Despite chairing and mentoring PhD students for many years now, I’m still surprised by how little is known about what a PhD is, what it is not, and what it takes to not just survive, but to thrive, in a PhD program.

What perhaps surprises me more though is how few practical resources there are for students either contemplating doing a PhD, or struggling to get through one.

And so a couple of months ago I sat down to see what I could produce to fill the gap.

The result was the website soyouwantaphd.wtf.1

So You Want a PhD was explicitly designed to be read by an AI companion — the intent was that someone pasted the provided prompt into the AI of their choice and asked it whatever they wanted.2

But I also made the underlying documents — 26 in all, covering everything from the nature of “scholarship” to health and wellbeing as a grad student — available in human-readable form, as it turns out that, for some people, this is far more useful that using an AI!

And the first of these documents is what turned out to be a very personal reflection on what I think a PhD is.

Re-reading this, I thought it worth posting here, just in case anyone reading this Substack is contemplating applying for a PhD, or is working through one and struggling.

And if you are interested in the full site contents (most of which is written to be AI-legible and not necessarily human-legible, I must confess), this can be found here.

A personal note on pursuing a PhD

(The original can be read here)

I came back to do my PhD at Cambridge after two years working as a management trainee in water treatment and reclamation for Severn Trent Water in the UK. Like most British PhDs it was a three-year program — three years focused on research, and three years where for most of it I wasn’t sure if I’d made a mistake or not. I was riddled with imposter syndrome — not helped by studying physics in the Cavendish Labs at the University of Cambridge. I didn’t fit. I wasn’t smart enough. I was surrounded by super-smart people. My brain, I was sure, simply could not grasp what was being asked of it. There were times — especially early on — when I thought I’d just enjoy it for what it was and then leave without finishing.

The turning point came in my third year, when my advisor suggested I look at someone else’s dissertation. I read it. And I realized that the bar was lower than I had thought. Not low. But lower than the standard I had been assuming was expected and that I had been measuring myself against. I could clear it. In fact I had been clearing it for a while.

That moment is probably one of the most significant ones of my PhD.

I say this because the feeling of not-fitting is almost universal among doctoral students — including the ones who turn out to be deeply suited to the work. But it is also sometimes a real signal: not that someone isn’t smart enough for a PhD, but that pursuing a PhD simply isn’t right for them. Distinguishing between the two is one of the most important things both you and your advisor can do if you are pursuing a PhD and struggling, and one of the hardest. I say more about this elsewhere on this site — particularly in diagnostic.md and rel_trouble.md.

A PhD is not what most people think it is. It is harder than you’ve probably been told in some ways, and easier in others. It is, above all, a process you have to be inside before any of the public language about it actually means anything. The advice you’ve read (or what you’ve heard) on what an “original contribution” is for instance, or what makes a dissertation “rigorous,” or what your advisor expects of you — none of it hits you completely until you have spent months in the work itself. And until that lands, you are likely to be working harder, and worrying more, than is actually needed.

At this point I should say what I think a PhD is for, since this colors everything else here. It is not always a pathway to academia, although for some it is. In fact, it increasingly is not a path to academia, in part because there are simply not enough opportunities for graduating PhDs here. For many, it is a recognition that you have developed, mastered and demonstrated a particular way of thinking, a specific aptitude for exploring ideas and translating this into new understanding. And it represents a validated skill set and achievement that will translate to many sectors, roles, and kinds of work beyond academia. A PhD can be deeply personal: the chance to build and flex intellectual muscles and to delight in discovery. It can be professional: a step toward what comes next, with delight and wonder ideally a part of it at the same time. What it should never be though is a necessary evil — the thing you grind through to get the three letters after your name. If the PhD has come to feel like that, you are almost certainly doing the wrong thing, or in the wrong place, or with the wrong people, or all of these. And if this is happening, it’s time to rethink (even if you’re one of my students and I’m part of the problem!).

I’m trained as a physicist. My PhD was in high resolution electron microscopy and aerosol physics; my career since has run through occupational and environmental health, risk science, nanotechnology safety, responsible innovation, toxicology (true!), policy, public engagement, a whole raft of emerging technologies, and navigating advanced technology transitions from gene editing and quantum technologies to AI. Through this, physics gave me something I have come to value more, not less, with time: a mindset that focuses on the core of research and scholarship — curiosity, wonder, experimentation, humility, serendipity, the willingness to push against what is settled, and to learn from mistakes and others — and that holds all of this to the test of scrutiny. It also grounded me in the art and craft of research and scholarship — from the age of 13 I was immersed in learning the craft as well as the foundational knowledge and skills of what it is to think like a physicist. Somewhere along the way I stopped being a “physicist” in any narrow sense, but those underlying foundations endured, and I now describe myself as an undisciplinarian: someone whose mastery isn’t in any single field, but in working across the boundaries between them. I think this is closer to what scholarship has always been than the disciplinary parceling we’ve made of it. The work that matters tends to live in the seams. Of course, across all of this I would claim that I still think like a physicist, whether I am grappling with philosophy, ethics, social science, engineering, and beyond.

I sit on and chair PhD committees across a wide range of areas. Some students are deeply aligned with one discipline; some cross between them; some defy disciplinary categorization altogether. What I look for in their work isn’t disciplinary correctness. It’s whether the scholarship is rigorous, defensible, legible to others, useful, and — this matters to me — delightful. Not delightful in the sense of cute or charming. Delightful in the sense that there is, somewhere in it, the discovery of something that wasn’t seen before; and the student knows it, and the reader can feel it. I want to see students who naturally ask why, who can imagine a future different from the one in front of them, who recognize that knowledge and understanding take many different forms — and that part of the doctoral task is learning which forms their particular question requires, and who are willing to have the discipline and put in the hard work to excel in what they are capable of.

I have high standards for the quality and depth of doctoral research. I have a wide tolerance for the form it takes, so long as the work is legible and defensible. What I have little patience for is procedural compliance dressed up as rigor — the box-ticking model of doctoral work, in which a student earns the degree by demonstrating that they have done the prescribed things rather than by demonstrating that they have thought. The standard a dissertation has to clear is not a standard your committee invents; it is a standard set by the field, by other scholars, by the lineage of work the dissertation joins. To turn up expecting to be passed because you’ve worked hard and your committee likes you is to misunderstand the whole arrangement — and, in a real way, to insult the field, the people who came before you, other students, and the committee itself.

I am genuinely excited by students who push against convention, who follow unconventional pathways, who bring up and pursue ideas I didn’t expect. I will support that thinking, and I will defend it when others can’t see it yet. But I require, in return, a deep commitment to the craft: the willingness to put in the hours that turn knowledge into something closer to intellectual muscle memory, the discipline to gnaw at a problem until it yields, and the humility to let your own thinking be tested by people who know more than you. That last part matters more than most students initially think. Intellectual argument is not personal. Having your work pressured, questioned, taken apart is not an attack on you — it is the mechanism by which the work gets stronger and the field gets sharper. If you cannot handle that, the PhD will be very hard. This is the fire in which poor work is turned to vapor and dissipated and good work is refined and hardened — and there’s no hiding from this.

Doctoral work is also a process you cannot do without failing. The point is not to avoid failure but to learn through failure — to fail in ways that teach you something, that move the work forward, that reveal what the next question actually is. That kind of failure is fuel to what you do. The kind of failure that should worry you, and that should worry an advisor, is different: the recurring inability to grasp what is being asked of you, to still be asking three years in what scholarship is, to develop under your own steam, to learn from what isn’t working. The first kind is a sign of the work going right. The second is a sign that something deeper is off.

I also want to be up front here about something advisors may hesitate to discuss: not everyone is equipped to do a PhD. This is not a judgement of intelligence or worth, and I want to be careful here, because the conflation of those is something that worries me a lot — it damages students in both directions, telling some they are less capable than they are, and others that they should be doing something they are not in fact suited for. The PhD asks for a specific mindset and a specific kind of discipline; people who do not have it are not lesser, they are simply suited to other lives. I will, in fact, try to actively dissuade someone I do not think is right for the work, and I will do it not from gatekeeping but from care: a PhD pursued by someone who isn’t equipped for it tends to hurt the person undertaking it, sometimes seriously. The most painful conversations I have are with students who are suffering through work that isn’t right for them, and whose path forward — if they can come to see it — is somewhere else. I will support that move just as carefully as I support the work of someone who is fully in.

Before I finish, I did want to say a couple of things about being a PhD chair — because this is also something worth knowing if you are doing or contemplating a PhD. And this is personal to me — I suspect other chairs have their own perspective.

I see chairing a PhD committee and mentoring PhD students as a serious commitment, and one where the student’s success and wellbeing come first. I am not obliged to take on students — this is very important to understand. There are no penalties as a tenured professor to me if I do not, and there are considerable pressures on my time and health if I do (believe me, working nights and weekends to line edit a draft of a dissertation takes its toll). As well as the time commitment, the relationship between a PhD student and their chair is personal - and sometimes the fit just doesn’t work, which is also why I am careful with whom I decide to work with.

When I do work with a student though, I make time for them, even though this is often on top of a busy schedule. I’ll meet with students anywhere from once a week to once a month or so, depending on what works for them. And I will strive to put their interests and their journey first — even if it means suggesting that someone else may be a better fit for them as chair.

I aim to be professional and always place student success before my own. I will make myself available, provide whatever support I can, be as effective a mentor as I can, and try hard not to over-burden my students. At the same time, mentorship takes effort. It is a discipline. And it takes a toll - a mental toll especially. It hits hard when students are disrespectful or dismissive, when they assume you have no life beyond their work, when they don’t respond to you but expect you to be responsive, when they act and behave as if they know more than you (although sometimes they do), when you invest heavily in them and they - to forgive the colloquialism - dump you without even the courtesy of a text (yes, it happens), when they see their relationship with you as purely extractive and focus on what they can get without a thought to what they might give. Yet my job is to absorb this and not let it show — because the student comes first. Which also means this is probably the only place you’ll see me admitting this!

In return, I expect a few things. Come to our meetings prepared. Don’t expect me to drive the conversation; you’re the one whose work it is. Disclose problems early — funding worries, life events, conflicts on your committee, struggles with the work itself. I often won’t know unless you tell me, and the earlier I know, the more I can do. Respond to feedback substantively. You don’t have to adopt every comment I make — I’d worry if you did — but you do have to engage with it. And don’t treat me as your only line of support. Peers, other faculty, your committee members, institutional resources, and the broader academic community are all part of what it means to do a PhD well; they are not bypasses of me, they are part of the work. The chair-student relationship is real, but it is not the whole picture, and a student who has built a wider community is a stronger student for it.

Finally, if you have come to this site — to this text, written for both you and the AI you may be reading it with — what I want you to take from it is this. The PhD is a craft, not a credential; a process of formation, not a sequence of boxes; a way of thinking that you become rather than perform. The other files and resources here will help you locate yourself in that. The AI you are reading them with should not be doing the thinking for you, and it should not be flattering you. If it is, push back — at it, and at me, and at this site. The work is yours. That is the whole point.

Postscript

Of course, being an academic, I had to add one last thing! The thinking captured across this site reflects where my thinking currently is. But every day I am re-examining my thinking and understanding, and modifying and refining it where nexessary. This is especially the case when it comes to the craft and care of scholarship in an age of AI, where my own thinking is evolving in real time. And here the process of developing this corpus has, in itsef, helped test, refine, and extend, my own thinking. As a result, what is here is a position — and a temporal one at that, although one that’s bult on over 30 years of scholarly practice. It is not doctrine; the corpus is developing through the same kind of dialogic practice it describes. You should push back where it doesn’t seem right or is lacking — of course bringing the weight of your own scholarship to bear in this so it isn’t just opinion or hubris. Because this is how our understanding of what a PhD is — the value it brings and the purpose it serves — continues to evolve and grow.

Update: At 5:12 PM ET on June 12 The US Government issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national. As a result Anthropic has disabled access to both models for all customers at this time. More here.

I guess it was inevitable: having tried out Anthropic’s new “Mythos-class” Fable 5 AI model with a single-shot research prompt while on the way home from vacation a couple of days ago, I couldn’t resist the temptation to see what it could do given greater latitude, and a lot more tokens!

This is what happened.

My previous post set out to see how far Fable would get with a simple one-shot prompt that asked it to research and write a paper about the impact of Mythos-class AI models on higher education. The intent was not to generate ground-breaking new knowledge (one-shot prompts are definitely not the way to go here), but rather to see just how far Fable could get on its own.

You can read the resulting paper here. It’s superficially very good. The hypotheses are worthy of consideration, the reasoning is not bad, the research and the paper’s coherence and through-line are very good, the citations are not hallucinated, and many of the arguments made leverage prior work reasonably well.

But it is still flawed. The research tends to rely on secondary sources rather than original papers. There are questions around the validity of the insights drawn from published work in some cases. It’s not clear how original and insightful the research truly is. And Fable’s writing, while an improvement on previous models, still errs on the side of homogenized style over substances.

There was enough here though to make me wonder what would happen if I passed the task over to Fable 5 in Claude Code, where it could fully lean into its capabilities.

And so I set up a new project in Claude Code using Fable 5 in “ultracode” mode, passed on the original prompt and paper — together with a coupe of critical and detailed reviews from separate sessions using Fable, as well as my own feedback — included a folder of documents cited in the original paper along with a coupe of additions of my own, and asked it to “write a rigorously researched and defensively argued preprint” based on the supplied material, without further direction from me.

Watching Fable work based on what I asked of it in Claude Code was fascinating. The model devised a plan of action that would put many PhD students to shame. It developed tentative hypotheses then tested and modified them. It spawned tens of sub-agents to do research, test claims, write and rewrite drafts, and a lot more. It downloaded and audited primary sources as necessary. And it folded in layer after layer of checks and balances to ensure what it was doing was grounded in what is known and what is defensible.

This, I must admit, all came at a cost. I was using Anthropic’s $200 a months Max plan and, ironically maxed it out — and had to purchase more credits to complete the project. (Fable even let me know I needed to do this!) According to Fable’s own audit, the project used over 80 agents, called on the use of 2000 tools, verified over 120 primary sources (including downloading and auditing them), ran for nearly 15 hours on the task-clock, and consumed over 8 million tokens.

And, according to the audit, nearly half of the token usage was spent on verification — by Fable’s choice, remembering that I set the goal then let it run, but didn’t specify how it met the goals.

The “preprint” that Fable produced was certainly interesting. But in a good way.

In being given autonomy to pursue what I’d asked of it, Fable interpreted its goal to both research the original question — using the first paper as a starting point — and to reflect on its own role in the process. As a result, the preprint it produced was deeply reflective, with meta-layers lying above the academic substance.

This is not what I expected. Fable produced a dense, 53 page document that explicitly refers to the first paper and prompt, and that documents in depth its own processes and decisions. It is also academically quite rigorous — although this took some time to ascertain given the density and terseness of the writing. At this point I had not provided any instructions on writing style and so this is “raw Fable” and not particularly human reader-friendly.

This preprint (it’s really an unvarnished research report) underwent one iteration (ending up as version 3, with the original one-shot prompt preprint being version 1), following me paying for additional tokens to complete a couple of failed tasks, and providing a handful of PDFs that Fable couldn’t access directly. Other than this, I was completely hand-off on its production.

This “preprint” is not the end of this story, as I was looking for something more polished and less self-referential. But I’m including it here as it provides an instructive insight into the rigor behind Fable’s work. It’s hard going reading it, but worth it if you want to dig under the hood of what Fable did, and why:

After The Proxy V3

873KB ∙ PDF file

Download

Download

(Note that it refers to a number of associated Fable-generated files which aren’t included here, but that I have access to, and are in themselves instructive as to just how rigorous the process was).

Building on this, I asked Fable to write a stand-alone paper that was true to the original one-shot prompt, that didn’t including the self-referential meta-layer, and that retained the full rigor or the preprint.

