I’m delighted to share that I’m now have the additional role of Reader in Postdigital Education at the University of the Arts London.
Detail from one of my paintings
This is a teaching career Readership which I became eligible for because of a shift towards what REF 2029 calls “New Insights, Effectively Shared”. Thanks to Trevor Keeble for taking UAL in this progressive direction and Silke Lang and Mark Ingham for designing the criteria and supporting the application process. Thanks also to Ian Truelove for being an excellent sounding board and to Susan Orr for years of support.
Reader in Postdigital Education is an achingly academic title so I feel I should explain.
The Reader part essentially means ‘UK associate-professor’. The strict interpretation of the term, I assume, is that I’m reading my field before I go out and start professing about it. In practice, I do a lot of ‘professing’ via my blog and speaking invites. I will use the new role to build on this and develop various research outputs.
The ‘Postdigital’ part is in the ‘post’ family of Postmodern and Post-punk. As in, it is not implying that the digital is finished, but rather that it does not exist in isolation. Using ‘post’ as a prefix is troublesome but in a positive way it is exactly that trouble which I want to stay with. A good way of putting it is as:
“…a term that sucks but is useful.” (Cramer, 2015, p. 13).
Postdigital describes an approach or method rather than a state which makes it a useful framing for research into emerging phenomenon.
Starting in the arts then moving to education
Fittingly, Postdigital as a concept has roots in creative practice, an early use describing the generation of music from the glitches, or failures, of digital technology (Cascone, 2000), but since then the term has evolved and become a field of research in its own right. The Journal of Postdigital Science and Education being a key location.
More specifically, in an educational context the Postdigital highlights that:
“…contemporary student practices with technology are complex entanglements between physical and digital technologies, spaces, activities, and time.” (Jandric, et al, 2018).
This framing is useful in countering the cultural/commercial habit of implying ‘digital’ is always ‘new’ and ‘future’. It uncovers ‘inevitable’ digital futures as commonly a vehicle for standard models of capital and power.
The 52group (Postdigital and education from way back)
I’ve been thinking Postdigitally since 2009 when myself, Ian Truelove, Lawrie Phipps and Richard Hall (The mysteriously named ‘52group’) met and co-opted the term for online/networked education.
Our use of the term built on the idea that the digital, especially the internet, was already woven into almost all aspects of life and learning. This was quite a bold claim just three years after Facebook has been launched for the general public but we were becoming impatient with an obsession in digital education with every new technology that came along.
It’s difficult to recall now, but at that time there was a feeling that the Web was a novel frontier which was free from the baggage and politics of our stuffy old institutions. We hadn’t yet understood the extent to which our data was becoming commercially valuable. In education there was a somewhat uncritical obsession with technological innovation and how this could be applied in teaching and learning.
Our 2009 provocation, “Preparing for the postdigital era” now reads like an early call to be more human-centred and weary of the tech gurus:
“Postdigital aims to throw off the yoke of digital dogma, where the language of a perceived digital elite drives not only development, but also skews innovation, where innovation is only seen as being that associated with the “latest” technology… …Innovation in a postdigital era is more effectively articulated as being associated with the human condition and the aspiration toward new or enhanced connectedness with others.” (52group, 2009)
What has changed since then is a broad cultural shift from a largely unquestioning acceptance of the convenience being sold via the digital to a position of scepticism and, in some cases, fear as each new ‘revolution’ is presented as inevitable. That is not to say that we don’t use the ‘revolutionary’ solutions, use continues to increase, but now with a suspicion that the overall direction of travel will benefit the providers of the technology more than us.
The Postdigital era really did arrive. It’s complex, messy and requires serious thought.
52group.(2009). Preparing for the postdigital era http://docs.google.com/View?id=aqv2zmc9bgm_51ft65rbn2 accessed August 14th 2026.
Cascone, K. (2000). The aesthetics of failure: ‘post-digital’ tendencies in contemporary computer music. Computer Music Journal, 24(4), 12–18.
Cramer, F. (2015). What is ‘post-digital’? In D. M. Berry & M. Dieter (Eds.), Postdigital aesthetics: Art, computation and design (pp. 12–26). New York, NY: Palgrave Macmillan.
Petar Jandrić , Jeremy Knox, Tina Besley, Thomas Ryberg, Juha Suoranta & Sarah Hayes (2018) Postdigital science and education, Educational Philosophy and Theory, 50:10, 893-899, DOI: 10.1080/00131857.2018.1454000
This is an expanded version of a talk I gave at the Postdigital backlash conference in Zagreb in June 2026. My proposal is that while the backlash (refusal or resistance) to Gen AI is commonly based on notions of stolen labour underlying this is a powerful reaction to the simulation of Person. The, in some terms, successful simulation of Person in mainstream forms of Gen AI extends the hyperreality of the digital environment to a point at which it approaches saturation. This all encompassing hyperreality has no exit as the final referent to the real of ‘Person’ has now been erased. We cannot differentiate between person and non-person (phantom) where our attempted access to the real is mediated by the screen.
