AGI Economics: Coordination vs. Competence

Contents

Contents Jagged agents, mosaic organizations

How AI differs from humans

Competence sets the level but coordination sets the scale

The Four Regimes of AGI

Seeing the future

Oct 29, 2026 The Economics of Intelligence: How and when does AGI lead to economic transformation?

Smarter AI will increase productivity, but real economic transformation hinges on agents learning to coordinate.

This essay draws on the paper Organizing Intelligence which presents the full economic model and derives the results formally.

How do capability improvements transform the economy, and when? In this essay, we define thresholds of economic capabilities, e.g., minimal AGI, as a function of how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.

The last few years have seen exponential growth in the capabilities of machine intelligence. Every few months, we see a frontier AI model clear another milestone that seemed years away: writing production software, outperforming domain experts, and solving Millennium Prize problems in math. But looking around at the broader economy, it seems remarkably unchanged. Most people still go to recognizable jobs, headcounts look very similar to a few years ago, and aggregate productivity numbers have barely budged.

Part of the reason for this disconnect is that the individual AI agent is the wrong economic object. The vast majority of capability benchmarks measure individual AI agents solving problems in isolation, but economic transformation is an equilibrium outcome where organizations compete, subject to the forces of supply and demand. Workers do not operate in a vacuum. Organizations assemble people with complementary strengths and divide up tasks to create teams far more capable than any solo worker. Because existing benchmarks only test whether AI can outperform individuals, a model can exceed the performance of every person one-on-one and still lose to a well-structured team. This is why organizations have sometimes been referred to as a form of superintelligence.

At the same time, standard economic models would miss the transformational potential of AI because they still tend to model machine intelligence as previous technological innovations—either as a better tool in a worker’s hands or a machine that is swapped in for people one task at a time. General-purpose machine intelligence does not fit that mold due to two main features. First, while the supply of human workers is fixed, AI agents and their associated capabilities can be scaled on demand at the cost of compute. Second, the constraints that limit the size of human teams such as span of control, communication frictions, and misaligned incentives need not apply to machine agents. When they can eventually work directly with one another rather than sitting inside human hierarchies, we will be dealing with a genuinely new kind of economic input.

So, when does machine intelligence actually lead to economic transformation—and what capabilities need to be achieved in order to see the impact of AI on market structure, wages, and jobs?

To answer this question, we derive economic scenarios under different levels of AI capabilities. By examining how intelligence functions inside organizations, the answer to when the economy transforms depends on when two key capability thresholds are crossed:

Mapping where AI sits along both dimensions reveals distinct regimes of capabilities based on how they impact what the economy looks like, who gets employed, and how much they get paid. We term these regimes minimal AGI, supercompetent AI, supercooperative AI, and Artificial Super Intelligence (ASI). Tracking the transition between regimes shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet capture.

The vast majority of current benchmarks don’t attempt to measure coordination. They treat intelligence as an individual score rather than a jagged profile that can coordinate with complementary teammates.

To show why this is important, we gave 100 people a quiz spanning six domains: math, verbal reasoning, science, patterns, culture, and economics. Their performance in one domain turned out to be not very predictive of their performance in another. The population didn’t contain copies of the “average intelligence”; it contained specialists who were jagged along different directions (strong in some areas, weak in others).

For example, one person may be very good at many of the domains but abysmal at pattern matching; on the other hand, another participant could excel at pattern matching while showing middling performance on everything else. (Figure 1 shows data from six participants.)

Figure 1. Jagged human capabilities across six domains and organizational mosaic intelligence.

But workers almost never participate in consumer and enterprise markets in isolation. They are part of organizations that exist largely to do something really clever with humans’ inherent jaggedness: they combine complementary specialists so that the team’s capability profile covers ground no member covers alone. This is Ricardian comparative advantage operating inside the firm—the idea that people sort into jobs that they are relatively better at. Roles aren’t filled randomly, but structured to maximize each specialist’s comparative advantage.

The result is mosaic intelligence: the organization builds a collective capability that captures the strengths of every specialist while eliminating the gaps between all of them. By filling in the jaggedness across worker profiles, mosaic intelligence outperforms every individual in it. Figure 1 shows how this works in our data: the blue envelope represents the average ability of the six participants on the left, but the red (much larger) envelope corresponds to the intelligence of an organization that matches specialists to the skill that they are best at. When participants are paired to complement each other to solve a specific task, they outperform random pairs by 32%.