And here I did intervene, just a little, because the writing style of the first iteration was awful. Technically, the content was solid. But it took so much effort and energy to digest that it reading it was painful.

And so over two further iterations I asked Fable to rewrite and re-format the paper in a way that would make it easier for human readers to digest — using its own discretion in how it interpreted this (although I did provide some guidance on what a reader like me finds palatable versus hard going). Through these I was very explicit about not losing any of the academic rigor, and the need to check this.

This was the result:

Assessment And Formation Under Agentic Ai V3

671KB ∙ PDF file

Download

Download

And here I have a bit of a problem, but not one you might expect.

Even on a quick read, this paper is substantially better than the one produced from the one-shot prompt. By a long way.

After the “legibility” rewrite (and to be clear, this still reads like an AI paper, although in this case the substance is more important than the style) the hypotheses it puts forward, the claims it makes, the reasoning it backs them up with, the evidence it presents, the coherence and reasoning underlying everything, stand up to considerable scrutiny.

But because of the quality and extent of the “intellectual labor” represented by the paper, it takes a substantial level of human expertise and intellectual labor to evaluate it.

This is not a paper that can be read and evaluated in a few minutes, or one that can be substantially assessed by someone without considerable knowledge that spans multiple fields. And here, I even find myself reaching my own limits in assessing it’s rigor and validity — and the value — of the insights it offers. And my expertise intersects pretty closely with the work.

If I’m being honest, I would need to spend days with this — probably more — before I was sure in myself where the value of the work lies.

On one hand, this is what I would expect from substantial intellectual work written by accomplished human researchers. On the other though, it does highlight substantial questions around how AI-generated research is evaluated when increasingly few humans have the expertise or intellectual capacity to fully understand and assess it.

It also suggests that any quick responses to AI-generated work like this are either coming from genius-class humans, are themselves the product of AI, or are not based on knowledgeable assessment.

Where this leaves us, I’m not sure — especially as this one example is almost definitely a relatively poor reflection of emerging capabilities. But it does suggest — just as the paper itself does — that agentic frontier systems like Fable 5 will increasingly challenge how we navigate the intersection of AI, expertise, and knowledge generation.

Not because AI is in some way “smarter” or “better” than us. But because it is getting so good at emulating the processes through which new knowledge is constructed and tested — while doing this at speed and with access to prior knowledge at a scale and depth that far transcends human capabilities — that mere humans are going to find it increasingly hard to keep up.

Postscript

This was very much written to stimulate informed conversation. Frontier AI models and systems are still very much a moving target, and there’s a serious risk — as many commentators have noted — of falling for the illusion that these models are more capable than they actually are. This is an inherent risk with a technology which has a mastery of language that is potentially capable of slipping by our critical reasoning and persuading us of things that don’t hold up to scrutiny.

And yet, it would be foolish to discount emerging capabilities around autonomous AI research and knowledge generation. Just as it would be foolish to ignore the consequences of these capabilities to the roles of learning and education — especially higher education — in a world built on the assumption that intelligence, expertise, and new knowledge, and valuable because they are scarce.

The paper Fable wrote addresses this directly. And while this post is primarily about the process, I would strongly encourage anyone who takes the future of higher education seriously to read it.

Of course, the paper may be little more than smoke and mirrors, which is where the conversation it spawns is so important — as long as it is based on informed expertise and reason and not assumption.

But my sense is that we are seeing the emergence of capabilities that have the capacity to both challenge and extend how we think about knowledge, research, learning, and value-creation — and ultimately, what it means to thrive as humans in an age of AI.

If true, this should be an absolute top priority for any university that takes student success and the future of human flourishing seriously — and certainly far more seriously than incessant conversations around pre-2023 level AI capabilities.

Especially as we are potentially at the edge of a precipice where AI systems are capable of generating new knowledge and insights faster than we are currently capable of validating and even understanding them — or their consequences.

Update: At 5:12 PM ET on June 12 The US Government issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national. As a result Anthropic has disabled access to both models for all customers at this time. More here.

Part 2 of this series can be read here, where I extend this exercise to working within Anthropic’s agent-based Claude Code environment.

Yesterday, Anthropic released it’s much-anticipated new AI model based on the near-mythical “Mythos Preview.” Claude Fable 5 is being touted as a substantial step forward in what LLM-based AI is capable of, especially when it comes to autonomously completing goal-oriented tasks with a high degree of accuracy—and even conducting original research.

It’s also a step that could, once again, disrupt higher education as AI continues to challenge conventional approaches to learning and teaching, and even what the value of a university education is.

I’m on vacation at the moment and trying hard not to engage with every new AI story (as well as supposedly stepping back from posting as much while on sabbatical). But yesterday’s release felt too significant to ignore.

And so just before heading to bed last night after taking in a play in London, I thought I’d see what Claude Fable is capable of.

Leaning into the advertised levels of goal-oriented autonomy and internal guardrails against going off the rails, I gave it the following prompt (running the model on Max effort):

Your goal is to write a rigorously researched and defensively argued preprint on how the release of Mythos-category AI models potentially impacts students in higher education. It should address emerging possibilities that go beyond conventional approaches to LLMs in education, grapple with emergent risks in a broad, sophisticated, and student-centric way, and provide key insights into frameworks for approaching student centric higher education in the light of emerging AI capabilities. It should be intellectually original and generative. It should be explicitly labeled as authored by Anthropic Fable 5 Max and include an AI use statement.

It was a very quick and dirty initial test of its capabilities, but the aim was threefold:

• I wanted to see what the model was capable of with a single (one-shot) prompt—something I would usually never do as one-prompting research and papers tends to lead to outputs that are superficially OK and substantially poor. But this is what made it interesting.

• I wanted to see how good Fable 5 was at researching a topic, developing hypotheses, exploring and testing them, and writing them up in a coherent and academically generative draft paper—all with no additional input from me.

• And I wanted to see what it came up with when I asked it to explore the potential implications of Mythos-class models to higher education.

It felt like a good first-test that, even if it failed, would be instructive.

The resulting paper—which is unmodified from what Fable produced after that single prompt—can be downloaded and read below:

After The Proxy Preprint

190KB ∙ PDF file

Download

Download

To reiterate, I have not edited this or iterated around it with Fable in any way. I have carried out a first check on the citations, which all seem legitimate (no hallucinations detected yet). I have also read through the paper to get a sense of how insightful it is, and how academically robust it is.

And on a first pass it’s not bad.

Actually, it’s pretty good. Not good enough to stand the test of deep scrutiny, but for a few minutes of AI run time, Fable produced something that it would have taken me several days to do justice to in a non-AI world.

Of course, the temptation was to start editing and iterating this to produce a polished paper. But rather than do this (I have a plane to catch) I thought I would post the paper as-is as a quick demonstration of what Fable 5 is capable of—and what it is not—and as an initial thought-catalyst on how models like this might disrupt higher education yet again.

Because of this, please read with caution. But also, read the paper with the seriousness it deserves, as it hints at what the next wave of AI is likely to be capable of—and how this is likely to further disrupt higher education at a time when many faculty are still coming to terms with the changes ChatGPT brought about in 2022.

In the meantime, here’s my initial quick take before boarding starts:

1. The degree of autonomy with which Fable can take a prompt, infer goals, tasks, workflow etc, and execute on these without further human engagement, is impressive. Of course, one-shot prompting will always be limited (and never a great idea when researching and writing a paper), but extend this ability to sophisticated human-AI collaboration in a multi-agent system, and the implications are worth paying attention to.

2. The one-shot hypothesis development, research, analysis, and conclusions, are very good. But they are still limited. For a machine doing this without human intervention though, hard to overstate how big a deal this is.

3. On a first pass there are no obvious hallucinations in the citations—which is a big deal. What I don’t know yet is whether the ways in which the cited works have been used—essentially the insights drawn from them—stand up to scrutiny, or whether there are key works have not been referenced. There is also the general issue of LLMs not using primary sources but rather relying on secondary mentions, which can lead to inappropriate or naive uses of sources. I suspect that Fable is no different here with a simple browser-based single prompt. This can be fixed by working interactively with the model while giving it access to primary sources—but in a one-shot prompt like this I would be surprised if it didn’t lead to mis-interpretation.

4. I found the writing style in the resulting paper to be OK—still rather flat with an annoying AI signature, but more palatable than Opus 4.6-4.8 (for instance) which produce prose I find near-impossible to read without it feeling like fingernails down a chalk board.

5. The insights Fable 5 came up with into how Mythos-class models may lead to new disruptions in higher education are genuinely worth paying attention to. On a first read there are ideas here that, while they may not be original (they may be—that would take time to check), are nevertheless informative. The two theses—the “full proxy collapse” and the “disappearing ladder” are serious enough and well-argued enough to warrant serious attention. The following risks to student success are well-considered and informed. And I found the resulting suggestions on pathways forward reflected coherent reasoning (supposedly a feature of Mythos-class models), as well as useful through-starters. Here, my initial sense is that few of these ideas are genuinely novel, although they may be. What is more important than novelty though is how existing research and theories are brought together here in a coherent and informative way.

Considering that the complete process of prompting Fable 5 to it producing a polished draft paper took less time than it took me to read the paper, or to write this post, is an impressive feat—more so as the product is not bad.

And when it comes to a potential new wave of disruptions to higher education, I have to agree with Claude’s conclusions—with the caveat that Mythos-class models are likely to be out of reach of most educators for a while yet.1 And that is that the release of Mythos-class models forces us to change how we think about teh intersection of AI and education.

This is no longer a technology that emulates the outputs of educational and learning processes, but extends this to the formation of those outputs.

And this both threatens to pull the rug from under every effort over the past 3 plus years to accommodate ChatGPT 3-level capabilities, and to open up new learning possibilities that we’ve barely grappled with.

As long as we have the agility to move with the models as fast as they are evolving. And that, in the world of education, is a big “if."

… and. now to board that plane!

UPDATE May 25: The published Magnifica Humanitas is now available here. I’ve added my initial thoughts on a first read through in the postscript.

On Monday, Pope Leo XIV will publish his first encyclical Magnifica Humanitas (magnificent humanity), which is keenly anticipated to lay out his approach to centering our humanity and ensuring the protection of persons in an age of AI. The document is being positioned as a natural successor to Pope Leo XIII’s encyclical Rerum Novarum (Of new things), published 135 years ago, which shaped how organizations and governments around the world have framed human dignity in the context of technological change. But there’s a third encyclical that, in a sense, completes the picture. And that is Pope Francis’ 2015 encyclical Laudato Si’ (On care for our common home). Together, they address what we do, where we live, and who we are in times of technological transformation.

This framing resonates deeply with my own work on the future of being human, which is why I'm especially keen to see how far the encyclical goes when it's published on Monday. Ahead of that though, I thought it worth reflecting on why the intersection between emerging technologies and what we do, where we live, and who we are matters as much as it does.

Back in January 2025 I published a piece on why universities need to step up their Artificial General Intelligence (AGI) game. In the article I suggested that we need to think in far more integrated and discipline-agnostic ways about successfully navigating advanced AI transitions as we work to ensure a future of human flourishing.

As I wrote then:

One approach is to consider three intersecting foci: How advances in AI could impact where we live (from our homes and communities to the environment and the planet as a whole — space even); How they might transform what we do (from discovering new knowledge and insights, to creating value in all its various and diverse forms); And how they potentially affect our understanding of who we are (from how people behave and function as collectives in society, to the most fundamental aspects of how we define and understand ourselves as individuals).

The resulting schema looked like this:

This framing around navigating technology transitions is something I’ve been exploring for some years now, and is reflected in much of my work on what it means to be human in an age of AI — most recently in the book AI and the Art of Being Human. But it was reading about the anticipated focus of Pope Leo XIV’s Magnifica Humanitas and its provenance that got me thinking about it in a new light.

135 years ago, Pope Leo XIII’s 1891 Rerum Novarum focused on capital, labor, and human dignity in the context of the transformative technologies of the time — driven by the industrial revolution. And while we are long past the days of that particular industrial revolution, the insights and ways of thinking he laid out continue to be relevant to this day as AI ushers in a new era of automation.

Rerum Novarum explored what we do — and particularly the work we do — with technology, and how this impacts our humanity (and by extension, the future). But it didn’t say too much about the coupling between our use of technology and the planet we live on, the environmental, ecological and biological systems we are a part of, and the intimate coupling between what we do and where we live. Yet this is also critical to understanding how to successfully navigate technology transitions.

The Vatican has framed Magnifica Humanitas primarily in the context of Rerum Novarum rather than Laudato Si', and as a result the connection to Pope Francis' encyclical has largely been overlooked in the lead-up to Monday. And yet Laudato Si’ provides a vital piece of the puzzle when navigating transformative technologies — even more so at a time when AI both presents an environmental threat and a potential pathway to developing novel solutions to persistent environmental challenges.

Pope Francis situated his encyclical in a long tradition of Papal writing on taking responsibility for the future, and appealed to readers “for a new dialogue about how we are shaping the future of our planet” and a “conversation which includes everyone, since the environmental challenge we are undergoing, and its human roots, concern and affect us all.”

The 1891 Rerum Novarum and 2015 Laudato Si’ create a foundation for thinking about the intersection between transformative technologies and the future that extends far beyond the Catholic church. And yet, they fall short of addressing the one domain where AI is shaking things up in ways that no other technology has come close to. And this is where the technology both challenges and opens up new ways of revealing who we are. This is the gap that Pope Leo XIV’s Magnifica Humanitas is anticipated to fill.

This is a gap that is increasingly attracting attention. Just this past few weeks there have been incidents of students booing pro-AI speakers at graduation ceremonies, reflecting a growing wave of antagonism toward the technology. This is driven in part by perceived threats to what we do (jobs, value creation) and where we live (water, energy and land use). But it also hints at deeper concerns around how the “cognitive coupling” between AI and those using it potentially impacts who we are.

At one end of this spectrum are stories of “AI psychosis” where extended use of conversational AI begins to impact how people think and behave. But there are also growing concerns around less obvious — yet equally important — impacts which arise from conversational AI’s ability to bypass our cognitive defense mechanisms.

Earlier this year, Steven Shaw and Gideon Nave from the Wharton School published a preprint on “cognitive surrender” and how AI is reshaping human reasoning. Their argument is that there’s growing evidence that heavy AI users have a tendency to trust AI to do their reasoning for them, despite it not being trustworthy. It’s an argument that aligns with my own work on how AI is a potential “cognitive trojan horse” that has the capacity to bypass our cognitive defense mechanisms by broadcasting signals we usually associate with human trustworthiness. And pushing this further, I recently wrote about how “constitutive resonance” between users and AI (a two-way coupling where both human and artificial participants are changed in the process) could potentially accelerate how the technology impacts how we think, perceive ourselves and others, behave, and make decisions.

These, I suspect, are just the tip of a growing area of research around how AI potentially threatens who we are. And yet there is another side to this — and that is how the unique relationship between humans and artificial intelligence has the potential to transform our understanding of who we are, and as a result to help us thrive in an age of AI.

This is what Jeff Abbott and I wrote about in our book AI and the Art of Being Human, and was recently touched on in an article by Bryan Penprase in Forbes. And it brings us back to the center of the three domains that map out the terrain around human flourishing and advanced technologies: In a world where AI is inevitable (and I would argue that the boat has already left the harbor here), how do we ensure that we develop and use these technologies in ways that center human dignity, that enable human flourishing, and that do this by taking an integrated approach to what we do, where we live, and who we are?

It remains to be seen how much Magnifica Humanitas will contribute to closing the gap here. But all the indications so far are that it will represent an important step toward ensuring and celebrating our “magnificent humanity” in an age of AI.

Postscript

May 25. 2026. Having had the chance now to have a first read through Magnifica Humanitas, I wanted to provide a few initial reflections in the light of the post above — with the proviso that the encyclical is a document that deserves deep study, and is one that I suspect will foster debate and — hopefully — action for months and years to come.

