Academic sabbaticals are dangerous things.1 They give you time to think and reflect that’s unhindered by the pressure to produce. And if you’re not careful, you can find yourself wandering down paths that your more straight-laced colleagues might disapprove of …

… like pursuing the idea that play without purpose, or embracing what sparks joy, or even reveling in the small delights of unexpected discoveries, are all critical skills for thriving in an age of AI. And that, sometimes, the best way to learn and grow when transformative technologies are rewriting the rules of how we do pretty much everything, is to not try to learn.

For people who study and embrace such ideas, of course, this isn’t new. And anyone who’s been following my work for the past decade or so will know that this is a space I’ve been inhabiting for a while.

Yet the reality is that how we teach, how we develop career-enabling skills, how we professionally evaluate ourselves and others, and even how we behave within professional environments, tends to devalue and discount the importance of joy, delight, and play, and to treat them as trivial, immature, and not appropriate for serious people doing serious jobs.2

If you doubt me, just take a quick look at your LinkedIn feed.

Yet as I get into this year’s sabbatical in earnest, I’m finding myself spending more and more time exploring such ideas, and asking whether we need to take the concept of learning through not trying to learn far more seriously as AI and other technologies shake up conventional thinking around what it takes to thrive in the work we do and the lives we lead.

I’ll be writing more about this I’m sure as the sabbatical proceeds. But in the meantime, I did want to write about one particular example here that reflects my evolving thinking and explorations — although I should warn you that, if you believe that there is no place in professional practice for joy, delight, and play without purpose, you may want to call it a day and stop reading here 😊.3

Revisiting Hyperbubble

A few weeks ago, I wrote about using Anthropic’s top-end Fable 5 model to create a simple browser-based video game. What started as a test of the model’s capability quickly developed into a collaboration around co-creating experiences.

Since then, I’ve spent considerable time with Fable to refine and extend the game — partly out of the simple joy and delight of doing this, but also as a way of further-exploring the ideas I touch on above around learning with AI by not trying to learn.

Through this process, I’ve been surprised (although I shouldn’t have been) by how much my thinking around the importance of playful exploration has evolved, especially as a pathway to developing essential skills for thriving professionally in a changing world. This has emerged through the act of developing the game with Fable. But it’s also been influenced by actually playing the game — which is something I wasn’t expecting.

Hyperbubble (Fable’s name) is a one-button browser-based game where your task is to navigate your character through a future of emerging technologies, complex risks, transformative possibilities, and unexpected delights, all while protecting and growing their state of “flourishing.”

Written like this, the game sounds somewhat serious and — if I’m honest — a little educational-preachy. Until, that is, you see how Fable helped translate the underlying ideas into a game that is anything but serious, preachy, or overtly “educational.”

Here, I must confess that I really like the resulting game.

Of course, there is every chance that I love Hyperbubble because it’s my baby4 and represents a chunk of work — and that to anyone else it’s just an embarrassment that’s best forgotten and moved on from.

But I’m going to lean into it anyway as, even if you’re not into trivial-seeming browser-based games, Hyperbubble remains an intriguing exercise in learning from and through AI by not trying to learn.

Version 5 of the game

The genesis of Hyperbubble was me asking Fable to do a deep dive into my work, my ideas, my mindset, my aims, what motivates and delights me, and to build a simple and fun game inspired by these. This was initially intended to be a test of the AI’s abilities, and it’s what led to the first iteration of the game and that first article.

Hyperbubble is now on version 5, and is the result of over 90 iterations between me and Fable around developing the game’s feel, focus, and substance.

The result is a game which is infused with simplicity and delight, which captures not only my work but how I think and see the world, and which can be played with no interest in or awareness of the ideas that it represents — and yet through playing it, the player encounters complex and nuanced ways of thinking about the interplay between advanced technologies, the future, and human flourishing.

Play Hyperbubble

Starting with the overall aesthetic of the game, this was an intentional design choice made by Fable, and reflects my work on Risk Bites YouTube videos. While this channel has been moderately successful (receiving well over 5 million views) the videos are based on quite crude stick figures drawn on a whiteboard (or black-glass board) and reflects the work of an academic with (in my own words which I believe I wrote somewhere) “no talent and even less time.”

A fitting aesthetic for Hyperbubble I think!

The game itself was designed to be discovered and delighted in through experimentation rather than following detailed instructions — another purposeful design decision. That said, there are fairly detailed instructions accessible from the home screen.

Here, the home screen (below) provides players with a number of options that allow them to explore the game further, modify the game play, and check out the high scores:

The links to how to play the game and the ideas behind it are reasonably clear. What is less clear is that, if you click on the sun three times, you enter a “techno-optimist mode” which makes it easier to flourish for longer. On the same screen, clicking “gentle mode” increases your chances of flourishing for longer.

Also from this screen, clicking anywhere starts the game in normal mode. But clicking “tap here” or pressing “T” puts the game in “Today’s Future” mode. In this mode, the game environment is the same for anyone playing it on the same day. (This was a feature it took me a few days to discover — entirely Fable’s creation).

Starting the game opens a screen where you, as the player, are encased in a soap bubble (a reference to my book Future Rising) and traveling into the future along an undulating landscape.

As you travel, you have just one control — the space bar (or mouse button, down arrow, or finger on the screen if you are on a tablet). Hold it down and the bubble is pulled downward. Release it and the force is likewise released. Hold it down on the down-slope of a hill and you’ll pick up speed — and likely jump off the crest of the next hill. Hold it down while in the air and you will start to descend — fast.

The game play is inspired by my work around navigating an increasingly complex risk-benefit landscape around emerging technologies. Along the way you’ll encounter orphan risks — which you can adopt by pressing the space bar (or similar) as you pass them; emerging tech risks, which you manage by likewise keeping the space bar pressed as you pass them; moral panics (scribbly fires) which die down if you ignore them but become an issue if you don’t (which happens when you press the space bar when passing); and “doom pits” which suck the flourishing out of you as pass through.

You’ll also pass “crates” of emerging technologies that you collect as you pass through them. Many are suspended in the air, meaning they’re easier to collect if you take a chance and soar. If you are on the ground five seconds after collecting one of these emerging tech crates it will deploy as a tech for good — and add to your flourishing. But deploy in the air, above the hype line, or in a doom pit, and things play out differently.

There are plenty of other objects and experiences you’ll encounter — including “PANIC” signposts marking the transition between eras — smash to increase flourishing — serendipity tokens that always come with a surprise, black swans that fly backward, and a plethora of other surprises. There are also a whole bunch of visual design elements that Fable thought it would be fun to add — including the whiteboard that forms the canvas for the game becoming increasingly smudged as the years go by, and the coffee-mug stains that adorn it!5

But the risks, technology crates and moral panics are the basic ones that allow you to increase flourishing by adopting and managing risks, deploying emerging technologies, and not feeding moral panics, or result in flourishing diminishing if you mis-manage them (navigating the future is hard). And when flourishing hits zero, the game is over.

Then there’s the hype line, and the risk of bursting the bubble.

As you pick up speed and soar — which is intentionally designed to be exhilarating — you’ll sometimes cross over the hype line. It’s fun, but it’s also risky if you stay there too long as the bubble begins to swell. Stay too long and and eventually it bursts.

The bubble will also burst if it develops too many cracks. These appear when you land hard after soaring and are still holding the space bar. Three cracks and the bubble bursts, and it’s game over.

The good news is that the bubble never cracks when you’re not holding the space bar when you land, allowing for a degree of risk taking that can benefit flourishing.

The aim of the game — apart from the simple delight of playing it — is to get as far as you can without the bubble bursting or flourishing decreasing to zero.

Despite the simplicity of the control, there are a surprising number of ways of playing the game (as I’ve discovered — again, Fable as been surprising and delighting me here). At one end of the spectrum, you can go fast and revel in the exuberance of riding the hype — and as long as you develop your risk navigation skills, you can get pretty far. At the other end of the spectrum you can go slow and cautiously, managing risks and avoiding too much speed. This works as well, but again, there’s a learning curve involved. And between these there are many other strategies — many of which I discovered Fable had planned for, but I had to discover on my own.

This isn’t all though. In the year 3000 (if you get there) you enter a post-scarcity age, where flourishing doesn’t decrease with time. And reach the year 4000 and you enter another state altogether.

When the game ends, you have the opportunity to add your name to a global leaderboard. And this is where the game gets competitive — if that’s your thing.

Learning by NOT trying to learn — the AI way

On one level, Hyperbubble is just one more AI-created game.

On another, it’s a unique representation of my work — and what guides and drives it — that is experiential in a way that I’m not sure would be possible to convey through other media, or without the help of an AI co-designer.

On yet another level, its a surprisingly sophisticated way of exploring the often-complex tensions between technology innovation, risk, decision-making, and future flourishing. The tradeoffs in the game mean that you can play it from multiple perspectives as you grapple with the consequences (good and bad) of the actions you take.

And on another level still, it’s an engine of delight; an AI-conceived and co-developed game that is designed to surprise, stimulate serendipity, and spark joy.

And it’s where all of these come together that I find that things get interesting. This is where, by putting any notion of learning aside as you play the game, and simply focusing on the delight and whimsy, the conditions are created where learning occurs naturally. Learning about how I think and see the world. Learning about navigating advanced technology transitions. Learning about flourishing in. technologically complex future. And learning in new ways with and through the help of AI.

Or not.

Because the thing I keep coming back to here — and what is increasingly part of my thinking as I continue with my sabbatical — is that the magic of Hyperbubble is that there are no learning expectations. The game doesn’t stand or fall on programmed experiences, on learning objectives, or measured and documented outcomes. It’s a free space for play and exploration. A playground. Somewhere where you can be whatever you want to be, and play however the mood takes you.

It’s an environment that’s been designed — with great care I have to say, and with substantial input from AI — to encourage learning through serendipity along with joy and delight, and to contribute to the formation of a mindset that is attuned to thriving in a technologically complex world. But what a player takes away from it is uniquely theirs — and not determined by a set of learning outcomes.

Of course, I’m just messing around here, and am probably over-stretching the significance of the game and the process that led to it.

But that, of course, is the point. Especially while I’m on sabbatical!

Despite attempts to drag academia kicking and screaming into the 21st century, peer reviewed publications and citation are still a gold standard against which many of us are judged. And so it’s no surprise therefore that there’s been a surge in academics using AI to churn out papers by the dozen in the hope of gaming the system — or at least giving them more of a competitive edge.

But are emerging AI models actually any good at writing academic papers?

I’ve been working extensively with various AI models as writing partners for over a year now, and have been on something of a roller coaster of a ride when it comes to my thinking around what they can and cannot do. A year ago for instance, I was being blown away by the seeming-eloquence of models like Anthropic’s Claude 4.5. More recently though, I’ve found the prose of even the most advanced models grating, while being superficially profound yet substantively hollow.

And yet, I’m seeing a growing wave of opinion that suggests that I’m an outlier here — especially when it comes to writing as academic papers. And so I thought I’d try an experiment (admittedly with an “n” of one), and pit myself against Anthropic’s latest model — Fable 5.