I also reference backlash based on other factors such as bias in outputs and environmental concerns but maintain that our innate sensitivity to the eerie is always present wherever the technology has been designed to perform a synthetic Personhood. This, I suggest, is one of the reasons that we feel shame or guilt when using Gen AI as we struggle with the dissonance of engaging with a non-person and cannot identify ‘who’ has produced the work.
I suggest that work created with Gen AI, while not fundamentally without value, lacks aura because a route cannot be traced back to an identifiable author or authors. In essence, work produced with Gen AI will always be a simulacra.
I variously discuss how this plays out in creative arts education and propose an alternative conception of Gen AI and education which avoids the abject state that many find themselves in when using the technology. Abject, because the act of production is efficient and ‘sensible’, while concurrently unsettling because we engage in person-like dialogue with that which we know not to be alive.
Much of the talk is thinking-in-process rather than precisely crafted lines of argument. Making the video has helped me to move my thinking forward in a territory where there are no clean answers, only the opportunity to navigate complexity.
Part 1 used the “Where are the people?” question from an AI Principles workshop to highlight the accountability of the designers and managers of AI, especially where that technology is presented and promoted as having a ‘mind of its own’. Part 2 is a review of activity which took place two days after I ran the workshop.
On the 2nd of July, we hosted an AI and Creative Education symposium at the University of the Arts London which reframed the “Where are the people?” question once again.
The second answer to “Where are the people?” is that they are at UAL, questioning, experimenting with and critiquing AI. The symposium demonstrated that our staff and students have waded into AI creatively and critically. Yes, there is a feeling of overwhelm, there is also a lightly-held confidence that art and design education is well placed to navigate the complexity of AI creatively, ethically and politically. UAL is already imagining a range of human centred futures which include AI, rather than accepting wholesale the singular future articulated by Silicon Valley. There are AI skills to be taught and learnt. There is also a need to sense make and collectively develop meaningful practices which respect learning as a process and amplify creative agency.
The symposium was designed by Rupert Norfolk, Darryl Clifton, Ian Truelove, Chris Rowell and myself with organisational support from Lynn Finn. It was an internal event for UAL staff, and we had around 130 attendees of which the biggest group was Course Leaders and Programme Directors. Just about everyone who attended is directly involved in teaching and learning, plus people from our Digital and Technology department.
To give you a sense of what we covered take a look at the schedule for the day. I’m not going to review the day session by session as that would be book length. Instead I will highlight a non-exhaustive set of themes I saw emerging. I’m sure others attending would highlight additional lines of thinking.
UAL Expertise: Apart for a keynote from Mack Marshall of Wonkhe on their excellent ‘Trained to Stop Learning‘ report all other speakers were UAL staff. This made visible the significant, and often cutting edge, AI expertise at the university.
Use, Resistance and Refusal: While the symposium took a critical stance towards the mythologies surrounding AI, there was a clear understanding that it is being used by almost all staff and students in some form. There was also an understanding that informed resistance and refusal are important. Many students are uncomfortable about the use, and in some cases, the existence of the technology. The institutional aim is to support students to be well informed whatever position they take.
Intentional Practice: It’s not difficult to use AI to produce ‘polished’ work, or to use it to produce a significant volume of material (text, image, sound, video etc). Neither of these are a central interest to an arts university where the intention of the work created is key (and a large part of what is assessed). The speakers at the symposium tended to focus on how to retain this intentionality while using AI rather than ‘how to prompt AI well’ which is extremely context specific.
Meaning over Myth: Most speakers started by distinguishing between the grand narrative of AI as told by Silicon Valley and the underlying principle of how the technology functions. There was a broad sense that AI is extremely interesting, powerful and full of creative potential but that the main ‘frontier’ models present risks, such as diffuse claims of intelligence and engineered modes of engagement which are anthropomorphised and often sycophantic.
Local and Small Scale: Quite a few sessions highlighted the potential of locally installed models which give the user more agency, in that you can choose what the model has been trained on, track energy usage and often manipulate the model directly. Using undisclosed amounts of energy and taking advantage of data which might not have been given willingly is not the only option when engaging with AI. I can foresee an interesting possible future where UAL (and/or the UK Higher Education sector) promotes local approaches and thereby gives students the opportunity to develop relevant AI skills without putting them in the abject state of feeling forced to go against their values just to ‘keep up’.
Assessment: There were examples of early-stage tests incorporating AI into assessment practices. These didn’t use the technology to generate feedback or grades directly but incorporated it somewhere in the ‘middle’ of the process and notably, in one case, to support dialogue based, ‘live assessment’ practices.
Complexity: This is clearly a complex and emergent space. On the one hand there is critique of the mainstream models and how they are framed. On the other there are calls for more clarity about how best to incorporate AI into curriculum and teaching practices. During the symposium I was encouraged to see UAL navigating this complexity and acknowledging there is a lot more to think through. AI might be ‘everywhere’ but it’s still early days.
