I have watched thousands of people learn.
Not in the passive sense of attending a lecture, but in the active sense of working through a problem they have not seen before. Forming a hypothesis. Testing it. Discovering they were wrong. Revising. Trying again.
There is something you notice, if you watch enough people learn, that is not captured in how we usually talk about intelligence.
The Story We Tell About Intelligence
The standard story about intelligence goes roughly like this.
Some people are good at mathematics and logical reasoning. Others are better at verbal things. Some people “get” technical concepts quickly. Others find them difficult. These differences are relatively stable — your capacity for technical thinking is something you have, like the colour of your eyes.
This story shapes how learners approach unfamiliar material. The person who was told at some point that they were “not a maths person” approaches a new quantitative concept not with curiosity but with pre-emptive defeat. They expect to fail before they begin.
And the tragedy is: this expectation is self-fulfilling. Not because they genuinely lack the capacity, but because the expectation changes their behaviour in a way that produces the predicted failure.
What I Actually See
After teaching thousands of learners in AI, machine learning and data science, I have come to believe the standard story is almost entirely wrong about most people.
What I actually see is this:
Lack of confidence masquerades as lack of ability. When a learner says “I’m not good at this,” what they almost always mean is “I have not yet had an experience in this domain that made me feel capable.” The incapacity is not cognitive. It is historical.
Most people can learn technically demanding material when given the right conditions. The conditions that matter are: a pace appropriate to where they currently are, problems that are approachable but genuinely challenging, and feedback that helps them understand their errors rather than just marking them wrong.
The moment of real learning feels like surprise. Not the mild surprise of getting an answer right, but the deeper surprise of suddenly seeing something you did not see before. When I ask learners to describe what learning actually feels like when it is working, they often use words like: I suddenly saw it, something clicked, it felt like a fog clearing.
Confidence compounds. A learner who successfully works through one challenging problem approaches the next one differently. The evidence of their own capability changes their internal model of what they can do. And that changed model makes them more likely to persist, which makes them more likely to succeed, which generates more evidence of capability.
This is why the moment of discovery matters so much. A learner who discovers something for themselves does not just gain knowledge. They gain a data point about their own capacity to think.
A Specific Memory
One interaction has stayed with me more than most.
A learner — a working professional, probably in her thirties — sent me a message after completing one of my courses. She wrote that she had spent her whole life believing she was simply not good at mathematics.
She described how this belief had shaped her choices. She had avoided quantitative courses. She had apologised for herself when numbers came up in professional contexts. She had come to accept it as a fixed fact about herself.
After the course, she said something had shifted. She was not suddenly a mathematician. But she had had the experience of working through problems she had previously considered beyond her, and of actually succeeding. She could feel that the boundary she had accepted was not real.
She ended by saying she had started teaching mathematics to her young niece — not because she had become an expert, but because she had developed enough confidence in her own thinking to trust herself to guide someone else.
That message changed how I think about education.
What This Means for How We Teach
If intelligence is more about confidence and accumulated experience than about fixed capacity, then the most important thing an educator can do is not to deliver information efficiently. It is to create conditions for genuine discovery.
The learner who discovers something has a different relationship to that knowledge than one who received it. They know, through direct experience, that their mind worked on something hard and found an answer. That experience is evidence they can draw on in every future encounter with something difficult.
Conversely, the learner who is always receiving pre-formed knowledge has no evidence that their own mind can do the work. Even if they can recall and apply what they were taught, they have no reason to trust themselves in the face of something new.
The Implication for AI and Education
I am often asked what I think about AI tutors and AI-based education.
My view is: it depends entirely on what model of intelligence the AI system is built on.
An AI that treats learning as information transfer — that answers every question immediately, that provides hints before the learner has struggled, that optimises for correct answers rather than productive struggle — will be efficient and useless in the way that bad lectures are useless.
An AI that understands that the goal is the development of a learner’s confidence and self-trust — that knows when to be quiet, that recognises the moment before discovery and does not disturb it, that celebrates the learner’s reasoning rather than just their answers — that AI would be genuinely valuable.
The technology to build the second kind of AI exists. What is needed is the right model of what learning actually is.
I write about education at the intersection of technology and human development. The Learning by Inventing section of this site explores these ideas in more depth.