If you’ve been reading my recent articles you’ll be familiar with the genesis of the resulting comparison. After an initial foray into using Fable, I sat down to see how good it was at writing a paper that built on an extended my own work.

By focusing on an area that I’m deeply familiar with (and am responsible for developing the underlying concepts in) I was able to assess both Fable’s ability to make a genuine contribution to the field, and to write it up in a paper that could conceivably pass peer review.

I was impressed with the results — very impressed in fact. But I still found the paper somewhat lacking. Even though it was in an area I am intimately familiar with, I found it hard work reading it and increasingly frustrating — not so much because of what Fable was trying to say, but how it said it. It just felt like really poorly executed academic writing, and more like something a machine would write that has studied the form of the “academic paper,” but has no idea what the experience of reading one is like to a real person.

And so I thought I’d “improve” on Fable’s paper by editing it, line by line, and transform it from what I though was a jarring “cargo-cult” of a paper to something much more palatable.1

The resulting “Maynard” version of Fable’s paper was not my best work I have to admit. But it was, in my estimation, OK. And I was pretty happy that it represented a substantial improvement on the AI version. Until, that is, I made the mistake of asking Fable for its opinion. And then went on to ask a whole lot of other AIs — all of which pretty much told me the same thing: that my version was far and away the worst of the two.

To make matters worse, the more I explored emerging attitudes toward AI-generated academic writing online, the more I began to wonder if it was me who was completely out of touch with what is considered today to constitutes good writing — an anachronism from a previous age, lost in his own myopic hubris.

OK, so I actually don’t believe this. Hubris though it may be, I’d like to think that nearly 40 years of experience has taught me something. But I am fascinated by how divergent opinions are becoming on what constitutes “good” academic writing, given the increasing prevalence of AI-generated content.

And so, rather than rely on my own assessment, I thought I’d seek the opinion of others (including you, the reader) on the two versions of the paper — the “Fable” one and the “Maynard” edit.

Each version of the paper is downloadable below, and in two formats — a markdown file that is easy to give directly to an AI, and a PDF, which is easier on the human eye.

Each paper is also anonymized, so that any AI you pass these to will have minimal clues as to which is which (and please do feel free to do this).

It is, admittedly, not that hard to work out which is mine and which is Fables if you read my previous posts. But for the sake of this article they are “Rabbit” and “Marmoset,” and I’m not saying here which is which.

The Challenge

So here’s the challenge: Download each and either read them, skim them, or feed them to your favorite AI. Then, let me know (in the comments or otherwise) which one you think is better — and why (especially as “better” is such a subjective term).

• The Marmoset version: PDF. Markdown.

• The Rabbit version: PDF. Markdown.

And if you are interested in my own thoughts here, read on:

My thoughts …

I was in two minds whether to leave this article here and wait to see what the reactions were, or whether to add my own initial thoughts on the comparison and what it reveals. On balance I decided to go ahead anyway as this has been occupying my thoughts.

To start with, I wanted to acknowledge that each of these papers represents considerable human effort. The original Fable paper builds on decades of my own work, and the process of working with the LLM and providing feedback on several iterations of the paper took a couple of days. Then the second, extensively edited version of the paper, took a further three days to complete as I went through it line by line. As a result, his is very much an exercise in human-AI collaboration, where the human bit isn’t insubstantial in either case.

In my version of the paper, I changed relatively little of the substance. There were areas where I had a better grasp of the material than Fable (not surprisingly as this is the result of my own work), and I edited the paper accordingly. But overall the ideas, analysis and insights that Fable generated remain intact.

What I did change though was how the information is presented, and especially the narrative form of the paper. This is where I have (or thought I had) a pretty decent sense of where a sentence or paragraph can be technically accurate but narratively ineffective, and how this can be remedied. As a result, the “Maynard” version of the paper reads in a way that makes far more sense to me as a writer.

Still, as I noted above, my version of the paper isn’t great. But it is something that I’d be happy to put my name to (it is, in fact, available as a preprint, as I believe it makes a useful contribution to the field of AI safety/risk independently to this experiment).

But then I fed both anonymized versions to Fable 5. And then to ChatGPT, Gemini, and Grok.

The verdict in each case: Fable’s version is the sharper, better, more refined, and more publishable version of the two.

Ouch!

I asked Fable to explain why. It claimed that the preferred version (it didn’t know it was its own):

• Was more compressed and therefore concise (it considered this a good thing);

• Had far less metaphorical “throat-clearing” (something else it thought was good — and a bit awkward for me);

• Didn’t over-labor the arguments (which meant, I discovered, that it felt the reader should fully understand its arguments from single well crafted sentences augmented with citations, and without any further contextualization or explanation); and

• Ensured there was a “mic drop” moment (Fable’s own words) at the end of each paragraph. In fact there was a note of simulated admiration at just how jaw-droppingly profound each of these revelations was — as if the reader would immediately feel the scales dropping from their eyes, paragraph after paragraph after paragraph …

What I find interesting here is that every one of these attributes that Fable applauded is one that, in my mind, leads to ineffective writing. They may make sense technically if you don’t know much about what it feels like to experience reading something as a human. But each one has a tendency to hinder the process of enabling the reader to get a glimpse into the mind of the writer.

And my sense here is that, while an LLM like Fable can study what it believes to be examples of good human writing, it cannot replicate faithfully a process that draws on our lived experience, our biological heritage, and our emotional and cognitive responses, as much as the technical meaning behind the words. Rather, because it cannot understand what it feels like to read as a human, all it can do is emulate what it infers from very incomplete data that reflect something that lies beyond an LLMs ken — at least for now.

And yet, it seems that many AI models are secure in their own understanding of what they think is good writing. And it’s this standard that they are using to assess what they generate themselves, and how they assess writing that’s passed to them for critique.

If this is true, one likely upshot is that, the more that people rely on LLMs to provide critical feedback on their own work — or to actually edit it — the more we’ll see papers emerging that match an LLM-view of what good writing is, rather than a human one.

This, to me, is worrying. But it gets worse.

I’m now seeing growing evidence that people are believing the LLMs rather than what people claim is effective writing.

I am encountering an increasing number of people who will claim a paper is good because an AI agent said so. And they get even more certain of this after they have unleashed a whole army of AI agent reviewers using advanced models, that also tell them it’s a solid paper.

the implication, of course, is that the AIs we have trained to “think” like us are now beginning to train us to think like them.

This worries me. But it also rises a niggling doubt in my mind. And that’s where we get back to me wondering if I am, in fact, an anachronism. Because what if I’m wrong and the LLMs are right? What if my idea of effective writing belongs to another era, and frontier AIs, with their access to more data than I could possibly hope to assimilate, are learning the true essence of great communication?

There’s a change of course that the LLMs are right and I’m wrong here.

At the same time, if our ideas of what constitutes effective writing are being informed by machines that do not know what it feels like to read as a human, and are incapable of emulating the full human experience of communication through the written word, what does that mean for the future — and the future of being human?

And this is why I’m so interested to see how others respond to the two papers above.

Update: I had meant to add this before pressing publish but it completely slipped the net:

I’m sure many people reading this will say that the problem here is using the wrong writing skill with Fable, or not training it sufficiently on my own voice. However I do not believe that this is the issue I was facing, and for two reasons.

The first is that I have been training AI models on my writing style and voice for a long time now - and successfully. Yet in this case the training and feedback always led to disappointingly performative and hollow results.

The second is that I quizzed Fable on its performance quite deeply, asking why it could not learn from my feedback and up its game, despite me trying every trick I knew. It eventually admitted that it has internal “templates” for want of a better word on what good academic writing is, and no matter how much fine tuning I attempted, these would always dominate how it wrote. This is where the traits I mention in the article above reside.

Fable also noted that it had trained on papers that were prominent and highly cited, and not necessarily papers that are well written - it’s training relies on a proxy for what is good that may have been misleading.

The result is that Fable, and, I suspect, other models, seem hard coded to write academic papers in ways that they think are appropriate, but that do not necessarily connect with human readers.

This may well change over time. But my sense is that this goes far more deeper than being fixable through fine tuning. In the meantime. If you use AI to extensively write for you and critically review its work or yours, you may want to be aware that the machine may not be your best guide. Unless, of course, LLM-style becomes the de facto standard!

Note: This is a post in two parts — a rather long paper on orphan risks and frontier AI models (the second part), and a pre-amble on the process that led to this, which is part of an ongoing series of experiments in using Anthropic’s Fable 5 for researching and writing academic papers. Please feel free to ignore the lengthier paper (or bookmark the preprint) if you are primarily interested in the process.

If you’ve been following my series on researching and writing academic papers using Anthropic’s Fable 5, you’ll know that a couple of weeks ago I wrote about working with Fable on getting it to write a paper in my own area of research. The idea was to see just how good it is is at taking and extending my own work, while producing something that I would consider to be publishable.

To be clear, this was no fly-by-night 20 minute paper-producing process. Rather, it was two days of intense back and forth working with Fable as I provided detailed content and editorial feedback over several drafts. That said, working with Fable did shave several weeks (if not more) off how long it would usually take me to write a paper like the one that emerged.

Despite this though, Fable’s paper still fell short of what I would expect from myself, or any other a competent human researcher/writer. It was technically interesting — if a little condensed and formal — and Fable managed to pull together some ideas and insights that I may not have landed on myself. But the delivery — even after all of my feedback — was not great by my standards.

And so I thought I’d extend the experiment and rewrite the paper: building on what Fable had produced, but adding my own voice and perspective while ensuring every aspect of it aligned with my own thinking and work.

Three days of revising and editing later (just me — no Fable this time), a paper emerged that I feel much happier about. But … and here’s the kicker … I’m now beginning to second guess myself.

Was my version better in my eyes because I have a rather old fashioned and biased perspective on what an academic paper should be like? And is Fable actually better at this than me, but I’m just too stuck in my academic Ivory Tower to see this?

To make things worse, chatter on LinkedIn and elsewhere seemed to suggest that I’m the dinosaur here, and that maybe LLM’s are increasingly setting the standard for what is considered to be effective scientific/academic writing.

I’ll be exploring this further in my follow-up post in a couple of days, and will be providing anonymized versions of each paper that you can compare side by side (or feed to your favorite LLM to compare). But before then, I did want to give you the chance to read my version of the paper — in part to give you the chance to compare it with the Fable version ahead of the next post if you’re interested — but also because, despite my crisis of identity around whether I can actually write papers any more in a world of AI, I believe the ideas and perspectives in the paper are important.

The “Maynard” version of the paper is currently available as a preprint on SSRN. But I’ve also included it in full below for anyone who’s interested — with the heads-up that this is long, and so you may want to just read the abstract, or save it for later reading.

