In the 1970s, a team of researchers set out to build a computer system that could teach surgery.

The idea was straightforward. Find the best surgeons in the world. Record everything they did and turn it into a training program that could transmit world-class surgical skill to the next generation of doctors.

They found the surgeons. They recorded everything. And then they ran into a problem that nobody had anticipated.

The best surgeons couldn’t explain what made them good.

When they sat down and tried to articulate what they were doing and why, the accounts they gave were incomplete. They described the mechanics. But they couldn’t describe was the accumulated judgment of ten thousand procedures compressed into instinct that had become invisible even to themselves.

The researchers had set out to capture expertise. What they discovered instead was that expertise, at its highest levels, resists capture.

That discovery has a name. It is one of the most important and most underappreciated ideas in the history of human knowledge. And understanding it is the only way to understand why building systems that perform at expert level is so much harder than it looks.

The Philosopher Who Saw It First

Michael Polanyi was not the kind of person you would expect to reshape the field of artificial intelligence. He was a Hungarian-born chemist who had fled Nazi Germany in the 1930s, eventually landing at the University of Manchester where he spent the second half of his career not doing chemistry but thinking about what scientific knowledge actually is, how it develops, and how it moves from one generation of scientists to the next.

By the 1950s Polanyi had become increasingly troubled by a dominant assumption in Western philosophy of knowledge, the idea that genuine knowledge is knowledge that can be made fully explicit.

That if you truly understand something you should be able to state it clearly, defend it logically, and transmit it to anyone willing to pay attention. Knowledge, in this view, is essentially propositional. It lives in sentences. It can be written down.

Polanyi thought this was fundamentally wrong.

And he spent the better part of two decades building the argument against it.

His most concentrated statement of that argument came in 1966 in a slim book called The Tacit Dimension. The book opens with a single sentence that contains the entire problem: “We can know more than we can tell.”

It sounds simple. It is not.

What Polanyi Actually Meant

Polanyi’s argument begins with perception, the most basic act of knowing something.

Consider how you recognise a face. You can look at a photograph of someone you know and identify them instantly, across years, across changes in weight and hair and age.

You are doing something genuinely sophisticated – processing a complex pattern and matching it against memory with remarkable reliability. But you wouldn’t be able to explain ( which features you used, how you weighted them, what the decision rule was) exactly how you did it to someone. Not because the knowledge isn’t there.

Because the knowledge doesn’t exist in a form that can be stated.

Polanyi called this tacit knowledge or knowledge that we hold and use reliably but cannot fully articulate. He distinguished it from explicit knowledge i.e. knowledge that can be stated, written down, and transmitted through language and instruction.

The distinction sounds straightforward but its implications are radical. Because Polanyi’s claim was not just that some knowledge happens to be tacit. His claim was that tacit knowledge is foundational.

He believed that all explicit knowledge rests on a substrate of tacit knowledge that can never be fully surfaced. You cannot make everything explicit because the act of making something explicit always relies on tacit capacities that are doing the work underneath.

He illustrated this with what he called the subsidiary-focal distinction. When you use a hammer, your attention is focused on the nail. The hammer itself (its weight, its balance, the feel of it in your hand) is present to you, but subsidiarily – you are not focusing on it. You are focusing through it. If you shift your attention to the hammer itself, you lose your grip on the task. The tacit knowledge that makes you competent with the tool only functions when it stays tacit.

This is why expertise is so hard to teach and so hard to transfer.

The expert is not withholding anything. They are focusing through their knowledge, not on it. Asking them to articulate it is like asking them to stare at the hammer instead of the nail. The act of articulation disrupts the very thing you are trying to capture.

The Iceberg

The most useful way to think about expertise is as an iceberg.

Above the surface sits explicit knowledge, everything that can be stated, taught, written down, encoded in manuals and training programs and textbooks. This is the knowledge that moves easily. You can put it in a document and send it across the world. It survives the death of the person who held it. It can be transmitted to ten people as easily as to one.

Below the surface sits tacit knowledge. It’s vastly larger, and entirely invisible from above. This is the knowledge that makes the difference between someone who knows the rules and someone who can actually perform.

It includes:

Perceptual knowledge: the ability to notice what matters. The experienced radiologist who sees something in a scan that a resident misses. The fund manager who reads a room full of executives and knows within minutes whether the business is actually healthy. They are perceiving things that are genuinely there, but their perception has been trained by years of experience into a sensitivity that cannot be directly transmitted.

Procedural knowledge: knowing how, as distinct from knowing that. You can read every book ever written about riding a bicycle without being able to ride one. The knowledge of how to ride lives in the body, in the calibration of balance and response that only practice builds. Professional skills work the same way. The senior copywriter who reads a brief and knows immediately what angle to take is not applying a rule. They are drawing on something built from thousands of briefs processed over years.

