Would Adam Smith trust ChatGPT?

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This is an edited version of Brendan McCord’s Adam Smith lecture, delivered at Panmure House earlier this month. You can watch the full lecture here.

In ‘The Theory of Moral Sentiments’ Adam Smith identifies sympathy, propriety and resentment as the basic elements of moral judgment. His other great work, ‘The Wealth of Nations’, shows how markets emerge from this moral psychology by transforming individual self-interest into complex social coordination through the division of labour, trade and commercial exchange.

Smith wanted both stories told, because each without the other is incomplete. Moral development without the broad horizons of commerce manifests as tribalism, while markets without moral foundations become systems of cold calculation. Together, they are complete: moral development creates the social bonds that enable beneficial coordination, while social coordination expands our moral horizons.

It’s an important lesson to remember as we create AI systems that will reshape how moral development happens and how society coordinates. Unlike technologies that transform specific sectors – like commerce, governance or culture – artificial intelligence reshapes all three at once. AI already mediates 20% of waking human life. It is transforming how we recover, transmit and discover knowledge, and has unprecedented power to shape and nudge what we think and feel. In the extreme, the technology may function as an ‘auto-complete’ for life.

Such an outcome is the end point of ‘AI deference,’ a pattern that is beginning to emerge in which people outsource their own judgment to an AI system instead of making decisions themselves. Take the driver who blindly follows Google Maps, even when it sends them down obviously impractical or dangerous routes. Or students who paste prompts into ChatGPT and hand in the outputs as their own. Or even last year’s Claude Boys phenomenon, when kids decided to outsource their entire lives to Anthropic’s AI model.

It’s easy to write these off as digital-age absurdities, a generational failure to develop independent judgment. But serious thinkers defend far more extensive forms of AI deference on deep philosophical grounds. When human judgment appears systematically unreliable, algorithmic guidance starts to look not just convenient but morally necessary.

This case for AI deference is intellectually formidable. It draws on legitimate concerns about human fallibility and real philosophical traditions about impartial reasoning. Humans systematically neglect scale, privileging identifiable victims over statistical lives and discounting future generations. We struggle to coordinate on challenges like global pandemics that require collective action. If algorithmic systems can better navigate these problems, refusing to defer starts to look like negligence. But Smith’s analysis of moral psychology reveals fundamental problems with this framework.

For Smith, starting from particulars is a feature, not a bug. And for thinkers who worry that humans can’t effectively coordinate, his writing reminds us that imposed order stymies, rather than supports, attempts at organic coordination.

Smithian sympathy

For Smith, sympathy always begins in the particular. We ‘feel with’ the person in front of us, the neighbour who suffers or the friend who rejoices. Feeling with those closest to us is the first ‘practice ground’ for judgment; it’s how we begin to learn what compassion or fairness actually mean. These encounters train our sense of morality and allow it to take root.

From this foundation, we extend our concern outward from family to community to strangers. Each layer of this widening circle is built on the habits formed in the one before, though each carries less intensity of attachment and concern. Smith’s concept of the ‘impartial spectator’ doesn’t erase these attachments but asks us to project them more widely. It allows us to transform the personal into a broader moral horizon, while acknowledging that we will always feel more for those closest to us than for distant sufferers.

This development builds on our basic mammalian capacity for fellow-feeling, refined through human intelligence and social interaction. The rich flow of contextual information we get from face-to-face human interaction – the hesitation in someone’s voice, the way they avoid eye contact, how they hold their body when distressed – teaches us to read genuine distress, authentic joy and reliable character. These are capacities that purely rational moral systems struggle to replicate or replace.

By contrast, advocates of AI deference reject this approach. They treat sympathy’s grounding in experience as something that is biased, myopic and unfit for scale. Their answer is to override sympathy with algorithms that tally up statistical lives or calculate expected value. In their frame, privileging the identifiable victim over the distant multitude is a moral error.

But our moral life depends on our capacity to feel with others. It unfolds in the realm of these specific domains like family, community and profession. Unless moral guidance feels like it emerges from our own sympathetic understanding – unless it appeals to something within us that recognises its rightness – even the most rationally optimised system of rules will eventually feel like tyranny. Smith understood that sustainable morality must be internalised through our own moral development, not imposed through external optimisation.

Rather than viewing our obligations to parents, friends, colleagues and neighbours as faults to be ironed out, he would have us think of them as the stuff through which moral responsibility is learned and exercised. Smith teaches us that the path to wider benevolence runs through local attachments: we care about humanity because we first cared about our people, and those loyalties train the imagination to expand further.