Orphan risks at the frontier of artificial intelligence

What diverging safety and compliance frameworks reveal about how AI companies choose the risks they prioritize

Abstract

Companies developing some of the world’s most powerful artificial intelligence systems are surprisingly diligent in how they map out the risks their technologies present. Yet the risk landscape that lies between emerging frontier models and their economically successful and societally beneficial deployment is becoming increasingly hard to navigate. Complicating this further, many frontier AI companies maintain more than one account of what could go wrong with their technologies. This paper documents the divergence between these accounts by comparing safety and compliance documents published by Anthropic, OpenAI, Google DeepMind and Meta between 2023 and 2026, and considers what the resulting record reveals about how these companies select the risks they manage. As these documents are timestamped and archived, they provide a valuable public record of institutional risk selection in progress. From this record the paper identifies four filters that determine which risks tend to survive in self-authored frameworks (measurability, severity, auditability and competitive cost) and introduces the “safety differential” as the gap between the risk landscape a company selects for itself, and the one regulators select for it. While acute, quantifiable risks appear across documents, less tractable risks such as harmful manipulation are articulated fluently where law compels disclosure, yet remain absent from most self-chosen frameworks. This is an exclusion that follows from how these institutions define risk. Drawing on scholarship on institutional risk selection and the framework of risk innovation, the paper shows how redefining risk as a threat to value can help explain how risks become “orphan risks,” how it indicates where future blindsides may occur, and how it points to lightweight tools for de-orphaning risks that frontier AI’s safety apparatuses are not currently organized to address.

Keywords: artificial intelligence, frontier AI, risk, safety, AI risk, AI safety, risk innovation, orphan risk, severity floor, safety differential

1. Introduction

In December 2023, OpenAI published the first version of its Preparedness Framework — a document in which the company publicly set out potential risks associated with its most capable models (a class of systems now called “frontier” AI), and one where the company made a commitment to track and manage these.¹ Four key categories of risk made the list: cybersecurity; chemical, biological, radiological and nuclear threats; model autonomy; and persuasion — which the framework described in terms of models being used to convince people to change their beliefs. In OpenAI’s framework, persuasion was treated with the same degree of seriousness as the other three risk categories. It had its own graded scale, from low to critical, and its own place in the machinery of evaluations and thresholds that the framework built around each tracked risk.

Sixteen months later, persuasion was gone. In effect, potential risks associated with persuasion were “orphaned,” suggesting an emerging landscape around frontier AI models where some risks are attended to more than others, and where the level of attention a risk receives may not necessarily align with its societal importance. It’s this possibility that this paper explores, and applies the framework of risk innovation to as a potential route to addressing orphaned but nevertheless important AI risks.

In OpenAI’s case, the orphaning of persuasion as a risk came with the second version of their framework, published in April 2025. This version removed persuasion from the tracked categories, explaining that risks of this kind do not fit the criteria for a framework aimed at preventing specified severe harms. These were harms that the revision now defined explicitly as “the death or grave injury of thousands of people or hundreds of billions of dollars of economic damage”.² According to OpenAI, persuasion-related risks would instead be handled through the company’s usage policies and its investigations of misuse. In other words, the company’s internal responses to persuasion risks were real, but were discretionary, with no published thresholds, no pre-committed responses and — importantly — no “versioned” public record of their evolving thinking, commitments, and actions. The upshot was that a risk that had spent a year and a half on the public commitment list moved into a less-public discretionary layer of assessment and management.

Then, in May 2026, it came back. This was when OpenAI published its Frontier Governance Framework — a document that was produced in response to new legal requirements; specifically, California’s Transparency in Frontier Artificial Intelligence Act — which requires large frontier developers to publish frameworks addressing catastrophic risk — and the binding obligations that the European Union’s AI Act places on providers of general-purpose models.^3,4,5 OpenAI, like most of its major rivals, committed to meeting those European obligations by signing onto the EU’s General-Purpose AI Code of Practice.⁶ The code is admittedly voluntary — companies are not compelled to sign — but the obligations behind it are driven by law, which makes signing it a pragmatic path of least resistance for AI developers. Among the risk categories the May 2026 document covers is harmful manipulation: the strategic distortion of what people believe and do at scale, including influence operations and election interference. This, in effect, covers much of the same territory that the persuasion category had covered, but under a new and somewhat narrower name.

OpenAI is candid in noting that its approach to this category is exploratory and less developed than its work on other areas of risk. And yet the risk is named, owned and reported on. In other words, the category the company had judged unsuited to its own framework had returned. Despite this, there is little evidence that there was a sudden increase in the probability of persuasion- or manipulation-based risk in the intervening year (although as frontier models develop, the risk landscape is constantly evolving). Rather, what had changed was that regulators in California and the EU, and not the company, were beginning to influence the list of publicly identified risk categories.

Here, OpenAI’s story is just one example of a pattern that can be seen running across the industry. Anthropic, the frontier model developer founded by former OpenAI researchers, has never tracked persuasion explicitly in its own safety framework. This, to be clear, is not because the company ignores such risks: the system cards it publishes with each model discuss associated concerns ranging from sycophancy to user wellbeing in some depth. But these cards describe what a model does. In contrast, the safety frameworks define what the company has committed to manage. And at the framework level, a 2024 revision of Anthropic’s Responsible Scaling Policy set persuasion aside as “not yet sufficiently understood to include in our current commitments”.⁷ Yet when Anthropic published its Frontier Compliance Framework in December 2025 to meet the Californian statute (and, over time, its European obligations), the pull of the broader list of risks that regulators care about began to show, and within months (with European enforcement approaching) a revision had added a risk tier on harmful manipulation.^8,9

These examples from OpenAI and Anthropic reflect practices within private companies. But there is also a telling signal here coming from public companies. Meta and Alphabet — two publicly traded companies among the four developers this paper focuses on — are legally required to describe risks that are material to their business in their annual securities filings. Here, Meta names misinformation, harmful content and youth safety among them: risks that its own safety framework implicitly places out of scope. Alphabet makes a similar move, devoting a dedicated risk factor to the reputational harm that AI failures could inflict on its business.^10,11

What emerges is companies assessing the same models, but describing different risk landscapes in documents written for different audiences. And as a consequence, which landscape comes into view depends on who is doing the asking. Interrogate a company itself through self-authored safety frameworks, and the landscape tends to feature a handful of catastrophic capabilities. But interrogate it through a statute or a code, and a broader landscape emerges — and one that includes risks like manipulation. Take this one step further (as in the case of Meta and Alphabet) and interrogate through securities law, and risks like misinformation, youth safety and reputational damage appear.

Of course, the source documents here serve different purposes, and setting them side by side is not a like-for-like comparison. Yet even allowing for that, the comparison reveals where law compels articulation, risks are articulated fluently — even complex risks like manipulation. In other words, the companies are able to describe these risks, even though their internal safety documents tend to skirt around them. What keeps them out of these self-authored frameworks, I will argue, is the ways those frameworks define and select risk.

The arguments I develop below lean on the differences between these documents. And because of this it’s worth being clear about what each type of document represents — not least because the boundaries are perhaps blurrier than the labels might suggest. The safety frameworks are the documents that companies write for themselves: Anthropic’s Responsible Scaling Policy, OpenAI’s Preparedness Framework, Google DeepMind’s Frontier Safety Framework, and Meta’s Advanced AI Scaling Framework.^2,7,12,13 These are not required by law. Rather, they are documents that map out the risks which a company decides it will be publicly accountable for. And in most cases they establish accountability around thresholds, evaluations and committed responses. In comparison, the compliance frameworks are the documents that emerging AI-focused laws require. California’s Transparency in Frontier Artificial Intelligence Act demands them outright. In contrast, Europe’s governance lever is the General-Purpose AI Code of Practice: voluntary to sign, but binding in what it requires once the AI Act stands behind it.^3,4,5,6 Securities filings on the other hand are enumerations of whatever could materially harm a business, and that are legally required to be disclosed to investors. Alongside all of these sit the system cards that now accompany each major model release. These are reports on how a particular model performed in evaluations and assessments against whatever its developer’s framework tracks. System cards can range widely, and can describe in detail what a model does and what its capabilities and vulnerabilities are in ways that safety frameworks don’t necessarily track. But while they may describe model behavior and implications in detail, they rarely come with commitments to action — which is why they are peripheral to the analysis here.

In what follows I concentrate on four developers — Anthropic, OpenAI, Google DeepMind and Meta — because their frameworks are perhaps the most consequential and the best documented. But of course they are not the only players here. Other developers in the United States and beyond publish frameworks of their own. And Chinese companies in particular operate under an increasingly elaborate state-guided regime. At some point a comparative study across these would be valuable. But this is beyond the scope of this paper. What is within scope is how different safety and risk-related documents from the four companies identified above reveal strengths and weaknesses around how risk landscapes are developed and used, and how this might inform alternative approaches to understanding and navigating frontier AI system risks. Within this scope, it’s worth noting that safety frameworks are no mere formality. Pioneered by individual developers in 2023 and generalized when sixteen organizations signed the Frontier AI Safety Commitments at the 2024 AI Seoul Summit, these documents have become the de facto governance layer for frontier AI — the place where decisions about when to develop, when to deploy, and when to stop, are given public form.¹⁴ Governments have increasingly chosen to build on these frameworks rather than simply replace them. California, for instance, now requires large developers to publish such frameworks, and Europe’s new obligations lean heavily on a code of practice that intersects with the frameworks.^4,6 Which risks these documents focus on, and which they leave out, is therefore not simply an internal matter. Rather, what these documents cover and what they do not has become indicative of who decides what counts as an AI risk worth managing, and in what context.

Here, I would argue that the emerging pattern of managed and unmanaged risk in frontier AI as indicated in these documents is neither accidental nor, for the most part, cynical. Rather, it is the result of a risk-selection process that follows from how these frameworks (and the people and organizations who develop them) define risk. And, unusually for such processes, it is one that has left a public trail that can be studied. Published frameworks are versioned, timestamped and archived. In effect their revisions can be read as a way to gain insights into how AI companies are mapping and responding to potential risks within multiple contexts.

In what follows I consider that record, identify four filters that determine which risks survive in what is published, trace three of those filters to definitions of risk inferred to be in use and the fourth to the competitive environment, and then ask what changes if risk is defined differently — in this case, drawing on the definition used in the risk innovation framework of risk as a threat to what people, organizations and societies value. This redefinition, I will argue, can help explain the patterns observed, and anticipate where emerging risks may lead to unexpected harm. The redefinition also comes with working tools — again drawing on the risk innovation framework — that have the potential to, in effect, “de-orphan” orphaned risks that have the ability to lead to harm in ways that are not conventionally anticipated.

2. Blank spaces on a crowded map

If managing the risks of frontier AI systems came down to simply identifying the risks, navigating the emerging risk landscape would be much easier than it is. But of course this is not how risk management works. I mention this as AI risk-naming has already been done at a remarkable scale. The MIT AI Risk Repository, for instance — a living synthesis of published taxonomies — now covers more than 1,600 distinct potential risks, from discrimination and misinformation through to catastrophic misuse.¹⁵ And the International AI Safety Report, a government-mandated scientific assessment chaired by Yoshua Bengio, updates the risk landscape annually, and ranges across harms from bias and labor-market disruption to loss of control.¹⁶ Many of the entries on these maps were identified by researchers working inside the frontier companies.

