Where to Look, and What Just Disappeared
Rory Sutherland made an interesting observation in a recent YouTube video. It was a point about leadership. He argued that he is less interested in telling people what to do than in telling people where to look. It is both annoyingly good and inviting given the common fare around leadership. It has the quality of something said casually over coffee while quietly reversing a forklift through three management theories and a strategic plan.
Most organisations are addicted to telling people what to do. Policies tell people what to do. Strategic plans tell people what to do, although usually in language that appears to have been assembled from wet cardboard, airport signage and the minutes of a committee that died in 2018 but has not yet been told.
Universities are especially gifted at this. They can produce a 42-page document whose main achievement is to make the reader feel both governed and abandoned. Sutherland’s line then becomes attractive.
Leadership is not just command. It is attention. The leader says: look here. This matters. That does not. This is evidence. That is noise. This is urgent. That can be safely ignored until it becomes a crisis with muffins. There is something right about this and also something slippery. Telling people where to look is not innocent. It can reveal what has been ignored. It can also redirect everyone away from the large smoking hole in the floor.
“Look here,” says the leader, pointing at innovation. Meanwhile workload is in the corner eating the furniture. “Look here,” says the consultant, pointing at efficiency. Meanwhile care has been converted into a spreadsheet and is now being asked to justify its colour scheme. “Look here,” says the AI strategy. Meanwhile judgment has slipped out the back door wearing a fake moustache and carrying a small suitcase labelled “professional discretion.” This is why the where to look line matters for GenAI.
A lot of the AI conversation still asks the dull question: What can the machine do? Can it write, can it summarise, can it draft a lesson plan, can it produce a grant outline, can it write a diplomatic email to someone whose main contribution to the project has been to repeatedly reopen settled matters like a cat proudly delivering dead mice to the ethics committee? The answer, of course is yes, it can do all those things, more or less. The better question is not what the machine can do. The better question is: where does it make us look?
GenAI pulls attention toward fluency, speed, coverage, neatness and plausible structure. These are not minor gifts. A blank page can behave like a domestic tyrant. A first draft can feel like a ladder with no final rung. A summary can stop a document setting like wet cement. A list of possibilities can loosen a thought that has jammed itself in the doorway. Used wisely, GenAI can help. But fluency is also its trick.
The sentence arrives before judgment has found its shoes. The answer appears before hesitation has had a chance to clear its throat. The category settles in before the case has even entered the room. Then comes the confident paragraph, wearing a tiny academic hat, nodding gravely, and hoping nobody notices it is only pretending to be knowledge. This is where academic work gets interesting.
A large part of academic craft is not content, it is attention. Knowing where to look. Knowing what to ignore. Knowing when a sentence is bluffing. Knowing when a concept has been dragged from another field and is now stands awkwardly beside the cheese table. Knowing which footnote is quietly holding up the argument. Knowing when “further research is needed” means “we have reached the end of our courage and would now like a coffee break.”
This is part of what might be called secret academic business [1]. We teach the visible rituals: literature reviews, referencing, methods, theoretical framing, argument structure. Fine. Necessary. OK. But the hidden craft is harder. It is knowing what smells wrong. It is knowing when a field has quietly agreed not to ask the awkward question. It is knowing when the student’s problem is not a student problem at all, but a task design problem wearing a lanyard. It is knowing when a beautiful paragraph has performed the intellectual equivalent of rearranging cushions while the house burns.
LLMs can mimic some of this. They can perform critique. They can say “problematise” with a straight face. They can generate a paragraph that looks as if it has read widely and slept badly. Mimicry does not support apprenticeship. The machine can point. It does not know why pointing there matters [2]. Or, more accurately, it does not care. And not caring is part of its charm. Also part of its menace. Like a vending machine that dispenses plausible interpretations and occasionally drops a can on your foot.
This is basically why middle managers are in such a strange position with GenAI. From above comes the great hymn of efficiency: streamline, automate, optimise, transform, scale. These words now roam freely through universities, grazing on sense and leaving small piles of key performance indicators behind them.
From below comes the less glamorous question: who does the work after the miracle? Who checks the output, fixes the errors, spots the subtle damage, and explains to the student, dean, committee, partner organisation or future ombudsman that the system was used “appropriately” in a setting where nobody had time to ask what appropriate meant? This is the part that often disappears. GenAI does not simply save labour. It redistributes labour.
The time saved by one person does not always disappear. It often reappears elsewhere as checking, cleaning, correcting, documenting, soothing, translating and apologising. Usually lower down. Usually invisibly. Usually on the desk of the person already holding three collapsing systems together with Outlook calendar invites, institutional memory and a small, renewable supply of spite.
The useful middle manager may not be the one with the grand AI strategy. It may be the one who keeps asking irritatingly practical questions. Not, “How do we use AI to go faster?” but, “What are we no longer noticing because we are going faster?” Not, “Was this written by AI?” but, “Can the person defend the choices?” Not, “How much time will this save?” but, “Whose time, and where does the unsaved time go to die?” That last question is unlikely to appear on a slide with a blue gradient, which is usually how you know it may be useful.
So, at this point, “where to look” is starting to admire itself in the reflective surface of its own cleverness. The thing is that sometimes people already know where to look. They can see the problem perfectly well. They know the workload is impossible. They know the policy is nonsense. They know the assessment invites AI mimicry. They know the committee has become a retirement village for unresolved decisions. They know the bold new initiative is last year’s bold new initiative wearing a different scarf.
The problem is not attention. The problem is permission, time, protection and authority. Telling people where to look is not the same as giving them the capacity to act on what they see. This is where leadership slogans begin to wobble. “Where to look” is useful, but it can become another elegant idea floating above the swamp in clean shoes. It makes leadership sound like a matter of insight, when often it is a matter of courage, resources and not punishing the person who says the boat is both sinking and described in the annual report as “aquatically agile.”
Maybe Sutherland’s line needs an extra clause. Leadership is not only telling people where to look. It is being responsible for what your pointing hides.
This applies to GenAI too. Reward speed and the work will get faster. Reward polish and it will become smoother. Reward compliance and the room will get quieter. Reward scale and people will start to shrink inside the machinery. Reward judgment and we might get something resembling education. No guarantees, obviously. This is still planet Earth. But judgment seems the better target.
That means asking students and staff to show the choices, checks, hesitations, refusals, revisions and reasons behind their work. Not merely “did AI write this?” but “what did you do with what it gave you?” Not “is this authentic?” but “can you account for it?” Not “where is the human text?” but “where is the human judgment?”
The bot can produce text. It cannot take responsibility for what the text does. That remains our problem. Lucky us.
So yes, Sutherland is right. Leadership is partly about telling people where to look. The more troublesome version is better:
Good leadership makes better things noticeable, gives people some capacity to act on what they notice, and remains accountable for what has been pushed out of view.
This is less elegant. It has too many clauses and will not fit neatly on a coffee mug, which is a serious disadvantage in a civilisation increasingly governed by mugs, slides and laminated wisdom. But it has the virtue of being less likely to become nonsense by lunchtime. It also gives us a useful rule for GenAI: do not ask only what the machine can do. Ask where it is making you look. Then ask what has disappeared. Then ask who has been left to clean it up.
Notes
[1] This blog post is a longer account of secret academic business and GenAI.
[2] It is often easier to imagine a mind than explain the maths.
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