Now apply this logic to an entire team. Consider building and shipping software, which requires both strong coding and clear writing. Pairing specialists in both areas balances out individual weak spots. The resulting productivity gain means each coder and writer within the firm can earn more than they can on their own outside of it—not because they each become more capable in isolation, but because complementary specialists reinforce one another’s comparative advantage, allowing both to command higher wages. (The left panel of Figure 2 shows how this works with a simple numerical example.)

Organizations also unlock value for a third kind of worker: coordinators. Judged by any standalone single-agent benchmark, a pure coordinator looks like the weakest worker in the economy, average at coding and average at writing. Working by themselves, their economic output is negligible. Inside a team, however, a skilled coordinator eases handoff friction across colleagues, enables the team to grow larger without losing focus, and makes every coder and writer noticeably more productive. The coordinator allows the team to benefit from what each specialist individually excels at. In equilibrium, coordinators—or as they’re more commonly called, managers—earn some of the highest wages in the economy, not because they excel at a particular task, but because they maximize the output of the mosaic.

Now what about machine intelligence? Much has been said about the jaggedness of AI agents-–they can solve advanced math problems but stumble on what appear to be fairly easy problems. Human capability is uneven too, but while human intelligence is jagged in different directions, AI models are jagged in ways that are very similar to one another. We can add a complementary human specialist to a team to fill a gap, but spinning up another copy of a model only adds capacity without increasing breadth of capabilities.

A model whose capability profile outperforms the average of every individual human can therefore still lose in the competitive market to a team of jagged humans who reinforce one another, none of whom could best the AI on their own. Asking "is the model better than a human?" is therefore focusing on the wrong object of economic analysis. In a competitive market, the right question is whether an organization of AI agents can surpass an organization of humans, or a blend of humans and agents.

Figure 2. Mosaic intelligence across skills and organizational scale curves across firm sizes.

Beyond how capability profiles fit together, AI agents differ from human workers in two key ways that are key for understanding economic transformation: duplicability and coordination.

Human expertise is in fixed supply. Each person has a finite number of hours in a day and cannot be scaled at will. To expand, industries are required to hire further up the talent distribution, which inevitably drives up wages. This is the essence of what Ricardo (the same one as before) taught us: the highest economic returns accrue to those whose skills cannot be reproduced.

AI removes that scarcity bottleneck at scale. Once a model reaches a given capability level, it is available in any quantity at the rental rate of compute. This becomes the backbone of how wages change when AI is introduced into the economy: whenever a model can match human capability, human wages become tied to the cost of compute.

The second difference is coordination. As the Nobel-winning economist Ronald Coase famously asked: if organizing work inside a firm cuts costs, why isn’t all production carried out by one big firm? A century of organizational economics has answered Coase by pointing to human limits. Two colleagues working side by side hold the full design of a project in mind and resolve mismatches immediately. Expand that team to 20 or 200 people and attention starts to stretch thin, implicit assumptions drop out in handoffs between workers, and private career incentives create misalignment amongst employees. Much of human knowledge is tacit—captured in Polanyi’s Paradox of "we can know more than we can tell"—and passing work from one person to another compresses rich context into lossy summaries. Jacob Marschak and Roy Radner built an entire theory of teams around the information that is lost in communication alone.

History shows us what happens when there is a shift in coordination constraints. In the 19th century, the emergence of the telegraph and the railroad didn’t make individual workers better—they raised the speed and fidelity of handoffs, and that alone reorganized production into structures that had never existed. It was improvements in coordination technology rather than advances on individual skills that led to the rise of the modern firm.

Importantly, none of the constraints that limit human coordination have to apply to AI agents. Today’s AI systems coordinate poorly in practice. But because of their high communication bandwidth, their potential for coordination is immense. Copies of an agent share identical weights and representations. They can pass along a complete working state—including entire codebases, intermediate derivations, and full context windows—and execute a common objective without conflicting career concerns.

The limits that have historically bounded firm size stem from human bandwidth and misaligned incentives. In order to predict an AI-driven economic transformation, it’s critical to understand if these limits apply to agents.

Now imagine a market where firms assemble any workforce they like, hiring scarce humans at market wages and renting AI copies at the price of compute. Competition then selects organization structures that survive new entrants.

Any organization’s overall capability can be decomposed into the competence and coordination dimensions