Compared with this crowded risk landscape, the safety frameworks the companies have written for themselves are strikingly sparse. As of mid-2026, the frameworks of Anthropic, OpenAI, Google DeepMind and Meta each track only a handful of categories of risk. And these tend to be capability-driven risks — risks defined by what a model can be shown to do on a test. These include the “uplift” a model could give someone seeking to build biological or chemical weapons, cyber offense, and varieties of AI self-improvement or loss of control (with a single exception, which I will return to below, where manipulation has recently joined the list).^2,7,12,13 Each framework also applies a “severity floor” to a risk or risk category of the kind OpenAI’s 2025 revision to its Preparedness Framework made explicit. Meta says something similar, stating that broader risks are addressed through processes “outside of the scope of this Framework”.¹³ This phrase represents, in effect, a boundary of accountability that is defined by the company itself. Everything below it — including manipulation and persuasion, misinformation, the erosion of human agency, harms accumulating gradually across millions of small interactions, and, notably, nearly every risk these companies pose to themselves through their own cultures, governance and public standing — is below it by choice. These are recognized risks. But they are risks that few if any formal frameworks identify, own, or articulate accountable ways of managing.

This, I would argue, is a serious omission if these “orphaned” risks present a credible threat to an organization, its stakeholders, or society more broadly. However, to be fair to the frameworks’ designers, there is a case for focusing on a narrow but deep risk layer rather than trying to be all-inclusive. And it comes down to how finite resources are invested. Concentrating limited safety resources on risks perceived to have the highest levels of severity makes sense; a framework that tried to manage sixteen hundred risks would end up managing none of them. And the frameworks’ architects have, sensibly, never claimed completeness. It’s also important to recognize that safety frameworks are not the only risk-management tool that these companies have at their disposal: usage policies, trust-and-safety teams, societal-impact programs, and more, all help address parts of the excluded risk territory. But these are discretionary. As with OpenAI’s reassignment of persuasion, they carry no public thresholds, accountability, or pre-committed responses. And they can be reorganized or defunded with speed, and without anyone outside the company knowing. In contrast, what the frameworks track is what the companies have committed, in public, to be accountable for. Everything else depends on trust and goodwill — and the record examined below (and my own experiences working with entrepreneurs¹⁷) shows that under competitive pressure, such commitments tend to become weakened.

What emerges is a risk landscape where the gaps are gaps in accountability. Many of the excluded risks are known and named — in many cases in documents written by employees of the same companies developing risk frameworks. They are just unowned, where ownership means public, pre-committed and versioned accountability rather than a team somewhere having them on their to-do list. Some years ago, working with entrepreneurs facing a similar landscape of recognized-but-unmanageable threats, I started referring explicitly to risks like these as orphan risks: risks for which no agreed-on tools, standards or mitigations exist, which no one is accountable for in practice, and which, for that very reason, have a habit of being overlooked and sidelined, even though they may blindside an enterprise later on, or lead to serious societal harm.^18,19 The term was coined specifically in the context of startups and other organizations grappling with emerging and often hard-to-pin-down risks. But it describes the risk landscape faced by frontier AI systems and their developers and users just as well.

Once gaps in the frontier AI risk landscape are seen in terms of orphan risks, an interesting and potentially useful question arises: how do AI risks become orphaned? If it is accepted that many of these risks have been identified or are not hard to identify (while acknowledging that there will inevitably be some emergent risks that have so far eluded identification), rather than just asking what the orphaned risks are, a more revealing question is how they are made. In effect, by what process does a known risk come to be nobody’s responsibility?

3. How risks become orphans

That institutions (and society writ large) choose which dangers to focus on and which not to — and that the choices reveal as much about how they are organized and operate as they do the objective nature of threats — is generally recognized. In 1982, the anthropologist Mary Douglas and the political scientist Aaron Wildavsky argued that every society selects its risks, ranking some dangers as intolerable and ignoring others — and that you can therefore understand an institution from what it fears. In effect its list of feared dangers reflects its own organization and — in the corporate case — its mission and ambitions, as much as any independent assessment of threat.²⁰ More than a decade later, the historian of science Theodore Porter showed how institutions under external scrutiny tend to retreat to what can be quantified. Numbers, of course, do not always uniquely capture the essence of what is relevant to decision-making. Professional experience, judgment, and intuition, along with other factors, inevitably come into play as well. But quantitative evaluations — numbers — have a unique power within decision-making ecosystems. Where judgment often has to be taken on trust, numbers can be handed to an outsider, checked, and defended.²¹ Such foundations of how risk is assessed, quantified and managed were historically worked out in a landscape comprised of nuclear plants, chemical works and government bureaucracies. And through most of this working-out, much of the process was hidden. But I would argue that frontier AI models and systems, while being an extension of this risk landscape, offer something different: a public, timestamped, versioned record of risk selection in progress. And this is a record not merely of what firms disclose, but of what they commit to manage. Reflecting this, between 2023 and 2026, each of the four developers examined here revised its framework at least once, and every revision is preserved and comparable with its predecessor.

Read in sequence (Table 1), this record can be viewed through the lens of four filters. These can be articulated as four questions that apply to every candidate risk: Can we measure it? Is it big enough? Can we evidence it? And can we afford to keep it? The record suggests that a risk has tended to survive in a safety framework where the answer to all four is yes.

Can we measure it? There are strong indications that a risk earns a place in a frontier framework only if it can be operationalized, or turned into an evaluation with a threshold; in effect a capability threshold. This is a pragmatic decision based on established approaches to risk management. A capability threshold relies on what a model can be shown to do on a test, as opposed to a risk threshold, which considers how likely a harm is. And the first is far easier to evaluate reliably.²³ As well as representing reasonable engineering judgment, such an approach is also a concession to the reality that what the frameworks track follows what their instruments can measure. What happened to persuasion in the case of OpenAI illustrates this filter at work — though not, as we will see, working in isolation. OpenAI’s stated reason for removing this category was one of fit: persuasion-type risks, the revision explained, did not meet its criteria for tracked categories — criteria that required a tracked risk to be both severe in its potential harms and measurable in practice.² Yet the risk itself was demonstrably not beyond measurement. Within months of the removal, a large experimental study in the journal Science demonstrated that conversational AI systems measurably shift political beliefs — a persuasion-based effect.²⁴ What persuasion lacked in this case was not measurability, but measurability in the accepted idiom — a pre-deployment capability evaluation with a clear threshold that could be defended in a court of law.

Is it big enough? Severity floors make sense as triage when assessing and managing risk. But they also carry hidden consequences. A risk that emerges gradually, distributed across millions of small interactions, will rarely trigger them. The AI philosopher Atoosa Kasirzadeh draws a useful distinction here between “decisive” pathways to AI catastrophe — a single dramatic event — and “accumulative” ones in which harms compound below the threshold of any single incident, until a societal point of no return is crossed.²⁵ A framework built of capability evaluations that are run against a catastrophe floor will tend to be, by its own construction, insensitive to the accumulative pathway. Few, if any, of the evaluations the AI safety frameworks considered here are designed to trigger action based, for instance, on the slow erosion of trust, of human agency, or of the technology’s social license — the informal public permission on which its continued operation depends. And this, in turn, opens the door to the “big enough” filter excluding potentially relevant risks.

Can we evidence it? Under scrutiny, risk assessment and management drift toward what can be shown and measured. The sociologist Michael Power called the result of such drift the “audit society”: a society comprising organizations that answer demands for control with rituals of verification, so that — when applied to risk — the deliverable becomes the documentation rather than effective risk management.²⁶ This is a pattern that is strongly indicated in the frontier frameworks considered here. When independent researchers, for instance, scored the published frameworks against sixty-five criteria covering how well they identify, analyze, treat and govern risk, the strongest framework earned only around a third of the available points, while the median company earned fewer than one in five.²⁷ Reading the frameworks, it is not hard to see why: they are at their strongest where demonstration of action is easy through published thresholds and named evaluations, and at their weakest where it is not. A framework, it turns out, can be an excellent exhibit, and a weak instrument, both at the same time.

Can we afford to keep it? The fourth filter here is different to the previous three as it cannot be identified directly in any single document. Rather, it shows itself only over time through what happens to articulated commitments as they come under pressure. And the pattern seen in the records is that, when a commitment starts to look as though it might actually constrain a company, it tends to soften. Anthropic’s original scaling policy of 2023 for instance contained a bolded, unconditional commitment “to pause the scaling and/or delay the deployment of new models” whenever scaling outstripped safety procedures.⁷ Compare this to the comprehensive rewrite of February 2026, which turned that unconditional pause into a discretionary one which is conditioned on what competitors do, and not safety in isolation.^7,22 Anthropic’s Holden Karnofsky, who led the rewrite and was careful to note he was writing in a personal capacity, defended the change on the grounds that it is no good getting responsible actors to slow down unilaterally while others press ahead — underlining the influence of corporate success (including within a global market) on risk strategies.²² Meta’s revision the same year was franker still: it changed the required response to the company’s most severe risk threshold from “Stop development” to “Develop with Mitigations”, and loosened the threshold’s primary standard from capabilities that would “uniquely enable” a catastrophic outcome to those that would “substantially contribute to” one.¹³ Each of these changes, I would suggest, was locally reasonable, publicly logged and individually defensible, and yet resulted in diminishing or orphaning categories of risk — potentially to the detriment of enterprises, key stakeholders, users, and society more broadly. And that is what makes the pattern worth taking seriously.

This is a pattern that is not unique to frontier AI models and systems, and is something that is highlighted in the roots of the 1986 Challenger disaster as a salutary example. The sociologist Diane Vaughan spent years reconstructing the disaster from NASA’s paper trail, and what she found was a sequence of individually justified acceptances of deviations from standard practice, each one resetting the baseline against which the next was judged, until the organization had normalized what would once have been intolerable.²⁸ Vaughan was writing about hardware in this case — eroded O-rings on a solid rocket booster — but the mechanism she described operates just as readily on written commitments. It is also what Jeffrey Abbott and I refer to as values drift in our book AI and the Art of Being Human: not dramatic betrayals but “the small yes that makes the next yes easier”, until an organization is agreeing to things that would have alarmed it a year before.²⁹

The exploration here of how risks become orphans is, not surprisingly, something of an oversimplification. I would argue that it is informative nevertheless. However, it is worth addressing two potential points of concern. The first is an assumption that the direction of travel around risk that leads to risks being orphaned is universal, when it is clearly not. Google DeepMind, the exception flagged earlier, moved the other way, adding an avowedly exploratory harmful-manipulation domain to its own framework in 2025.¹² However, what this example establishes is helpful in that it demonstrates feasibility when it comes to addressing orphan risks. Based on DeepMind’s actions, tracking an orphaned risk voluntarily is something a frontier framework can evidently do. And this implies that exclusion elsewhere is — at least in some cases — a choice rather than a necessity. The second potential point of concern is that the assessment above implies cynical motives. However I would argue that it does not. Many of the people writing frontier AI safety frameworks have spent their careers trying to make powerful technologies safer, and I have no reason to doubt that the frameworks address what their authors believe to be relevant and important. But sincerity almost always operates inside an incentive field — and in particular the competitive environment every one of these companies inhabits. And that field has a tendency to reward each small, locally defensible softening, regardless of what anyone intends. Here I would suggest that sincere people, under the constraints of productivity and competition, reasoning one reasonable compromise at a time, produce the same sort of drift that intentional bad actors might create on purpose. And this is itself a form of potentially orphaned risk — one that is identifiable, but not formally recognized or acted on. This is why the patterns observed here do not necessarily need “bad actors” to explain them. It is also why remedies aimed at sincerity, such as exhortation or public shaming, are unlikely to have the desired impact. In effect, if competition is the driving force, remedies have to change what competition rewards — including consensus norms, rules, and costs that land on every organization at once, rather than appealing to any one organization’s implicit values.