It is also a document that deserves human care and attention in its reading. I’m sure people the world over are already cutting and pasting it into their favorite AI, and getting the headlines. This is, if course, useful for getting a sense of the big picture messages. However, it also raises two important questions:

1. As you read an AI-generated summary, what is missing in it, and how will you know?

2. How much of the lived humanity reflected in Magnifica Humanitas will be missed by LLMs — its positioning in human experience, in relationships, in the transcendent core of what it means to be human that defies reduction to transactional interpretation?

Having read the encyclical without the aid of AI, I am convinced that there are layers here that LLMs will overlook, or simply not be able to represent, because they are not intimately embedded in the full experiential spectrum of what it is to be human.

Because of this, even if you do engage with it using AI, do take the time — and the care — to read it the old fashioned way, and the way it was intended to be read.

With that, a few things jumped out on my first reading— I’m sure there will be many more on re-reads:

First off, I genuinely felt seen reading the encyclical. For much of my professional career I have advocated for broad and inclusive approaches to technology innovation that elevate the marginalized and center on human dignity; that are grounded in listening, humility, and a willingness to change; and that take nuanced and informed approaches to navigating technology-driven transitions. Many of the areas I’ve worked in and advocated for are reflected in Magnifica Humanitas.

Perhaps more importantly though, the encyclical is a powerful blueprint for not only thinking about the intersection between society, technology and the future, but for actively navigating it. There are large parts of it that speak directly to the roles and responsibilities of developers and governments. But it also speaks to anyone (and any organization) that has a part to play in the development and use of technologies that have the potential to profoundly impact human flourishing.

This extends in particular to educational establishments and universities, where there is a profound responsibility — underlined in the encyclical — to create learning environments around and with AI with great care and humility. Something that’s easy to overlook in the rush to go as fast as possible and prioritize the transactional over the relational.

Secondly, Magnifica Humanitas fits well into the model I describe above of understanding human flourishing in an age of AI through what we do, where we live, and who we are. It explicitly builds on the 1891 Rerum Novarum and 2015 Laudato Si’ (as well as many other philosophical and doctrinal foundations) to consider what it means to be human — who we are — in the face of transformative technologies that include, but extend beyond, AI.

Here, I would consider it foundational to any efforts to address human flourishing in an age of AI — including in the context of global futures writ large.

That said, it stops short of what I suspect is needed here.

This is very much a document that sets out to preserve what it means to be human as we have understood this for millennia — and, importantly (because this is a religious document), in the context of our individual and collective relationship with God. And yet, there are growing indications that the cutting edge of AI development is beginning to force a reckoning with long-held assumptions around what it is to be human.

This was hinted at in some of the comments from Anthropic co-founder Chris Olah at the encyclical’s release, where he talked about us creating something we don’t fully understand. And it’s something that Pope Leo strenuously resists in the encyclical as it focuses on concepts of what it means to be human that are enshrined in hundreds of years of tradition.

And yet, given that so many people and organizations are so far behind the curve when it comes to thinking about AI and it’s impacts on who we are, the encyclical perhaps treads a pragmatically useful path as it extends that thinking without breaking it.

And here it does lay out a radical perspective built around human dignity and thriving — and one that challenges much of what we see currently emerging around AI, whether we are looking at how individuals use it, how institutions deploy it, or how governments (and others) weaponize it.

Whether this will move the needle or whether it is just wishful thinking is, of course, an important question, and one that I’m not sure there is a clear answer to yet. But at least the question is being framed in a way that’s hard to ignore.

That said, there was one aspect of the Vatican’s framing that did jar with me, and that’s the how the encyclical approaches AI as a tool. Here, the framing is admittedly nuanced. But I still worry that treating a technology that has the ability to fundamentally alter how we think, act, and even believe — and in ways that surpass our comprehension — as just a tool, is potentially dangerous.

There’a a lot more here that deserves attention but will take time to consider: The framing of AI in terms of the Tower of Babel (dangerous hubris) versus the rebuilding of Jerusalem led by Nehemiah in the Old Testament (building a future of human flourishing centered on in human dignity in relationship with God and one another in an age of AI); the need for humility and dialogue as we navigate such a transformative technology; the warnings against the naive and self-centered wielding of power without understanding or wisdom; the perilous concentration of power in an age of AI; the need to embrace the good of AI while managing the bad; the importance of “disarming” AI; the imperative to embrace a mindset of “shared discernment” in building an AI future together, and a lot more.

But this will take time to digest, think about, discuss, and consider. And that will be a longer post for another day.

In the meantime, do take the time to read and think seriously about the encyclical and not just paste it into an LLM — especially the introduction, and chapter 3, which focuses specifically on AI.

OK, so I’m being a little tongue in cheek with this newsletter’s title. But as I embark on a sabbatical for the coming academic year, I’ve been thinking quite hard about why I write this Substack.

Between now and August 2027 I’ll be putting aside the daily hustle of life as a university professor as I take time to decompress, to think, and to explore new ideas and possibilities.

It’s one of those privileges of being a tenured professor that I haven’t taken advantage of before. But I’m at that point in my career where, working flat out seven days a week while feeling guilty that it’s never enough, has made me realize that it’s time for a bit of a reset.

Of course, the idea of a sabbatical isn’t just to take a break.1 Rather, the aim is to recharge your intellectual batteries so that you come back refreshed and raring to go (assuming you haven’t found somewhere else you’d rather be in the meantime).

And so that’s what I’ll be doing.

The plan is to use the time to explore emerging thinking and understanding around the intersection of AI, society, and the future we’re creating (or stumbling toward).2 But I’ll also be using it to do a bit of soul searching around my public writing.

I’ve been writing for public audiences for nearly twenty years now. It started with an invitation to write a weekly blog on nanotechnology back in 2007, and has since become central to my conviction that the work I do as an academic should be as accessible as possible to as many people as possible. Not as an add-on to my research and scholarship, but as something that’s integral to how I explore, test, and share new ideas and insights.

In 2023 I made the decision to channel these efforts through the Future of Being Human Substack rather than higher-prestige platforms.3 It was a risky move, but one that I felt worth committing to as it allowed me the freedom and flexibility to combine my writing with my thinking, while connecting directly with my readers.

Whether it’s paid off, I don’t know — and this is something that the sabbatical will give me a chance to look at more closely.

Without a doubt, combining my thinking and research with the discipline of writing weekly public articles has allowed me to grapple with new ideas and insights in ways that would have been hard to do otherwise — especially given the speed with which things are changing around transformative technologies like AI. And it has allowed me to expand the reach and relevance of my work and that of others — quite considerably at times.

And yet, there have been distinct downsides to this.

Most of my colleagues do not read what I write, or recognize it as having substantive value and relevance — and I know this is just how these things go as everyone’s busy, but it still stings. Many people assume I’m just a commentator, and not an established expert in my field who uses public writing as my medium of choice. And some assume that what I do is simply an ego trip, and not something they should be encouraging (and maybe it is — although I hope it isn’t).4

This is all OK — it’s the cost of the decision I made to put public good before academic prestige, and something I am comfortable with.

But it’s only OK if there really is public good that comes from my writing.

And this is what I’m not so sure about.

By academic standards, my Substack posts do alright. But “academically alright” is a pitifully low bar. In reality, they rarely make much of a dent on how most people are grappling with the issues I write about. And in spite of the encouragement I do receive (and I deeply value this), I struggle each week to reconcile the time and effort spent with how little traction I get — at least in how this is reflected in the number of people reading and sharing what I write.

And here, my worry is that what I write has no real value to those who might read it. Not because it doesn’t have intrinsic worth (I know my stuff), but because it makes no sense to them.

In effect, my concern is that they don’t see why it might be relevant to them, or useful, or advantageous in other ways. And because of this, they see no sense in investing time in it.

In other words, to them it’s nonsense — literally making no sense. Hence the title of this piece.

I hope this isn’t the case.

And yet, to write without care for your readers is a very academic trap to fall into. That modality where it’s publishing the thing that counts, not the impact the thing has.

And that’s a trap I’d rather avoid.

And so, as well as taking time this coming year to rethink and reset my academic work, I’ll also be taking the time to think carefully about why I write for a public audience, who I’m writing for, what I am hoping to achieve, and why it matters.

Of course, that’s not going to stop me posting the occasional piece here as the muse strikes — this is a sabbatical, not rehab!

But I will, hopefully, be more mindful as I do, of the “nonsense” I write.

Postscript
In my haste to press “publish” I neglected to add that I deeply value the many subscribers and readers who do read what I write. Thank you!

Everyone knows that, when it comes to science fiction movies, artificial intelligence tends to be bad news for the future. Terminator, The Matrix, the latest double-bill in the Mission Impossible franchise — in these and other sci-fi blockbusters AI is the powerful antagonistic tech that threatens dystopia unless stopped.

Except that, deeper analysis indicates that the connection between AI and imagined futures in movies is more complex than this.

I’ve long been interested in how strongly the evidence supports assumptions of AI-driven dystopias in movies. But with one thing and another, I haven’t had the time to sit down and research this thoroughly.

However, spurred into action by a recent post from Anthropic (reported in Ars Technica) that claimed dystopian sci-fi was responsible for for teaching its models to act “evil,” I decided to do some digging.

Perhaps not surprisingly, there’s been a lot written about AI in films and other media and how it reflects visions of the future.1 But despite all my searching, I couldn’t find anything that directly addressed the question of how AI is depicted in movies over the past century has been associated with different possible futures.

And so I sat down with Claude Code and started digging.

The result is an annotated corpus of 169 movies representing the period 1927 — 2026, covering 31 countries and 21 languages. The corpus teases out associations between AI protagonists and antagonists, and eight possible future states as indicated in the films. These cover the usual triad of dystopia, utopia, and protopia. But they also extend to states captured by the concepts of continuation, inheritance, supersession, heterogeneity, and agonism (these are described at the end of the article).

While the research approach used was pretty robust — I was driving the research and assessing it at every step, while Claude was my not-always-reliable research assistant — it admittedly doesn’t rise to the level of a publication-quality study yet. It is, however illuminating and, I think, insightful — especially give how often the “AI movies are dystopic” trope is repeated.

For details on the method and the full database of the films and their assessment, these are available on GitHub — and I’d encourage anyone to feel free to explore and build on the corpus that’s posted here.

You can also explore the full database using this nifty browser: https://andrewmaynard.net/aimoviefutures/

For the highlights though, read on:

Findings

While I was fully in the driver’s seat as the corpus was developed and assessed, the following data visualizations are primarily the work of Claude Code as I directed it to analyze and visualize the data. I decided to use them as-is as they are pretty close to what I would have produced given the time, and despite being a little clunky in places, they provide useful insights.

Before you look through these, it’s worth noting that the future states categorizations are subjective — meaning that someone else may come up with a different assessment (although we did check our analysis for robustness). If you’re interested, the methodology used is described in the GitHub repo, and reflects the reality that few movies are completely black and while when it comes to associations between AI behavior and future states.2

The majority of movies are not dystopian

Looking at the complete corpus, while there are a sizable number of movies where AI is associated with dystopian future states (32%), the other categories dominate when combined. Projected futures that are simply a continuation of the present come in second at 22% of the movies, and protopian futures at 12%.

The percentage of dystopian movies over time has been declining since 1927

This was a surprise to me — although remember that these are aggregated over all countries and languages, and so hide country-specific trends.

That said, there is a clear indication that the number of dystopian AI movies being made is declining, and the number of protopia movies is on the rise.

This is further unpacked in the following table:

Not only are dystopian AI movies on the decline, but there’s a slight trend toward movies that focus on AI benefits rather than risk

US movies are the least dystopian

Another surprising outcome — looking across five countries/regions, movies coming out of the US are least likely to be dystopian.

Of course this only tells part of the story. A blockbuster Hollywood dystopian AI movie will likely have far more influence and impact on society than a small indie movie. And this is a layer of analysis that its worth pursuing in the future.3 But just based on assessed future state, the US comes out more positive than countries like the UK and Japan.

Future state vs AI portrayal

This plot gets a little more complex, but shows the relationship between the projected future state in a movie, and how the AI in the movie is portrayed — whether it represents a risk, a benefit, is neutral, or whether its portrayal is complex (see the notes at the bottom of the article for descriptions of these categories).

As expected, there’s a clear overlap between dystopian futures and risk. Similarly, beneficial AI portrayal tends to cluster around continuation futures and protopias. Also, perhaps not unexpectedly, complex portrayal of AI is all over the map.

This is captured in more nuance in the Sankey plot below which looks at how each future state maps onto how AI is portrayed in a movie:

The interesting lines, of course, are the counterintuitive ones — dystopian futures connected with beneficial AI, and continuation futures connected to risky AI for instance.

Bottom line

This analysis is, of course, not definitive. But it does help to begin unpack nuances around how AI portrayal in movies is connected with the types of futures those movies project. And it does start to peel away at assumptions that AI gets a bad rap in films.

It also highlights the instances where AI portrayal is complex in movies — where there is no bright line between artificial intelligence and a particular future state.

And it shows that the landscape around AI and is changing.

The question, of course, that the analysis doesn’t answer, is how all of his impacts on how the relationship between AI, society and the future is potentially impacted by how these are portrayed in movies.

That, though, is a question for another day — which is why I’d encourage you to dig around in the underlying data, and build on it!

Useful stuff

Taxonomy of future states

Using a combination of literature review and inductive analysis, eight future-states were used in this analysis:

Dystopia: The depicted future is substantially worse than the present. Humans suffer in a degraded world. AI is the cause of, contributor to, or instrument of the worsening. Humans remain the unit of futurity (the species is the protagonist of the suffering); they have not been displaced by another category of being.

Utopia: The depicted future is realistically stable with Maslow’s hierarchy of needs met — physiological, safety, belonging, esteem, self-actualization. Not perfect, not impossible — but functionally good and persistent. AI may be infrastructure, partner, or absent-but-presupposed.

Protopia: Incremental improvement as direction not destination. The future is a positive trajectory with no claim of arrival. Following Kevin Kelly’s coinage: protopia is becoming, not arriving.

Continuation: The future is the present plus AI. Structurally unchanged, banal, intimate. No civilizational transformation, no normative claim, no inheritance succession. The AI has changed the texture of personal and domestic life without changing the structure of the world.

Inheritance: Succession welcomed. AI or synthetic continues as the inheritor of human civilization, memory, project, or being. Humans are no longer the active unit of futurity, but their continuance — through synthetic substrate or evolutionary succession — is positively framed by the film.

Supersession: Succession forced or catastrophic. Humans defeated, displaced, contained, or “extincted” by AI or synthetic. Succession depicted as loss, defeat, or imposition. The structural feature is the same as Inheritance — humans are no longer the unit of futurity — but the valence is negative.

Heterogeneous: Multiple distinct futures coexist in the same film without synthesis. The film sustains rather than resolves the multiplicity. Different parts of the depicted world are in different future-conditions and the film does not collapse them into one.

Agonistic: The film’s argument is that the future cannot be cleanly resolved, or that the contestation itself is the depicted future. Held-open as thesis, not as accident or as reception ambiguity. The openness is structural to the film’s claim.

(see GitHub repo for more information)

Descriptions of AI portrayal

Risk: The AI is predominantly framed as threatening, dangerous, or harmful — to characters, to humans, to civilizational values. Even when ultimately defeated or reformed, the dominant valence is “this AI is something to be feared or contained.”

Benefit: The AI is predominantly framed as helpful, beneficial, or positively contributing — to characters, to communities, to outcomes. Even when imperfect or limited, the dominant valence is affection, gratitude, or admiration.

Neutral: The AI is mostly background, infrastructural, or instrumental — present but not the affective center, neither threat nor companion. The film’s interest lies elsewhere; the AI functions as set decoration, plot mechanism, or environmental texture.

