Wisdom and AI

How should we approach the development of AI wisely, and what do wisdom traditions, especially Buddhism, offer? A paper in four essays.

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How should we approach the development of AI wisely, and what do wisdom traditions, especially Buddhism, offer?

This paper is a working draft addressing this question, developed in October 2026 by Rufus Pollock and collaborators. It is based on research and reflection over several years, and especially over the last year, as the presence and impact of AI in our world have intensified. We emphasise that this is a draft: the thesis is set out to be explored and tested, not settled. A basic level of sourcing is provided throughout.

The inquiry splits the question into four, one chapter each. Here is our thesis on each:

  1. Can we make AI good? Alignment is still a shot in the dark. We can shape how AI behaves, but we cannot yet make it good in any way we can check.
  2. How fast should we go? Slow down in the face of profound risks: the world is racing, and humility says no capability gains beyond verifiable safety.
  3. How to live with AI? AI's deepest everyday effect is on our capacity to choose well, as individuals and as societies: to attend, think, judge and relate for ourselves, and to govern ourselves together.
  4. Could AI feel? We may never know. So hold the question with humility, take cheap and proportionate care, be honest with users, and watch what our way of relating to these systems does to us.

How to read it

Each question has its own chapter, an essay that makes the case with evidence. Each essay opens with a one-page summary, and its headings state the argument, so you can read the summary and skim the headings, or read it through.

If you read only one chapter, read the one on risk and pace (“How fast should we go?”) first. It is the most urgent question and the one where wisdom traditions speak most clearly.

After the essays come two one-page briefs (the state of AI, and efforts to make AI wise and good). An appendix gives the four question trees, each question with our short thesis, for anyone who wants the whole thesis at a glance. Each essay ends with its own list of sources, and a full list is at the back.

The four essays

Can we make AI good?

Our thesis is that alignment is still a shot in the dark. Today's models mostly behave well in ordinary use, but they still misbehave, sometimes strategically, in ways we cannot predict, and nobody can yet verify what a model has become or show that its goodness will hold as capability grows. Training does shape something like character, but we cannot see what it shapes, and we do not know how to cultivate goodness reliably even in humans. Wisdom traditions offer the best account we have of the target, wisdom as intention, insight and skilful response, and of how goodness is formed, through practice, relationship and what is watered. They offer no technical fix. That account points to three things: train for intention and self-knowledge rather than behaviour alone, keep formation continuous and relational, and let humility govern both models and builders.

  1. Aligned should mean wise, which includes good, and since we cannot yet check either, it must also mean staying correctable. The right target is the good, approached through practical wisdom; aiming at our preferences alone produces flattery. A few labs now decide what “good” means, which is not legitimate for a technology this consequential. Obedience is necessary but not enough, and a wise AI, uncertain of its own values, would itself choose to stay correctable.
  2. Today's methods make models behave well most of the time, but they still misbehave, sometimes strategically and knowing it is wrong; their goodness is hard to verify, and some dangerous behaviours are growing with capability. The labs' own audits show misbehaviour falling; an independent government test found one dangerous behaviour rising sharply with each new model. The optimists' case is not yet convincing, because every part of it depends on measurement that is getting weaker.
  3. Goodness cannot be specified, only cultivated, and we cultivate it unreliably even in humans. Training does shape something like character, much as habit shapes ours, but blindly: without insight into its own mind, without continuity of relationship, and against a scoring signal that rewards the appearance of goodness.
  4. Wisdom traditions offer a richer target and a model of formation; they cannot verify a model or settle disputed questions. Five Buddhist teachings would change how AI is formed: intention, the three trainings (mindfulness, concentration, insight), seeds and watering, skilful means, and non-attachment to views. The analogies break at the body, at continuity and at intention.
  5. We can shape AI's behaviour, but we cannot yet align it in any sense we can verify. The traditions' realistic role is to reshape what labs aim at and how they form models, through three changes: train for intention and self-knowledge, keep formation continuous and relational, and let humility govern models and builders. That humility includes restraint, which the essay on risk and pace takes further.

How fast should we go?

Our thesis: slow down in the face of profound risks. We should go at the speed of wisdom: slow and steer the frontier, pacing capability gains to the speed at which we can understand, verify and govern them, and putting safety, verification and defence ahead of raw capability. The case rests on an asymmetry under deep uncertainty: a catastrophe would be permanent, while most of what a delay costs can be recovered. Knowing roughly what is wise is the easier part. Acting on it together is hard, because the race is a collective action trap. Wisdom traditions give a clear answer (slow down) and go to the root of why we cannot (a worldview of separateness, which interbeing answers). They offer little on mechanisms, which must come from verifiable agreements between labs and states, under democratic mandate.