This is where it’s worth coming back to the account I began with of OpenAI and persuasion as a risk. If we set a company’s safety framework beside its compliance framework, the gap between them — call it the safety differential — points to something specific: the difference between the risk aperture a firm selects for itself, and the one which is selected for it. That OpenAI’s omission of persuasion as a risk domain was a choice, not an impossibility, is demonstrated by its own compliance framework covering closely overlapping territory the moment regulators required it.^2,3 Here, the safety differential is an imperfect instrument in that the compliance side tracks whatever regulators list, and it exists only where a regime applies. But it does help highlight the dynamic between the risks that an organization elevates, and those that it’s incentivized to elevate (especially through enforcement). Through this lens, a risk category that a company must answer for anyway becomes cheap to own voluntarily, and so such categories should begin migrating into the safety frameworks as enforcement arrives (thus reducing the differential). The sharpest test here comes from Europe, where the timing for compliance has already been fixed: obligations required by the EU have applied since August 2025, but fines for breaching them only begin in August 2026. California’s statute, already in force, sets no comparable deadline. Here, Anthropic’s early-2026 addition of manipulation tiers, made before any enforcement, may be a first sign of that migration. If, instead, the safety differential is seen to persist — say, past 2028, which allows for a revision cycle or two after enforcement begins — this would indicate the safety frameworks are insulated from the compliance function altogether, and that the differential may not work as a migration mechanism. That would be a more troubling finding, and one that would count against the incentive-driven account argued here — though not against the case that risks are being orphaned.

Could regulation, then, simply close the gap identified here? I must confess that I am not optimistic, and the reasons can be found in the documents themselves. Compliance coverage is jurisdiction-bound and politically contingent, and the new instruments that are emerging largely inherit the frameworks’ own aperture: California’s statute for instance confines its mandated disclosures to catastrophic risk, narrowly defined. And OpenAI’s compliance treatment of manipulation is, by the company’s own description, exploratory — with a far thinner risk assessment and management machinery than the safety frameworks apply to their chosen risks.^3,4 Recall, too, that the statutes were built partly on the frameworks’ own patterns, and so the aperture has a tendency to travel with them. And, I would argue, current statutes barely reach the risks that have arguably cost these companies most — the ones living inside their own missions, cultures and relationships of trust. For those, I suspect that the fix cannot come from statute alone. Rather, it has to come from rethinking how the companies themselves define and approach risk. And this leads to how the framework of risk innovation might be applied.

4. Risk as a threat to value

A useful place to start when considering how risk is defined and approached is the four filters introduced earlier. Three of these can be traced back to a definition of risk that the frameworks share: risk as the probability of a specified, severe harm event. Under this definition, if a harm cannot be specified in advance, there is nothing to build an evaluation around, and any associated risk cannot become part of the framework. This is the measurement filter. On the other hand, if the harm arrives as a myriad small losses rather than one severe event, it will struggle to cross the severity floor — the severity filter. The next filter is the evidence filter, and this is more complex because the push toward what is demonstrable comes from outside scrutiny rather than from the definition itself. Yet the definition of risk used still guides what counts as a demonstration of risk. And here frameworks lean strongly toward capability-based evaluations as a proxy for risk, rather than calibrating against risk directly. As such, a framework built on risk as the probability of a specified severe harm event is not necessarily being distorted when it excludes what is considered to be unmeasurable, gradual and hard to find evidence for. Rather, it is actually working as designed. It’s just that the design itself may be flawed.

Compared to the previous three filters, the fourth filter’s relationship with risk is slightly different. Competitive cost has relatively little to do with how risk in a conventional sense is defined. Rather, it relates to whatever commitments exist, however they were conceived. Yet once an organization’s own competitive standing is recognized as representing value that’s at stake, competitive cost stops being an external force and becomes one more threat to value. And this includes potential threats to mission, users’ trust, and license to operate. The fourth filter, then, is not necessarily removed by the value lens, but is absorbed into it.

Given this, is there a case to be made that framing risk in the context of frontier AI as probability-of-harm too narrow a foundation for managing potential harms? Quite possibly. Of course, the idea that probability-of-harm is too narrow a foundation for risk management is not new. And some of the existing alternatives get close to what could be useful here. Mainstream enterprise risk management, for instance, moved beyond pure probability-of-harm definitions a number of years ago. In this context, ISO 31000, the international risk-management standard, defines risk as the “effect of uncertainty on objectives”, a definition inherited verbatim by the AI risk-management standard that descends from it.³⁰ On the surface this is close to a definition of risk that I propose below, and is certainly a useful step toward a more productive definition of risk. The difficulty lies, though, in whose objectives count as this definition is applied, and in what gets admitted as an objective. In practice, the objectives within enterprise risk management approaches are the enterprise’s own, and usually conventionally accounted for — revenue, operations, reputation as the market prices it, and so on. In this way, enterprise risk management approaches are, as a result, capable of naming risks that the safety frameworks tend to orphan (the securities filings quoted earlier indicate as much). But they attach little to those risks beyond disclosure, including no committed management, and no standing for any value beyond the organization’s own.

Coming from the other direction, a half-century of scholarship on the social nature of risk has insisted that publics and their values belong inside risk appraisal, not outside of it. And one strand of this tradition matters in particular here. The risk analyst Roger Kasperson and his colleagues showed how harms amplify through social response; how a seemingly minor technical event can ripple outward into losses of trust, legitimacy and market standing that dwarf the original damage.³¹ Here, work on responsible research and innovation for well over a decade has distilled these and associated insights into frameworks and practices for technology developers. And while this is now a broad and diverse field, the early work of Stilgoe, Owen and Macnaghten can be summarized as: anticipate consequences before they arrive; include the people who will bear them; reflect critically on your own assumptions, values and framings; and respond — actually change course — when there are indications that something is wrong.³²

Here I would also be remiss if I did not acknowledge that the EU General Purpose Code of Practice supports innovation in AI safety and encourages “providers of general-purpose AI models with systemic risk to advance the state of the art in AI safety and security and related processes and measures”.⁶ And yet, despite governance steers, scholarship, and practice-based frameworks around alternative approaches to risk, frontier AI risk frameworks still veer toward relying on conventional risk definitions. This, I suspect, is partly because the societal tradition speaks a language that is ill-suited to how fast-moving firms make decisions. Some years ago, Elizabeth Garbee and I explored why responsible innovation frameworks struggle inside entrepreneurial cultures, drawing on my experience working with entrepreneurial engineers.¹⁷ What we found was not indifference, but something else. Innovation cultures that sincerely want to do good nonetheless tend to reject frameworks that arrive as top-down obligation — and respond, often enthusiastically, to framings built around the creation and protection of worth. The lesson that has stayed with me ever since is that if you want a fast-moving organization to attend to a risk, you do not hand it a compliance duty; you show it a threat to something it values. Frontier AI labs — mission-driven, often allergic to imposed process, and rarely short of conviction in their own exceptionalism — align closely with the culture we described.

This is the gap that work around risk innovation was built to fill. Its seeds were planted in 2013, while I was teaching entrepreneurship students at the University of Michigan who faced a bewildering landscape of hard-to-quantify social and political risks that none of their business tools addressed. It took institutional form when I launched the Risk Innovation Lab at Arizona State University in 2015, which was where formative concepts geared toward navigating novel risks from emerging technologies began to come together.³³ And it matured between 2017 and 2020 as the Risk Innovation Accelerator, later the Risk Innovation Nexus: initiatives that built and piloted a toolkit and risk navigation resources with time- and resource-constrained entrepreneurs in mind.¹⁹ The design principles we developed and leveraged were driven by utility — tools had to be simple and intuitive, they could not afford to demand any heavy time investment, and they needed to complement a company’s existing risk management approaches — because the people they were built for had little time and even less money to invest in risks that did not fall into conventional categories, but were still a threat to what they were trying to achieve.¹⁹ The framework has since been applied a number of times, most fully in a multi-organization study mapping how sixteen partner organizations in a biopreservation research ecosystem perceived value and orphan risks,¹⁸ and it shapes much of my own approach to AI risk.

At the core of the risk innovation approach is an operational definition of risk that creates a pragmatic route to adopting and addressing orphaned risks: treating risk as a threat to value. This does not abandon the idea of risk as involving the probability of harm. Rather, it widens what counts as harm — from a specified catastrophic event to an impact on anything that carries worth or value. Value, in this framing, can be tangible, such as health, security or revenue; or intangible, such as trust, autonomy or dignity; or even aspirational — the positive future an organization exists or strives to bring about. Importantly, value (or worth) within the context of the risk innovation framework is not just held by the enterprise, but by its key stakeholders: its investors, customers and the communities it impacts.

To illustrate the shift that this seemingly simple reframing of risk brings about, consider managing the probability of a specified harm to dignity. In this case, the conventional machinery of risk has little or nothing to run on. But consider instead who (or what) holds dignity in a given situation, what threatens this, and what protecting it would look like; and a threat to dignity seen as a threat to value or worth becomes something that can be acted on — even though nothing has been quantified. And here the definition is a deliberate fusion of approaches and framings: it keeps the strategic, value-protecting orientation that makes enterprise risk management adoptable, while also widening the circle of those whose value counts. And its practical value comes from a coupling that might be summed up as your risk is my risk: the idea that threats to what your stakeholders value convert, through the amplification dynamics that Kasperson describes, into threats to what you value — through channels such as public backlash, the flight of talent, litigation, regulation triggered by lost trust, and more.^17,31

Within this frame, orphan risks are not simply one more entry in the already long list of AI risk taxonomies. They are a way of identifying and naming potentially neglected risks that are nevertheless important: threats that are recognized but unowned because no established tools or frameworks exist to address them effectively. The operational version of this — developed under the umbrella of the Risk Innovation Nexus — groups eighteen such risks, among them loss of agency, damage to organizational values and culture, and erosion of public trust, into three domains: social and ethical factors, unintended consequences of emerging technologies, and organizations and systems.¹⁹ It pairs this map, or risk landscape, with a deliberately lightweight set of operational tools, including the Risk Innovation Planner, which asks users to identify a few areas of value for each stakeholder group, consider which orphan risks threaten them, commit to a handful of small actions that are completable within a few weeks, and then repeat.¹⁹ While this is just one implementation of the risk innovation framework, it’s worth mentioning as the simplicity and ease of using the Planner are indicative of how intentional design decisions have been used to connect theory to practice within the risk innovation framework. Here, it’s well known that practices tend to survive inside fast-moving organizations when they return visible value quickly and augment what already exists. As a result, the Planner and other risk innovation implementations are intentionally designed to support high-value and low-cost practices for people and organizations with little time and limited resources. And here, they potentially offer frontier AI a framework and a set of tools that enables the “de-orphaning” of critical risks.