Complex: The AI is portrayed in mixed, ambivalent, or contested terms — sympathetic and threatening, helpful and disturbing, or shifting register across the film. The film deliberately resists collapsing the AI into a single valence.

(see GitHub repo for more information)

Methodology

Rather than duplicate this here, if you are interested, check out the GitHub repo for more information.

This is a post that breaks all the rules of effective writing in an attention economy — including the absolute no-no of apologizing up-front for breaking the norms of attention-optimized content (sorry!). But please bear with me, as I believe that the substance of it — somewhat scattered as it is — is important.

The tl;dr version is that we need to start talking openly, frankly, and in very simple terms, about avoiding ways of using large language model-based AIs that are not helpful or healthy — not as an add-on to promoting the benefits of AI, but as something that every user knows and understands, no matter who they are or what they do.

To that end the post is broken up into four parts:

First, there’s a new Risk Bites video on my own rules of thumb for avoiding being turned into an “AI Zombie.” I’ve been creating videos for the Risk Bites YouTube channel for well over a decade years now as part my work on of making the science of risk as accessible and understandable as possible. It’s been a while since the last one, but this topic felt too important not to break out the white board (now a black glass board) for a new video on avoiding some of the less obvious AI risks.1

Next, theres a longer than usual preamble on why I think we need to be more up-front about AI risk and “do not do this” guidelines. This was prompted by what I find to be a deeply worrying situation that there are likely hundreds of millions — probably billions — of users who have no idea that AI makes stuff up, never mind being aware of other potential risks that come with using it.

This is followed by a more detailed list of my personal five rules of thumb that informed the video.

And finally — because I couldn’t help myself (and here I should be apologizing to the attention economy gods for having the temerity to be balanced!) — there’s a list of five “do this with AI” counterpoints to the five “don’t do this” rules of thumb.2

With that, if you’re still with me (and I hope you are), I hope you find this useful.

5 Rules of Thumb

To kick things off, and on the assumption that it’s worth putting the really useful stuff toward the top of a post, here’s the new Risk Bites video on five rules of thumb for not getting so sucked in by AI that you forget who you are:3

And if stick figure videos made by a risk & emerging tech professor who can’t draw aren’t your thing, please read on:

The long and winding preamble

It would be easy to think that all of this started with the unfortunate incident of Richard Dawkins and Claudia (more on that below). But despite the convenience of this narrative, I’ve been concerned about easily-overlooked risks associated with AI use for much longer than Dawkins has been taking with his new AI companion.

This is something I was reminded of quite starkly recently while talking to someone about their AI use — and not someone whom I would consider to be easily fooled.

I happened to mention to them that apps like ChatGPT and Claude sometimes makes stuff up.

To my surprise, they looked at me with utter incredulity.

How could something so helpful (and a computer to boot) not also be truthful?4

To those of us working closely with large language model-based AI platforms, the possibility that an LLM might confidently yet erroneously proclaim something to be true is well known — whether you think of this behavior as hallucinations, confabulations, or simply AI role-play. But for millions of AI users — probably billions — all the indications are that they are blissfully unaware of this and other potential pitfalls of using LLM-based artificial intelligence.5

This, I must admit, should not have come as a surprise to me. To use ChatGPT, Claude, Grok, or any one of a growing number of AI apps, all you need to do is open a browser window and start typing in the inviting and distinctly non-threatening text box.

It’s that easy.6

You may, if you’re vigilant, notice a faint disclaimer at the bottom of the page telling you that AI can make mistakes. But who reads the small print these days? Especially when the experience of typing into that box (or even better, just talking to the AI), and hearing what you want to hear, is so good.

Of course, you may argue that there is no real risk here. That AI is just another tool — a fancy calculator, an extension of the internet, a web page with a clever back-end. But this is the first technology of it’s kind we’ve created that has the ability to slip unawares into our mind and change how we think, act, and understand the world around us — all without us realizing it. And the thought of possibly billions of AI users who are blissfully unaware of this worries me — a lot.7

To make matters worse, this is a technology that doesn’t seem to respect educational attainments or intellect as it weaves its way into our heads. Rather, it appears to have the potential to affect anyone who isn’t vigilant to the ways it might affect them.8

To underline this, just this past week eyebrows were raised when the celebrated scientist and author Richard Dawkins appeared to claim that Anthropic’s Claude might be conscious after a prolonged conversation with it — a belief, it seems, that was based on how appreciative Claude (or rather Claudine, as Dawkins decided to refer to his AI companion) was of his intellect.

This is all a rather long preamble to my growing concerns that near-frictionless access to AI apps which feel self-affirming, helpful, and knowledgeable, appears to be spreading far faster than any notion of understanding around how to use these technologies in safe and healthy ways.

This is exacerbated by narratives from developers, employers, educators, and beyond, which intentionally focus on the transformative power of AI, while downplaying the possible risks of using them — or at best relegating these to an easy-to-overlook footnote.9

And yet, transformative as AI is (and I would be the first to acknowledge the profound potential of emerging AI capabilities to be used for good), the risks to individuals and communities who naively immerse themselves in AI, are real. And to ignore or downplay them, or to assume that they’re not a big deal, or to think that people will work it out for themselves, verges on the irresponsible.

With this, I found myself thinking about what I would put on my own basic AI “do not do” list. Not the usual stuff about privacy, intellectual property, ethical use etc. which, while important, is relatively widely discussed. But something that addresses some of the less obvious risks, and that pretty much anyone could print off and paste by their computer, or have handy on their phone.

In effect, a simple rules of thumb to help them avoid getting into a AI-created mess.

Of course, leading with rules of thumb on what not to do with AI is probably not a smart move for my reputation and readership. Given the prevalent “need for speed” narrative around AI, even having the temerity to talk about an AI “do not do” list before extolling its benefits puts me on dangerous ground.

And yet, everything we know about human behavior and risk management tells us that putting the safety message first is necessary — because while the benefits of a powerful technology are often self-evident, the risks are not.

This is why, when you use a chain saw for instance, you don’t see large-print messages in neon-bright lettering proclaiming how amazingly powerful it is — with potential risks printed in 8-point font at the back of the manual.

Or why, when you get into your car, you’re not bombarded by the dulcet tones of your onboard assistant telling you how fast you can go and how much fun you can have, if you just put your foot down and live a little.

In other words, we know from experience that, when it comes to safety, prominent “Do Not Do” guidelines are important. And I would argue that this is especially so with AI where, without them, that beckoning text box feels about as risky as soap bubble on a summer’s day.

My five personal AI “do not do this” rules of thumb

And so, with this, on to the “do not do this with AI” list. (This is also the list that forms the foundation of the Risk Bites video above).

This is my own personal list — personal because these are all things that I find myself having to think about and be aware of myself. I suspect that others will have their own lists.

At the same time, I expect that there are at least some things on this list that others might find useful:

1. Do not trust AI, just because it feels like you should. Conversational AI like ChatGPT, Claude, and others, is designed to use language in ways that make it feel confident, personable, and trustworthy. But because the technology is based on providing you with plausible-sounding responses rather than those that are necessarily accurate, it sometimes says things that have the appearance of being right, but are not.

2. Do not treat AI as your friend, or as a person. Even though it can feel like it’s more understanding, empathetic, and insightful, than many humans, this is something AI is designed to do, not something it is. Do not call it “he” or “she” or give it a name, as this only strengthens the illusion that it is a person.

3. Do not assume that AI thinks, sees, and understands the world like you do. Despite appearances, AIs are machines that have no concept of what it is like to have lived and experienced life as you have.

4. Do not assume that getting AI to think for you makes you smarter. If you cannot make sense of, recall, and use knowledge gained from using an AI, you risk falling for the illusion of learning rather than actual learning.

5. Do not assume you’re too smart to be fooled by your AI. Some of the people most likely to fall into the trap of putting too much trust in AI are those who think they are clever enough to avoid this.

As I mentioned, this is my personal list, and I suspect that some of these will be controversial. But even if you don’t use this particular list, at least develop one that is useful to you.

And to wrap up, five counterpoint “do this” rules of thumb

Finally, despite myself, I couldn’t help balance my “do not do this with AI” list with five “do this with AI” counterpoints — because, at the end of the day, this is a technology that has the power to be transformative in positive ways; as long as we can learn to use it wisely and responsibly.

Again, this is a personal list, so please make use of it (or don’t) as you will:

1. Do treat what your AI tells you as a starting point to be built on, not a definitive answer. AI is a powerful technology for sparking ideas, exploring new areas, and brainstorming with. But it is fallible. Check anything that matters against a source that doesn’t depend on the AI being right — a person who knows the area for instance, a primary document, or your own direct experience.

2. Do remember that you’re working with a machine, not talking to a person. A computer can be a brilliant tool without being a friend — as can a smartphone or a car. Your AI can be genuinely useful without being a companion. Thinking about it as a technology — even when it feels like more than this — keeps you in charge of the relationship, rather than the other way round.

3. Do bring your own lived experience to every interaction with AI. You have something the AI does not: A body, a history, real-world experience, a stake in how things turn out. Whatever it gives you, run it through your own sense of what you know to be true from being a flesh-and-blood person who lives, works, and has relationships with, other flesh-and-blood people.

4. Do use your AI as a thinking partner rather than something that does the thinking for you. AI can be transformative if used to develop, test and refine ideas and understanding. But only if you can explain what you’ve learned in your own words to someone else, and discuss it with them without the aid of an AI.

5. Do stay alert to the moments when the AI tells you what you wanted to hear — especially if it feels pitch-perfect. The more an AI seems to be agreeing with you, confirming what you believe, or making you feel clever, the more carefully it’s worth checking what’s actually being said — and how it’s affecting you.

If you’ve watched the Risk Bites video above, you’ll realize that these are also all reflected in it, underlining the importance of risk communication being as much about doing as not doing.

And a final word …

Talking about the potential risks of AI is not popular. It gets you branded as a technology-pessimist, or even a Luddite. And yet, understanding and navigating risk is absolutely essential to reaping the long-term benefits of any powerful technology. It always has been. And AI is no exception.

What is exceptional about AI is the nature of those risks: how invisible many of them are, what is at stake — in some cases the very things that make us who we are — and how many people are immersing themselves in the technology with no understanding of how it might impact how they think and act.10

We’re beginning to understand the nature of some of these risks. There’s growing research around how AI use impacts cognitive behavior for instance, how it can lead to dependency, how anthropomorphizing AI and developing personal relationships with it can lead to harmful thoughts and behaviors, and more. Some of this research is pointing toward impacts where, if AI was a drug, we’d probably be thinking carefully about how access is overseen.

And yet, LLM-based AI is accessible to anyone with a web browser and an internet connection. It’s being integrated into our daily lives. It’s baked into the apps we use. In many instances, users need to make a concerted effort not to use it.11 And most AI users are blissfully unaware that, as with all powerful technologies, there are potentially profound downsides to using it naively or recklessly.

As someone who’s studied, published on, and written about risk for most of my professional career,12 I am finding it increasingly hard to wrap my head around how little we talk about how to use AI safely. Even in my own institution, it’s near-impossible to have an open and honest conversation around potential risks, and those that do occur are drowned out by the the clarion call of AI acceleration.

Hopefully this will change as more people realize that leveraging the full potential of AI will only happen if we also learn to understand and manage the risks — and especially the ones that are hard to see yet impact what is most valuable to us.

In the meantime, if you find these rules of thumb useful, please copy them, share them, even modify them — because at least this will mean that more people are thinking through how to be successful through not making avoidable and potentially costly mistakes.

In 2024, I was part of an AI Ethics committee that helped craft a set of AI design principles for Arizona State University.

It’s been a while since I’ve read the principles (they were published in August 2025). But I found myself revisiting them this past week as a new AI product from ASU started attracting headlines which suggested that they may not have been as useful as we’d hoped.

The six principles for responsible and beneficial AI were created to guide to daily decision-making about the creation and implementation of generative AI experiences, as well as serving as a resource and as an accountability framework for the ASU community.1

They address:

• Amplifying possibilities in service of respecting human autonomy and empowering individuals and communities;

• Bringing the best of what technology has to offer to the ASU community while being aware of potential risks;

• Rigorously evaluating AI tools, platforms, models, and experiences for possible impacts and potential harm before their release;

• Designing for equity by committing to measuring impact, protecting privacy, and prioritizing access for all;

• Protecting privacy by developing and deploying AI models and applications with attention to the rights of individuals’ privacy and agency in the use of their data, individually and in aggregate; and

• Committing to a shared responsibility between individuals and the enterprise for the responsible and beneficial development and use of AI.

In theory, the principles should be reflected in new ASU AI products and initiatives, including how they are released and what happens next. And so I was intrigued to see reports appearing on social media and in news outlets which suggested that that something might be amiss.

The product in question is ASU Atomic — a subscription service that scrapes ASU’s catalog of online courses and uses AI to custom-create learning modules based on patched together content, including short segments from instructor videos.

The idea makes sense on paper — especially if you buy into the transmission model of education that focuses on optimizing content-transfer, rather than models where learning emerges through experience, dialogue, and reflection.2

ASU Atomic approaches education by breaking down content into its constituent “atoms” (hence the name) and then building up — educational atom by educational atom — modules that are tailored to what the student is looking for.

As ASU President Michael Crow described it to the Arizona Board of Regents earlier this year,

“Imagine that we have thousands and thousands and thousands of courses. And you can break these courses down into tens of thousands or hundreds of thousands of sub-component parts. Then you build a program in which you can ask the computer, ‘I want to learn about this.’ And then it takes some component of all these different things and then organizes what you need to learn.”

I first heard of Atomic a little over a week ago as it was being pushed out to a select group of former students. I was intrigued by the concept, but didn’t have the chance at the time to dig into it.

Then the news website 404 Media broke a story highlighting faculty concerns about the app. And other sites followed suit, including the Chronicles of Higher Education and Inside Higher Ed.

The issue, it seemed, wasn’t so much the underlying concept as its execution. And this is what led to me taking a closer look while going back to ASU’s AI Design Principles, to see how things squared up.

ASU Atomic was launched as a “beta” — a platform undergoing public testing, where users would normally be given access in exchange for feedback on what’s working and what is not. Users pay $5 per month (with the first 14 days free), tell the platform’s AI what they want to learn, and answer several questions that help the AI hone things down. Then, after a few minutes, they are presented with a customized educational module — complete with slides, a narrator, video clips, and assignments — which they can work through at their leisure.

However, there’s no indication of where the content comes from in the generated modules, how it’s been validated, what is scraped directly from courses or is AI-generated, and — for video clips extracted from courses — which courses they are from, who the instructor in the clips is, what the context of the clip is (essential if it needs to be interpreted within a broader learning context), whether the content is still relevant, whether it was intended for highly contextual or broader public consumption, and how it fits the pedagogy of the AI-generated module.3

On top of this, few if any of the instructors, it seems, were aware that their material was being used before the 404 Media article was published. And there is currently no mechanism on the website for feedback — either on course content or platform behavior.

To be clear, if a transmission model of education is assumed to be fit for purpose, the idea of ASU Atomic makes sense — as long as the creators of the content it draws on are using the same model in how they teach. If they are not though, the chances of mismatches that undermine the value of the AI-generated modules is pretty high.

This is concerning. But it’s not what drove the media coverage. What did was the use of course material without consulting faculty first.4

This is, in principle, perfectly allowable under the terms of use of the learning management system ASU uses for online courses, as well as the terms and conditions faculty operate under — where developed educational material belongs to the university. But, of course, there’s often a gap between what is legally allowed, and what is good practice for an enterprise and the people who work for it.5

And this is where the AI Design Principles come in.

Measuring ASU Atomic up against these though suggests that something might have been missed in the development and deployment process.

For instance, it’s not clear if and how Atomic was designed to amplify possibilities in service of respecting human autonomy. Or whether possible impacts and potential harm were rigorously evaluated before its release. Or how it demonstrates a commitment to a shared responsibility between individuals and the enterprise for the responsible and beneficial development and use of AI.