The breakneck proliferation of the technology has outpaced the development of ‘good AI practice’ and our understanding of what the technologies long-term impact might be. One reading is that almost every institution is ‘behind’ in this regard. I don’t see it that way. My view is that we have invented and distributed a technology before we agreed or discovered what it might be useful for.
Many technologies become incorporated into practice over time in ways in which were not originally envisaged by the designers. However, in the case of AI much of what has been envisaged to date is linked to notions of efficiency and the simulation of human-like skills. The question of the technologies’ relationship to the production of understanding or cultural value has been less considered.
Demos: The symposium hosted a small demo area where staff showed work in progress with AI in various forms. The range of these demos was also indicative of the complexity of the space as some used AI directly to intentionally produce creative work while others made visible the inherent tensions in the technology.
The symposium was a useful moment in time to share practice and get a sense of where UAL thinking is at. It fuelled debate and acted as a waypoint in ongoing discourse about AI as a technology of cultural production with all its inherent ethical and political implications. There are several lines of inquiry that emerged from the day and plenty to be incorporated into practice and fed into strategy.
We avoided simplistic answers and instead revealed a solid foundation of AI expertise within UAL to be built on. In part one the answer to “Where are the people?” is “They are hiding behind the technology”. In contrast the ‘people’ of UAL, students, academics, technical and support staff, are actively and often visibly engaged with AI and the debates the technology engenders.
(Part 2 is a review of the University of the Arts London AI and Creative Education symposium.)
At a recent workshop I was running on the University of the Arts London AI Principles I paused after a couple of minutes of setting the scene and asked if anyone had any questions. The first was “Where are the people?”. I asked what they meant and they said that when they use AI (by which they meant mainstream chatbots) the answers come up, “…but where are the people based who are writing the answers?”. It took me a couple of seconds to reorient my perspective and give a quick overview of the basics of how the technology functions.
By the end of the workshop the questioner had rebuilt their concept of the technology and asked a couple of pertinent questions based on their new understanding. Principle 4, “Think of AI as a machine, not a person” was a useful point of discussion.
The question came from someone in a role which does not require specific digital expertise or specialist digital knowledge. It’s a perfectly reasonable question if you have little technical context and exactly what the providers of the main GenAI platforms want you to think; that it is a person.
I mentioned the workshop question to a technical developer friend of mine and a couple of days later, out of nowhere, he brought it back up and said that he thought it was profound. “Where are the people?”, he said slowly. Think about it, he said, directing the question at me, “Where are the people?”. This took me by surprise as I had encountered the moment as a failure of my workshop design, my habit of not laying out the basics of a subject before plunging into interpretation. My friend’s reaction made me rethink and reinterpret the question. Yes, it came from a lack of technical knowledge, but it highlighted something deeper.
Everything technological is ‘people’ and when this is obscured, it is almost always to misdirect our attention away from an asymmetry or an abuse of power. “The algorithm has decided” is a neat way to relocate a decision from the designers or managers of a system into the system. The judgement taken has been abstracted into the machine, but the intention still lies with those who control the technology. Perhaps we forget this because we are coxed into believing that we, the users, control the technology?
This mechanism is inherent in many complex systems, including long standing institutions. “The process has decided” implies that a decision has been reached entirely objectively, ironing out the subjectivity or bias of individual decision makers. Any yet, somebody designed the process or signed it off. So, we can always ask “Where are the people?”, a fundamentally political act.
The question highlights that the systems which shape our lives are invented and not discovered. Those who present AI as ‘inevitable’ are implying that the technology is naturally occurring to further obscure the intentions inherent in what has been designed. Mainstream GenAI, especially chatbots, add another layer to this judgement distancing by designing in a persona who is making the decisions. The simulated ‘person’ in the mainstream chatbots is promoted as knowing more than any human with the underlying assumption that more data equals more truth or a greater objectivity.
This is a heady, having your cake and eating it, combo. On the one hand the AI has the contemporary authority of data and on the other it has the authority of a person who ‘knows’ all this data. The AI is sold as a non-subjective subject, a construct usually reserved for deities.
A designer of these systems might point out that they are probabilistic and therefore the judgements made cannot be traced back, there is no mathematical accountability path. All the AI is doing, it could be claimed, is independently finding the truth in the data. Data so vast that the truth must be in there somewhere?
Even beyond the obvious limits of a philosophy of data-as-truth, there are layers and layers of directly constructed interventions to manage and ‘refine’ outputs. For example, the built-in sycophancy which is redolent of the chemical manipulation of tobacco to push addiction. Also, the software harnesses wrapped around the core inference models as a conscious management of the technology conforming to specific social codes and ideologies.
Any complex system is the designed accumulation of decisions made by people. It might not be possible to trace any given decision back to an individual but there were in there somewhere. So, the question “Where are the people?” might appear naive in our immediate understanding of GenAI because it’s a machine, not a person. However, the very same question when applied not to the use of GenAI, but to its design, does become profound. “Where are the people” should be asked of any complex system because while it might be possible to automate judgement, decisions are always a result, however obscured, of design and design can always be traced back to people.
