I want to begin with a thought experiment.
Imagine training a language model on all the text produced in England in the year 1800. The model learns language, facts, customs, arguments, newspapers, laws, philosophy and literature. It becomes very good at generating text that sounds like 1800.
Now ask it: what do you think about slavery?
The model will likely produce something that reflects the dominant views of the period. Because those views — repugnant to us now — were common sense in 1800. They were woven through the legal system, the economy, the literature, the science, the church sermons and the political speeches. They were what people said and wrote and assumed.
The people who argued against slavery in that era were, by definition, arguing against common sense. They were not extrapolating from dominant patterns. They were imagining something that had not yet existed.
What Language Models Are Good At
I do not want to minimise what language models can do. It is extraordinary.
They can write code, summarise research, answer questions across a remarkable range of domains, reason through problems, explain complex ideas clearly and translate between languages with impressive accuracy. They have absorbed an enormous amount of human knowledge and can deploy it flexibly.
This is genuinely useful. In my own work building Terno AI and teaching at CloudxLab, I see daily how much AI can accelerate work that previously required significant time and expertise.
But what a language model is doing, fundamentally, is pattern completion. It has learned an extraordinarily rich and nuanced model of what human expression looks like — what comes after what, what ideas tend to appear together, what kind of text fits what kind of context. It is, in a deep sense, a machine that has learned common sense from the accumulated record of human expression.
That is its power. But it is also a limit.
The Ideas That Changed Everything Were Not Common Sense
Let me give a few examples.
Gandhi’s ahimsa. Non-violent resistance as a political strategy was not an obvious extrapolation from the dominant models of political conflict in the early twentieth century. Most political conflict — including anti-colonial resistance — assumed that power required force. Gandhi’s insight was that there was a different kind of force. This was not common sense. It was moral and strategic imagination that went against almost everything the existing evidence suggested.
The abolition of slavery. The argument that people could not be owned as property ran directly against centuries of law, custom, religion, economics and received wisdom. The people who made this argument were not slightly adjusting the existing consensus. They were insisting on something that the world had not yet accepted.
The germ theory of disease. When Semmelweis argued that doctors were transmitting illness by not washing their hands, he was rejected by the medical establishment. The idea that invisible organisms caused disease contradicted what was considered obvious and was professionally dangerous to assert.
These were not insights that could have been generated by averaging existing knowledge. They required someone to step outside the prevailing pattern and see something that the pattern obscured.
AI and the Amplification of What Already Is
There is a specific risk that concerns me about AI systems that are primarily trained on what humans have already said and written.
Such systems are very good at reinforcing existing patterns. They are good at producing things that fit within the space of what has already been done. They are, by design, trained to be coherent with their training data.
This makes them invaluable for tasks where the goal is to produce something that fits a known pattern: code that follows conventions, text that sounds professional, summaries that capture the main points.
But if we ask them to help us think about questions where the right answer requires going beyond the existing pattern — questions about justice, about the structure of society, about what human flourishing could look like — we should be careful about taking their outputs as authoritative.
The AI trained on dominant patterns will tend to produce dominant-pattern answers.
What This Means for Human Beings
I do not think this is an argument against AI. It is an argument about how to use it.
The extraordinary capability of AI systems to handle patterns, to process information, to generate and refine text — all of this creates space for human beings to do something different. To do the things that pattern completion cannot do.
What are those things?
Questioning the assumptions that hold patterns in place. The person who says: wait, why do we assume that? Why does this seem obvious? What would the world look like if this were different?
Imagining what has not yet existed. Not extrapolating from data, but reaching for something that has not yet been instantiated — in technology, in art, in social organisation, in science.
Making moral judgements that go beyond what consensus permits. The recognition that something currently accepted is wrong — not because the data says so, but because something deeper says so.
These are not small things. They are perhaps the most distinctively human things.
And here is the interesting possibility: as AI takes over more and more of the work of pattern application — of doing what has already been done, of producing what fits existing templates — perhaps it frees more human capacity for the harder and stranger work of imagining what could be different.
The Question I Keep Returning To
I do not know whether AI systems will eventually be able to do this harder work. Perhaps sufficiently powerful systems, trained on sufficiently diverse data, will develop some capacity for genuine novelty.
But even if they can, the question I want to keep asking is: what should we be doing?
What is the version of human intellectual and creative life that is called forth by a world where the routine work of pattern application has been automated?
I think the answer involves taking more seriously — not less — the capacity to question, to imagine, to dissent, to dream.
AI can help us with what exists. We must remain responsible for what does not yet exist but should.
This essay is part of a series on AI and human thinking. If these ideas interest you, you might also enjoy my writing on Learning by Inventing — a related question about how discovery, rather than instruction, creates original thinkers.