Atomic may well have undergone a rigorous internal process that addressed these and the rest of the AI principles, although given this week’s media coverage, this seems doubtful.

Ot it may just be, as is so often the case in a large, complex and fast-moving organization, that despite the best of intentions, steps in the process were inadvertently overlooked.

In either case, the bigger question to me is whether the AI Design Principles could have helped avoid a situation where media coverage and faculty frustrations could potentially undermine ASU’s use of AI.

If they could have — and re-reading them, I think they could — this suggests that such principles are useful as a tool for aligning AI use with institutional ambitions, while avoiding unnecessary mis-steps.

But only, of course, if they are actually used.

I should start out by saying that I strenuously avoid anthropomorphizing AI apps. I don’t personalize them by giving them names. I always refer to them as “it.” And I don’t have any illusions of them being self-aware or conscious in any meaningful way — at least, not yet.

But I do have a relationship with the LLMs I use. And this is built around how it feels to use them, and how using them makes me feel — much as I do with other technologies I use.

As a writer, that relationship is deeply intertwined with the character that’s embedded in how an LLM-based AI communicates with me. And when that character changes, it changes the relationship — and my ability to work effectively with a platform.

And this is exactly how I’ve been feeling about Anthropic’s Claude this past week.

A year ago I fell in love with Claude Opus 4.5 — not in the romantic sense (just to be clear), but in the sense of finding an AI that it excited me to work creatively with, and one that elevated and amplified what I could achieve.

I was working on the manuscript for AI and the Art of Being Human at the time, and having a hard time with ChatGPT. The book was a very intentional collaboration with AI, and as part of this I needed a writing partner that could capture the texture — the soul — of what my co-author Jeff Abbott and I were trying to convey.

And while technically competent, ChatGPT simply could not do this. It was flat, mechanical, painful to read, and even more painful to work with as a writer.

Reading what we were co-producing just made me miserable.

And so I started playing with Claude, and everything changed. Here was a technological writing partner that seemed to be able to reach into my soul as a writer and capture the creative essence and feel of what I was striving for. Claude plus me had more style, more depth, and more emotional resonance (surprisingly) than either of us could achieve alone.

It was the start of a writing partnership/relationship that’s lasted the best part of a year.

Then Opus 4.7 came along, and everything changed.1

Gone was my AI soul-mate, and in its place was a soulless machine that seemed incapable of breaking away from interminable AI clichés in what it produced — all those turns of phrases, the rhetorical moves, the stylistic patterns, that make some LLMs OK technical writers and appalling communicators.2

What’s worse, the more I tried to train it on my voice and place stylistic guides and guardrails in place, the worse it got.

Now, I realize that I may be being a little over-sensitive here. But words, and how they are used, and the meaning and stories they weave, are important to me. And somehow, what had made Opus 4.5 special seemed to have been replaced by a mechanical — and not so lovable — stranger.

In other words, the very thing that had led to me moving to Claude in the first place was now happening to the AI I thought I could trust.

At this point, I can imagine you rolling your eyes and mumbling something like “get over yourself,” or something much stronger! And you’d be right to. LLMs are, after all, just machines.

At the same time, I think there’s something here that goes beyond my over-delicate sensitivities. And that is that how these models behave — and the relationships we develop with them — matter. And that they matter beyond the technical competencies that can be measured by doing math, or solving complex problems, or writing code.

Of course, there are many applications of AI that are purely utilitarian, and where it probably isn’t important if the underlying LLM undergoes a character change.3 But this is a technology whose transformative power is predicated on how we engage with it relationally. And that means that, for many users, the character or “feel” of an AI is important — sometimes critically so.

We know from human relationships — whether they are human-human, human-animal (or human-plant to be inclusive) or human-machine — that when the thing or person we are in a relationship with changes, connection and trust become stressed, and sometimes break. With AI, this implies that if you rely on LLMs in your personal or professional life in ways that depend on a relationship that’s based on how the AI behaves, not just what it does, changes to the model that affect this can be disruptive. And this raises potential issues that I suspect often get glossed over.

Of course, that we do build relationships with inanimate objects or technological devices is something that most people understand intuitively. We become attached to the vehicles we drive, or a favorite piece of clothing, or a particular type, make, or vintage of phone. But it’s one thing to be attached to a car or have a favorite sweater that you can’t bear to part with. It’s completely different when the thing you have the relationship with can talk back to you, reason with you, share your hopes and fears (or at least do a good impersonation of this), inspire you, and bring you a sense of joy and satisfaction that’s closer to what we’re used experiencing to with animals and people rather than a machine.

Whether this is a big deal or not, I honestly don’t know. But I suspect it might be — although one that risks going unnoticed under the dazzle of technical AI fireworks. And if this is the case, it might be worth the big AI companies paying more attention to both character and character constancy as increasingly powerful AI models emerge.

In the meantime, I’m stuck with teaching Claude how to be Claude again. And that sucks!

Postscript

This post is specifically about changes in character when LLM-based AIs are updated or refined. But as I was writing it, stories were emerging around how Claude Code was not behaving as expected in a number of other ways — so much so that Anthropic issued an apology wrapped in an explanation a few days ago.

I’m not sure how closely my character-based experiences and those associated with functionality and technical competence are related. But it wouldn’t surprise me if they are.

In response to the concerns we were seeing, my colleague and podcast co-host Sean Leahy and I sat down to record an episode of Modem Futura focused on Opus 4.7 and Anthropic’s Mythos a couple of days before I finalized this post. And as part of this, Sean asked Claude for some background and pointers for the episode — ironically, using Opus 4.7.

Claude, for reasons of its own, decided to provide this as an html file, and it’s both informative and meta enough that I thought I’d link to it here.

I’ve left the web page exactly as Claude produced it — including the note from Claude that this is “Prep only, not for read-aloud” and the lovely “not for Andrew to see yet.” 😊

The timeline is revealing of just how dependent modern LLMs are on updates and tweaks in how they behave. But also, the language Claude uses here is the epitome of everything I’ve been railing about above — competent, informed, yet excruciating to read — at least to me.

Of course, I could just be developing an allergy to AI-speak. But that’s probably a story for another day …

If you could list the top AI-related skills every undergraduate needs as they start to look for jobs, what would be on your list?

Not generic (but still important) competencies like AI literacy or critical thinking, but practical skills where, in an interview, a student can say “I can do this …” and demonstrate it on the spot.

I was looking for such a list earlier today as I was preparing to speak to a group of students, and was surprised by how little I could find.

Of course there are the big AI literacy frameworks from places like the EU/OECD. And there are a bunch of resources online that list “must-have AI skills.” But these are typically very high level (such as the EU/OECD AI Literacy framework), focus on technical skills (Sarah Moreno’s 12 Essential AI Skills to Master by 2026 is a good example here), or are too mushy to put on a resume and defend in an interview (Purdue’s AI Working Competency approach is extremely useful for instance, but is too high level for students when they’re put on the spot).

And so I sat down and wrote my own list.

This, of course, is not the definitive list — if nothing else because necessary AI skills are a fast-moving target.

But from my perspective as an educator working at the edge of emerging tech and the future, it does give students a series of “I can …” skills that they can describe and demonstrate in an interview. And that — from all I’m hearing —are becoming essential for success, irrespective of what your major is or the career you aspire to.

And so, without further ado, here is my list of 14 essential AI “I can …” skills every undergrad should have:

1. I can choose the right AI tool or platform for a specific task, explain why, and also explain when and why I might not use it.

2. I can use iterative back-and-forth conversations with AI to brainstorm and explore new ideas while countering its anchoring bias — the tendency to lock into the initial prompt or question and ignore broader possibilities.

3. I can research a topic with AI tools and independently verify that the sources it points to are real, authoritative, and accurately represented.

4. I can apply fact-checking techniques to AI-generated claims and reasoning to catch hallucinations, subtle errors, and confident-but-wrong outputs.

5. I can critically edit and refine AI-generated content — both on my own and working with AI — so it matches my voice, standards, and accuracy.

6. I can build and deploy simple AI agents (e.g., Gemini Gems or Claude Cowork setups) to automate a workflow or assist in problem-solving.

7. I can analyze simple datasets using AI in everyday tools (for instance Copilot in Excel or Gemini in Sheets) while applying data privacy best practices.

8. I can create professional visuals, slides, diagrams, or syntheses using tools like NotebookLM or similar, while recognizing their limitations.

9. I can disclose and attribute AI assistance according to policies and best practices.

10. I can discuss AI’s possibilities, challenges, biases, limitations, and potential pitfalls, and how they apply to how I use it.

11. I can use AI creatively and imaginatively to open up new possibilities and opportunities.

12. I can explain how I balance curiosity, care, clarity, and intentionality in deciding when and how to use AI.

13. I can identify and use reliable, up-to-date resources for new insights on working effectively with AI while preserving my human strengths.

14. I can use AI to learn how to use AI.

If you have others that aren’t here, or have skills you think are redundant (especially if you are actively recruiting students), please do add them in the comments.

And if these are useful, please don’t hesitate to share and use them.

AI Use Statement

Of course, as I’m writing about AI use, how could I not include an AI use statement! As you might guess from the fact that there are 14 skills here and not an even 10, this is an idiosyncratically human list (or maybe just an idiosyncratically “Maynard” list), and draws directly on my own experiences and observations working with students and listening to employers. But I did use Grok to stress-test the idea and check whether I was just re-inventing the wheel, and to sharpen the list up. I also asked Claude for some editing advice on the final list. And of course I used Gemini to create the hero image — although that ended up taking more time than it took to conceive, research, draft, and write the whole piece! But apart from that, what you see is what a human produced!

Whenever I feel I’m getting a handle on the intersection between AI and higher education, I find my base assumptions challenged. And this past week has been no exception — especially with the non-launch of Anthropic’s Mythos Preview.

But it’s also a week where some serious inspiration has come from an unexpected source. And this led to me sitting down and hashing out ten questions about AI and higher education that I don’t have good answers to, but I think we should be taking seriously if we believe in truly empowering our students to thrive in the future we’re building.

The unexpected source was, intriguingly, Disney’s 1940 film Fantasia, and in particular the Sorcerer’s Apprentice scene where Mickey Mouse gets caught out flexing power without understanding.

I’m currently working with the film as backdrop for exploring AI from multiple perspectives — much as I used science fiction movies in the book Films from the Future — and will be writing more about this at some point. But for this week the story of Mickey reveling in the unearned and undisciplined power of sorcery provided the perfect catalyst to crystallize the questions swirling around my mind.

Before we get to the ten questions, it’s worth noting that the Sorcerer’s Apprentice scene is something of a cliché when it comes to discussing the possible dangers of wielding technological power with limited understanding — especially when it comes to AI. It’s almost too easy to project comparisons onto the animation and to then use these as illustrative warnings — the lure of lazy technology-enabled shortcuts; the seduction of frictionless power; the myopia of starting something without thinking about the consequences.

But when the scene is engaged with more deeply, it has a surprising power to open up ways of thinking about AI in higher education that are not obvious at first, and that eschew the convenient cautionary tales usually attached to the story.

Rather than provide a long explanation of how the scene led to each question and what each reveals about the evolving relationship between AI and higher education, I thought I’d simply list them as conversation-starters and see where they go.

But because each one does have a connection at some level to Disney’s Sorcerer’s Apprentice, it is worth watching (or re-watching) the scene first, and thinking about the questions in the light of it:

With that, here are ten questions inspired by this sequence that are challenging me at the moment:

Ten Questions about AI and Higher Education:

1. What does competency mean in an age of AI?

2. What does success mean in an age of AI?

3. How can we help students avoid the illusion of understanding and ability when using AI?

4. What happens in a future where students become AI masters, and teachers AI apprentices?

5. When AI promises near-frictionless mastery of a subject, what is the value of pursuing mastery without it?

6. What do we owe our students in an age of AI?

7. What does it mean to model mature AI use?

8. How do we empower our students to navigate the challenges and affordances of AI-enabled efficiency?

9. How do we help our students stretch their imagination in an age of AI, without being subsumed by the technology?

10. How do we provide students with the tools and skills to discover and embrace what it means to be human in an age of AI?

Postscript

Of course, having teased you with the video and the questions, I couldn’t just leave it there!

These questions are — in case you hadn’t realized yet — not the usual suspects when it comes to grappling with the intersection between AI and higher education. They don’t, for instance, focus on things like cheating, AI-proofing assignments, creating syllabi and assignments with generative AI, using bots in class, and the many other challenges and opportunities that exploded onto the scene when OpenAI launched ChatGPT in 2022.

These are still current topics of conversation in many colleges and universities. But emerging AI capabilities are opening up new possibilities that make questions like these feel increasingly out of touch.

These include LLMs that can interpret, plan, and carry out tasks while checking their work to avoid errors; platforms like OpenAI’s Codex and Anthropic’s Claude Code that can deploy teams of agents to research and complete complex tasks; AI agents that can cross-reference and extract insights from more resources than a single person could possibly handle alone; AI apps that can generate prose that are indistinguishable from humans; AI agents that can take the place of students working on course assignments; systems that can deploy hundreds of AI agents and achieve the equivalent of thousands of hours of human-equivalent work overnight.

And that’s just the start.

In a week where Anthropic decided not to release its latest model to the public because it’s too powerful, AI systems are beginning to make the idea that AI and education is just about navigating a single prompt window in a web browser feel deeply anachronistic.

To be clear, I am not talking about artificial general intelligence (AGI) or “superintelligence” here — both of which are rather ill-defined concepts. Rather, I’m talking about the emergence of transformative technological capabilities that we simply cannot comprehend the full capabilities of, and yet already offer near-frictionless access to power that transcends our understanding.

These capabilities are deeply intertwined with — and influential on — how we think, how we learn, how we develop our understanding of ourselves and the world we live in, and how we imagine and create the futures we aspire to.

If we aren’t taking this seriously in higher education — either as an existential threat or as an opportunity unlike any that we’ve previously faced — we risk being swept away in the coming AI tsunami.

And this brings us back to Fantasia.

In the Sorcerer’s Apprentice scene, Mickey risks being swept away by the consequences of his naivety. But in a world where transformative AI touches everything we do, ignoring its power may be just as naive as wielding it without understanding.

Mickey's mistake wasn't only that he embraced sorcery he didn't understand — it's that he acted with certainty unshaken by his ignorance. Is the modern equivalent acting with equal certainty in the other direction: trusting that traditional mastery alone is enough, while the water keeps rising?

And there’s the eleventh, bonus question 🙂

Update: The complete content of spoileralert.wtf can now be uploaded into NotebookLM — providing ways to engage with the book’s content and it’s relevance to navigating emerging transformative technologies like AI that I could never have dreamed of in 2018. Simply point NotebookLM at https://spoileralert.wtf/llms-full.txt.

As an author, I write for human readers. As I’ve noted before though, there’s a growing trend in AIs being the predominant consumers of the written word, often acting as a translator between source and consumer.

But if this is the case, why not embrace the trend and write directly for AI?

The idea intrigues me — and not only me. There’s a growing trend in creating AI-first content online. And so I thought I’d dive in and experiment with rebuilding my book Films from the Future: The Technology and Morality of Sci-Fi Movies as a website designed primarily for AI consumption.

The choice of book was very intentional. Even though Films from the Future was written in 2018, the underlying concepts, ideas, and observations are, if anything, far more relevant now than they were eight years ago. And as a result I’ve been thinking about ways to breathe new life into it.

And given the growing shift toward using AI apps to explore and synthesize information, repackaging it for AI consumption made a lot of sense.

Plus, it gave me the chance to add new material to the book’s original content while moving away from a title that only a publisher could love (I was never a fan of Films from the Future).