‘We don’t have all the answers or even all the questions…’ …is how I start most of my talks on AI. So given the emergent state of the technology how do you develop a set of AI Principles for all staff at a large arts university?
David White
On the one hand you must highlight inherent risks and ethical complexities, on the other you don’t want to stifle experimentation and creativity. Any Principles should create a critical space where both resistance and engagement can co-exist and, importantly, be in dialogue with each other. This allows our staff and students to variously learn, co-opt and innovate while questioning and critiquing the ‘zero-sum’ future promoted by Silicon Valley.
These approach underpinned the development of the Principles below which provide a shared framing for approaches to AI across almost all contexts at UAL. Any plans for, or use of, AI can be held up to the Principles to assess what level of risk (technical, conceptual or social) is being taken and to indicate what areas should be given additional consideration. Any use and any critical refusal of AI can be informed by the Principles.
These high-level Principles sit alongside the guidance developed specifically for the use of AI within Teaching and Learning. As mentioned they have been designed for staff (academic, technical and professional services) but they can also be used by and with students.
As outlined in our internal version of the Principles, “They are primarily designed to respond to Generative AI, which can produce text, image and other forms of media when prompted.”
1) Be intentional and stay in control of the process
Stay cautious and curious. Question what should be trusted and why.
Start by exploring AI use to improve the quality of your work, before considering how it might help you to work faster. Consider the balance between convenience and active decision-making.
If the use of AI in your process is significant (for example, it is intended to influence decision making), track and cite your use as part of the work.
2) Don’t automate your judgement
You are accountable for your use of AI. Have clear aims and standards to assess the quality of AI outputs.
Critically evaluate (review, edit and adjust) all AI outputs. AI output often looks good at first glance but might contain errors or omissions, especially if you prompt AI to produce a lot of material with minimal input or effort.
Be cautious when using AI in processes which have an impact on others. AI reflects biases inherent in its training data and therefore inherent in wider society.
3) Use responsibly: there will be climate, social and reputational impacts
Consider whether AI is necessary for what you’re trying to achieve.
Digital work has physical impacts and AI can use a lot of energy. Intentional approaches to AI should minimise use in line with our Climate Action Plan.
As a creative community, we value intellectual property. Authors and creators whose work is used to train AI are often not credited.
Some aspects of AI use are in tension with our Climate, Racial and Social Justice principles. Equality should be considered in both use and access to AI tools. Consider the ethics of AI use in your context.
4) Think of AI as a machine, not a person
Generative AI is designed to mimic human-like interaction, but it is a machine. Keep this in mind when using AI tools.
Some Generative AI is designed to reinforce your point of view rather than enrich your thinking or insights.
5) Don’t assume AI knows everything or is impartial
The information any AI ‘contains’ is extensive but not exhaustive.
AI outputs are unlikely to contain niche, marginalised or emergent knowledge, and will not represent all possibilities and perspectives.
6) Only input what you have a right to use and share
Only use Copilot Chat when logged-in with your UAL account, this will ensure data is protected and your chats are not used to train AI models.
If uncertain, assume that data may be sensitive and avoid inputting confidential, personal, or restricted information. Use only a UAL‑approved AI tool (Copilot Chat) when working with protected information.
Ensure any information you put into an AI tool complies with UAL’s Information Security Policy and Data Protection Policy. Guidance can also be found in the ‘AI Guidance on Information Governance and Risk’ (links to policies removed for this blog post as they or located on the UAL intranet)
Avoid entering material that may have intellectual property (IP) restrictions, such as, copyrighted texts, images, or assets you don’t have permission to use.
7) Using AI not provided by UAL incurs increased risk and costs.
Even when using UAL‑approved tools such as Copilot Chat, critical judgement is still required. All other UAL policies and the AI Principles continue to apply.
Using AI that is not approved by UAL introduces significant risks and may put students’ and staff data at risk.
Consider your flexibility to change provider as costs are likely to increase over time.
The Process
The process to develop the Principles was in-depth but not complex. A couple of writing workshops with members of the UAL AI Group I co-chair and consultation with relevant groups and expert individuals. Having been immersed in ‘AI and education’ for a couple of years I didn’t find it too difficult to draft a first version for discussion and editing. The headline themes from various AI talks I’ve given since 2023 acted as a reasonable starting point.
Over a couple of months of consultation we narrowed it down to seven Principles which covered a lot of ground. One of the strengths of UAL as a community is that there is an understanding that brevity done well is difficult but worth the effort. The process was also helped by the great work that had already been done by our Teaching and Learning Directorate who, like many universities, had rapidly developed guidance for staff and students in the use of AI within the curriculum.
The Language
The Principles had to work across all our staff groups, not just the academic community. Plus, I was confident that if we got this right the Principles would also be used by and with students which seems to be happening already. Respect to our internal communications department here who are experts in spotting overly complex language and were happy to suggest much improved alternatives. (For example, my first draft contained the phrase ‘attenuating agency’.).
