A friend and I were talking about AI on the bus to last weekend’s football game and, as usual, ended up on the familiar questions. How good are these systems getting? Which jobs will go first? What happens if they eventually become better than us at most things?
As the bus wound its way through the inevitable traffic outside the stadium, we got onto a different question. If the cost of producing useful intelligence keeps falling, what happens when it becomes so cheap and readily available that we begin to treat it like a utility?
For most of human history, useful intelligence has been expensive. Diagnosing an illness, interpreting a contract, writing software or analysing a company usually requires somebody who has spent years learning how to do it. Societies responded by building institutions around that scarcity. Universities produced expertise, professions certified it and companies assembled it.
When you pay a good lawyer or engineer, you are not really buying an hour. You are buying a small slice of everything that lawyer or engineer has learned to make that hour useful.
The cost of intelligence is already falling quickly. When GPT-4 appeared in 2023, OpenAI charged $30 per million input tokens and $60 per million output tokens. More capable models now cost a fraction of that. The comparison is not perfectly like for like, but the direction is clear enough. More intelligence is being delivered for much less money.
Electricity offers a useful precedent. Before electrification, factories had to produce power close to where it was consumed. Electricity changed that. Once power became reliable and cheap, firms stopped designing around its scarcity and started assuming it would simply be there.
The important effect was not cheaper machinery. It was what followed. Elevators made tall buildings practical. Refrigeration transformed food distribution. Washing machines changed domestic life. Computing eventually grew on top of the same infrastructure. Once power was abundant, whole categories of activity that had previously made little sense became viable.
AI may do something similar to cognition.
There is a great deal of thinking that simply does not happen today because it costs more than it is worth. Companies do not investigate every small opportunity. Doctors cannot ask ten specialists to review every patient. Scientists cannot explore every plausible hypothesis. These limits are so familiar that we barely notice them.
However, lower the cost far enough and some of those limits start to disappear. Imagine every medical diagnosis being looked at from several directions. One system checks drug interactions. Another looks for unusual conditions. Another scans the latest research. A fourth is told to challenge the conclusions of the others.
That does not necessarily make the doctor redundant. It makes something that is currently exceptional, several expert opinions on one case, routine.
The same logic extends much further. A small business might have something resembling a lawyer, finance team, developer and market analyst available whenever it needs them. A student could keep asking for a different explanation until a difficult idea finally clicks. A scientist might explore a hundred possibilities before deciding which are worth testing in the real world.
This is why it is important not to get trapped solely in the immediate questions about AI: which jobs disappear, which tasks are automated, which professions are disrupted. We also need to think about what a post-AI economy looks like once intelligence is cheap and widely available.
History suggests that when one thing becomes plentiful, something else becomes scarce.
The internet gave us almost unlimited information and left us fighting for attention. Digital cameras made photographs virtually free without making everyone a good photographer.
If AI gives us almost unlimited analysis, then another analysis may not be worth very much.
A management team may soon be able to generate a hundred plausible strategies before breakfast. The difficult part will still be choosing one and living with the consequences.
The same is true of financial analysis. If models and reports can be produced almost endlessly, value shifts towards people who can explain what the numbers mean, spot dubious assumptions, understand the business behind them and help somebody decide what to do next. Good analysts may spend less time calculating and more time interpreting.
Producing an answer is also not the same as knowing that it is right. AI might produce a legal opinion in seconds, while somebody still has to decide whether they trust it enough to act on it. Lines of software can be generated quickly, but proving that the result is safe enough to run a bank is a very different problem.
So I do not think expertise simply disappears. It changes shape. Knowledge and analytical skill matter, but so do experience, credibility, judgement and responsibility. AI can make the first two much cheaper. The others are more stubborn.
This could alter the shape of companies too. Organisations exist partly because useful knowledge is scattered across people and coordinating those people is costly. If AI reduces the cost of both expertise and coordination, some activities that currently require a large organisation may no longer do so.
A small company may gain access to capabilities that once required entire departments. Large organisations will still enjoy advantages in capital, reputation, distribution, proprietary data and physical assets. But the ability to employ large numbers of clever people may no longer confer quite the same edge.
By the time my friend and I reached the football, fortunately, we had stopped talking about AI. Like it or not, football is already drenched in analysis: expected goals, tracking data, tactical models, performance statistics. AI will provide vastly more of it.
Yet it is hard to imagine that wealth of data making the match itself more meaningful for the fan. There is still only one game taking place. Nobody knows exactly what will happen. If your team misses a penalty, a computer can simulate a thousand versions in which it goes in, but none alters the afternoon you actually experienced.
Perhaps that hints at another consequence of abundance. Some things remain stubbornly scarce.
And this is where the electricity comparison breaks down.
Electricity is passive. It can power a television but does not tell you what to watch. Intelligence can. An AI helping with a decision can also choose which evidence to show, how to frame the question and which possibilities appear sensible.
That makes intelligence a peculiar sort of utility. We may come to depend on it while it is also helping to shape the way we understand the world.
Questions that sound technical today may therefore become important. Who owns these systems? What are they optimising for? How independent are two supposedly different opinions if both depend on the same underlying models? Who takes responsibility when everyone follows the answer because everyone assumes somebody else has checked it?
Many of our institutions were built for a world in which intelligence was difficult to find and expensive to organise. Schools, professions, companies and bureaucracies all reflect that scarcity in different ways.
AI will not sweep them away. But if competent intelligence becomes something we can summon almost without thinking about the cost, it would be surprising if they all continued to look much the same.
We spend an enormous amount of time debating how intelligent AI will become. I think we need to spend much more time thinking about the world that follows if intelligence becomes cheap and abundant.
Technology can change very quickly. Institutions, education, companies and social norms usually do not. If intelligence really is becoming a utility, the danger is not simply that we underestimate what AI can do. It is that the technology arrives long before we have worked out what we want to do with it.