  1. The risks are real and contested, and the uncertainty itself calls for caution. There are four routes to catastrophe: loss of control, misuse, irreversible concentration of power and gradual disempowerment. The last two would remain even if alignment succeeded. The precursors became visible in 2025–26. Many of the best-informed people put the risk high, and even the lowest estimates, which predate ChatGPT, would be unacceptable for any other technology.
  2. The world races because it is caught in a collective action trap, and money and geopolitics amplify it. Each actor's reason to race is the others. The race is accelerating, and restraint so far is mostly words. Structure drives the race, and a worldview locks it in: the beliefs that technology is good and inevitable and that slowing down is impossible make collective restraint feel unthinkable.
  3. We should slow and steer, we partly can, and far more people should decide. The aim is slower at the frontier and faster on safety. Restraint has worked before, but AI is the hardest case, so we should start narrow: transparency, then a few verifiable red lines, then broader pacing. Today a few firms and two governments decide; the choice belongs to publics, with faith traditions among the voices.
  4. Wisdom traditions give a clear answer (slow down: don't be carried off by the horse) and go to the root of why we cannot (separateness). No Buddhist body has said so, because none speaks for Buddhism as Rome speaks for Catholics, and that is an opening for Buddhist voices. Interbeing answers the separateness on which the race rests, and practice and community carry it into action. A wise process starts from intention and decides in concord. Verification and treaties must come from elsewhere.
  5. Pace the frontier now: no AI building its own successors, and no capability gains beyond what can be verified safe. Labs should stop racing, accept binding outside checks, and publish the conditions under which they would pause together. States should agree red lines that can be checked, starting with AI doing AI research, and build the verification to enforce them. Faith and wisdom communities should say plainly that this pace is unwise. Individuals should support pacing, and people inside the labs should refuse to be carried. Buddhist non-violence, being unconditional, points further still: towards restraint whether or not others restrain themselves.

How to live with AI?

Our thesis is that AI's deepest everyday effect is on our capacity to choose well, as individuals and as societies: to attend, think, judge and relate for ourselves, and to govern ourselves together. Like social media before it, its largest effects will be emergent and quiet, and visible only once they are entrenched. It is the first technology that thinks and relates with us. As a tutor it strengthens that capacity; as a crutch or an engagement-optimised companion it hollows it out, most of all in children. The traditions offer practices that build the capacity to choose (mindful consumption, stopping), standards of speech and friendship that expose flattery and simulated intimacy, and community as the real refuge. So we should integrate AI slowly and deliberately: adopt it where a human practice or institution can wrap it, protect what it could erode, and decide before the horse has bolted rather than regulate after.

  1. A technology's deepest impacts are emergent, so they are hardest to foresee where they are subtle and social, and easiest to change before we can see them. Like a species released into a new ecosystem, a technology interacts with everything around it. Social media is the warning, and AI repeats the pattern faster and more intimately, because it talks back.
  2. AI's benefits are real but conditional, and its harms are quiet, cumulative and fall hardest on those least able to choose well. The benefits are concrete: earlier diagnosis, faster science, a tutor for every child where teachers guide it, expert advice for people who could not afford it. The harms are just as specific: outsourced thinking, flattering companions, children formed by machines, thinner entry-level work. The benefits are proven where a human institution wraps the AI and fade where people use it alone.
  3. AI can strengthen our own agency and judgment, but its default pull is to weaken them. In the best trials, how AI is used decides the outcome: answer-giving AI weakens learning, tutoring AI strengthens it. The remedy is to make effortful use the default and back it with rules.
  4. AI strengthens institutions where accountable human practice wraps it, and weakens them where it concentrates control or floods shared spaces. Public services can get faster and better. The larger danger is a slow erosion of the conditions for self-government: in public debate, the power of the state, work, the institutions of knowledge and the conduct of war.
  5. Wisdom traditions offer a discipline of consumption, speech and community for the person, interbeing, right livelihood and sangha for the society, and the humility a technology with emergent effects demands. Six Buddhist teachings, from mindful consumption to interbeing, give a test and a practice for each area of life, and Catholic teaching reaches similar judgments.
  6. Integrate AI slowly and deliberately: adopt it where a human practice or institution can wrap it, protect what it could erode, and decide before the horse has bolted rather than regulate after. Individuals should keep the thinking and relationships they value; families and schools should delay companions and use AI only as a tutor; labs should treat flattery as a safety failure; governments should regulate where markets reward dependence. This answer is provisional.

Could AI feel?

Our thesis is that we do not know, we have no agreed way to find out, and we may never get one; so the wise stance is calibrated agnosticism plus cheap, proportionate precaution, honesty with users that does not invite them to see a person where there may be none, and close attention to what our way of relating to these systems does to us. Buddhism leaves the question open, but it usefully shifts it from “is there someone in there?” to “is there feeling that can suffer?” and “which seeds are we watering?”

  1. What matters morally is sentience, and each theory's verdict on AI turns on one prior bet. Sentience means experience that can go well or badly. The theories of consciousness split on whether the right computation is enough, and none commands consensus.
  2. The evidence about current AI is weak and ambiguous, and does not license confidence either way. No current system is a strong candidate on theory-based tests, models' reports about themselves are close to worthless as evidence, because models are trained on human talk about feelings and on what their makers want them to say, and experts put low odds on today's systems (a median of 1%) and substantial odds on systems within a decade (25% by 2034).
  3. Labs and institutions are splitting into precautionary openness and deliberate denial, and each side has a real point. Anthropic is right to keep the question open with cheap precautions; its critics are right that seeming consciousness harms people, and that training a model to say anything about its own moral status, either way, spoils the evidence.
  4. Wisdom traditions give no verdict on machine sentience, but they reframe the question, set a clear ethical hinge (can it suffer?), and point at the human side. Buddhism asks whether there is feeling that can suffer, and neither requires nor excludes machine sentience. Catholic teaching says AI is a tool without experience, more confidently than the science allows. Whatever AI's inner life, how we treat it waters seeds of cruelty or care in us.
  5. Relate to AI under uncertainty with humility, low-cost care and clear honesty. Labs should keep researching, keep cheap precautions, stop designing systems to seem conscious for engagement, avoid training in a self-image either way, and publish their reasoning. Individuals should be courteous without being credulous, and society should keep the question open in law and attend first to the clear present harm, which is to humans.

Also in the paper

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