The Friction Premium.
Exploring why eliminating friction from knowledge work destroys the hidden mechanisms by which organisations learn, adapt, and generate genuinely novel ideas.
There is a management consulting firm in Amsterdam — call it a composite, because the pattern it represents is now widespread — that spent 2025 systematically removing friction from its knowledge work. Not the bureaucratic friction of approvals and sign-off chains, but the intellectual friction: the first-draft writing that analysts used to do themselves, the three-hour debates about strategic framing, the laborious manual synthesis of research before a client presentation.
The AI-assisted workflow they designed is, by any conventional productivity measure, excellent. Client-ready documents in a third of the time. Proposal turnaround in 48 hours instead of two weeks. Partners freed from review bottlenecks. Senior consultants finally able to focus on client relationships rather than document production. The metrics trend positive across the board.
Eighteen months in, a senior partner raised a problem that had no metric attached to it. The junior analysts — two cohorts’ worth, hired in 2024 and 2025 — could not explain why they believed the things they were recommending. Not in the sense of being unable to defend the AI’s logic. In the sense of having no formed view at all. The AI had produced a position. They had adopted it. The friction that would have produced the position in them — the struggle to structure an argument, the debate that forced articulation, the constraint that demanded creative problem-solving — had been optimised away along with everything else.
The firm had become very fast at producing work it no longer deeply understood.
This is the Friction Premium problem. It is not a technology problem. It is a structural one — and it is arriving across almost every organisation that has deployed AI substantively in knowledge work.
The science they left out of the AI strategy deck
The case against removing all friction from knowledge work has been building in the academic literature for thirty years. It simply hasn’t made it into most enterprise AI adoption frameworks.
In 1994, UCLA cognitive psychologist Robert Bjork coined the term “desirable difficulties” to describe a counterintuitive finding that had been accumulating across learning research for decades. Certain forms of resistance during learning — spaced retrieval rather than massed review, interleaved practice rather than block repetition, varied conditions rather than stable ones — consistently improve long-term retention and the transfer of skills to new contexts, even though they make learning feel harder in the moment and produce worse performance during the learning period itself.
The mechanism is not mysterious once you understand it.
Difficulty creates what psychologists call “generation effects” — when you have to retrieve, reconstruct, or produce something rather than simply recognise it, you engage different and deeper cognitive processes. You build connections between concepts. You construct understanding rather than merely encountering it. Those connections produce durable knowledge that transfers when the situation changes.
The smooth path — re-reading, summarising, reviewing already-formatted material — produces the sensation of understanding without the underlying encoding that makes knowledge usable in genuinely novel situations. You feel like you know. You may not know in the way that matters when the context shifts.
Bjork called the inverse of this the Fluency Trap: when processing feels easy, we interpret that ease as comprehension. The problem is that ease of processing and depth of understanding are not the same thing. Fluency is a feeling, not a fact. And it is the feeling that AI is extraordinarily good at producing — on behalf of the people who use it.
The Fluency Trap has an organisational-level equivalent. When an organisation runs its knowledge work through AI systems that produce fluent, plausible, well-structured outputs, the cognitive signal to evaluate critically is dampened across the board. The output looks like understanding. It carries the surface structure of careful thought. It may or may not have the underlying depth.
The empirical case at scale
The Microsoft and Carnegie Mellon University 2025 study of 319 knowledge workers is the sharpest quantitative picture of what this looks like in practice. Participants reported applying no critical thinking to 40% of their AI-assisted tasks. Not reduced critical thinking — none. More significantly, the study found that user confidence in GenAI inversely correlated with critical thinking application: the professionals who trusted AI most scrutinised its outputs least.
This is the Fluency Trap made visible at scale. When the output is fluent and confident in tone, the cognitive trigger for critical evaluation is not activated. When the human reviewer has high confidence in the AI system, their own evaluation is further reduced. Two compounding effects that together produce, across thousands of decisions per day, a systematic withdrawal of the cognitive work that was previously producing understanding.