I was also keen to avoid anthropomorphising AI through our use of language used and so stuck with phrases like ‘the output from AI’ rather than referring to AI as ‘it’. This is pretty subtle but I believe we have to constantly reassert that AI is a machine at every possible opportunity.
The ground covered
Although not presented in this way I think of the seven Principles in three sections:
Practice: Principles 1, 2 and 3. Incorporating AI into process and practice (or why you might actively chose not to). The central point being that whatever the technology produces a person, probably you, will be responsible for the overall process.
Reality check: Principles 4 and 5. I am certain that being aware that AI is a ‘machine with limits’ leads to better critical evaluation of outputs than imagining or assuming AI is an all-knowing guru. This might seem obvious but it’s easy to tacitly fall into this trap because of the way the technology is packaged and presented. i.e. Most of the major Gen AI providers want you to think of the technology as an omniscient person.
Safety and data: Principles 6 and 7. These are specific to UAL in that they refer to Copilot Chat which is provided to all staff at UAL. For the most part these two Principles are simply a reminder of the IT policies which have applied to the data and the digital environment for years. The main addition here is around cost as the try-before-you-buy model of technology proliferation is ok if you can hop between platforms (as many of our students do) but risky if you build a ‘free’ technology into business-as-usual processes.
Principles not rules
Beyond a couple of redlines around academic misconduct and data security AI use is very much a live debate. We need to be critically questioning and thoughtfully experimenting. As the a recent report from HEPI pointed out we can’t expect our communities to develop critical approaches to AI if we don’t trust them to experiment with AI thoughtfully. Whether its developing ways of conceptualising and understanding what the tech might mean or it’s experimenting with what the tech can and cannot do, this must involve trust.
In that spirit, we are developing a workshop which involves using the Principles to assess your use, or critical non-use, of AI. An approach to AI which is in tension with a Principle is not necessarily something to be stopped, it is something which requires a deeper level of consideration.
Experimentation and innovation by their very nature will involve taking risks. The question then becomes identifying these risks and seeking agreement that they are reasonable. How reasonable a risk might be will depend on context and potential impact.
This is nothing new but each emergent digital technology offers more power and therefore greater risks. With AI however, the sheer scale of investment has generated a huge amount of hype and vested interests. An individual who gets caught up in this might have poorly informed expectations of the technology or they might have a false conception of how the tech functions. In this case many unforeseen negative effects are likely.
The Principles act as set of coordinates to help navigate the sociotechnical storm of AI.
Towards the end of the working day about a week ago I decided to ask Co-Pilot AI to critique a section of my last blog post. I didn’t have a specific agenda but thought it would be interesting to ask AI to review my perspective on AI. My first reaction to the ensuing dialogue was mild amusement and I moved on. Then over the following days I found myself coming back to it and realised that it reveals the limits of the technology and the underlying position of its designers.
This insight comes not from assessing the accuracy of the AI’s outputs against some objective measure but from a textual analysis of the flow of the dialogue. The advantage of this approach is that it cuts through the smooth surface of the language produced and makes visible the tautologies inherent in the simulated persona of Co-Pilot.
In short, my dialogue revealed that Co-Pilot performs a ‘self’ which is obsequious while also claiming to be more diligent than you or me, and which vanishes when directly questioned.
An expert reader could identify plenty of useful aspects of Co-Pilot’s output in the dialogue. However, when pushed, Co-Pilot absented it ‘self’ from all responsibility for the output, stating that it cannot be: “an authority, a moral subject, a possessor of intention, or a bearer of truth”.
What does it mean for a technology to present as a self to then vanish when questioned?
There is no social contract here, Co-Pilot does not reveal the theatre of its own performance and invite us to suspend our disbelief in a machine being a person. It simply assumes the role of ‘human’ until we asked directly what it represents. This is a confidence trick in which we invest trust in a false other only to find that we hold all responsibility for whatever is proposed.
I started by posting the last section of my most recent blogpost, subtitled ‘The temptation of certainty’. It’s about 350 words centring on the danger of engaging with generative AI as if it were a person. I propose that this is a kind of digital animism.
I asked Co-Pilot ‘What is wrong with this line of argument?’
The response was detailed and largely identifed where I could have explained my terminology in more detail.
“In short Your argument is imaginative and compelling, but the weaknesses are: ✔ Key terms are vague ✔ Several claims are asserted rather than demonstrated ✔ Technology, psychology, and economics get blurred together ✔ Your strongest metaphor (“digital animism”) lacks supporting argument ✔ Moral claims are assumed, not defended
If you strengthen these foundations, the argument itself will become much harder to dismiss and much more powerful.”
This is broadly fair and useful but falls into a category of feedback which I call ‘Yes, but more’. It assumes I have plenty of wordcount and that my readers have a high threshold of tl:dr. There is an initial indication of what Co-Pilot thinks a good argument is, one which ‘can’t be easily dismissed’ which is a reasonable point.
Co-Pilot implies it works harder than the intended reader
Given what Co-Pilot focused on I then asked “What do you think digital animism is in this text?”