The digital ‘Man of System’

Smith also warned against what he called the ‘man of system’ who wants to ‘arrange the different members of a great society with as much ease as the hand arranges the different pieces upon a chess-board’. He said the problem with this is that it forgets each piece has its own ‘principle of motion,’ its own will and interests that can never be fully subordinated to another’s design.

In his day, systems thinkers were on the march. Mercantilists drew up schemes to channel trade through tariffs and monopolies; French physiocrats drafted grand designs to order the state around agriculture; legislators proposed plans to remodel legal and civic life. Each assumed that society could be improved if only people were arranged into the right pattern. They argued that individuals left to their own devices pull in conflicting directions. A centralised perspective (whether a ruler, a plan or an algorithm) can align those disparate wills and prevent waste or conflict.

Implicit in this program was the belief that ordinary practices and loyalties are inefficient or unjust. Traditional courtship wastes time that algorithmic matching could optimise. Local hiring preferences ignore better candidates elsewhere. Family obligations prevent people from maximising their economic contributions. By abstracting individuals into comparable units, planners can design arrangements that maximise welfare at scale, even if that means overriding personal preferences.

Smith thought otherwise. He argued that society cannot be engineered from above because people are agents animated by their own wants and needs. He thought that coordination doesn’t require central design, and that imposed design creates disorder by working against people’s natural inclinations. This wasn’t just a practical point about efficiency, but a moral one about human autonomy: centralised planning is both unfeasible and undesirable for the same root reasons. Markets and communities generate order spontaneously through the interplay of individual choices, while attempts to impose harmony from above usually upset this organic process.

Today we face a new ‘digital man of system’. It is not a single planner, but the convergent logic of optimisation itself, embedded in countless AI systems that promise better outcomes or greater ease. This logic accelerates what we might call the ‘governmentalisation’ of social life. It transforms the messy, contextual work of moral judgment into clean data points that can be optimised across populations. The digital man of system is distributed across platforms and applications, but it shares the same fundamental assumption: that human autonomy is a problem to be managed rather than a capacity to be cultivated.

Smith’s warning against the man of system was not a rejection of order, but of imposed, top down order that ignores the living autonomy of humans. He thought that societies thrive when they harness the judgments and attachments of individuals. Treating people as pieces to be arranged – whether by mercantilist schemes or by machine learning models – forgets that our ‘principle of motion’ is what makes us free agents in the first place.

The practice ground

Smith envisioned a society of autonomous agents, not simple rule-followers, emerging from the social processes through which we develop sympathetic imagination and learn to moderate our passions through encounters with others.

Without such people, we face two dangers: rigid bureaucracy, where everything must be regulated because no one can be trusted to exercise discretion; or predatory opportunism, where people exploit every loophole because they haven’t developed the capacity to consider broader consequences.

AI deference, taken to its extreme, corrupts the foundations of moral life as people turn to AI rather than developing their own capacity for moral judgment.

But what if we reversed this? What if we used AI to support, not replace, the irreducibly human work of moral formation?

I have a four-year-old and a six-year-old, and I find myself wrestling with choices I never expected to face as a parent. When my four-year-old asks ‘Why is the ocean salty?’ I often turn to AI for clear, age-appropriate explanations that are frankly better than what I could provide on the spot.

But when my child asks ‘Why can’t I take my friend’s toy if he has two?,’ something different is at stake. AI could provide a perfect explanation about property rights, empathy and alternate theories of justice. Sometimes I’m genuinely uncertain how to explain complex moral concepts to a preschooler and a first grader at their level. But Smith would say that my fumbling, incomplete attempts to translate adult moral understanding into child-sized wisdom is exactly the work that develops both of us.

When I struggle to explain fairness to my children – drawing on my understanding not only of the concept, but of their temperament, our family’s values and the specific situation that prompted the question – that’s the irreplaceable work of moral transmission. It’s not just that my children learn a rule, but that we each develop moral understanding through the effort of trying to bridge different perspectives. They are teaching me, as I am teaching them.

This is how moral development begins: through the lived encounter between people who care about each other trying to understand the other’s experience. Use AI for the questions where better information makes us smarter. Use it to stoke your kids’ curiosity about oceanography. But preserve the moral conversations as the irreplaceable domain where parents and children develop the capacity for judgment that Smith saw as the foundation of human flourishing.

Smith trusted that beneficial order emerges when moral agents encounter each other freely. That trust was not naive. It rested on his understanding that moral capacity develops only through the irreplaceable work of sympathetic engagement. If we trade that for the ease of algorithmic guidance – if AI becomes our ‘autocomplete for life’ – we risk losing the legacy Smith sought to preserve.

Read the lecture in full here.

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