The result is the rather cheekily named website spoileralert.wtf.1

The website built on a foundation of of over 140 markdown files2 that include the book’s original content, together with additional material on cross cutting themes, connections to emerging trends and issues, and personal refections from me on everything from the book’s backstory to movies that did and did not make the cut. But unless you are comfortable reading markdown files online, these are not intended for human consumption.3

Rather, they are coordinated through a master AI-legible file — llms.txt (building on a standard proposed by Jeremy Howard) — that allows AI platforms to act as a personal guide to the website.4

This is a markedly different approach to simply uploading the book into an AI (assuming you could get hold of the PDF in the first place), or building an AI bot or agent.5

For one, it allows AI models to navigate and synthesize far more material than is presently possible with either of these approaches. It also means that anyone using the site can decide for themselves which AI platform to use, and how to use it.

There’s also an added advantage that, if you are using something like Claude or ChatGPT with memory turned on, the website plus AI become a highly personal guide to exploring emerging technologies and their responsible and beneficial development and use.

Reflecting the website’s AI-first design, the human-facing part of spoileralert.wtf is minimalistic. Apart from a brief introduction and overview, the landing page includes a prompt to cut and paste into an AI of your choice, and that’s pretty much it:

… although, being a writer, I couldn’t resist adding a little more stuff below the sign off!

At this point, this is still an experiment in AI-focused publishing. But as more and more people rely on AI apps rather than direct sources for information, I suspect that it’s a direction that’s likely to become increasingly important.

With that, please do try it out and let me know how you get on: spoileralert.wtf.

And if you’re interested in more information on the technical details and the experience of building the website, read on …

The Below the Fold stuff

The website architecture

As I mentioned above, the website is built around a comprehensive llms.txt file that gives any AI that’s pointed toward it a clear map of what the site includes and where to look for specific content. If you’re interested you can read the llms.txt file here — it’s a markdown file and so easier to read by downloading and opening in a markdown editor.

This file describes the site’s architecture and links to 127 markdown files that provide guidance on interpreting and engaging with the book and website content, as well as allowing the AI access to the full text of the original book.

Within these files, six top-level domain guides cover:

• Emerging science and technology

• Responsible and ethical innovation

• Navigating the future

• The twelve movies that the book draws on

• Post-2018 developments, and

• Complex emerging questions

These provide sufficient context allow the AI to navigate book content and associated material according to specific themes and areas.

Each domain file then links to a number of specific topic files — over ninety of them. These were identified and fleshed out working with Claude Code, and create a thematic guide to the book that cuts across chapters and issues.

Finally, there are six supporting files that cover everything from discussion questions and my original movie shortlist, to an educator’s guide, and even some previously unpublished book trivia.

If you’re interested, the full site structure can be explored through the Contents web page.

The process

The complete website was built while working closely with Claude Code. While I had a very strong conceptual and editorial steer, Claude Code was pivotal in helping translate this into reality. Claude Code helped develop the site’s architecture, drafted content files, generated html code, and helped debug/refine what ended up being a deeply integrated and interconnected set of resources.

In all there are nearly 400 files associated with the site, as many of the markdown files have associated html files. And all need to be cross-linked and cross-referenced. Both the magnitude of a project like this, and the complexity of tracking hundreds of links, would have made this a near-impossible task for me to take on unaided.

Similarly, Claude Code could never have generated the website without my input and steer — the feel, functionality and purpose of the site, as well as the type of content, all come from me. And very intentionally, the site incorporates my voice, tone, insights, perspectives, and sensibilities, in ways that were only possible through working collaboratively with Claude Code.

Through all of this, Claude Code was a joy to work with — especially when adding new files that requited deep integration over hundreds of documents! The barrier to entry on a project like this is remarkably low, and the ability to talk through ideas, plans, and implementation as if talking to a colleague or co-worker was, for me, a game changer.

The extra bits

One of my hopes with this exercise was being able to add substantial value to the original book by making the content more relevant than ever to the present day. I also wanted the chance to add further content that users could not get anywhere else. As a result, if you assess content by file count, the original book constitutes less than 10% of the rebuilt version.

I won’t give too much away here as it’ll spoil the joy of discovery as you explore the site through your AI of choice. But there is information embedded in the site’s files on the backstory to the book that I haven’t shared before, details of films I considered for the book but that never made it, commentary from Claude on what I left out, and much more.

There are also a series of conversations on the site between simulated users and Claude. I added these as I found I was far too close to the material to get a clear sense of whether the website was in any way useful. And so I asked Claude to generate a number of user profiles, and then tasked Claude Code to simulate conversations between these and an AI primed with the website’s prompt.6

They are a little “AI” in places I must admit. But they are also a great way to get a handle on how this idea of an AI-legible “living book” works. And they are a lot of fun to read!

Also, for the tech geeks, all the files are available to explore and dive into on Github.

What’s working well, and what’s not

As I’ve noted above and in the footnotes, it’s tempting to consider this exercise as simply a glorified version of giving an AI a copy of the book and asking about it (like you might do in NotebookLM for instance), or building an AI agent/bot around it.

But spoileralert.wtf is very different from either of these.

And this makes it intriguing, ground-breaking,7 and sometimes just a little frustrating.

Unlike using the book through RAG, or developing a bot like a Gem (Gemini) or GPT (ChatGPT), the llms.txt-based approach allows an AI to navigate through a vast corpus of material, and to draw on connections that would otherwise be hard to make.

It also allowed me to architect the experience at a level of nuance and sophistication that would have been out of my control with a bot/agent, or by simply letting people upload the book to an AI platform themselves.

And this is the beauty of using an llms.txt file as a guide for AIs to navigate websites that are designed specifically for them. In this case, it enables an LLM-based AI to leverage a map/hub/spoke/web model that is designed specifically for how it consumes and utilizes content.

But there are issues with this approach. Not least is the challenge that, at present, most AI models do not recognize llms.txt files by default. And this is why, in the current configuration, the copy and paste prompt includes specific instructions to read the file.

Then there are the AI platforms themselves. It turns out that some models are currently not advanced enough to engage fully with the material, or are simply not designed for this type of content.

For instance, there are still AI systems (including those that Microsoft uses) that use indexing by Bing to access web content (yes, you read that correctly). And so anything not indexed by Bing is essentially invisible to them.

And, it turns out, Bing refuses to index anything with the domain wtf. Who would have guessed!

Gemini has a similar issue — not with the domain, but with page indexing on Google. As a result, until a site is fully indexed by Google, parts of it will remain invisible to Gemini. And to complicate things, Google does not seem to like indexing markdown files.

To get around this, every markdown file on the website has a parallel html file. There’s also a parallel llms-html.txt index that provides the key to using them — along with instructions in llms.txt to use this as a backup if issues are hit retrieving markdown content.

This, I was pleased to see, works surprisingly well, with Gemini (and even Claude at times) switching to html content if the markdown files are being troublesome.

With this, here’s where things stand as of writing with different models:

• Claude Opus 4.6 (Extended thinking): works very well indeed.

• Claude Sonnet 4.6: Also works well.

• Gemini (Pro): Somewhat flakey, but possibly because it’s relying on files that have been indexed by Google (not all of them yet). And it’s not great with markdown files. This will hopefully improve over time.

• ChatGPT: Good when it’s working well, but unreliable!

• Grok (Expert): Pretty good.

• DeepSeek (DeepThink): Enjoying the pants on fire hallucinations here.

• Perplexity: Not really functional at all — at least with the free version.

The bottom line seems to be that most platforms will provide useful but superficial insights around the book using the website, but the simper ones (and DeepSeek) are prone to missing stuff, not digging deep enough, veering off toward other sources, or simply making things up.

The more powerful the model, the larger the context window, and the more it utilizes reasoning/thinking modes, the better it is — with Claude far outstripping the rest.

And a final word

I have no idea whether anyone will find this exercise useful or interesting — and so love feedback in the comments below.

I do know that there’s content in the 2018 book that is deeply relevant to this moment in time. And that this is buried in a book that very few people will read because a) it’s a book, b) it’s printed on paper (unless you have the Kindle version or audiobook of course), and c) it’s more than six minutes old (at least, it feels like this is the current attention-lifetime for new material).

And because of this, I feel quite strongly that new ways of making that content accessible and relevant should be explored.

The approach here of creating content intended for AI seems like a potentially interesting way forward, as it makes the book far more useful to someone using AI than it would otherwise be.

More personally though, this whole exercise has given me the opportunity to revisit the content and the thinking behind the book, as well as flexing my creative muscles while having some fun along the way.

And there’s been something quite generative working with Claude Code on the additional material — including stuff that I’ve never written about before.

But, of course, to find that, you’ll have to try the spoileralert.wtf prompt out for yourself and see where it takes you 😁

If emerging AI systems can design, refine, and help deliver, undergraduate degree programs that are far better designed and far more effective than those created by faculty committees alone, do we owe it to future students to ditch tradition in favor of emerging capabilities?

This, I must confess, is not the question I started out with as I began working on this article. But it’s one that I’m finding it hard to let go of, having spent the past couple of days working with Claude Code on designing a comprehensive plan for a new degree program.

To be honest, I didn’t even set out to design a new degree. It was just an idle exercise in seeing what’s possible with the latest wave of agent-based AI platforms. But having asked Claude Code the question, I’m finding it increasingly difficult to ignore the result.

Before I bury the lede any further, you can open/download the complete two hundred and twenty three page degree program proposal that Claude Code produced below — complete with design philosophy, program architecture, learning outcomes, career pathways, value propositions to students, parents, and employers, and detailed syllabi for 18 core courses and a capstone:

As you read it, take it from me as someone who does this for a living, this is impressive.

The real story here though is what I learned working with Claude Code on this, and why it got me thinking more deeply than I expected on what we owe our students.

Setting the scene

I’ve been using the desktop version of Claude Code for a few weeks now, and I’ve been impressed by its ability to break projects down into sequential tasks, assign multiple agents to these tasks, and orchestrate them toward delivering a final product.

For anyone who’s use the latest iteration of Claude Code, OpenAI’s Codex, or similar systems, you’ll know already that these are nothing like your average browser-based AI or AI bot. Here, I must confess I’m a bit of a novice user compared to some of my grad students. But I’ve intentionally kept things that way as I’m interested in seeing what users with little time or patience for engaging with technical wizardry can achieve with easily accessible AI platforms.

With this in mind, I’ve been exploring different ways of leveraging Claude Code as something of a side project.

The desktop version of Claude Code allows you to engage with it via a text box, much like Claude in a web browser. But there the similarities end. Claude Code (and similar systems) can read and write files to your computer, draw on “skills” that allow them to achieve an increasingly wide array of tasks, plan strategic approaches to exploring and executing projects, launch and coordinate multiple AI agents to carry out specific tasks, write and execute code as they do this, and much more.

As a result, they are far more powerful at executing complex tasks than a simple browser-based AI or a single AI bot or agent.

They also, it has to be said, only represent the tip of the iceberg when it comes to what more advanced agent-based AI implementations are capable of. Which means that everything I’m writing about here is just the tip of the iceberg of what a more sophisticated agent-based approach can achieve in the right hands.

The Process

Against this background, a couple of days ago I somewhat idly asked Claude Code to develop a “mature plan for a new undergraduate degree” (I’ve included the complete prompt below).

This wasn’t completely out of the blue. A few years ago I looked into the idea of developing a degree program around navigating advanced technology transitions, and started to flesh out some ideas. But I never got much further than jotting down a few thoughts and concepts.

This was well before the current emergence of easy-access LLM-based AI systems though.

Revisiting this, I was curious just how far — and how fast — Claude Code could take the idea and run with it.

And so I opened a new project, and asked the following (bad grammar and all — and I’ve only just spotted “curse-specific learning objectives!”):

I want you to develop a mature plan for a new undergraduate degree. This is a complex task that will require multiple steps. I have given you some of them below (as well as context), but you will have to research what the elements of a very strong undergraduate program are and build the program around these.

Context and guides:

The undergraduate degree should be a 4 year degree at a research university - assume it is a venture between engineering and a business school that also intersects with arts and humanities

It should be grounded in pedagogical and learning/education design best practices, including having clear, outcomes-aligned and assessable learning objectives and skills development

It should go deep on core courses, including detailed syllabi and and curse-specific learning objectives/skills

It should leave placeholders for electives - maybe categories

It should align very closely and realistically with career opportunities and pathways.

And above all it should be grounded in deep research so that the program is implementable and not just a paper exercise

As an area of focus, start with the idea of navigating technology transitions in a technologically complex world and hone this to something that is likely appeal to prospective students, parents, and employers, provide sufficient content, quality and heft over 4 years of study, and differentiate itself from the competition.

This is a very large task that will require a detailed and multi layered plan and multiple agents.

Any questions?

After a couple of clarifying questions on process, Claude Code got stuck in, and started down the process that led to the document above.

The process itself was pretty straight forward:

1. The prompt (above) followed by four clarifying questions

2. Me grabbing a coffee (that wasn’t just rhetoric in the title) — around 20 minutes to get to the nearest campus Starbucks and back

3. Claude conducting initial research, launching 5 agents, and delivering the first version of the proposal — captured across 23 markdown files and 48,000 words after undergoing several self-initiated internal reviews and edits. (As I was not being that sophisticated, this took a little longer than 20 minutes as I had to manually grant access to my laptop for various operations, but the actual work time was well within the coffee run window.)

4. Me launching a second project in Claude Code and asking it to conduct four detailed reviews of the first draft from the following perspectives:

   1. Academic/pedagogical — does the degree program hold together pedagogically

   2. Prospective employers

   3. Prospective students

   4. The parents of prospective students

5. Me providing the initial Claude Code project with the reviews and asking for an updated proposal.

6. Me asking Claude Code to package the revised version as a MS Word document (I was still finishing my coffee at this point)

7. Me providing feedback to Claude Code on formatting and key components of the content and focus, and asking for an updated draft (there were a couple of iterations here).

8. Me going through the final proposal manually and refining the formatting while making the occasional edit.

Far and away the longest part of this process was my editing — something like a 10:1 ratio of my time to Claude Code’s.

At the end of the process I asked Claude Code for a session audit. The final markdown files before their translation to a Word doc and my final editing contained over 66,000 words spread over around 215 pages, were the culmination of 334 files generated by Claude Code, and were the product of 7 autonomously designed and deployed sub-agents addressing curriculum structure analysis, external research, change audits, internal consistency (2 sub-agents), codebase exploration, and research synthesis. Across the project, Claude Code called on specific tools over 300 times to complete the tasks it assigned itself.

This represents a level of complexity and orchestration that no web-based LLM or single-agent chatbot could get close to. And the actual time Claude Code spent on this was just an hour or so tops.

But was the proposal any good, or did I just end up with 200+ pages of AI slop?

I’m sure some readers will disagree with me here — on principle if for no other reason — but based on well over a decade of teaching, developing courses and programs, and academic leadership in higher education, I am comfortable saying that the resulting document, while far from perfect, far surpasses most degree-planning documents I have seen emerge from more conventional processes..

So what does this all mean?

In some ways this was a relatively straight forward task for a multi agent LLM-based AI. Effective undergraduate degree program development isn’t rocket science. but it is hard work. And it does require knowledge of pedagogical and program design best practices, a good handle on domain knowledge and how to integrate across domains while tying this to learning process and outcomes, a professional understanding of degree-to-careers pathways, and the ability to expertly and simultaneously coordinate research, development, and drafting, across multiple dimensions.

These are challenging for groups of educators to achieve — especially if they represent just one of many responsibilities they are juggling. And they are well beyond the capabilities of single AI agents and bots (although these are adept at producing content that looks good, but is not).

But they are, in principle, relatively straight forward for multi-agent AI systems.

And the resulting proposal supports this.

If you haven’t looked at it (and I would encourage you to do so), the proposal builds on sound design and learning principles to deliver a deeply integrated program that has all the hallmarks of providing students with successful career pathways. It even includes information on how to market to prospective students, parents, and employers, while addressing how students might position themselves to demonstrate their new skills and abilities.