Contextual judgment: knowing when the rules apply and when they don’t. This is perhaps the most valuable and most elusive dimension of expertise. Textbooks describe how things work in general. Experts know how they work in this situation, with these constraints, given what happened last time. That situational sensitivity is almost impossible to encode because it is not a rule at all.

The knowledge of what to ignore is perhaps the least discussed but most practically important. Experts are not just better at processing relevant information. They are better at filtering out irrelevant information. They have learned, through experience, what doesn’t matter. That negative knowledge is as hard to transfer as the positive kind.

When Tacit Knowledge Is Lost

The organisational implications of tacit knowledge loss are severe and largely invisible until it is too late.

When an expert leaves an organisation what walks out the door is not just the explicit knowledge they held. That part, if the organisation was reasonably diligent, has probably been documented somewhere. What walks out the door is everything below the surface. The perceptual sensitivity built over decades. The contextual judgment that knew when the documented process didn’t apply. The feel for what mattered and what didn’t.

This loss is structurally invisible because explicit knowledge is easy to see and tacit knowledge is not.

Organisations inventory what they can measure. They document processes, capture decisions, build knowledge bases. And then they are surprised when the person who wrote the process document leaves and everything quietly starts going wrong gradually, in the accumulation of small decisions that the documentation doesn’t cover and the new person doesn’t know how to make.

NASA experienced this in one of its most documented forms. After the Apollo program ended in the early 1970s, the organisation went through waves of restructuring and downsizing.

When NASA began planning a return to the moon decades later, it discovered that significant tacit knowledge about how to build certain components had simply ceased to exist within the organisation. The documentation was there but embodied, practiced, judgment-laden knowledge was not.

This pattern repeats across industries and new graduates, however well trained, cannot replicate what the experienced staff did without being able to say why.

Why This Problem Is Acute Now

For most of the history of organisations, tacit knowledge loss was a serious but manageable problem. It was addressed, imperfectly, through apprenticeship and practice rather than instruction.

The medieval guild system was essentially a tacit knowledge transfer mechanism. So is the residency system in medicine or the partnership track in professional services. You spend years watching someone who knows what they’re doing, and eventually some of what they know moves into you.

Apprenticeship is slow and expensive. But it works, because tacit knowledge can be transferred through observation, practice under guidance, and through the accumulated experience of being in the room when an expert makes a hundred decisions and slowly developing a feel for why.

The agentic era has broken this in a specific and important way.

The promise of AI agents is that you can encode expert-level performance into a system and deploy it faster, cheaper, and more consistently than any human expert. The appeal is obvious. The problem is that the entire premise depends on being able to get the expertise into the system in the first place.

The real question is transferring expertise that is mostly tacit.

This means that most agent implementations are not actually encoding expertise. They are encoding the explicit layer – the documented processes, the stated rules, the guidebook version of how things work.

Most organisations have deployed that explicit layer at scale and called it an expert system.

What they have actually built is a very fast, very consistent, very scalable average.

It performs well on the cases that the explicit rules cover. It fails, sometimes catastrophically, on the cases that require the judgment, the contextual sensitivity, the feel for when the rules don’t apply that lives below the surface of what any expert can easily say.

The gap between a competent agent and an expert-level agent is almost entirely a tacit knowledge gap.

It is not a technology or a model problem. It is the same problem the surgical researchers hit in the 1970s, the same problem Feigenbaum hit sitting with chemists in the 1960s, the same problem Polanyi was describing in 1966.

We can know more than we can tell. And until you have a method for surfacing what can’t easily be told, you are building on the visible part of the iceberg and wondering why the system keeps running into things it didn’t see coming.

What This Means in Practice

Polanyi’s insight makes this problem legible. And this is where every solution begins.

If tacit knowledge cannot be extracted through direct questioning, it can be approached through other means. Through observation rather than interview. Through cases rather than principles. Through contrast rather than description. Through the careful, patient work of watching experts perform and finding ways to surface the knowledge they are focusing through rather than on.

That work has a name and a methodology.

It is the discipline of elicitation and it is where the practical response to the tacit knowledge problem lives.

But before elicitation can work, you have to understand what you are trying to elicit. You have to know that the knowledge you need is not sitting on the surface waiting to be asked for.

You have to know that the iceberg is mostly underwater, and that the part you can see is not representative of the part you can’t.

That understanding is what Polanyi gave us. And it is why, sixty years after he wrote it, his single sentence still contains everything you need to know about why this problem is hard.

We can know more than we can tell.