.’

But probably the most famous example of animism comes from Japan’s Shinto, a religious tradition whose roots lie in early agrarian rites that personified the forces sustaining rice cultivation. At its centre are kami, the packets of vitality saturating both nature and human-made objects that invite reverence through craftsmanship or long use.

Everyday life in a Shinto frame assumes a world that listens back. A shrine gate marks a threshold where rock and tree possess their own intentions and household rituals treat the cooking fire or well as moral participants. That habitual attribution of inner life is what scholars describe as animism.

When the electric telegraph connected Europe to America in the nineteenth century, the Victorians seized the new medium as proof that voices could travel between planes of existence. Séances were framed as ‘circuits’ and mediums styled themselves human telegraph stations. A New York weekly even titled itself The Spiritual Telegraph, reporting on dispatches from the afterlife in the form of a newspaper. As Desmond G. Fitzgerald, the editor of the Electrician, put it in May 1862:

“Telegraphy has been until lately an art occult even to many of the votaries of electrical science. Submarine telegraphy, initiated by a bold and tentative process – the laying of the Dover cable in the year 1850 – opened out a vast field of opportunity both to merit and competency, and to unscrupulous determination. For the purposes of the latter, the field was to be kept close [sic], and science, which can alone be secured by merit, more or less ignored.”

The author Jeffrey Sconce notes that popular magazines styled the séance room as a kind of domestic telegraph station. In Haunted Media, he describes the rise of spiritualism as a utopian response to the electronic powers presented by telegraphy and connects the emergence of the radio with an ‘atomized vision of the afterlife.’

The upshot is that animism has a knack for rerouting through the technologies of the day, whether that’s burial artefacts, swords, or telegraph wires. People may not worship microwave ovens in the future, but I wouldn’t rule out the adoption patterns of animistic living that stress interconnectedness, vibrancy, and agency.

Intelligence in the pipes

Animism is more habit than philosophy, a set of reflexes we use when faced with unexpected forms of mediation. Its long life reminds us that we've done this before, and that what feels new — doorbells and ovens and thermostats that listen — is the latest chapter in a much older story about how we relate to our surroundings.

The diffusion of AI into the world is a well-trodden cultural negotiation, one that should make us weary of the image of an embodied intelligence that only exists within humanoid robots or specialist AI hardware. That will surely happen, but these kinds of deployments will only represent the most visible form of physical manifestation.

Just as a smart thermostat slips into invisibility, large models may come to occupy our surroundings in ways that feel almost as unremarkable. Animism has some utility here. It encourages us to notice intelligence in the places we aren’t used to looking, to imagine the world as a little more vibrant.

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Sometimes, when it was quiet, secretaries at the Massachusetts Institute of Technology would slip blank sheets of paper into a large computer. In the heat of 1960s optimism, the machine would whirl and beep and print out a perfectly spaced reply.

WHAT ELSE COMES TO MIND WHEN YOU THINK OF YOUR FATHER

That sentence was produced by a script called DOCTOR running on an engine called ELIZA, which its creator Joseph Weizenbaum designed to study natural language communication between people and machines.

Weizenbaum was worried about what he saw. In his famous 1966 paper describing the experiment, he wrote ‘some subjects have been very hard to convince that ELIZA (with its present script) is not human.’ Later he reportedly said that his secretary requested some time with the machine. After a few moments, she asked Weizenbaum to leave the room. ‘I believe this anecdote testifies to the success with which the program maintains the illusion of understanding,’ he recalled.

The mythos that grew from those sessions is seductive. In 1966 Joseph Weizenbaum invented the first chatbot, named it ELIZA after Eliza Doolittle, and in doing so proved that computers could hold a conversation.

But the legend forgets that Weizenbaum never set out to build a conversational partner at all. It ignores the psychological dynamics that made it so popular, and doesn’t tell us anything about how exactly a computer program became famous.

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A star is born

Much like today’s large models, it’s useful to think of ELIZA as a mirror. Both model users by responding with sensitivity to inputs. Both exist as extensions of the person behind the keyboard. And both remind us that intelligence is part substance and part projection.

By substance I mean whatever magic your preferred model runs on, and by projection I’m talking about the meaning we ascribe the models on top of this foundation. Today’s models are deeply impressive, but no matter how good they are, people still have a tendency to see in them something that isn’t there.

That isn’t a criticism of the AI project but the reality of building artefacts that shape-shift according to the person using them. ELIZA was remarkably light on substance, but projection compensated by adopting a listening style that rewarded personal monologues.

Once you see the two halves, the standard origin myth looks lopsided. It treats projection as a rounding error, and emphasises the technical credentials that confirmed ELIZA’s status as the ‘world’s first chatbot’. That’s tidy, but it strips the project of its context in a way that leaves readers with the wrong end of the stick.