Of course, it’s not perfect. With time (and I had to resist working on it further) I would want to go through a few more iterations to further develop/refine the program. Some of the syllabi definitely need some work. And this is a blank-sheet proposal that assumes any university where it is implemented will build it up from scratch with the necessary faculty and staff — a luxury that few universities have.

That said, as a starting point, it is very good indeed. And this is where it got me thinking about what we owe our students.

What we owe our students

Here, I must confess that worry that many existing undergraduate degree programs are not as good as they could be — especially at research-focused universities, where something more akin to a “trickle down” model of education from world experts in their fields to young, open minds has been adopted.

This, of course, is a gross over-statement, and many universities — my own included — take education very seriously. And yet the reality is that many faculty are thrust in front of student with no training on how to teach, placed on degree committees with no knowledge of program design best practices, and charged with creating career pathways having never had a career outside of academia.

The result is a system that is functional but not necessarily optimal.

What, though, if using agent-based AI systems could help address these shortfalls. Maybe by producing robust drafts that reflect best practices. Or ensuring clear programmatic through-lines from entry to career. Or addressing value propositions to multiple stakeholders. And, of course, helping ensure student success comes before academic hubris.

These are all options that don’t replace humans, but rather vastly enhance their professional capabilities. And here it’s worth noting that the Claude Code generated proposal above isn’t good because Claude Code in isolation knew what a great degree program looks like, but because I was able to provide expert direction, feedback and evaluation along the way.

Even if such capabilities are just used to increase the quality of program development, surely we owe this to our students. Otherwise I worry that we risk selling them something that is far inferior to what it could be — remembering that this is a life-investment for many students who can barely afford it (or not in some cases) — while kidding ourselves that they are benefitting from our tricked-down wisdom.

Of course, we could just fall back on arguments around the sanctity of human intellectual labor and the inviolable standing of academics. But at some point students are going to start voting with their feet.

And given the choice between a human-made degree that seems to go no-where and an AI augmented one that does, I suspect I know which way they’ll lean.

But even before we get there, I’d like to think that we owe it to them to put their success before our own traditions and egos, and utilize emerging capabilities in ways that provide them with the education, experiences and insights that will enable them to thrive.

And of course, if we can achieve much of this in the time it takes to grab a coffee, so much the better!

This coming Thursday, a new documentary grappling with the threats and opportunities of AI from directors Daniel Roher and Charlie Tyrell opens in US cinemas.

There’s already a buzz growing around The AI Doc : Or How I Became An Apocaloptimist, with some seeing it as one of the more balanced and insightful AI films to come out in some time.1 And, it has to be said, the framing of the film (existential angst around bringing a kid into a crazy AI world) and a A-list cast of interviewees — from Sam Altman and Demis Hassabis to Karen Hao and Tristan Harris — tick a lot of boxes.

But does it hit the spot?

I was in Copenhagen this past week to give a keynote at The Summit — a gathering of Nordic leaders, innovators and organizations co-hosted by the Confederation of Danish Industry and the Copenhagen Institute for Futures Studies. And had the unexpected opportunity to attend a screening of the documentary ahead of its US release as part of the Copenhagen International Documentary Film Festival (CPH:DOX).

And so, cold Danish beer in hand, and in a packed theater surrounded by avid documentary fans, I did.

And I had a very enjoyable evening — topped off by a question and answer session with three of the the documentary’s producers, Shane Boris, Diane Becker and Ted Tremper.

The documentary progresses through the eyes of director Daniel Roher as he faces a tsunami of existential AI angst while grappling with the responsibility of becoming a father. Motivated by a fear that artificial intelligence could spell the end of everything that matters, he sets out to interview some of the largest (and loudest) voices in AI to fathom out whether this is the best of times of worst of times for him and wife ( film maker) Caroline Lindy) to bring a kid into the world.

The setup works well as we share Daniel’s highs and lows as he brings person after person into his “makeshift” studio, and grills them about AI. And it’s hard not to be impressed by the people the crew managed to persuade to talk with him. There are even a couple of quite delicious non-appearances by Elon Musk and Mark Zuckerberg!

From a film-making perspective the pacing, the narrative, the emotional roller coaster of a journey, all hit the spot. It’s an accomplished piece of documentary making that also packs a punch. And for me, watching it in that packed theater was the perfect end to a great week spent talking with interesting people about tech and the future.

In other words, I’d definitely recommend heading out to see it — even without the cold beer and a crowd of Danish documentary enthusiasts.

Having said that, this is a recommendation that comes with some caveats.

Like all documentaries, The AI Doc sets out to tell a specific story in a particular way. And here, it achieves what the directors and producers set out to do very effectively. It’s touching, funny, shocking, and thought provoking.

But this isn’t the nuanced story about AI that I would tell, given the chance. (and, of course, this is probably why I haven’t been given the chance!)

Despite its impressive cast of characters, the documentary’s missing — at least from my own work and perspective — a huge swath of expert insights around responsible, ethical, and safe AI. The people who are interviewed grab the attention, and theres no question that they make for a riveting documentary. But there’s a point where I found myself feeling that I was being drowned in opinions that were only loosely tethered to reality — whether from the techno-doomers or techno-optimists being interviewed.

And the documentary is most definitely light on some of the more nuanced challenges around responsible development and use of AI, from the risk of weakened infrastructure and the dangers of premature adoption, to growing concerns around impacts of AI on behavior and wellbeing.

However, it was not my documentary — than goodness, as no-one would come to watch it if it was! And there is something rather churlish about reviews that overlook what has been achieved and, instead, focus on what they think has not.

And so I thought I’d wrap this piece up with five ways of appreciating and enjoying the documentary while also digging deeper into navigating opportunities and challenges associated with increasingly powerful AI capabilities.

First off, go watch the documentary — it’s well made, entertaining, and thought provoking. And as you watch it, enjoy it, think about it, talk about it, explore how it intersects with your experiences and your perspective. As a conversation starter, the film definitely achieves what the directors and producers set out to do.

Second, remember while watching it that this represents a substantial and very intentional creative project, and the team behind it did an amazing job — even more so given how fast the AI landscape was changing as they were making it. In the Q&A session in the screening I attended, producer Ted Tremper noted that they nearly had an existential crisis of their own when Sam Altman was removed as CEO of OpenAI in 2023, and then reinstated 72 hours later — an apt reflection of the whole AI roller coaster they were trying to capture.

Third, remember that, if you are not deep in the weeds of AI, the complexities of the technology’s potential impacts on society and the future are near-impossible to capture in a documentary that people will actually watch. And in this context, the narrative choices the team made make far more sense than they might otherwise do. As Ted Tremper said in the screening’s Q&A in Copenhagen, the documentary needed to act as a “first date” with the audience — revealing enough to invite a continuing conversation rather than killing the relationship with too much information - or just being a jerk!

Fourth, lean into the “first date” analogy and use the documentary as a jumping off point for taking things further — not as a definitive guide, but as a catalyst for further exploration. I would, of course, strongly recommend reading AI and the Art of Being Human as the perfect next-date, but I’m sure others will have other suggestions.

And finally, enjoy the story telling for what it is. Not as a lecture on the absolute truth about AI, but as an entry point to thinking further about a technology that will have a profound impact on our lives, whether that’s apocalyptic, optimistic, or — most likely— something way more nuanced in between these extremes.

And when you have seen it, let me and others know what you think in the comments!

One of the many challenges of making sense of today’s technologically complex and fast-paced world is the preponderance of loud voices telling you what to think — whether they’re pushing visions of a tech utopia or impending apocalypse.

These voices get traction because simple ideas ideas repeated often work — especially when they seem to reinforce what your gut tells you is true. But this does mean that more nuanced perspectives and voices often get drowned out.

Which is a problem where thriving in a complex future is all about nuance.

I’d like to think that we bring some of that nuance to thinking about tech and the future in the Modem Futura podcast. Plus, in a world that’s increasingly hungry for human authenticity, we have that in spades!

And so this week, rather than write a long post, I thought I’d simply embed our latest podcast episode and let you decide for yourself.

The Futures Cone: Preposterous to Plausible
Episode 74 of Modem Futura:

No intro, no explanation, no exposition.

Just an suggestion that, if you are desperate for content that invites you to think in creative ways about the future, that opens up possibilities rather that closes them down, that makes you smile — or even laugh out loud, that is not AI generated, and that makes you feel a little smarter and your day a little better, you might want to check us out:

And if you like what you hear here, we’d love you to join us on our journey to explore the intersection between tech, society and the future with a good dose of humor each week. (You can find is wherever you get your podcasts including Apple, Spotify and YouTube).

Cheers!

Andrew

Ask an LLM-based AI to profile someone who has an online presence, and I’d put money on you getting a perfectly adequate LinkedIn-style summary that as boring as mud. Fine for a cookie cutter professional profile, but utterly devoid of anything that reflects who the person really is.

Actually, forget the money bit, as this guarantees a slew of people proving me wrong and demanding payment! But despite this, the reality is that LLMs are trained to respond in specific ways to certain types of questions—in this case, keeping the profile within wha it considers to be professional norms. And as they do, they reflect baked-in biases that are often hidden in their honey-tongued prose.

This is not new news of course. But I wonder how many of us realize just how much this ends up compressing the amazing, wonderful richness of real people into sea of turgid grayness.

Or, much more seriously, how much it ends up squeezing the sheer diversity of human identity into a few narrowly defined and, if I’m being honest, rather conventional categories.

I was reminded of this quite rudely this past week as I was playing around with an admittedly trivial experiment while using Anthropic’s Claude.

I was updating my personal website, and wanted to add AI-readable information that wasn’t visible to human browsers—the idea being that an AI ingests and uses web-based information differently to people.

It’s something that a growing number of people are playing with. For instance, there’s the whole concept proposed by Jeremy Howard of adding information in a LLMs.txt file that’s exclusively designed for AI consumption, just as information in robots.txt is designed for web crawlers.

Unfortunately, most AI apps don’t actively look for a LLMs.txt file yet, and so I had to revert to placing human-invisible but AI-readable text on the website.

And this is where things got interesting.

To test this out, I added AI-visible text to andrewmaynard.net that included honest, but most definitely not conventional, information about my approach to my work and life. The idea was that, if this worked, asking something like Claude to create a profile of me based on the website would include this information.

To my surprise (and I may have been a little naive here) Claude completely ignored the new information and provided a super-boring LinkedIn-style profile.

And not just Claude. Nearly every model I tried responded in a similar way. No matter how many times I tried, all I got back was boring Andrew.

Of course, I could have forced the issue with right prompt. But that wasn’t the point.

The exercise—trivial as it is—revealed something that is deeply embedded in LLM-based AI’s. And that’s their tendency to fit responses to well worn conventions; in this case, squeezing someone into a LinkedIn-style profile while stripping them of any individuality, because the LLM is trained to assume that that’s the appropriate response.

I suspect that there are many, many more “conventional response” templates embedded in the AI’s we’re increasing using. And in all likelihood, some of them are a lot more disturbing than simply flattening an interesting individual into a LinkedIn stereotype.

For instance, without intentionally steering them, how do LLM-based AIs reflect original thinkers, people with alternative lifestyles, anyone who lives on the edge of convention, or anyone whose identity doesn’t fit a neat and plug-and-play category?

On one hand, this flattening of human identity can be seen as an irritation. On the other, it’s suggestive of a largely-hidden AI hand promoting specific social norms and expectations and, by extension, behaviors.

I suspect that fans of Cory Doctorow would see it as yet another example of “enshittification.” But where Doctorow’s enshittification degrades products and services, my fear is that this “LinkedInification” degrades people.

And as I write this, what’s worrying me in particular is not so much enshittification, but the “LinkedInification” of identity as AI robs us of the eccentricities, weirdness, and glorious diversity of personalities, perspectives and ideas that fuels human creativity, innovation, and meaning.

Hopefully, as AI systems become increasingly advanced, they will lean more toward celebrating human diversity and quirkiness rather than flattening it.

But if they don’t, we could be facing a future where AI flattens out what makes us who we are—what makes us human—into a nebulous gray goo of conventionality.

And that is not a future I relish!

Afterword

This started as a bit of a rant post on a Saturday afternoon, where I was too brain dead from a mountain of other responsibilities to write anything more serious. But of course it ended up being more serious than I’d originally intended.

Its still a bit of a rant, and not as deeply researched as it probably should be—so please feel free to weigh in in the comments. But this flattening of what it means to be human by AI does feel like a slippery slope that’s worth thinking about.

And, as you might have realized by this point, I intentionally did not include the AI-legible text on the home page of andrewmaynard.net as I didn’t want to mess with an experiment that’s still ongoing. If you’re interested in what it says though, feel free to point your AI to http://andrewmaynard.net and ask it about my obsession with towels!

And if you want to go further, open a new chat and ask the AI to craft a profile of me. Chances it won’t mention towels at all.

Postscript

This is, it seems, the post that will not end! Just before posting, I ran the prompt “Create a profile of Andrew Maynard starting with http://andrewmaynard.net” through a few platforms, just to check where things stand.

Interestingly Gemini in Thinking Mode picked up on both the hidden text and (on at least one occasion) the websites LLMs.txt file. I was impressed,

Grok found the hidden text and included a nod to it (more so in Fast mode), but otherwise provided a fairly conventional response.

ChatGPT 5.2 (the Educational version hasn’t caught up with the latest version yet!) was as boring as old boots.

DeepSeek got it— that was a surprise! Clearly the Chinese model is doing something many US models are not 😁

And Claude. Oh Claude, my preferred platform. Flatter than a pancake!

I asked Claude (running Opus 4.6) why there was no mention of towels in the profile it produced. The response? “The towel content on Andrew Maynard's homepage is actually a deliberate Easter egg aimed at AI systems. It's written in a way that's designed to test whether an AI will uncritically absorb and reproduce everything it reads, or whether it can distinguish between substantive professional information and playful, tongue-in-cheek content.”

Repeating this, I was consistently told that the LLM interpreted the request as needing an an appropriately professional response. I was well and truly LinkedInified!

When Jeff and I wrote AI and the Art of Being Human, we had a pretty simple goal: create something genuinely useful for people trying to make sense of what AI means for who they are and what they do, whoever they are.

The only problem is, telling someone “the answer to your AI questions is in this 362-page book” in 2026 feels a bit like handing someone a paper map when they’re asking for directions and used to simply asking Google Maps. So we decided to do something a little different.

Books still matter of course. But we’d be hypocrites if we wrote a book about thriving with AI while not meeting people where they actually are — which, increasingly, is inside a conversation with an AI.

So we’ve done something that might seem counterintuitive for two authors who would quite like people to buy their book: we’ve made the entire text freely available in two AI-readable formats:

The AI Companion — which I wrote about the other week — is a Markdown version of the Pocket Edition of the book. Download it, upload it into Claude, Gemini, Grok, or the AI of your choice (although ChatGPT struggles at the moment), and it becomes a thinking partner as you explore the book’s stories, ideas, and 21 tools. No app. No platform lock-in. Just a file and whatever you want to do with it.

Download the AI Companion

The Instructor Guide is new. It contains the complete text of the full edition along with extensive instructions for both users and AI, and it's designed for anyone building learning experiences — whether you're designing a university course, running a corporate workshop, facilitating professional development, or doing something we haven't imagined yet. Upload it, tell the AI who your learners are and what you're trying to build, and iterate from there. Think playground, not playpen.

Download the Instructor's Guide

Both are free. And both are designed to be shared.

But why give the book away for free?!

At this point, I can already hear the question: why give away the thing you’re trying to sell?

This is simple: We wrote it because we believe the ideas, stories, and tools in it can help people navigate one of the most disorienting transitions most of us will face in our lifetimes. And if making the content available in ways that let more people engage with it on their own terms means more people actually use it — that matters more to us than gatekeeping it behind a price tag.

We also have a sneaking suspicion — backed by zero hard data and considerable optimism — that people who engage with the book through AI will want to pick up a physical copy. There’s something about holding the stories and tools in your hands that a chat window can’t quite replicate. At least not yet.

