What More Intelligence Will Actually Do
In a recent essay, Noah Smith asks what economic transformation we should expect from increasingly capable artificial intelligence. He considers robots as a new form of ‘smart matter’, AI systems capable of extracting distributed knowledge from firms, and the possible discovery of complex predictive regularities which humans could use without understanding.
The essay contains several suggestive ideas, and Smith repeatedly qualifies the idea of intelligence as a single, indefinitely expandable capacity. But he never quite consolidates those qualifications into the conceptual shift they require. ‘Intelligence’ remains the rhetorical subject of the essay even as its explanatory work is increasingly divided among quite different machine capabilities: speed, memory, replication, sensor integration, pattern recognition and embodiment.
A better question is not what more intelligence will produce, but how increased cognitive capability changes the structure of tasks themselves.
Most economic tasks have a bounded competence window. A warehouse-sweeping robot (or human cleaner armed with a broom) must be intelligent enough to navigate the building, avoid obstacles and meet the required standard of cleanliness. Below that threshold lies failure. Within a certain range, greater competence improves performance. But beyond an upper bound, additional abstract intelligence has little purchase on the task. The machine or human employee cannot sweep a floor much better merely because the cleaner could also prove difficult theorems or intelligently discuss Proust. Much of the tragedy of today’s graduate precariat lies precisely here.
That does not mean intelligence has become economically useless. It means that its point of application must move. A more capable system might redesign the sweeping routes, alter cleaning schedules, identify the sources of dirt, change packaging methods or redesign the warehouse itself. Intelligence has saturated the task of sweeping - but not the larger system within which sweeping occurs.
This distinction applies throughout the economy. Every task has some minimum competence threshold, some range in which greater ability improves performance, and some saturation point beyond which it does not. But there is generally a higher-order possibility: that the task can be redefined, reorganised or abolished.
The economically decisive property of AI may lie less in performing existing tasks at superhuman levels, except perhaps in open-ended disciplines such as mathematics, science (and perhaps politics and warfighting), than in changing the character of the tasks themselves.
So advanced AI will add value by its ability to move tasks from one category to another: from judgement to procedure, from tacit knowledge to measurable signals, from too much awkward exception-handling to standard workflow, from human coordination to software, and finally from a task requiring intelligence to one embodied in infrastructure.
This is an old pattern. Evolution turns flexible generalists into niche specialists whose competence is embodied in anatomy and instinct. Skill acquisition turns conscious effort into automatic expertise. The beginning guitarist like me worries about finger placement, timing and pressure; the experienced player simply plays. Engineering turns intellectual solutions into machinery. Bureaucracy turns judgement into rules. Software turns human decision-making into repeatable operations.
Intelligence is often most valuable at the frontier where structure has not yet been settled. But once it succeeds, its achievement disappears into habit, organisation, machinery or code. What previously required thought becomes something the surrounding system itself enforces. Hence the old joke that AI denotes those research areas which have not yet been productised: few people now describe satellite navigation as artificial intelligence.
This suggests a more useful research programme than asking whether AI has become generally ‘smarter’ than human beings, a question too decontextualised to be generally useful.
Which economic activities have wide competence windows, and which saturate quickly? Which apparent cognitive limits belong to the task itself, and which are merely imposed by narrow job descriptions, bad software or institutional restrictions? Where can AI redesign the surrounding environment so that less intelligence is required locally? And where do conflicting goals, veto networks, politics, responsibility and human preference prevent the task from being compiled into procedure?
Coding is an obvious test case. Writing a routine function from a precise specification may have a fairly low upper competence bound. Once the code is correct, clear and efficient enough, additional brilliance adds little. But discovering requirements, choosing architectures, anticipating failure and deciding what should be built have much wider cognitive windows. As code generation becomes cheap, the economic centre of gravity moves towards specification, validation and system design.
The central insight is therefore almost the opposite of the usual superintelligence story:
The economic effect of intelligence lies less in performing tasks more intelligently than in reorganising the world so that intelligence is no longer needed to perform them.
A genuinely transformative AI economy may not look like a world in which every machine displays conspicuous brilliance. It may look like a world in which immense quantities of intelligence have vanished into the mundane structure of ordinary life.
This essay emerged from a discussion between GPT-5.6 in High mode and me. It began with my objection that Noah Smith had largely reified the concept of intelligence, and developed into a wider discussion of intelligence, instinct, consciousness and Peter Watts’s science-fiction novel Blindsight. GPT-5.6 produced the initial draft from that discussion, which I then revised.
