How I Think About Learning

The model of learning that most people carry — absorption of information that previously wasn't there — is accurate about what learning looks like from the outside and wrong about how it actually works.

Information doesn't go into an empty space. It goes into a model of how the world works. The model determines whether the information is absorbed, how it is interpreted, and whether it changes anything or simply reinforces what was already there. The model is the thing that matters. Information without a model to receive it doesn't stick.

This means that learning is fundamentally a model-updating activity, not an information-accumulation activity. And the strategies that work for information accumulation — reading more, attending more courses, consuming more content — are not the same as the strategies that update models.

What model-updating actually requires

A model updates when information arrives that doesn't fit it. Not information that confirms the model — that doesn't produce learning, it produces reassurance. Information that contradicts the model, that reveals a gap or an error, that cannot be easily absorbed without changing something.

This is uncomfortable. The discomfort is not incidental — it is the mechanism. If there's no friction, there's no update. Learning that is entirely comfortable is not changing anything.

The practical implication: deliberately seek out information that challenges your current model rather than information that confirms it. Read the people who disagree with you seriously, not to defeat them but to understand why a reasonable person would hold their view. Work in domains where your current model clearly fails. Spend time with people who think differently about things you care about.

This is not the learning posture that most people take. Most people seek confirmation, avoid contradiction, and learn primarily within their existing framework. The output is gradual refinement of what they already believe rather than the more significant updates that would come from genuine challenge.

The specific learning debt of early expertise

There is a pattern I have noticed in people who became expert in something relatively young: the models they formed early are deeply embedded and hard to update even when new evidence demands it. The earlier a model forms, the more of subsequent experience has been interpreted through it, and the more that subsequent experience appears to confirm it.

I know I have this problem. The models I formed from watching how things worked in Kalahandi — about how institutions behave, about what motivates people, about what causes failure — are deeply embedded. Most of what I have learned since is compatible with those models. Some things are not. The models that formed early are the hardest to examine, which makes them the most important to try to examine.

The practice I use: when I notice strong confidence in a conclusion, treat it as a flag. Not that I'm wrong — I might be right — but that the strength of the conviction warrants checking whether I've actually thought it through or whether I'm operating on a model that formed long ago and hasn't been revisited.

Learning from people vs. learning from content

Most of what I know that actually works in practice — in recruiting, in building businesses, in managing relationships — I did not learn from books or courses. I learned it from watching people do things well and badly, from being in the room when decisions were made and seeing what happened afterward, from making mistakes and noticing the pattern.

Content learning — books, articles, courses — provides frameworks and information that give structure to experience. But the structure only becomes useful when it connects to something I have actually lived. An abstract principle about how organisations fail means little to me until I have watched a specific organisation fail in a way that the principle explains. Then the principle becomes a tool I can use, rather than a sentence I can repeat.

This shapes how I approach learning deliberately. I try to read after doing, not before. After I have experienced something — a failed search, a difficult client situation, a decision that didn't work — I look for frameworks that explain it. The framework lands differently when you have the experience to anchor it to.

The role of teaching

I learn best when I have to explain. Not because teaching is a performance that requires mastery — I often teach things I understand poorly, and the process reveals the gaps. Teaching forces the externalization of a model that is usually tacit. You have to put into words what you know but haven't articulated, and the articulation reveals where the model is unclear, inconsistent, or incomplete.

This is one of the reasons I write. Not primarily to share — though sharing is useful — but to discover what I actually think. The writing forces a specificity that internal thought rarely demands.

What I am trying to learn right now

At this point in my life, the domains where I most need model updates are:

How organisations actually change. I understand why they fail to change (incumbents, inertia, information silos) better than I understand what makes the rare successes work. The successful change cases often look like luck from the outside, which means I haven't yet built a good model of the mechanism.

How to distinguish between genuine insight and pattern-matching that happens to have worked in the specific cases I've seen. A lot of what passes for expertise is extrapolation from a limited sample. I don't know how to systematically tell the difference between the two in my own thinking.

How to maintain the beginner's mind in domains where I have experience. The longer I work in a domain, the harder it is to see it fresh — which is a real loss, because fresh eyes catch things that expertise misses.

These are not resolved. I keep working at them.