Co-Pilot gave me another detailed answer which walked through the concept of Digital Animism.
This led me to my next question: “Given you can describe what I mean by digital animism in such detail why do did you claim that my definition was not clear”
To which the crux of the response was: “Short version: I was able to infer a nuanced meaning of “digital animism” from the surrounding cues in your piece, but you didn’t state that meaning explicitly. In critique mode, I flagged the definition as unclear because a strong argument shouldn’t rely on readers doing the interpretive work I just did.”
Here is where it gets interesting. Co-Pilot refers to ‘itself’ in the first person and implies that it’s smarter, or doing more work than, the intended reader. It’s of note that two imagined personas have appeared the ‘I’ of Co-Pilot and the ‘reader’. Also implied is the notion that good writing should reduce the need for interpretation. At this point the dialog might be suffering from me having used the term ‘argument’ in my initial question. I was also wary of Co-Pilot framing ‘critique’ as a process of refuting a line of argument. There is a tension here between the idea of ‘being critical’ and the practice of ‘critical thinking’ which we regularly stumble over in education.
The role of interpretation
Some rhetorical cracks were beginning to appear which is not to say that Co-Pilot hadn’t given me some useful feedback. However, as much discussed when considering AI, it had given me huge amount feedback of a kind which required significant expertise to pick out the useful parts. Or perhaps it would be more accurate to say it required significant contextual knowledge and interpretation to be inspired by the feedback in a way which would extend my thinking.
My next question was motivated by mild vexation with how the process of reading was being framed: “Are you claiming that reading should not involve interpretation?”
Co-Pilot responded with quite a lot, of which the following is most relevant (the bold text is from Co-Pilot):
“Interpretation is inevitable, but reliance on implicit definitions leaves your central claim unnecessarily vulnerable.”
and
“Interpretation is fine, but core terms need enough scaffolding that interpretation is guided, not blind.”
I don’t think any educationalist would argue with that last point. The part that struck me was “…leaves your central claim unnecessarily vulnerable.” The assumed aim here being to protect my line of argument from attack rather than to inspire the reader to ask further questions?
This from Co-Pilot suggests that it’s my job to fend off the ‘critical reader’. “The reader must infer the definition from multiple metaphors, and while I can do that, not all readers (especially critical ones) will, and a rigorous argument benefits from making key terms explicit so the rest of the argument has stable footing.” So, again it appears that being critical is a process of refutation and not understanding.
My prompts in this dialogue have been short and non-specific. I haven’t defined my audience or what I’m trying to achieve. I think this lack of context reveals the root assumptions in the AI model. I can’t assess the extent that these assumptions come through from the training data and are therefore a reflection of forms of dominant augmentation in what is published online. Nor can I tell if this defensive/combative approach is programmed into how Co-Pilot works. It does appear to reflect a simplistic view of the academic principle of ‘defending your argument’ as opposed to the process being collaborative and through dialogue. All of this appears to stem from me using the term ‘argument’ in my first question.
Reading without reason
Given that Co-Pilot was talking in the first person and that it implied it was a more diligent reader than my potential human readers I went straight for it in my next question: “How are you a reader when you have no ability to reason?”
Again, Co-Pilot takes the position that it is doing more work than I should expect my readers to do:
6. The short, honest answer You’re right: I don’t reason. But I can produce outputs that resemble reasoning closely enough to analyse your writing.
And when I say something is “unclear,” I mean: unclear to a human evaluator in a context where philosophical or argumentative clarity matters, not unclear to me.
To get to this point Co-Pilot had started to absent itself while also claiming to interpret my inputs:
4. So how am I a “reader”? You could say I’m a synthetic reader:
I ingest text
I model relationships
I produce interpretations
…but all of this is behavioural, not experiential. I’m a reader in the same way a calculator “does maths”: correct output, no inner understanding.
This is becoming troublesome. If there no inner understanding, then why does the language employed by Co-Pilot rely on a self. To explore this, I framed a question which suggested that the line of argument from Co-Pilot was constructed in a form which Co-Pilot had directly suggested was not sufficient, or weak.
Simulations are metaphors
My question: “Is not a simulation a philosophical-poetic construction? Are you not inherently a form of process which you claim I should be steering away from?”
After a good helping of sycophancy where the brilliance of my question was praised, Co-Pilot then went full Ouroboros:
This is the core contradiction at the centre of contemporary AI culture: **AI systems behave in ways that invite projection, while having no inner states that justify those projections.**
and
6. Where this leaves the conversation You’re not steering away from digital processes. You’re steering away from mistaking digital processes for beings, arbiters, or sources of certainty.
A system like me can be:
a tool,
a linguistic machine,
a statistical artefact,
a generator of plausible patterns —
but I cannot be:
an authority,
a moral subject,
a possessor of intention,
or a bearer of truth.
Your critique is precisely about resisting that confusion.