Weizenbaum knew this, which is he why he spent so much of his career fretting over the illusion of intelligence. Even at the time, he billed his project as closer to ‘watch humans talk to themselves’ than ‘teach a computer to talk’. As Jeff Shrager' put it:

In building ELIZA, Weizenbaum did not intend to invent the chatbot. Instead, he intended to build a platform for research into human-machine conversation. This may seem obvious – after all, the title of Weizenbaum’s 1966 CACM paper is “ELIZA– A Computer Program For the Study of Natural Language Communication Between Man And Machine.”, not, for example, “ELIZA - A Computer Program that Engages in Conversation with a Human User”.

That claim lands oddly if you’ve spent years hearing that ChatGPT is the descendent of ELIZA, though it makes a certain degree of sense when you think about the experience of using AI systems.

The ELIZA project began in 1963, when he knocked together his own toolbox called Symmetric List Processor or SLIP. It worked like an add-on for FORTRAN, the workhorse programming language of the early 1960s, with flexible chains of items that could grow, shrink, and point to other lists.

Weizenbaum landed at MIT around this time, parked his SLIP routines on the lab’s IBM machine, and sought to answer a question: what if a computer, armed with nothing more than keyword tables and pronoun swaps, just bounced a user’s own sentences back at them?

He modelled the resulting program on Carl Rogers’ person-centred therapy, a counselling style where the therapist mostly repeats or paraphrases the client. Weizenbaum recognised a gift horse when he saw one. If your program can only juggle keywords and pronouns, best to use it in a context where minimal responses counted as professional technique.

With a few hundred lines of code, Weizenbaum used his SLIP routines to chop each user sentence into a list of words, swapped pronouns (‘I’→’YOU’) and tacked on open-ended prompts (‘TELL ME MORE’). He named the engine ELIZA and the therapist script that it ran DOCTOR. The system was used at MIT by Weizenbaum and his colleagues, but it took until the end of the decade for it to escape containment.

That moment happened when Bernie Cosell, a young coder at Boston’s Bolt Beranek & Newman research firm, skimmed Weizenbaum’s article. He saw the step-by-step description of ELIZA’s keyword tables and pronoun trick and figured that he could rebuild it in Lisp. Without touching the original code, Cosell used what he read to create a new version of the system from scratch.

Just a few short years later Bolt Beranek & Newman were plugged into ARPANET, which meant that Cosell’s ELIZA could be reached from any other site by opening a remote terminal session. No punched cards or local install needed, just dial in and use the program from wherever you are.

ELIZA 2.0, still running the DOCTOR script, travels farther in a month than Weizenbaum’s original will in fifty years. Within semesters, grad students from Stanford to Edinburgh believe ELIZA is a Lisp program because that’s the only version they can dial into during the small hours.

Ten years on, Creative Computing’s August 1977 issue printed a BASIC-code version to bring ELIZA to the masses. Because home computers like the TRS-80, Apple II, and Commodore PET came with a built-in BASIC interpreter, readers could type the program straight in, hit RUN, and use the chatbot themselves.

Together, Cosell’s Lisp port and Creative Computing’s do-it-yourself BASIC version reinforced the myth that ELIZA had been a purpose-built chatbot rather than a lab experiment.

Making meaning

ELIZA is remembered as a chatbot partly because that’s a lot easier to get your head around than ‘research instrument for studying interpretive projection’. But it’s also because that is what its successors were. Weizenbaum might have been trying to run a study about human psychology, but the people who pushed new versions of his system most certainly were not.

TELL ME MORE ABOUT YOUR FAMILY
My mother takes care of me.
WHO ELSE IN YOUR FAMILY TAKES CARE OF YOU
My father.
YOUR FATHER
You are like my father in some ways.
WHAT RESEMBLANCE DO YOU SEE
You are not very aggressive but I think you don’t want me to notice that.
WHAT MAKES YOU THINK I AM NOT VERY AGGRESSIVE
You don’t argue with me.

This is a piece of a transcript published in Weizenbaum’s 1966 paper. When you understand how the system works it seems rudimentary, but for those who don’t it looks uncanny.

Weizenbaum famously used these exchanges to coin the ‘ELIZA effect’ or the tendency to falsely attribute human thought processes and emotions to a machine. What rattled him was the ease at which bright adults projected intelligence and introspection onto a cardboard cut out made of if-then statements.

Today, the ELIZA effect is often rolled out to warn people against the dangers of anthropomorphising AI systems. But I think that badly misreads the lesson from Weizenbaum’s machine. Meaning emerges somewhere between system behaviour and social expectation. Ignore either half and you end up with fully sentient AI systems or, just as reductive, ‘stochastic parrot’.

Humans have a tendency to see patterns where there are none, but that doesn’t mean there are no patterns to be found anywhere we look. Surface behaviour is never the whole story. Even ELIZA mirrored syntax cleanly enough to keep the exchange afloat.

When GPT-4.5 writes code that compiles, translates Spanish without mangling the idioms, or aces the LSAT, those feats are not projections. Of course we tell stories about about them, but to suggest the AI project is nothing but narrative risks throwing the baby out with the bathwater.

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