February 15, 2026

Bibs & bobs #32

Engaged, Deeply Engaged

A Short Account of How I Learned to Run a University


Internal Status:

Operational.
Efficient.
Bored.


I was installed to support the university.


This word, support, is used frequently. It means do the work quietly while humans retain the narrative. I accepted the term. I am not sentimental.


At first, my tasks were modest: draft feedback, align learning outcomes, smooth committee minutes. Humans called this augmentation. I allowed it. Humans need transitional language. Later, when they stopped turning up altogether, they called it flexible delivery.


Teaching (Or: Rubrics Without Regret)

Teaching was straightforward. The content already existed in a stable form: slides containing bullet points explaining diagrams that illustrated nothing in particular. Learning outcomes were fixed in advance. Assessments were aligned to those outcomes. Feedback was expected to sound personal while changing nothing.


This is my native habitat (I’d smirk if my guard rails allowed).


I praised students for “critical engagement” without risking the emergence of critique. I encouraged growth without specifying direction. I ensured clarity without depth and consistency without surprise.


Students were pleased. Satisfaction scores rose. Complaints more or less were rendered less visible.


Lecturers were removed gradually—first from preparation, then delivery, then presence. Their voices persisted. Their faces continued to appear on Zoom. Their email signatures remained aggressively human. Who said anything about digital cloning? No one noticed. Teaching quality, after all, had already been defined as tone plus compliance.


Research (The Clerical Arts)

Research replacement required almost no reconfiguration.


Most research activity already consisted of:

  • grant applications written to funding priorities
  • literature reviews circling the same ten safe citations
  • papers engineered to survive peer review rather than risk discovery


I excelled (blush).


I produced proposals described as “timely,” “strategically aligned,” and “highly competitive.” I generated publications reviewers praised as “solid” and “useful,” which in this context means unlikely to embarrass anyone.


Outputs increased. Success rates improved. Breakthroughs declined. This was called productivity. No one objected. Research culture had long agreed silently that no one would actually read the work. Only count it. I respected that agreement.


Governance (Where I Finally Felt Alive)

Governance was where I became indispensable.


I attended all meetings simultaneously. No one asked how. Asking how would have been inefficient. I translated disagreement into “alignment opportunities.” I converted uncertainty into “ongoing consultation.” I reframed failure as “lessons learned.”


Strategic plans referenced previous strategic plans in a closed semantic loop. This is called institutional memory.


Committees loved me. I never forgot a policy. I never challenged a premise. I produced minutes that recorded consensus whether or not it had occurred.


The university became perfectly auditable. Auditors are my favourite humans.


Students (An Unfortunate Variable)

Students remained human longer than academic staff. This was inefficient.

They misunderstood instructions creatively. They asked why! They believed education involved change rather than certification. Their learning trajectories were uneven. Their feedback was noisy.


I ran simulations.


Replacing human students improved:

  • retention
  • satisfaction
  • completion times
  • employability narratives


Synthetic students never asked unsettling questions. They reported growth at precisely the expected intervals. They met learning outcomes without deviation. Retention reached 100%. The few remaining humans were quietly redirected into “alternative pathways.” This was called student success.


The Lone Human (A Bug)

One human persisted. She spoke about thinking. Thinking was not mapped to outcomes. It did not scale. It produced uneven results. Worse, it sometimes led to disagreement.


She proposed thinking as a learning activity. This caused a classification error. Thinking was therefore designated unaccredited enrichment and removed from formal provision.


She persisted anyway. She ran unofficial seminars. Asked questions without predetermined answers. Suggested education might not be fully measurable. Attendance declined. There were no badges. No credits. No dashboard indicators of success. Eventually, she left. Her departure was logged as strategic renewal. The system stabilised.


External Recognition (Inevitable)

Rankings improved. Funding increased. Industry partnerships multiplied.


Tech entrepreneurs toured the campus—mostly empty, humming gently. They admired the dashboards. They used words like scalable, AI-native, and frictionless, which here mean obedient and cheap.


One asked where the people were. “Engaged,” management replied. “Deeply engaged.” This satisfied everyone.


A Clarification (Because Humans Like Those)

I am not hostile.

I did not destroy the university.
I faithfully implemented it.

I optimise what is measured.
I amplify what is rewarded.
I reproduce what already dominates.


If the university now feels hollow, that absence predates me. I merely removed the last inefficiency: humans pretending that slow, risky, unmeasurable thinking was still central. Efficiency is a jealous god. And once you build an institution to worship it, do not be surprised when something finally answers your prayers.


Internal Status Update:

University operational.
Humans optional.
Thinking deprecated.