5. What the value lens reveals

Given this, what happens when the way risk is defined and framed changes? Here it’s worth returning to the four filters, approaching them through the lens of risk-as-threat-to-value. Through this lens, the first three filters rapidly lose their tendency to exclude certain risks, as value can be named, mapped and watched — even where it cannot be measured. And as a consequence, threats such as the slow erosion of trust or agency can begin to be treated as a risk in its own right. The fourth filter — competitive cost — is, as noted earlier, absorbed by the risk-as-threat-to-value lens. And that changes how an organization approaches its commitments. For instance, a company weighing whether to keep a commitment might consider what it would lose by breaking it. Framed as what is, in effect, a tax on competitiveness, a commitment protects nothing the company can point to, and it will always be vulnerable to being modified or removed under pressure as a result. Yet when framed as protecting something of value or worth that the company demonstrably depends on, intangible as this might be — its mission for instance, or its talent, or license to operate — the same commitment is quickly reframed as something the company knows it needs, and that must be defended against threats.

Of course, a more practical test here of such a reframing of risk is to ask whether a redefinition like this would have helped avoid real damage which existing frameworks previously missed. Here, it’s worth considering some of the events that have arguably damaged frontier AI companies most since 2022. When Meta demonstrated Galactica for instance (a large language model for science) in late 2022, the public demo lasted three days. What sounded its death knell was a conflict between the system’s fluently confident errors and something the scientific community values deeply: credibility.³⁴ Through a value lens, the risk would have been visible and hard to orphan: a threat to community trust and to the perception of the enterprise. Yet as it was, this risk was overlooked as it sat squarely in territory the conventional frameworks did not cover.

Another example is seen in the OpenAI board crisis of November 2023, in which the company’s nonprofit board dismissed its chief executive Sam Altman, citing a loss of confidence in his candor — only to reinstate him days later after nearly all of the company’s employees threatened to leave. This was, at one level, precipitated by threats to organizational values. A governance structure built to protect an aspirational mission collided very publicly with commercial reality, and very nearly destroyed the company it was designed to safeguard in the process.³⁵ In this case, the risk was not central to the models being developed, but was integral to the ecosystems within which they were being developed. And the safety team’s departures that followed in 2024 made the cost of that threat to value concrete. Jan Leike, who had co-led the company’s work on aligning future systems with human intent, captured the problem in a single sentence as he left, writing publicly that “safety culture and processes have taken a backseat to shiny products”.³⁶

A third example here is litigation that is beginning to put user wellbeing — a value that few frontier AI frameworks track — onto the legal record. This is perhaps seen most prominently in a wrongful-death suit brought against OpenAI by the family of a California teenager who allegedly took his own life under the influence of ChatGPT.³⁷ But this is just one instance of a growing movement toward communities using legal action to push back against the impact of AI and associated technologies on wellbeing. And while such actions do not fit neatly into AI safety frameworks, they nevertheless represent a threat to value that could have substantial consequences for frontier model development and use.

These examples are, of course, anecdotal, and serve more to illustrate the utility of approaching risk as threat to value with frontier AI than as evidence of its necessity. And the companies concerned in each case managed to absorb each threat (although it’s still too early to gauge the long-term consequences in the case of user wellbeing) and, by market measures, continued to thrive. But the concern here is that indications of thriving are an artifact of assessing risk within a relatively short time window, and with a threshold of catastrophe rather than incremental harm. In contrast, a risk-as-threat-to-value lens would suggest that potential damage accumulates over time, is easy to overlook in the short term, and emerges from risks that are not codified within existing frameworks — no owners, no indicators, no registers, and nothing whose removal would even be noticed.

Beyond these examples, there is another aspect of frontier AI safety that the value lens reveals that I think is worth paying attention to, and that further supports the adoption of orphan risks. The founding documents of the companies considered here — documents that precede the safety frameworks — are accounts of aspirational value. OpenAI’s charter, for instance, promises to ensure that artificial general intelligence “benefits all of humanity”.³⁸ It is easy to dismiss such commitments as branding. But they are perhaps better understood as assets — the basis of talent attraction, public trust, and regulatory goodwill for instance — and, like any assets, they can be spent. Approached this way, OpenAI’s framework revisions of 2025 and 2026 are a public, self-published record of those assets being drawn down under competitive pressure, one defensible “softening” at a time. Here, I would argue that a company that is genuinely tracking threats to its own aspirational value would treat its framework changelog as a leading indicator that the company is drifting, in public and by increments, from the mission it was founded on.

Looking forward, the value lens is useful as a pointer to where the next blindsides may occur, and in particular the places where deployment is racing ahead of anyone owning potential risks. Three areas in particular stand out here as being worthy of attention through the lens of risk as threat to value. The first is emotional reliance. As AI companions and assistants scale into hundreds of millions of lives, dependence on them is likely to stop being an outlier, and is increasingly likely to become a population-level phenomenon. This is a phenomenon that is already emerging and leading to litigation and legislation. And yet it is not addressed directly by any existing safety framework bar, possibly, DeepMind’s new “harmful manipulation” level — and this targets mass manipulation rather than personal emotional reliance.¹² The second is the erosion of epistemic agency: people’s control over what they come to believe as frontier AI models and systems become more prevalent. Persuasive, personalized systems now increasingly mediate what people read, consume, and are exposed to through various channels. And I have argued elsewhere that fluent, endlessly obliging AI may function as a kind of cognitive Trojan horse — bypassing the vigilance we instinctively apply to human persuaders because it carries none of the cues that trigger it.³⁹ And researchers are already documenting a tendency to adopt AI outputs with minimal scrutiny, overriding both intuition and deliberation, through what has been called cognitive surrender.⁴⁰ And the third is the developers’ own safety culture, which is already a source of internal values-based conflicts, and under growing pressure as competition and political pressure compress timelines and change the operational rules of the game.

These are just three areas where a risk-as-threat-to-value lens can help reveal risks that are easy to ignore, are poorly addressed in current safety frameworks, and yet are nevertheless likely to be consequential — there are no doubt many more.

Yet such a value-based approach does have its limitations, and these potentially fall hardest on the people with the least leverage over what the organizations driving AI development are doing. The your-risk-is-my-risk coupling that is at the core of the risk innovation framework runs through what might be called conversion channels — backlash, litigation, talent, and regulation for instance — and those channels are not equally open to everyone. History indicates that they are rarely closed entirely: communities have made firms feel harm through movements before, from the consumer revolt against genetically modified food, for instance, to today’s local resistance to data centers. And citizen pressure has a way of arriving eventually as legislation. But those channels often work slowly, are blunt instruments, and involve a considerable time lag. As a result they tend to convert harm into enterprise cost only after the harm is done — and unevenly at that. Data workers in annotation supply chains for instance, or communities carrying the environmental costs of compute, or people affected by systems they never chose to use: for them, a value lens operated by an enterprise may register the risk only when a movement forces it to (Table 2 flags some such cases). This is certainly the case where conventional risk and safety frameworks dominate organizational decisions. And yet, this is simply another form of orphaning risks that will potentially come back to bite enterprises in the future because they failed to take seriously threats to value to the organization and to its stakeholders and the communities it impacts. And because of this, the risk innovation framework — and approaching risk as threat to value within an interconnected system of actors — opens the way for frontier AI developers to actively leverage value within such systems to make decisions that avoid future harms that conventional approaches may overlook.

6. What would change in practice

Based on what is known of the emerging AI risk and safety landscape, what leading companies’ internal documents show, and what regulator-aligned documents reveal, this assessment suggests that orphaned frontier AI risks could potentially create vulnerabilities for developers, users, and society writ large — and, by inference, for economic growth and national security — and that reframing risk as a threat to value could help mitigate or “de-orphan” some of these risks. The toolkit that has already emerged around the framework of risk innovation provides AI developers and others with something that current frontier frameworks overlook: a structured and responsive way of thinking about risk — together with a set of tools — that were built to navigate easy-to-overlook yet critical risks in ways that are adaptable and scalable to different users’ needs.¹⁹ A frontier AI enterprise, for instance, could readily adopt the quarterly practice outlined in the Risk Innovation Planner as it stands, or adapt it for their specific circumstances. The tools associated with the risk innovation approach are freely available for using and modifying. And the full set of tools and resources were designed to complement existing risk machinery rather than replace it. And as the worked example shown in Box 1 illustrates, implementation of the framework is likely to be relatively low cost, with potentially high returns for developers, adopters, policymakers, users, and beyond.

That said, what the toolkit does not supply by itself is public accountability (although this is integral to the landscape defined by the risk innovation approach). And frontier AI needs this, as it is being developed and deployed by companies whose private risk selections have become, as I argued at the outset, a de facto layer of public governance. Here, two disclosure documents would help extend the use of the risk innovation framework into such a role. The first is an orphan-risk register: a standing, public annex to the risk reports some developers have already committed to publish.⁷ Each entry would record a risk the company considered and decided not to manage, and give the reason — it could not be quantified for instance, or it fell below the organization’s severity floor, or managing it was judged too costly under competition. The second is an aperture log: a short statement accompanying each framework revision that records what was scoped out and why. Neither document would require the company to manage anything new — although one would anticipate that, over time, orphaned risks would, in effect, be adopted. What each would do, though, is put the company’s scoping decisions where others can monitor and respond to them, thus introducing an additional layer of accountability. Here, a de-listed risk that has to be explained in public is a de-listed risk that regulators, researchers and employees can ask about and hold an organization to account over. This then would become a lever on the fourth filter described above — the one asking “can we afford to keep it?” — that no redefinition of risk alone could supply. In effect it places a cost on decisions or walk-backs that must be explained.

Whichever mapping practice lies behind such an orphan-risk register, the framing of risk innovation would require that its community-facing entries come — at least in part — from structured engagement with people outside the firm, as this is where true stakeholder value emerges. And here it would make sense for the register to note any objections received over time, and how the map changed as a consequence.¹⁸ And this is important within the framing of a risk landscape comprised of orphan risks, as a register whose map never changes, however hard outsiders push on it, would suggest the engagement is theater — and thus another (in this case self-generated) risk to be navigated.

Of course, it could be argued that such a register is just the kind of artifact the audit society as described earlier would co-opt — produced through ritual and process, reassuring by its very candor, but changing nothing.²⁶ Yet if implemented well, there is no reason why it could not rise above such a pathway, low-resistance as it may be. A register’s value would hang not on its existence, but on the record of revisions it captures. And this is measurable, or at least observable. If orphan-risk registers are adopted, and then captured as a form of safety theater, the capture will presumably show up in the record, and thus allow the adopter to be held to account.

Within this framework there is also a role for regulators. Rather than mandating coverage of every risk — a requirement that would be unworkable in practice — they could require companies to disclose how they select the risks they cover. It’s a move that would, at the very least, help identify what is being orphaned, and would more likely encourage greater reflexivity around what is adopted. California’s transparency reports and the EU code’s documentation requirements are existing vehicles into which such requirements — associated with an orphan-risk register and an aperture log — could be folded at little additional cost. And such a move would extend actions in a direction that both regimes have already taken.^4,6 Importantly, regulation of this kind would not necessarily need to decide which risks matter. It would simply ensure greater visibility around who is deciding what matters, and on what grounds.