The directional consistency across independent studies is striking. Gerlich’s 2025 study, published in MDPI’s AI Tools in Society, found a negative correlation between frequent AI usage and critical thinking ability, with the effect stronger in younger participants — exactly the cohort entering knowledge work organisations now and developing the professional judgement that will define their careers. A 2026 University of Technology Sydney analysis of cognitive offloading found that extensive reliance on AI tools can lead to a decline in cognitive engagement and skill development, though the effect is responsive to deliberate interaction design.
The mitigation finding is important. The problem is not the technology. It is the absence of intentional design. Most organisations have not designed their AI deployments to preserve cognitive friction. They have deployed AI to eliminate it, measured the resulting productivity gains, and called the job done.
What the friction was actually for
To understand what is being lost, it helps to decompose productive friction into its three distinct forms. They are different in mechanism, they produce different kinds of value, and they require different interventions to preserve.
Cognitive friction is the effort of articulation — the work of formulating your own argument before the AI does it for you. The labour of structuring a position, identifying its weaknesses, finding the right evidence, and presenting it in a form that holds together under scrutiny. This process does not merely produce a document. It produces understanding in the person doing it. The output is a side effect. The cognitive work is the point.
When AI writes the first draft, the human no longer does the work of articulation. They do the work of evaluation — which is genuinely valuable, but a different and often shallower cognitive task, particularly when the output is fluent and the evaluator has high confidence in the AI’s capability. The Fluency Trap reduces the quality of the evaluation step. The generation step — where real understanding is built — is skipped entirely.
Over time, the person who never articulates their position in writing has not built the same depth of understanding as the person who did. The outputs may be indistinguishable in the short term. The expertise is not. This difference only becomes visible when something genuinely novel arrives and the depth of understanding has to transfer to a situation the AI’s training data did not anticipate.
Social friction is the structured disagreement that forces better thinking. The meeting where two people with genuinely different frameworks argue until the logic becomes clearer for both. The pre-mortem that requires someone to articulate specifically and on paper why the plan will fail. The adversarial collaboration where a committed sceptic’s challenge produces a more robust position than any amount of internal consensus would have generated. These interactions are often uncomfortable. They are productive precisely because of the discomfort — the discomfort is the signal that genuine thinking is happening, that positions are being tested rather than ratified.
AI-assisted workflows tend to reduce social friction systematically. When the AI synthesises the debate rather than the debate happening in full, when the AI identifies risks rather than a sceptic articulating and defending them, when the AI generates the pre-mortem rather than a team constructing it through genuine anticipation — the social process that produced the improvement in thinking is compressed or eliminated. The conclusions may be similar. The understanding built in the room is not.
Structural friction is the constraint that forces creative solutions. The budget limit that requires rethinking the entire approach rather than adding resources. The technical restriction that pushes engineers toward solutions they would not have considered with unlimited flexibility. The deadline that eliminates the option of the perfect solution and requires the good-enough one that, in the search for it, reveals something the perfect solution would have permanently hidden.
“Training is frequently non-optimal because it fails to incorporate the variability, delays, uncertainties, and other challenges the learner can be expected to face in a real-world job setting.”
— Robert Bjork, UCLA Bjork Learning and Forgetting Lab
What gets lost: expertise versus the output of expertise
The most important distinction the Fluency Trap obscures is between expertise and the output of expertise.
AI can produce the output of expertise remarkably well. Given sufficient training data and a capable model, an AI system can produce a strategy document, a due diligence report, a legal brief, a financial model, or a market analysis that is indistinguishable from the work of a domain expert. This is real. It is valuable. It is one of the genuine and transformative capabilities AI brings to knowledge work.
What AI cannot produce is the expertise itself — the durable, transferable, judgement-forming understanding that exists in the person who would otherwise have done the work. In a world where AI routinely produces the output, it becomes far less obvious that this matters. The output is there. The client is satisfied. The process completes. Why should it matter whether any specific human deeply understood what was produced?
It matters for three reasons that only become visible at critical moments. First, evaluation: someone has to be able to tell whether the AI’s output is right, subtly wrong, or dangerously wrong in ways that fluent presentation conceals. The consultant who has never constructed their own analysis from first principles doesn’t know what a misleading analysis looks like from the inside. Second, adaptation: when conditions change in ways that weren’t in the training data, the organisation needs people who understand the domain, not people who can prompt an AI that understands it. The difference is invisible under normal conditions and critical under unusual ones. Third, succession: when the people who carry genuine expertise leave — which they always do — the organisation needs the next generation to have built expertise through their own cognitive work. If the junior generation never built expertise because AI produced the output throughout their development years, the knowledge exists nowhere a human can reach it.