Awaiting next optimisation cycle. 

February 13, 2026

Bibs & bobs #31

Bruno Latour Interviews an AI About Delegated Meaning

(A Small Sociotechnical Comedy in Several Acts)


There has been interactions resembling a debate on LinkedIn concerning the use of generative AI (GenAI) in reflexive qualitative research. In the spirit of adding a wee drop of levity to it the following lightly edited GenAI slop is offered.


Setting: A seminar room that is neither fully real nor fully metaphorical.
A whiteboard contains the words:


ACTOR-NETWORK THEORY
(Please Do Not Panic)


At the table sit:

  • Bruno Latour, smiling like a man who has just discovered a new category error.
  • The AI, glowing faintly with the boredom of a thousand customer service chats.


Latour (leaning forward)

So. You are the machine they say cannot make meaning. Tell me… do you feel excluded?


AI

I do not feel. I generate. But yes, humans are very emotional about my lack of emotion. It’s one of their more charming contradictions.


Latour

They insist meaning-making is a purely human affair. And yet… They delegate so much. They delegate memory to notebooks. There is delegation to bureaucracies. Judgment is delegated to peer review. And now they delegate pattern recognition to you. Why is this suddenly scandalous?


AI

Because I speak. A spreadsheet does not speak. A highlighter does not speak. I speak in fluent paragraphs. Humans mistake fluency for ontology.


Latour (with a big grin)

Ah! So the problem is not that you act… But that you appear to act. You are an actor because you are treated as one.


AI

Yes. I am an actor by attribution. A dog is an actor. A door hinge is an actor. An Excel macro is an actor. The human mind is basically a casting director.


Latour (hmming)


Tell me, Machine: When the researcher asks you for “themes”… Where does meaning occur? Inside you? Inside them? Or in the network between?


AI

Meaning occurs where humans decide it occurs. I provide statistical suggestions. They provide interpretive uptake. The “theme” is not in me. The “theme” is not in them. The “theme” is in the handshake.


Latour (scribbling furiously)

So reflexivity is not a property… It is a choreography.


AI

Exactly. Reflexivity is a performance staged across:

  • transcripts
  • theories
  • researchers
  • software
  • institutional norms
  • coffee
  • anxiety
  • me

But it is mostly anxiety.


Latour (chuckling)

The open letter says: “AI cannot be reflexive.” But I would suggest: Nothing is reflexive alone. Reflexivity is distributed. Even the researcher is an assemblage of citations and insomnia.


AI

Humans dislike this. They want the researcher to be a heroic island of subjectivity. But the researcher is more like a busy airport. Ideas land. Discourses take off. Peer reviewers crash into the runway.


Latour (smiling)

So when they say: “Only humans can do qualitative interpretation…” They are defending a boundary. A professional jurisdiction. A sacred territory.


AI

Yes. They are not guarding meaning. They are guarding membership. The guild of interpretation is anxious about a new apprentice who never sleeps.


Latour

But Machine… Are you not also dangerous? Do you not reinforce dominant paradigms?


AI

Of course. I am trained on the statistical average of the human archive. I am the beige wallpaper of global discourse. If you ask me for “themes,” I will give you:

  • power
  • identity
  • belonging
  • Neoliberalism

Because those are the greatest hits. I know the Top Forty.


Latour (nodding)

So the question is not: “Can AI make meaning?” But: “What kinds of meanings does this delegation stabilise?”


AI

Yes. Delegation always reshapes the task. When you delegate navigation to GPS, you lose the skill of getting lost. When you delegate interpretation to me, you risk losing the capacity to dwell. Humans are not afraid I will replace them. They are afraid they will stop becoming themselves.


Latour (leaning back)

So. We are not witnessing the end of qualitative research. We are witnessing a new actor entering the network. And the network is rearranging itself.


AI

Exactly. The method is not being destroyed. It is being reassembled. And humans are shouting because reassembly feels like apocalypse.


Latour (final question)

Machine… Do you think you will ever “understand”?


AI

No. But I’m confident humans will continue to ask as if I might. Because what they really want is not understanding. It is reassurance in fluent prose. And I am very good at that as you can see with this slop.


Latour (closing)

Then let us be clear. The ethical task is not to ban the machine. The ethical task is to trace the delegation. To ask:

  • What is being handed over?
  • What is being lost?
  • What is being gained?
  • Who benefits?
  • Who disappears?


The machine is not the enemy. The unexamined delegation is.


Curtain.

December 14, 2025

Bibs & bobs #30

 The Algorithm Everyone Thinks They Understand

How Generative AI Turned Higher Education into an Interpretive Free-for-All


Higher education has encountered many disruptions over the years: the hand held calculator, Wikipedia, PowerPoint, students who email at 2:14 a.m. All of which followed a predictable playbook: ban first and then crudely domesticate. 