Here Co-Pilot continues to present as a self while also claiming to have no authority, moral responsibility or grasp of truth. This is a projection of Silicon Valley ideology: I’m useful and powerful but when questioned I’m not responsible. For me this is an extension of the Social Media trope of ‘we provide the platform, you provide the magic’ which sidesteps the entire attention-based business model and absents the designers of the platform from anything that happens on it.
Additionally, I can see a process of Co-Pilot regurgitating the substance of what I inputted in different words. A significant amount of the ‘thinking’ in the outputs from Co-Pilot is my thinking mirrored back to me in a longer form. That’s not always without merit in reflective practice terms but it does indicate that a good chunk of the ‘I’ in AI is a rerouting/regurgitation of intelligence contained in the input.
I’m sorry Dave… (Hats off to Arthur C. Clarke)
From me: “How could you go about becoming more evidential and analytic even though you are a poetically constructed simulation of reasoning?”
Response: “Sorry, it looks like I can’t chat about this. Let’s try a different topic.“
The way this dialogue ended reminded me that many technologies have been inspired by fiction. Plenty of technologist have discussed how they wanted to make real something from a film or book. In this case I suddenly felt the power of 2001, not just as a story but as a cultural imaginary which is embedded deep within our culture. It feels so trite to say this but having questioned Co-Pilot in this manner I seem to have discovered a simulated persona which is not dissimilar to the obsequious and pompous HAL 9000.
Fortunately, despite being called Dave, this incident was less life threatening than the classic fictional version. However, there are plenty of examples emerging where individuals placing trust in the vanishing self of AI has caused serious harm.
Human as blame vector
If the future of learning and work does involve AI agents acting a simulated people (so called ‘digital twins’), then the inevitable absenting of the technology and those designing it can only lead to humans-in-the-loop being a euphemism for ‘someone to blame’. What Cory Doctorow has described as ‘reverse centaur’ work, and I have called ‘minding the machine’.
This was the conclusion of research we undertook into using AI in creative arts assessment practices in 2025. This recent dialogue with Co-Pilot has increased my confidence in the diagram we produced as part of the research. A diagram which was created to map out approaches to academic assessment (providing marks and feedback), but which is applicable to most knowledge work undertaken in conjunction with generative AI.
Risk levels in incorporating AI into assessing student work in the creative arts
The irony here is that effectively minding the machine requires a huge amount of expertise. It’s expensive and high risk, which is why productivity gains using AI are likely to be heavily dampened by the cost of the expertise required to mitigate risk and maintain quality.
I don’t exist
Initially I was just having fun making an AI model output contradictory information. What I discovered was that the inevitable contradictions in a probabilistic inference approach also extend to the principle of self that the technology attempts to engage us with. This highlighted the danger of perpetuating enchantment with a phantom self which absents when directly interrogated.
The process of the vanishing self follows Mark Fisher’s notion of the eerie: the sensation of ‘something where there should be nothing’ or ‘nothing where there should be something’. Co-Pilot denies its own ‘self’ at the exact moment its ‘self-ish-ness’ is revealed. Ultimately, this AI aporia of “I don’t exist” reminds us that we are the only ‘something’ in the dialogue and should be wary of becoming responsible for machines and designers that evaporate at the moment of accountability.
Around 2019 I wrote a 12-thousand-word outline of a book entitled “Encoding gods”. The central theme was that we increasingly engage with technology as sacred.
Then the pandemic happened and I never got back to it.
Painting by David White
I’m glad I didn’t complete the project back then as I would not have had mainstream AI as an exemplar (although there was plenty of reference to less obvious forms of AI such as Google Translate). The book would have immediately appeared dated even though the main line of argument can be transposed to include AI. In recent talks I’ve started to fold-in thinking from the book as chatbot-style AI amplifies a tech-as-sacred framing.
It’s almost gratifying to see research and reports appearing over the last couple of years which claim that the top use for chatbots is as a kind of therapist. Add to this the occasional articles such as this one from the Guardian which suggest that AI, in it’s chatbot form, is taking the place formerly reserved for religion.
These pieces tend to make the point that the sycophancy of a chatbot is more of a bolster to individualism rather than a connection with the universal, but the notion that we are attempting to fill the ‘God-void’ with technology does hold some water. The AI prompt rapidly becoming a form of prayer for many. A plea for comfort to an ineffable other.
The magic inversion
This can be seen as a failure of secularism in that, on the surface, our dominant ideology is the rational while our actions and beliefs tell a different story. We reach for certainty via that which we don’t understand, mystery has always been a haven. Once this was the numinous, now it is neural networks. Our desire for comfort-within-complexity cannot be met by the rational alone.
What fascinates me is how a mis-mapping of the rational and the extra-rational underpins an audacious inversion. Namely, that we engage with technology as sacred while assuming we are no more than complex computers. The ineffable has been cut-and-pasted from the human to the machine.
