Back to deep divesJuly 28, 2026

Noah Smith: AI can transform productivity even if intelligence has diminishing returns

Jobs, GDP, And Economic Growth
World Models And Robotics
AI For Science

Thumbnail for Noah Smith: AI can transform productivity even if intelligence has diminishing returns — Image: What will more intelligence actually do for us?

In a Noahpinion essay, Noah Smith asks why rapidly improving AI has not yet produced an economic singularity. He examines possible limits to intelligence, then describes three computer-native sources of productivity: replicable machine cognition, captured tacit knowledge, and “cloud laws.”

AI capability has risen faster than visible economic changeSection 01

But looking around, not much has changed since we entered the intelligence explosion. There’s a huge data center boom, and most people use AI on a daily basis, but we still live basically the same lives — driving to work or taking the train, sitting in front of a computer, scrolling on our phones, collecting a paycheck.

AI can solve difficult mathematical problems, while jobs and daily routines look much as they did before the latest capability gains. Smith calls AGI or ASI already present, but says its arrival has been incremental.

Ruxandra Teslo points to governance and other frictions. Clifford Sosin argues that current systems are powerful tools whose intelligence did not unlock immediate control over physical and economic reality.

Intelligence may face diminishing returns from limited information and chaosSection 02

So although we don’t know yet, it’s possible that humans were already hitting the point of diminishing returns with regards to individual cognitive capacity, and that superintelligent machines will never be as far beyond us as we are beyond dogs. But even if that’s true, I can think of at least three reasons why machine superintelligence could still deliver huge productivity gains.

François Chollet describes intelligence as a bounded conversion ratio. Once a reasoner is near the optimum, each improvement adds less.

Smith gives two possible limits. Observations are finite, so a model cannot extract information that was never measured. Complex systems can also be chaotic: small measurement errors grow until reliable prediction becomes impossible. Arvind Narayanan, Sayash Kapoor, and Sosin make related arguments about irreducible error and the boundary between reasoning and new facts.

Replicable machine intelligence can turn more capital into smart matterSection 03

This doesn’t mean economic output will explode to infinity. But what it does mean is that humans will be able to use physical capital — GPUs and robots — to do more and more tasks at once, including many cognitive tasks that we used to do the hard way.

Machine cognition can be copied. More data centers can run more agents in parallel, while the supply of human minds depends on population. Robots extend that capacity into physical work, and remote compute can control machines at many scales.

Smith expects a higher capital-to-labor ratio. Each person could direct many intelligent machines without any one machine becoming incomparably smarter than a human.

AI could capture and spread distributed tacit knowledgeSection 04

AI will be able to synthesize all that information and very rapidly suggest small ways to improve the production process. Many of those little experiments will fail; others will succeed and will quickly be adopted, allowing another round of experimentation and improvement to begin very quickly.

Zeiss mirror production and Chinese rare-earth refining depend on thousands of small techniques spread across workers and organizations. Much of that knowledge is not written down or consciously understood by the people using it.

Smith proposes recording work through glasses, gloves, and plant sensors. AI could combine those observations, suggest process changes, and learn from the results. If the loop works, lagging firms could adopt better methods faster and frontier firms could shorten production experiments.

AI may exploit cloud laws that humans cannot simplify or communicateSection 05

Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate.

LLMs reproduce human language without giving researchers a simple theory of how language works. Smith calls similarly useful but hard-to-explain regularities “cloud laws.”

He suggests that AI might find them in social systems, plasma, materials, or turbulence. A machine can hold patterns that human teams must split into smaller pieces for communication. If those regularities exist, AI could make parts of nature controllable without first turning them into human-readable laws.