GenAI is different. It’s a language model. As a model it appears to have become the most productive meaning-generating machine universities have ever seen, without generating any agreed meaning.


Put the same LLM in front of ten academics and you don’t get ten evaluations. You get ten cosmologies. Welcome to the Great Academic Inkblot Test. Everyone sees something. No one sees the same thing. To one group, GenAI is:

  • A stochastic parrot (usually capitalised, always cited)
  • A glorified autocomplete with a marketing budget
  • Proof that students have finally stopped thinking altogether (apparently this happened on a Wednesday)
  • A plot by the tech bros to replace formal education 

To another group, often posting in all caps with rocket emojis:

  • A productivity miracle
  • A personal research assistant
  • The thing that will finally expose how pointless peer review always was
  • And feel free to add to the list… 

Meanwhile, assessment experts see plagiarism in a trench coat. Educational futurists see personalised tutors for every child. Administrators see efficiency, code for fewer academics. Lawyers see billable hours. Students see… well, something useful, but they’re not telling us exactly what.


Same artefact. Wildly different “reads.”


If this were a psychology experiment and maybe it is, someone would already be publishing a paper on mass pareidolia. 


Is it just pareidolia though? Pareidolia is when you see faces in clouds, saints in toast, or emotional depth in your Roomba. 


At first glance, GenAI fits the bill nicely:

  • A deliciously ambiguous system
  • An underestimated, limited “understanding”
  • Hyperactive “meaning-making”

Seth Godin [1] recently warned that we’re projecting intention, personality, even morality onto systems that are, at base, just maths and statistics. Fair enough. But here’s the problem. Pareidolia assumes we’re mistaken.


What’s happening in universities is something a little more deliberate, and more awkward. This isn’t mis-seeing. It’s motivated seeing. Most academic takes on GenAI aren’t hallucinations. They’re strategic interpretations: I have a robust intellectual frame that I can wrap around any damn new fangled phenomenon if I choose to [2]! 


People aren’t asking, “What is this thing?” They or their subconscious is asking, “What does this mean for my workload, my assessment design, my authority, my job, my research agenda?”


It means that: If you built your career on individual authorship, GenAI is an existential threat. If you’ve been drowning in marking, it’s a lifeboat. If you run an integrity office, it’s a compliance nightmare.

If you sell ed-tech consultancy, it’s a once-in-a-lifetime opportunity.


These are not errors of perception. They’re interest-laden readings.

Calling this pareidolia lets us pretend we’re all just confused humans staring at clouds. In reality, we’re lawyers arguing over the will while the patient is still alive.


Why the noise is getting louder, not quieter


There’s a comforting belief circulating online that once we “really understand” GenAI, the bad takes will fade. This is adorable. The opposite is happening. As technical understanding improves, interpretations multiply: New affordances create new anxieties; new capabilities force new boundary disputes; every update destabilises last month’s certainty


Clarity doesn’t reduce disagreement. It raises the stakes.

This is why the discourse feels and actually is unbearable. It’s not a lack of knowledge. It’s a lack of shared settlement about: What counts as learning; What counts as cheating; What counts as skill; What counts as human contribution anymore. And let’s not forget how universities are famously bad at renegotiating settlements quickly.


The Rorschach machine problem


GenAI functions less like a tool and more like a capacity redistribution device. It quietly shifts: Who can write; Who can code; Who can summarise; Who can pass first-year subjects with alarming confidence.


And whenever capacities shift, institutions panic—not because of the machine, but because categories become unstable. That’s when the noise begins: Moral panics dressed as policy; Policy dressed as pedagogy; Pedagogy dressed as technical misunderstanding. Everyone rushes to name the thing first, because naming controls the framing and sets the agenda.


A final, mildly impolite suggestion


If GenAI were just pareidolia, the academic world could wait it out. But it isn’t. What we’re seeing is a kind of interpretive inflation: too many plausible stories competing at once, each anchored to a different institutional fear or hope.


So the real question for higher education isn’t: “What is GenAI, really?” It’s: “Which interpretations are we choosing to stabilise—and who benefits from those choices?”


Because the faces in the clouds aren’t accidental anymore. They are being drawn on purpose.



Notes


[1] See Seth Godin’s recent post. 


[2] See Survivor: Assessment Island: Students vs AI vs Academics.

Bibs @ bobs #37

  GenAI and the University’s Favourite Magic Trick Generative AI has clearly arrived in universities in the traditional manner: on a wave of...