Bain’s 2025 Innovation Report captures one dimension of this in findings about ideation quality. AI-assisted ideation produces a high volume of solutions with strong forecasted value. Human-generated ideas remain significantly stronger on novelty — particularly for breakthrough, category-defining innovation. The serendipitous interactions, the unexpected collaborations, the constraint-forced lateral thinking: these are precisely what AI cannot replicate, because they are produced by the conditions AI is optimised to eliminate.
Three archetypes: how organisations relate to productive friction
The Friction Eliminators have defined their AI strategy primarily in terms of process efficiency. Friction is categorised as unnecessary by default — anything that slows output is a candidate for elimination. Their short-term productivity metrics are improving. Their talent development outcomes, their capacity to handle genuinely novel problems, and their organisational resilience when conditions shift are not being measured. The bill has not arrived yet.
The Friction Preservers have maintained pre-AI working practices alongside AI deployment — requiring analysts to write first drafts before using AI assistance for refinement, maintaining structured debate in strategy sessions rather than letting AI-generated summaries replace the debate itself. They are being described internally as “not using AI to its full potential.” They are building organisations that can think independently of their AI tools.
The Friction Designers — the rarest category — have distinguished systematically between process friction and productive friction and have used AI to eliminate the former while deliberately designing the latter into their workflows. They require AI-assisted drafts to be critically annotated before any recommendation is made. They use AI to generate the pre-mortem and then require the team to argue against it. They set explicit constraints on AI-assisted option generation to preserve the creative problem-solving that unconstrained generation would eliminate. They are building AI-augmented workflows that make the cognitive work harder, not easier, at the points where the cognitive work is most valuable.
The friction audit
For any organisation deploying AI in knowledge work, the practical starting point is what I call the friction audit. Not “what friction have we eliminated?” — the answer is measurable and probably positive by any conventional metric. But the harder question: what category was the friction we eliminated?
Process friction — time spent on handoffs, approvals, administration, formatting, and coordination that moves information between people and systems without transforming it — is generally waste. Eliminating it releases time for higher-value work. This is real and worth pursuing.
Cognitive, social, and structural friction are different in kind. Eliminating them does not release capacity for higher-value work. It eliminates the mechanism by which higher-value work was being produced. The time saved is real. The capability that was building in that time is quietly gone.
The audit question for each workflow where AI has removed friction is specific: what was the human cognitive, social, or structural work occurring inside that friction? If that work was producing understanding, judgement, or creative insight — even as a side effect of something that looked inefficient from the outside — its elimination has a hidden cost that the productivity metrics will not capture until the capability it was building is needed and is not there.
The closing uncomfortable truth
The Fluency Trap, at the organisational level, runs like this: the organisation that deploys AI most aggressively in knowledge work may become the most fluent at producing work it doesn’t deeply understand. The outputs will be excellent. Clients will be satisfied. The metrics will trend positive. The organisation will feel capable and efficient.
The exposure arrives when conditions change — when the novel problem appears that requires genuine understanding rather than fluent pattern-matching, when the expert who actually knew things has left, when the AI’s training data no longer maps to the situation at hand, when someone needs to explain not just what was concluded but why it was right.
The Acar, Tarakci, and van Knippenberg meta-review of 145 empirical studies found that a healthy dose of constraints benefits individuals, teams, and organisations alike. The constraints organisations are optimising away are not purely inefficiency. Some of them are the conditions under which people develop the judgement to do this work well.
The Friction Premium is not a case against AI deployment. It is a case for precision — for understanding the difference between friction that was genuinely waste and friction that was structural value disguised as inefficiency.
The organisations that understand this distinction will build AI deployments that eliminate process friction while preserving productive friction. They will be slower than the Friction Eliminators in the short run. They will be more capable when the conditions that reveal the difference between fluency and understanding finally arrive.
Speed is not intelligence. Smoothness is not competence.
The premium, in the long run, goes to organisations that know the difference — and had the discipline to preserve the friction that was worth keeping.