When Intelligence and Coordination Costs Collapse Simultaneously
Two foundational economic inputs - the cost of generating cognitive output (i.e. intelligence) and the cost of coordinating exchange - are being compressed simultaneously and faster than output can adjust. This dual compression will generate a significant economic surplus that will be contested and unevenly distributed, and profoundly change how businesses operate and the way we work.
This dual compression, which we refer to as The Convergence Economy, will create structural economic shifts forcing us to rethink the foundational logic of the firm, the distribution of income, and the architecture of entire industries. The converegence will enable firms to deliver the same output at fraction of the cost and with a fraction of the workforce, resulting in large waves of displaced high-value knowledge workers with limited transition opportunities for the first time in the modern age.
We call this The Great Displacement
Unlike cyclical contractions or sector-specific disruptions, what is now underway involves the simultaneous and persistent compression of two foundational economic inputs: the cost of generating cognitive output and the cost of coordinating exchange.
The first is driven by the diffusion of general-purpose artificial intelligence. The second is driven by programmable settlement infrastructure — smart contracts, tokenized payment rails, and real-time clearing mechanisms — that materially reduces the friction of transacting across institutional, geographic, and temporal boundaries.
The resulting macro-institutional environment — what we've dubbed the "Convergence Economy" — demands an analytical framework calibrated to its specific structural properties, rather than one borrowed from prior periods of technological change.
The sectors where this contraction is most pronounced are those where production is information-intensive, output is digitally deliverable, and quality verification by counterparties is feasible without physical inspection. Professional services — legal, financial advisory, consulting, software, research, design — are the primary domain. They represent a significant and growing fraction of GDP in advanced economies and employ a disproportionate share of high-income workers.
The structural adjustment will unfold across four overlapping phases. We are currently at the boundary of Phases 1 and 2.
AI tools reduce internal costs ahead of competitive response. Headcount is drastically reduced and early adopters enjoy margin expansion.
A comparison of the current BLS data on U.S. job openings to corporate earnings/performance, as proxied by the S&P 500 index, is a simple way to gauge the impacts of AI on the labor market and validate the core tenets of this thesis. To see this analysis, click the button below.
"The surplus is real. The compression is underway. The question is who captures it."
This surplus formation model projects the annual structural surplus generated by AI-driven labor compression across the U.S. knowledge economy from 2024 to 2036. Adjust the sliders below to explore how changes in the wage base, raw compression rate, usable output fraction, and headcount elasticity alter the trajectory and magnitude of the surplus over time.
Once the structural surplus is generated, it must flow somewhere. This model illustrates three scenarios of how the surplus may be distributed across three cohorts — corporate profit, micro-enterprise income, and infrastructure rent. Select a scenario card below the chart to highlight its distribution profile and explore how the balance of economic power shifts under each regime.
This model operationalizes Figure 10 from the Full Paper. As intelligence and coordination costs compress, the expected firm boundary shifts from internalized production toward orchestrated external networks. Adjust the two decay rates to test faster or slower compression relative to the baseline path.
Each successive technology wave has been adopted faster than the last. Electrification took four decades to reshape manufacturing; generative AI reached widespread enterprise deployment in under three years. This rapid rate of technological diffusion and adoption means the structural window of time to adjust is shrinking — institutions have less time to adapt before competitive dynamics shift irreversibly.
Compression does not fall evenly. This heatmap scores six knowledge-intensive sectors across four exposure dimensions — task exposure, adoption speed, margin impact, and structural disruption. Sectors with high scores across all four dimensions face the most acute near-term transformation, while uneven profiles suggest more complex, phased adjustment paths.
The simultaneous compression in the costs of cognitive labor and coordination of exchange, and the resulting contested economic surplus, will bring about a difficult transition period for many.
Our Convergence Economy thesis presents an analytical framework to help people understand the magnitude and direction of the coming change, the mechanisms driving it, and the key parameters influencing decisions. Knowing these strategic imperatives will help when navigating this unprecedented transition.
Technology shapes the production frontier. Institutions, governance, and policy determine where on the distribution of outcomes the economy lands.
Governing this transition requires re-thinking policy, the social contract between governments and their citizens in addition to re-architecting the organization. We identify four policy levers that need to be considered.
Public investment in capable open models keeps frontier AI infrastructure broadly accessible, increasing competitive pressure on proprietary providers and reducing the share of surplus absorbed by infrastructure rent.
Portability requirements for fine-tuning data, system prompts, and workflow configurations reduce switching costs, prevent platform lock-in, and keep early capability advantages from becoming permanent structural barriers.
As knowledge work shifts toward independent operation, benefits must move with the worker through portable healthcare, retirement, and tax structures that cushion transitional income compression.
If infrastructure rent remains concentrated, governments may need targeted tax or utility-style frameworks that capture above-normal monopoly returns without suppressing normal returns on frontier innovation.
The central economic consequence of the Convergence Economy is the generation of surplus at a scale that is historically unusual. The surplus arises because two major cost inputs — cognitive labor and coordination overhead — are being reduced faster than output prices in competitive markets can adjust.
In a frictionless economy, this surplus would immediately dissipate through competition. In the actual economy, institutional rigidities, regulatory lags, first-mover advantages, and infrastructure concentration mean that surplus persists, at least in the medium term, before being competed away. The central question of the coming decade is not whether this surplus exists, but who captures it.