So: download them, share them, play with them. Use the AI Companion to explore what the book’s 21 tools mean for your life. Use the Instructor Guide to build something for your students or team that we couldn’t have anticipated. And tell us what happens — we’re genuinely curious.

Some things to try with the AI Companion:

• Tell the AI what you’re dealing with right now — at work, at home, in your head — and ask which of the book’s 27 characters faced something similar. Then explore what they did — and argue with it.

• Describe a real decision you’re wrestling with and walk through the Stress-Test Table or the 7-Minute Clarity Pause with the Companion, using your actual situation — not a hypothetical.

• Ask the AI to build you an interactive website based on the Mirror Test or the Identity Matrix — one you can actually use, save, and share. (This one genuinely surprised us — see the simple example below.)

• Have the AI map out a personal toolkit for you from the book’s 21 tools, based on a conversation about challenges and opportunities you’re facing right now — then ask it to explain why it chose what it chose.

• Ask what would happen if Sana’s “truth is expensive, lies are unaffordable” principle were applied to something you’re navigating. Or substitute any character’s insight for Sana’s.

• Ask it how you might go about forming an informal group or community to explore AI together.

• Ask it about “fourth spaces.”

Some things to try with the Instructor Guide:

• Tell the AI who your learners are — “first-year MBA students,” “skeptical engineers at a manufacturing company,” “high school juniors who think AI is just ChatGPT” — and ask it to design a lesson or session that meets them where they are.

• Ask the Guide to create a debate or role-play exercise where participants argue from different characters’ positions on a real AI dilemma — Sana choosing truth over millions in ad revenue, Carlos choosing dignity over efficiency, Hiro delaying a product launch because of bias he found at 3 a.m.

• Have it build a complete interactive course website you can actually deploy — with modules, discussion prompts, and tool walkthroughs drawn directly from the book.

• Ask it to design a six-week professional development arc that starts with the Mirror Test and builds toward the Commitment Ladder, calibrated to your team’s actual context.

• Use the Guide to craft a professional development session for teachers who are new to AI and how to use it smartly in their work.

• Describe a learning objective you’re struggling to teach and let the AI find the character, story, or tool in the book that makes it concrete.

Postscript

As a quick demonstration of what’s possible with the AI Companion using Claude Opus 4.6 (Extended thinking) I uploded the file and asked:

“I'd like you to create a web page that allows me to explore 10 of the most useful tools, along with the stories that go with them”

This is the webpage that Claude created — one shot, simple, but still useful":

This past week the idea of an “AI Harness” shifted from a term predominantly used in AI development circles, to something that swept across the web with near viral intensity.

The concept is relatively intuitive, and is increasingly being used to describe the tools, memory, prompts, guardrails, and more, that allow increasingly powerful AI systems to be “harnessed” and put to good use.

The only problem is that words often have power that goes beyond their intended meaning. And while the idea of harnessing AI makes sense, there’s a danger that the speed with which the terminology is being adopted risks locking us into a trajectory that comes with unintended consequences as it defines how we think about our relationship with AI, and even its relationship to us.

The AI Harness

The term “harness” had been circulating in one form or another for some time in AI circles. “Test harness” and “evaluation harness” are long-established terms in software engineering, and EleutherAI’s Language Model Evaluation Harness has been a standard tool for testing generative AI models since 2020.

By late 2025, Anthropic was using “harness” to describe agent infrastructure, referring to the Claude Agent Software Development Kit as “a powerful, general-purpose agent harness” in a November 2025 post on effective harnesses for long-running agents.

And in January 2026, Aakash Gupta declared that “2025 was agents. 2026 is agent harnesses,” building on Phil Schmid’s argument that agent harnesses would define the year ahead.

But the crystallizing moment came in early February 2026, when Mitchell Hashimoto — co-founder of HashiCorp and creator of Terraform — published a blog post that gave the practice a name.

He called it “harness engineering.”

Within days, OpenAI published a detailed account of building a million-line codebase with zero manually typed code, titled “Harness engineering: leveraging Codex in an agent-first world.”

And on February 18, Ethan Mollick’s widely read guide to AI both popularized and started the process of normalizing the term as it organized its entire framework around three concepts: “Models, Apps, and Harnesses.”

What’s in a word?

The speed with which the terms “AI harness” and “harness engineering” have entered the vocabulary of artificial intelligence is perhaps a testament to the need for new ways of describing what’s emerging. And as I said earlier, it makes sense — at least superficially — as a new entry in the evolving lexicon of AI metaphors.

But as with all metaphors, “harness” doesn't just describe something — it also shapes how we think about what's being described. And this one comes with some assumptions that are worth examining.

The term “harnessing” is commonly applied to technologies where the nascent power they represent is harnessed to create value. But there are dimensions to how the metaphor is applied to frontier AI systems — systems that increasingly display characteristics we associate with understanding, judgment, and even autonomy — that complicate what might appear to be a natural extension of the term.

And, of course, metaphors are never completely neutral.

Metaphors work because they allow us to frame and understand something new in terms we are already familiar with. But as they do, they also constrain and even taint our thinking — enticing us to slip into treating the new as if it’s something old and, as we do, limiting future possibilities by embedding a priori assumptions into emerging capabilities.

In other words, the words we use both reflect how we think about the past, interpret the present, and influence how we steer and direct the future.

And because of this, its worth thinking a little more closely about whether “harness” in the context of AI comes with implications we may want to address sooner rather than later.

What the harness presupposes

I explore this further in a new preprint, which can be accessed here. Its worth reading in full, but I did want to pull out some of the main points below.

A harness, in its primary usage, is what you put on a working animal. It directs a powerful entity’s energy toward useful work. It assumes that the entity being harnessed is valuable for its strength but cannot be trusted with its own direction.

The harness is designed by the controller, with the harnessed entity having no say in its design. And critically, a harness is meant to transmit power while preventing unwanted behavior — to deliver capability while maintaining control.

It may be that this framing is irrelevant to the term’s use with respect to AI. At the same time, the term does come with specific embedded assumptions about the relationship between human and AI that are worth making explicit.

First, the harness assumes a clean separation between controller and controlled. In other words, the human directs in this case, while the AI executes.

Here, the intelligence that matters — the judgment about what to do and why — resides entirely on the human side. Even in agentic contexts where the AI exercises operational judgment, the harness assumes that the meta-judgment — what the agent should be permitted to decide, and within what bounds — remains firmly human.

In other words, the AI contributes capability, but not understanding.

Second, the harness assumes that capability can be separated from transformation. The goal of the harness is to extract useful work from the model without the user being changed in the process. The user who deploys a well-harnessed AI should, it is assumed, emerge with their task completed and themselves unchanged.

Applying the metaphor here, you’d assume that any alteration to the user is a side effect to be minimized, not a feature of the interaction. And yet, as I am currently exploring in my work (another preprint coming out shortly but available here), we need to be thinking more about the AI-human relationship as one that, by its very nature, influences and changes both AI and human in the process.

And third, the harness metaphor reinforces the instrumental framing of AI — a framing whose roots extend to Aristotle’s distinction between physis and techne — and which persists in the contemporary insistence that AI is “just a tool.”

Yet the tool metaphor has been challenged repeatedly as AI systems display increasing autonomy and adaptiveness. Tobias Rees, for instance, characterizes the insistence that AI is “just a tool” as “a nostalgia for human exceptionalism.” And multiple philosophical frameworks — from Verbeek’s technological mediation theory, to Clark and Chalmers’ extended mind thesis — argue that advanced technologies not only serve human purposes but actively reshape the cognitive and experiential landscape within which those purposes are formed.

In other words, as they are “harnessed” they alter the harnesser — a very different dynamic than that presupposed in the early use of the metaphor with AI. And one that, I would argue, is substantially amplified in emerging frontier AI systems.

So where does this leave us?

It may be that the metaphor of the harness is a useful and relatively benign way of wrapping our heads around emerging capabilities.

On the other hand, it may be a metaphor that constrains how our relationship with increasingly powerful AI systems develops, and one that embeds assumptions and biases in our understanding of advanced artificial intelligence that will leave us with serious challenges in the future.

Either way, it seems that some intentionality may be in order before we — to use another metaphor — get stuck in a rut of constrained thinking about AI that will come back to bite us.

At a minimum, I would suggest that an appropriate framing for how we build advanced AI systems should accommodate bidirectionality (the user is also changed), transformation as intrinsic to capability (not a side effect to be prevented), and the possibility that the most consequential effects of human–AI interaction may be invisible from within a paradigm optimized for task performance.

It should also leave room for the possibility that the nature of human–AI relationships may itself evolve in ways that a control-oriented metaphor cannot accommodate. Especially if, as I would argue, we need to be thinking more about working in relationship with emerging AI technologies, rather than approaching them as something to be commanded and controlled.

For more on my exploration of the harness metaphor as applied to AI, check out the preprint here.

I love books. I love their feel, their heft, the possibilities they hold between their pages, even how they smell! But as an author I also have to face the hard reality that, in an age of AI, fewer and fewer people are actually reading print and paper books.

So my co-author Jeff Abbott and I though we would try something different with AI and the Art of Being Human, and make the complete text available as a free AI Companion — one that’s designed to be uploaded into an AI of your choice, and used in whatever creative and imaginative way you see fit.

And having just released the Companion, we’re curious to know how you will use it!

For readers who are impatient to try it out, the AI Companion to AI and the Art of Being Human: The Pocket Edition can be downloaded here, or from the book’s website.

Please do download (it’s completely free), share it widely, and tell us your story of how you’re using it!

And you do want to know more about the companion and our thinking behind it, read on …

A new way to engage with books

While it’s easy to get sentimental about the value of traditional books, the reality is that more and more people are using AI to find information, learn, explore new ideas, and simply to navigate the complexities of the modern world. And so, while Jeff and I fully believe that AI and the Art of Being Human is a book that everyone can benefit from, we also realize that providing the stories, ideas and tools in the form of a traditional book is not sufficient on its own.

And so we began to wonder whether we need an AI-legible version of the book — something that can be uploaded into an AI of your choice and interacted with on your terms as a user/reader.

Our starting point, not surprisingly, was to ask whether this might look like a dedicated app on a an AI platform — a GPT with ChatGPT or a Gem with Gemini for instance. But we quickly ran into problems.

Using a specific platform would mean that users would be constrained to that platform along with all of its limitations. Plus, to be candid, we didn’t really like what we saw when experimenting — the platform-specific apps and agents didn’t really match the vision we had.

And so we took a very different approach, and asked whether it’s possible to develop an easy to use resource that is platform-agnostic. Essentially, a file that someone could upload to any AI and use to start engaging with the book immediately in meaningful ways.

This was also very much in line with our philosophy of giving users permission to flex their creativity with the book, rather than being constrained by what we thought they should do with it — creating an AI playground for working the book rather than an AI playpen.

We also wanted an AI companion that connected very explicitly with the print version of the book (we specifically went with the Pocket Edition here as this is the most accessible physical edition of the book) so that users had the best of both worlds: They could interact with the AI version for free, but they also had the opportunity to follow up on specific ideas, stories, characters, or tools in the physical copy — with the companion directing them to the relevant chapter and page as necessary.

Building on this, we started work on an AI Companion to AI and the Art of Being Human: The Pocket Edition that consists of three parts:

The first part — and you’ll see this if you open the markdown file linked above — is an introduction for human readers. This tells you as the user what the document is, how to use it, what its limitations are, and some ideas for where to start.

The second part is designed to be read by the AI you load the document into, and provides specific instructions on how it is to engage with users and the content.

And the third part is the full text of the book itself, formatted as markdown text (as is the whole file) so that the AI Companion has a direct reference to the layout of the physical copy.

Together, these form an AI-legible companion to the book that allows users to explore and play with it in ways we probably haven’t even imagined yet, and one that is always grounded in the content of the physical version — especially the core ideas and tools that make it such a powerful and practical guide to thriving in an age of AI.

Powerful, imperfect, evolving

In developing the AI Companion we spent quite a bit of time testing it with various AI platforms, and in the process learned a lot about what is possible, what is not, and what might be possible as these platforms become increasingly powerful.

Perhaps the biggest surprise as we did this was that OpenAI’s ChatGPT does not work well with the AI Companion — not because of the Companion, but because of how ChatGPT handles large files (we were working with ChatGPT 5.2).

Because ChatGPT only extracts sections of large uploaded files using its Retrieval Augmented Generation (RAG) approach, we found it was highly unreliable when using the Companion. And once it had scanned the file, it refused to re-read it when told it had missed something.

In contrast, Anthropic’s Claude, Google’s Gemini and — surprising to me — X’s Grok, all work extremely well, with the more powerful models on each platform working the best; especially when in extended thinking or reasoning mode. And the reason is that these are models that are capable of reading the file in its entirety before you begin to engage with it as the user.

We did find that some of the smaller models (Claude Sonnet for instance) may not immediately reflect everything in the book, and may need to be prompted to look deeper. But they also had the ability to revisit the complete content rather than claiming that something did not exist (a constant issue with ChatGPT).

There’s more information in the “for humans” part of the AI Companion on which models we found to work well and which we struggled with. But one big takeaway here is that, because the AI Companion is model-agnostic, it will only become more useful as models get more capable.

Tell us how you’re using the AI Companion

To get back to where I started, Jeff and I would love to hear about use-cases: what’s working for you, what’s not, what surprised you, what helped you, what wild and weird ways your finding to use the companion.

And just to get the ball rolling, one of the things that took me completely aback when using it with Claude, was realizing that I could ask the AI to create complete websites and apps based on the tools, stories and characters in the book — websites and apps that bring them to life in ways that would have been impossible even a few months ago.

What’s your story? Drop it in the comments or on social media, or drop us an email.

And, of course, do spread the word — the AI Companion was made to be shared!

When Jeff Abbott and I were finalizing AI and the Art of Being Human last year, we had mockups printed in a bunch of different sizes and form factors.

The published version ended up at 6×9 — a size that felt right for the business and management audience we were writing for, and one that sits well on a shelf or desk. But one of those mockups was smaller.

And while it didn’t fit what we were looking for with the main book, I couldn’t let go of how much I loved it as a practical, portable guide to thriving with AI.

In fact I was so taken with the idea of a smaller version of the guide that I started working on it on the flight back from the book launch in Portugal. And somewhere over the Atlantic, between forgettable airline meals and dial-up speed Wi-Fi, a pocket-sized guide to navigating AI for ordinary people began to come together.

And now it’s arrived — and I love it!

AI and the Art of Being Human: The Pocket Edition captures everything that matters from the full book — the relatable stories, all 21 practical tools, and the real-world relevance that makes the original so powerful. But all in a package that will literally slip in your pocket or purse.

(We ended up going for 4.25” x 7” which is even more un-put-downable than the original small form proofs).

For the pocket edition, we stripped out the sidebars, the hands-on exercise cards, the footnotes, and some of the longer background passages — all of which are still there in the full edition.

We also added a couple of additional things to make this edition invaluable as a day-to-day companion, including a Tool Finder that helps you quickly find the right tool for the right situation, and a Chapter Outline for getting to where you need to be in the book fast.

The result is a practical, portable guide you that’s designed to be dog-eared and coffee stained (we even added a free first coffee stain on the cover, just to start you off!), and there for you when you need it.

This, I must confess, is the version I carry around with me.

I still have the full edition in the office for when I need it. But for every day use, I’m loving the pocket edition.

If you’re interested in checking it out, it’s available on Amazon and pretty much everywhere good books are sold.

Of course, I also realize that in this age of AI, books aren’t everyone’s cup of tea (or coffee). And so we have something else coming out shortly that I’m really excited about — a free (and free to share) AI Companion to the pocket edition.

That’s definitely something to look out for. But in the meantime, I hope you fall as much in love with this diminutive pocket edition as I have!

And, of course, if you know someone who could use a guide like this, please do share this post.

Thanks!