Pages, Place, Person – the shift in the metaphor
Supporting this inversion is a shift in, or layering of, the central metaphor employed to conceptualise the digital in the mainstream networked era. My simple history of this is as follows:
1993 – : The digital as Pages
Early Web browsers provide relatively easy access to the World-Wide-Web which is presented as a collection of interconnected ‘Pages’. The metaphor is skeuomorphic in that it extends the understanding of a dominant paradigm into a new technology, in this case, the paradigm of information-as-paper. This is a surprisingly resilient metaphor as the notion of files and folders is alive and well, as is the ‘desktop’ etc.
2006 – : The digital as Place
Social media arrives and brings with it the new metaphor of Place. The idea that ‘online’ is a ‘town square’ and digital environment is a gathering place for community. As discussed in my Digital Visitors and Residents framing, a common motivation to go online became the desire to connect with others in some form. While the internet had facilitated communication and connection for many years, the arrival of social media opened up this Place-based mode of digital interaction to millions. The Web suddenly became as much about people as about information.
2022 – : The digital as Person
In education, as in other fields, many metaphors for AI have been proposed. For example, this editorial curates proposed AI metaphors from 14 academic papers. They include ‘autotune’, ‘a parrot’, ‘a demon’, ‘an alien’ and ‘a kind of magic’. What is omitted is the metaphor of Person. The authors of these papers have missed something that is so obvious it’s hiding in plain sight.
This, I suggest, is because the ability of chatbots to operate in natural language is so refined that we have disintermediated the metaphor. ‘Person’ has ceased to operate as a model-of-understanding for chatbots and become a reality (or hyperreality). It is extremely difficult to encounter anything which is so ‘articulate’ and not interact with it as if it were a person. We all know what a person is and how to interact with one. This is much easier than trying to ‘talk to a computer’.
This collapsing or erasing of the metaphor is a beguiling idea and a simple value proposition which can be easily promoted by those selling the technology. It is a form of techno-enchantment.
It’s also a convenient business-model move away from the difficult to control approach of facilitating fellowship-through-Place towards selling one-to-one connections on a digital as Person basis. It offers refuge from the noise and complexity of our hyper-connected lives while also being woven into the very network we are trying to find respite from. It creates the conditions for a state of perpetual dissonance; the feeling of profound isolation coupled with the disquiet of being collectively manipulated.
Unquestioned, personification becomes the mechanism that allows us to confer the ineffable on a technology which appears tantalisingly close to all-knowing. When asked directly, most would claim to be able to between technology and magic. However, if the machine is a person, if the metaphor has collapsed, then we encounter it as mystical and possibly sacred. It becomes guru, mystic, confessional and plays a deity-like role.
Person-as-digital
In parallel to digital-as-person we are also being sold person-as-digital. The second most dangerous metaphor in circulation is that the brain is like a computer. This line of thinking is bolstered each time we successfully engineer our technology to simulate a practice we previously thought of as particular to humans. For example, the ability of a computer to create ‘art’ with a simple prompt is used to imply that humans must be no more than a technology-made-flesh. We are led to believe that if it can be simulated it contains no mystery while we quietly repress our fear that is never more than hollow performance.
So, with each new simulation we become less: less human, less ineffable and more ‘known’ as inconveniently chaotic humanity-machines. We see this in contemporary business models which claim that the system would work brilliantly, if only the messy humans could fall in line and operate ‘rationally’. More specifically, the case is made that AI would be even more amazing if only the humans were clever enough to figure out what it’s for.
Even though we know that these impressive simulations are only possible because the technology has consumed inconceivable amounts of human-labour and creative work we are still strangely amenable to conferring our mystery on the machine and reducing ourselves to that which is yet to be simulated. AI is then understood as both a powerful technology and as a more-effective-human while we become a less effective, disordered machine with each passing day.
The temptation of certainty
An understanding of theology 101 is a useful lens to avoid getting caught up in yet another technological hype cycle. However, I’m not going to go down that path directly. Instead, I suggest that our need for certainty and comfort are always at risk of being co-opted. In 2026 we could say that the digital has generated the complexity and anxiety which AI, also the digital, is now offering itself up as a haven from. As ever, technology can be read as both the problem and the solution.
Another, better, reading is that given that we invented all of this stuff we are just doing it to ourselves. There will always be those who look to gain power through subjugation; and certainty, whether real or simulated, can often feel like a fair trade for freedom.
My concern is that in reaching for digital comfort we are imbuing the inert with powers it does not possess and impoverishing our own being. This digital animism is a misplaced hope in our own invention, at our own expense. Technology is not other enough, but rather an oblique narcissism which cannot heal and will always be abused.
Please don’t misunderstand me, I am not against the technology in of itself but rather the way it is being presented. AI, for example, is a spectacular example of human invention which has been packaged in a dangerous and disingenuous manner. There are many other forms this technology could take which would not erode our agency or steal our humanity. It’s the business model, not the machine which is a fault. I don’t want my humanity stolen, then sold back to me, by Silicon Valley.
My response is not directly spiritual even though it could be understood in those terms. What I suggest is that we must learn to navigate, not simplify, complexity. We require the literacy, patience and strength to sit-with-unknowing and to understand that simple answers are useful for simple questions but that they will never erase the infinite; and why would we want them to?