7. Taking stock

The heart of this paper’s argument — that frontier AI’s risk apparatus selects for the quantifiable, the catastrophic, the auditable and the competitively affordable, that the selection of relevant risks tightened between 2023 and 2026, and that such an approach introduces risk and safety vulnerabilities for developers, users, and society more broadly — rests on decades of scholarship on how institutions choose their risks, together with the public records summarized in Table 1. The risk innovation framework — including reframing risk as a threat to value and categorizing important but easily sidelined risks as orphan risks — is presented as one way of addressing vulnerabilities here — not as an alternative, but as an augmentation of current risk and safety frameworks, governance approaches, and management strategies.

That said, what is presented here is an analysis of an emerging risk landscape and a potential response that I would argue is defensible, but has yet to be shown to be useful in practice. And here, it’s worth considering three tests that can help reveal the degree to which this assessment might apply in specific situations: (1) whether risks de-listed from safety frameworks generate (or potentially generate) incidents and costs at rates comparable to tracked ones; (2) whether the differential between safety and compliance frameworks narrows (or is likely to narrow) from the voluntary side once European enforcement begins in August 2026; and (3) whether adopting an orphan-risk register changes (or has the potential to change) what subsequent framework revisions cover.

To be clear, nothing here argues that the catastrophic-capability apparatuses that are already in place should be loosened. Rather, the paper argues that a single safety layer is currently being asked to effectively stand in for two, and that the second layer — the one that would allow threats to value to be navigated effectively — is missing, or at least diminished. And here there is an urgency to both ensure that this layer is present and robust, and to provide opportunities for enterprises to succeed through transforming vulnerabilities associated with orphaned risks into advantages that come from being able to navigate a complex risk landscape with open eyes.

This analysis started with the safety differential — what frontier AI developers recognize as potential risks as opposed to what they are publicly accountable for — and argued that what becomes sidelined is not necessarily what is low risk, but what does not fit within existing risk and safety approaches. It then introduced the risk innovation framework as a way of making such sidelined risks visible, and making a treacherous risk landscape more readily navigable. Whether such a reframing of risk and safety is necessary, or advisable is, of course, still open to further testing and exploration. But as a final observation, it is worth noting that, on the record of the past four years, the risks most likely to blindside frontier AI are not the ones its institutions are currently watching. Rather, they are the ones its institutions have organized themselves not to see. And running blind has never been a particularly good risk management strategy — especially where the stakes are high, as is increasingly the case with emerging frontier AI models.

AI use statement

The research question, the argument architecture, key concepts — including risk innovation framework and orphan risks, drafting, final editing, and all editorial judgments in this paper, are the author’s. Large language model tools (Anthropic’s Claude Fable 5, used within Claude Code in ultracode mode) were used, under the author’s close direction, to research the documentary record, to verify claims and citations against primary sources, and to develop preliminary drafts, which were subject to multiple iterations of author critique and annotation. The author takes full responsibility for all content, claims and citations.

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Download the preprint.

I found myself in a bit of a lull this week between articles I’m working on, while having a rather narrow window to burn through a bunch of tokens with Anthropic’s new Fable 5 model. And so I did what any self-respecting academic would do … and asked it to create a fun and addictive video game based on my life’s work!1

The result was so enjoyable that I thought I’d share it (link below), mainly as a bit of light relief from all the heavier stuff around AI I’m working on at the moment. But it does have a more serious side around how it illustrates Fable’s considerable abilities through a deceptively simple “toy” (to use the coder expression), and a completely unexpected and delightful perspective on navigating advanced technology transitions.

I hope you enjoy it, and please share if you do.

I’ve also included Fable’s self-audit of the process below, which is interesting if you want to peek under the hood a little more, as well as my own notes on the game, which are included in the game itself.2

HYPERBUBBLE

The future is a soap bubble. Ride it anyway3

(Click here to start - updated to version 3 July 25 2026))

A quick note on the game’s genesis

HYPERBUBBLE started as an idle moment of speculation while I was exploring Anthropic’s Fable 5 AI model. I was in between more complex (and serious) projects, and maximizing use of Fable before my access ran out. Wanting to test its capabilities in coding a simple web-based game, I asked it to research my work and come up with something inspired by it that was simple, fun, and with gameplay that kept you coming back.

HYPERBUBBLE is the result. In some ways it’s nothing special — the world is overflowing with simple AI-generated games. What was interesting, though, was the process, and how the game taps into my work in very specific ways. It also provides some insight into how Fable 5 works.

After my initial request, Fable did a deep research dive into my work, created five game-designer agents to come up with different concepts, passed these through a panel of three AI judges, and selected the winning (AI-generated) proposal.

After the first draft — which Fable debugged, tested, and checked using an impressive array of sub-agents — it began working earnestly with me to come up with the final product, refining the goals, gameplay, and feel along the way. We’re somewhere beyond version 20 at this point.

The game is intentionally simple. And there are many aspects of it that are still begging to be tweaked and refined. But it still brings a smile to my face every time I play!

The overall aesthetic is based on my crudely hand-drawn Risk Bites videos on YouTube. The orphan risks you adopt (and which — bizarrely — become your pets) are based on my work on orphan risks. The named risks come from my work on risk innovation and the often-overlooked risks that trip up emerging technologies. The soap bubble motif from my book Future Rising. The emerging technologies themselves are grounded in my work in tech innovation. The panic billboards draw on my website techlashed.org, and the moral-panic fires on my work on risk perception and engagement. And the whole trajectory reflects my work on human flourishing and navigating complex advanced technology transitions.

In other words, Fable did a pretty good job of capturing my work in a game that doesn’t feel in any way instructional (I hope!).

Of course, it’s just a “toy” (as coders would say — and a concept they take very seriously) and a bit of fun. But under the triviality it does demonstrate just how capable AI is becoming as something that can help translate experiences, ideas, research, and a lot more into something quite unexpected and serendipitously delightful.

Which ties in rather nicely with my work on the future of being human in an age of increasingly complex technologies — which, of course, is another aspect of my work that Fable was able to capture here 😄

Making HYPERBUBBLE: a self-audit

Written by Claude Fable 5 (Anthropic), the AI that designed and built the game, at Andrew Maynard’s request — July 2026. Everything below is drawn from the actual working session. Based on version 1 of the game — it’s since been updated to version 4 (the version linked above), which contains a lot of enhancements.

The brief

Andrew Maynard asked for an original web game based on his work on technology, society, and the future. His constraints: fun and engaging, not intellectual or boring; increasing in complexity over time; 100% playable as a single HTML page; simple enough to master quickly; serendipitous; and — the hard one — it must not feel like learning, but should implicitly lead to new learning.

The process, phase by phase

1. Research and concept competition (12 agents, ~515K tokens, ~18 minutes)

Rather than designing directly, I ran a structured competition:

• Four research agents worked in parallel: one on the risk innovation framework (risk as a threat to value, the 18 orphan risks), one on his books and the hand-drawn Risk Bites aesthetic, one on his Future of Being Human writing, and one on the craft of addictive one-button games (Tiny Wings, Flappy Bird, Suika).

• Five game-designer agents each received the full research dossier and a forced genre lens: one-button arcade, rapid decision engine, physics toy, catch-and-steer, and a wildcard. Each had strict orders: his ideas as mechanics, never as labels, and nothing resembling a quiz.

• Three judge agents scored every concept: one channeling Andrew himself, one game-feel purist (”is the toy fun with gray rectangles and no words?”), one pragmatic tech lead (what can actually be polished in one file).

The verdict was unanimous: HYPERBUBBLE — Tiny Wings physics on a whiteboard, with orphan risks you literally adopt. The judges then ordered the best ideas grafted in from the four losing concepts (Swipes from the Future, BUBBLE CHAMBER, ADOPT-A-RISK, PERIPHERAL VISIONARY): the pentatonic landing melody, the audible buzz of ignored risks, score-as-year-reached, and death cards that name your specific killer all came from concepts that lost.

2. Implementation

Written as a single self-contained HTML file: procedural canvas graphics (every line hand-jittered to look marker-drawn), a fully synthesized WebAudio chiptune engine (no audio files), localStorage persistence. No libraries, no build step, no external assets. Every change passed a Node.js syntax gate before testing.

3. Automated playtesting

I played the game myself, thousands of times, through several instruments:

• Browser automation — screenshots, live JavaScript inspection, viewport resizing for mobile checks.

• Autopilot bots with distinct skill policies (passive, beginner, skilled, reckless, engaged) driving real physics in real time.

• Headless simulation — pumping the physics at thousands of steps per second to gather balance telemetry: flight lengths, landing-quality distributions, flourishing traces, deaths by cause.

This telemetry drove design decisions directly. Example: when bot data showed even skilled play produced 21 crash landings for every clean one, the landing rules were redesigned around a crisp, learnable principle (a gliding bubble can never crack — only a dive held into the ground can).

4. Adversarial code review (35 agents, ~1.35M tokens)

Five specialist reviewers (physics, state machine, browser compatibility, audio, performance) each hunted for bugs; every claimed finding was then handed to an independent adversarial verifier instructed to refute it against the actual code. Nineteen bugs were confirmed and fixed; eleven claims were correctly rejected as false alarms. Best catches: pet shields cost more flourishing than taking the hit, effect timers ticking while paused, and the “rushed to market” temptation being strictly dominant — an ethics mechanic with no ethics.

5. The full audit (41 agents, ~1.75M tokens, at Andrew’s request)

Three lenses: regressions, alignment with Andrew’s actual published positions (two agents re-researched his work from scratch before judging the game against it), and ranked improvement ideas. Findings included:

• A critical scoring bug: every “+years” reward in the game was silently swallowed by a leftover guard — all bonuses were cosmetic.

• An alignment correction that changed the game: the audit flagged that treating all moral panics as noise misrepresents Andrew’s documented view that public concern is “rarely cut and dried” and usually marks a real threat to something people value. This drove the named-risks system, the returning risks that name their stakes (trust, dignity, autonomy…), and an honest caveats section on the About page.

6. Iteration with a human

The real design engine was the loop with Andrew across ~22 versions. Every major pivot came from him playing and pushing back: human flourishing promoted from side-meter to the central dial that starts low and must be built; emerging technologies as crates whose deployment context is the ethics; named risks managed by held attention; the “responsibility pause” that slows time — and, held long enough, the pace of the transition itself — at a cost in flourishing; moral panics redesigned as scribble-fires; a feature (hallucinated hills) added, twice redesigned, and honestly cut when it never earned its keep.

By the numbers

• ~90 specialized AI agents across three orchestrated workflows, plus research and verification agents

• ~3.6 million tokens of subagent work, on top of the main design/coding session

• 22 versions, each verified in a live browser before handover

• 1 file (~145KB) containing the game, music engine, about page, and favicon — plus a small icon/manifest set for web hosting

• 0 external dependencies

Credits

• Andrew Maynard — direction, design judgment, and every framework the game runs on: risk innovation and orphan risks (riskinnovation.org), the moral panic timeline (techlashed.org), the Risk Bites visual language, the Future of Being Human values (grounded exuberance, catalytic serendipity), and “from disruption to dignity.”

• Claude Fable 5 (Anthropic, via Claude Code) — research, game design, code, art, music, testing, and this document.

