OpenAI Essay Argues Superintelligence's Real Job May Be the Boring Work

OpenAI has published the first essay in a series called "The Next Economy," written by Hemanth Asirvatham and Elliott Mokski, which argues that the most valuable use of highly capable AI may not be producing brilliant insights at all — it may be grinding through the coordination, paperwork and construction that turn insights into real things. The authors include a note stating the views are their own and not those of OpenAI or their colleagues. It is an argument about economics and bottlenecks rather than a product announcement, and it contains no model releases, benchmarks or timelines.

The framing matters for anyone building with AI, because it inverts a common assumption. If the authors are right, the scarce resource in an AI-saturated economy is not ideas or even taste, but the physical and institutional capacity to act on them.

The case that ideas already outrun execution

The essay opens with a gap: humans have modeled the universe back to its first moments but have never traveled past the Moon. The authors use that mismatch to set up two possibilities — either deep truths can be reached largely by thinking, or human minds have simply outpaced the ability to build what they imagine.

Their historical evidence leans toward the second. Galileo extended human sight with two lenses and a tube. Extending it again, they write, required the James Webb Space Telescope: a ten-billion-dollar observatory with eighteen mirror segments engineered to fifty-nanometer precision, built by three hundred organizations across fourteen countries and stationed a million miles away. The jump, in their telling, was not just in brainpower but in bureaucracy.

They cite research productivity work by Nick Bloom and coauthors: sustaining Moore's law now takes more than eighteen times the researchers it did in the early 1970s, economy-wide effective research effort has risen twenty-three-fold since the 1930s, and measured research productivity has fallen by a factor of forty-one. They add that the technician workforce is growing twice as fast as the scientist workforce, that the use of specialized scientific equipment doubled over four decades, and that a chip fab costs five times what it did thirty years ago.

These are secondary citations inside an essay, not new findings. Readers should treat them as the authors' selection of supporting evidence, and note that research-productivity measurement is contested terrain — "ideas are getting harder to find" is a well-known result, but what it implies about the future is not settled.

"Institutional intelligence" as the hidden input

The essay's central coinage is institutional intelligence: the unglamorous competence of execution, spread across laws, funding mechanisms, supply chains and support staff. The authors describe frontier intelligence and the capacity to realize its output as economic complements — more of one raises the value of the other. A better telescope makes a good question more valuable; a better question makes the telescope more valuable.

They push the point with a deliberately deflationary image: a civilization of a billion Einsteins would still need most of them mining quarries and doing accounting. Genius designs the monument; institutions lay the stone.

For product teams, this is a useful reframing of what agent systems are actually for. The commercial pitch for autonomous agents is usually framed around capability — can the model do the hard thing? The essay suggests the more durable value sits in reliability across long chains of mundane steps, because that is where ideas die.

The near term: execution gets cheap, taste gets scarce

The authors argue AI is already making execution less scarce — writing code, searching unfamiliar literature, turning a sketch into a prototype — so that work once requiring an organization can be pursued by one person. They point to filmmakers who never get a two-hundred-million-dollar budget and game designers who spend careers implementing other people's visions. The expected result is more idiosyncratic projects created outside traditional gatekeepers, with the scarce input shifting toward taste: deciding what is worth making.

That is the most immediately testable claim in the essay, and also the one most consistent with what founders report today. It is worth noting the authors offer no data for it — it is an inference, not a measurement.

Then comes the twist. As AI begins generating agendas rather than just labor — the authors say we may already be seeing this in domains such as math, without specifying results — the number of film concepts worth developing and hypotheses worth testing explodes. Good ideas would arrive faster than infrastructure can absorb them, leaving the world more execution-starved than before.

Depth versus width

The essay lays out two directional futures. A civilization of depth is one where superintelligence economizes radically on contact with reality: reasoning from principles, mining the enormous backlog of already-collected data, running hyperrealistic simulations and then taking only a few targeted measurements to settle the remaining uncertainty. The authors invoke Mendeleev predicting undiscovered elements and physicists expecting the Higgs boson decades before observation, plus a jigsaw-puzzle analogy in which a nearly complete science finds its last pieces easier than its middle ones.

A civilization of width is one where nature's complexity outruns any mind's ability to shortcut it. Their example is biology: simulation can now test drug candidates in silico, but the authors say those simulations are not faithful enough to replace large human trials, so more machine-generated candidates make empiricism a bigger bottleneck, not a smaller one. In that world, most machine intelligence gets deployed into logistics — automated labs, more factories, more energy — and a project like a Dyson sphere is mostly construction and coordination, bounded by the speed of light. They call such a civilization slow, with major changes possibly millennia apart.

The honest part of the essay is that it declines to pick. The authors say they do not know whether the complements to intelligence will grow, shrink or stay stable.

What the essay leaves out

This is advocacy-adjacent writing published on a platform run by a company whose business depends on enormous compute and infrastructure buildout. The conclusion that almost all machine intelligence should be deployed to "do the boring" is also, conveniently, an argument for scale. That does not make it wrong, but it deserves noting.

More substantively, an essay about machines absorbing institutional work says little about who controls those institutions, how accountability survives when bureaucratic judgment is automated, or what happens to the technician and coordination workforce the authors identify as the fastest-growing part of research. Safety, regulation and labor displacement are largely absent. The closing section defends human curiosity on three grounds — an obligation to keep seeking understanding, a possible comparative advantage in frontier thinking over routinized work, and the value of creative variety — but those are philosophical arguments, not policy.

What to watch

• Whether OpenAI's Next Economy series moves from essays toward empirical work, and whether the "independent voices" platform hosts arguments that cut against the company's commercial interests.
• Evidence for the authors' claim that AI is already producing original agendas in mathematics; the essay asserts it without citing specific results.
• Whether in-silico biology simulation closes the fidelity gap the essay describes, which is the single clearest test of depth versus width.
• Spending patterns at frontier labs: more investment in automated labs, robotics and energy would signal the width thesis; more investment in reasoning and simulation would signal depth.
• How agent products get marketed over the next year — as sources of insight, or as reliable executors of long, boring workflows.