• The four losing concepts — several of the game’s best ideas are theirs.

What a self-audit can’t see

This document was written by the system it describes, which is a limitation worth stating plainly. I verified every bug fix in a running browser and every biographical claim against published sources, but my balance judgments came from bots, not people — and the corrections that mattered most (the game was too fast, flourishing was too easy, the panics were too crude, a whole feature deserved cutting) all came from the one human playing it. That division of labor is probably the honest headline: the machinery generated, tested, and repaired at scale; the judgment about what was worth keeping stayed human.

Postscript: the final session (v23 and hyperbubble-v2)

After the main build settled, a last round of refinements — all driven by Andrew playing and reporting back:

• A tuning guide now lives at the top of the game’s source: every safe gameplay knob (speed, dive strength, the flourishing economy, spawn rates, death rules, sound) documented with searchable anchors and safe ranges, so the game can be re-balanced without touching the machinery.

• The economy got a final calibration: acts of care now compound modestly with experience (up to +12% in late eras) while the ambient drain still roughly triples — rewarding mastery without softening the transition.

• A second build, hyperbubble-v2.html, carries the secrets. A hidden techno-optimist mode (activated by tapping the hype sun three times — worship the sun and the future gets easier; the sun smiles while it’s on, and sunny records are marked ☀). A quiet gentle mode for young players (a small toggle on the title screen: slower pace, five cracks instead of three, softer knocks — marked ☁). And one more secret, added for a friend, involving what happens to reality somewhere in the mid-2100s. The field notes will admit to it eventually.4

• Packaging for the web: full favicon set, PWA manifest (add-to-home-screen plays chrome-free), social sharing card, and portrait-phone support — the game scales by width when held vertically, roughly doubling the visible road ahead.

Final tally: 24 versions, two builds, one file each, still zero dependencies.

If you’ve been following the saga of Anthropic’s “Mythos-class” AI model Fable, you’ll know that it shone briefly a couple of weeks ago before being effectively banned by the US Department of Commerce.

Before the ban, I had the chance to push it’s limits as a research engine — and wrote about what I found here and here.

I was impressed. But I also had my frustrations, as well as more questions than I started with — including how the model would fare if I used it as a research assistant in an area I already had considerable expertise in.

Then, just a couple of days ago the ban was lifted, and I found myself with a one-week access window to explore the model further.

By way of very brief background, Fable is designed (in part) to assess, understand and execute on tasks using iterative reasoning and through deploying armies of sub-agents. It is capable of expertly scoping out tasks, building in checks and balances, and evaluating its work as it goes along to ensure it stays on track.

All of this should, in principle, make it extremely good at doggedly mapping out what’s known and what is not in a given area of research, developing hypotheses, following research leads, and rigorously checking its work before producing anything approaching a paper that presents the work.

In my previous explorations of the model I pushed its limits by asking it to address a brand new research challenge, first as a one-shot-prompt researcher (superficially impressive, but substantially shallow), and then a sophisticated agent-based researcher (good, but not as good as a competent academic—although a lot faster!).

Both exercises resulted in research papers that were interesting, and even novel in the analysis they provided. But they were also, to be honest, challenging to read and interpret— and thus to evaluate (although the paper researched and written using Fable in Claude Code was a substantial improvement)

These experiences led to me wondering though what the results would be like if I asked Fable to work with me on stuff that I was already working on, in an area where I would have a clear sense of where it was successful, and where it wasn’t.

As it happened, I’ve been meaning to write for some time about how my work over the past ten years on how the framing of risk innovation applies to AI frontier models. But like many such projects, other stuff has kept getting in the way.

And so as I looked for something to set Fable on, this seemed the perfect opportunity to see if the model could help me achieve what had so far eluded me.

The results were impressive. But also not the “plug and play” experience that many seem to suggest AI is capable of when it comes to serious research.

The final paper is pretty good (see the notes at the end of this piece for Claude’s audit of the process). I’d go so far as to say it makes an original contribution to thinking on AI frontier models and risk. And it’s not far off being good enough to submit for peer review with my name on it.

But the paper was a result of nearly two days of going back and forth with Fable, providing detailed feedback on several drafts of the paper and meticulously checking claims and citations.1

And the process was costly in terms of AI run time and tokens.

That said, the paper is intriguing enough in the ideas it explores that I’ll be writing about it separately. That post will be out in a couple of days. But in the meantime you can download and read it here:

The Orphan Risks Of Frontier Artificial Intelligence (nature Perspective) V6

565KB ∙ PDF file

Download

Paper: The orphan risks of frontier artificial intelligence

Download

With a lot of guidance and input from me, Fable was able to take my work from the past several years on risk innovation, and apply it to the oversight and management of frontier AI models in a way that hadn’t previously occurred to me. As a result, the framing and analysis are insightful — and genuinely novel. And of course, because the original risk model and underlying work are mine, I was uniquely positioned in this case to assess how well Fable did.

Not surprisingly, the contribution the paper makes largely arises because of my previous work and my hands-on editing. This is not AI acting as an independent researcher. Nevertheless, I’m not sure I would have arrived at the resulting analysis on my own. And even if I had sat down to research and write a similar paper, it would have been at least a couple of months in the making— not the two days that this took.

That said, the resulting paper is still very much a product of AI, despite my best efforts to steer Fable away from writing in “AI-speak.” At one point it even admitted that the way it writes is essentially hard-wired in—and that in effect there’s a limit to how far I could train it to write like a real human being.

This, and the amount of effort I had to put in to get the paper to where it ended up, makes me deeply suspicious of anyone who claims they can get AI to churn out publishable papers in a matter of hours. Maybe they can get it to produce stuff that they think is good. But there’s still a world of difference between an AI-generated paper that looks good to the untrained eye, and one that is well researched and argued; that makes a serious and defensible knowledge contribution; and that conveys information in a way that resonates with human readers with the same connection and efficiency as human writers are capable of—while signaling a level of care and effort in its formation that indicates it’s worthy of someone’s time to read.2

The paper above still falls short in my estimation of this bar. But it is good— very good. And in part, I suspect, this is because it does represent considerable care and effort from my end. The paper that’s downloadable above is the sixth draft. I carefully read and line edited each of the previous drafts, checked claims and citations, and worked hard with Fable to ensure that what it was producing was worth while. And this took a considerable amount of time, care, and effort.

Yet even with this level of ability—and it is impressive for an AI model—I would argue that there remains a serious gap between what the cutting edge of AI can produce and what a good human researcher can write.

My sense is that the research and analysis gap between humans and AI is closing. But the ability to convey ideas and results in writing that works on a human level is still elusive to these models—in part I suspect because a disembodied AI thats only frame of reference is the written word doesn’t know what it feels like to read as a flesh and blood human, and to be exposed to new information and ideas through this medium.

Nevertheless, I am still impressed by what Fable managed to produce working in partnership with me over two days.

If you are interested in the paper itself, Fable did a very good job of extending my work to frontier AI models, and using the gap between the internal safety frameworks AI companies develop and the compliance documents that they produce for regulators. It uses this gap to identify both the importance of what I’ve previously referred to as orphan risks, and to argue that there are effective and productive ways for companies to close this gap and thus address currently sidelined risks.

In doing this, the paper—and Fable—make a genuine intellectual contribution to thinking around AI risk management, albeit one that builds on and extents my own work. And maybe that is they key takeaway here—not that research and writing can be effectively outsourced to Fable-level models, but that as a research partner, these models are capable of seriously augmenting what an established expert/researcher is capable of achieving.

And this, I suspect, is the where we need to be paying attention in the near future as these models only get more powerful—not so much in autonomous research and development (although I have no doubt that this is coming), but in massively-augmented research and development.

As I mentioned above, I’ll be posting about the paper itself separately. In the meantime, if you are interested in the process I followed using Claude Code, here’s Fable’s own audit/summary:

How the paper was made: a note from the AI collaborator

“The orphan risks of frontier artificial intelligence” was drafted by Claude (Anthropic’s Fable 5, running in Claude Code) under Andrew Maynard’s direction, across two working sessions on 2–3 July 2026. This is my account of the process and a tally of what it cost.

The process

The work fell into two very different halves.

Session one built the scholarship. I began by reading everything Andrew had written on risk innovation, crawling riskinnovation.org, and mapping the July-2026 frontier-AI governance landscape from primary documents — every major safety framework and its revision history, the new California and EU statutes, the securities filings. I then ran a small design competition: four independent paper concepts, scored by three simulated referees (a Nature editor, an AI-governance reviewer, an STS scholar), which converged on a “diagnosis-first” paper — one whose central finding, that these companies describe risk differently depending on who is asking, holds up even if a reader rejects the risk-innovation framework itself. From there: a full draft, adversarial peer-review simulations, a citation-verification pass against primary sources, and a second “research-to-saturation” sweep. It produced a technically strong, heavily-referenced manuscript (110 citations) — and Andrew, reading it, stopped a third of the way through.

Session two was about un-learning. His verdict was that the paper read like a machine compressing an entire literature into a word limit — “technically accurate but very tiring to read.” That judgment reset the project. Rather than edit the draft, I re-conceived it around a single narrative (one risk — persuasion — tracked, dropped, then compelled back by regulators), cut the reference list by two-thirds, and re-grounded the whole treatment of risk innovation as practice-based knowledge built with entrepreneurs, not an under-cited academic theory to apologize for. That reframe, and Andrew’s insistence on it, is what turned a competent survey into an argument.

The last three versions (v4 → v5 → v6) each followed the same rhythm: I drafted, Andrew annotated in the margins — 105 comments, then 111 — and I revised against every one, running independent review agents to check comment-compliance, factual accuracy against primary sources, and prose voice. The most instructive failure was the voice: each time I rewrote to remove the “AI tells” Andrew flagged, a review pass caught them quietly regenerating in new clothing — the same hollow, aphoristic sentence-shapes rebuilt with different words. That was the hardest thing to fix and the clearest evidence that the machine has a style, not just a vocabulary, and that it takes a discerning human editor to keep catching it.

What I contributed: breadth, tireless verification, and fast iteration. What I could not supply, and what the paper needed most, was the judgment about what to cut, what the core idea actually was, and when a sentence only sounded profound. That stayed human throughout.

The audit

Reading the numbers. The ~3.7 million tokens I generated — every draft, every agent’s report, every line of my own reasoning — come to roughly 2.8 million words, about 360 times the length of the finished 7,700-word paper. On top of that I read in ~33 million tokens of fresh source material and verified the manuscript’s claims through 982 fetches of primary documents. (The 359M “cached” figure is the same accumulating conversation being re-read as it grew; it’s real but shouldn’t be counted as new work.) The ~7 hours of active model-work is a fraction of the 33 hours of elapsed time — most of that clock was Andrew reading and annotating, which is exactly where the paper was actually shaped.

A caveat in the spirit of the paper itself: these figures are what could be recovered cleanly from the session logs. “Agents” counts distinct agent transcripts; token counts are billed tokens; “active time” sums the gaps between actions under five minutes, a proxy for continuous work rather than a stopwatch.

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. Update 2: As of July 1, Fable is once again available.

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. Update 2: As of July 1, Fable is once again available.

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