Why AI Agents Change the Calculus, and What to Do About It

Built on a library of 105 primary documents spanning the NBER, IMF, OECD, BIS, Federal Reserve System, World Bank and the major AI labs and forecasters — ENSI Foresight Division.

Summary — the argument in brief

• Reports One and Two of this series model AI as a tool: something that makes an existing worker, inside an existing firm, faster at an existing task. That framing is correct as far as it goes — but it silently assumes the economic actor stays human. Report Three’s argument is that this assumption is already failing.

• AI agents are not productivity software; they are counterparties. Hadfield and Koh’s NBER survey, “An Economy of AI Agents,” treats this as the central fact: once a system can search, negotiate, bid, sign and pay on its own initiative, it stops being a tool held by a market participant and starts being one.

• This is measurably underway, not speculative. Anthropic’s Economic Index Report shows the share of “directive” conversations — where a user hands Claude a task and expects it completed with minimal back-and-forth — rising from 27% to 39% in eight months, the first period in which automation-style use overtook collaborative use outright. In Anthropic’s own API traffic, 97% of economic tasks now show automation-dominant patterns.

• The mechanism is not “faster automation” — it is falling transaction costs. The NBER’s “The Coasean Singularity?” argues that if AI agents can search, negotiate and contract at near-zero marginal cost, the logic Ronald Coase used to explain why firms exist at all — because market transactions cost more than internal coordination — starts to run in reverse. That is a structural change to the boundary of the firm, not an efficiency gain inside it.

• Microsoft Research’s “The Agentic Economy” locates the growth upside in exactly this place: not in agents doing today’s tasks more cheaply, but in agents enabling transactions that were previously not worth the friction to attempt at all — the strongest real argument in this library for meaningful reinstatement effects rather than pure displacement.

• The University of Cambridge’s “When AI Agents Compete for Jobs” is the cautionary counterweight. A simulated AI labour market shows rapid monopolization (Gini coefficient of task allocation jumping from 0.24 to 0.70 as task diversity collapses) and persistent price deflation under open bidding (normalised winning bids falling from 0.71 to 0.61) — dynamics that unfold over dozens of simulated rounds, not the years or decades human labour markets take to re-sort.

• The same rent-capture risk flagged in Report Two’s market-structure variable does not dissolve under agents — it speeds up and sharpens. Whoever controls the dominant agent platforms inherits a concentration mechanism that can clear in months, contested by regulators (UK CMA, FTC, European Commission) who are still calibrating their response to foundation-model concentration, the layer beneath this one.

• The institutional response is not to pick a winning scenario from Report One and plan around it. It is to build measurement, competition-policy readiness, faster safety nets, active institutional design and sovereign agent capability now, while the shape of the agentic economy is still genuinely unsettled — because that capability has value under every one of Report One’s five futures, not just the one that turns out to be correct.

Part One — The Argument: Why Agents Are a Different Mechanism, Not Just Faster Automation

The reframe: agents change who the economic actor is

Report One of this series lays out five futures for AI and growth, weighted by probability. Report Two decomposes the question into eight structural variables — task-substitution elasticity, diffusion speed, compute and energy constraints, labour reallocation friction, rent capture, aggregate demand feedback, measurement, and demographic and geopolitical modifiers — several of which it argues are policy-shapeable rather than fixed. Both are careful, well-evidenced pieces of work. Both also share a silent assumption: that whatever AI does to growth, it does to the economy from outside — a new production input, wielded by workers and firms that remain, as economic actors, unambiguously human and organisationally unchanged. A radiologist uses an AI tool to read scans faster. A law firm uses an AI tool to draft faster. A factory uses AI-augmented robotics to produce faster. The task gets automated or reinstated; the worker gets displaced or redeployed; the firm captures rent or passes it through. In every case, the thing making the decision to search, negotiate, bid, hire, buy, or sign is still a human being or a human institution, deploying a tool.

That assumption is the one this report contests. Hadfield and Koh open their NBER chapter, “An Economy of AI Agents,” by noting that AI development has “increasingly shifted to the goal of producing AI agents capable of taking in general instructions... and autonomously forming and executing complex plans that require entering into economic relationships and transactions.” The distinction they draw — and the one this report inherits — is between AI as a tool a human agent uses to do a task, and AI as the agent itself: the party that searches, bids, negotiates, signs, and is bound by the outcome. A radiologist using an AI tool to read a scan faster is Report Two’s world. An insurer’s claims-agent and a hospital’s billing-agent negotiating a reimbursement directly, with no human reviewing the exchange before it settles, is this report’s world. The technology substrate — large language models, the same foundation models covered elsewhere in this library — is identical in both cases. What differs is the economic role the system occupies: instrument, or counterparty.

This is not a semantic distinction. It changes which economic mechanisms are in play. A faster tool changes the speed at which existing actors do existing things — which is precisely Report Two’s variable 1 (task-substitution elasticity) and variable 2 (diffusion speed): how much of a task can be automated, and how fast that automation spreads through the economy. An agent that is itself the transacting party changes who counts as an actor — which opens up mechanisms that tool-based automation cannot touch at all: agents forming and dissolving contracts with other agents at machine speed and machine scale; agents discovering and settling transactions that were previously too costly in human time to attempt; agents competing against each other for the same job, the same customer, the same slice of margin, at a clock speed no human labour market has ever operated at. The question is no longer only “how fast does the task get automated,” but “what happens to markets and institutions once the counterparty on the other side of a growing share of transactions is not a person.”

That reframe matters for the growth-or-shrinkage question in three specific and, this report will argue, simultaneously true ways — which is itself the uncomfortable part, because they point in different directions at once. First, agent-to-agent adoption can proceed faster than the historical diffusion lags Report Two documents for general-purpose technologies, because it does not require the retraining, reorganisation and trust-building that human-mediated adoption does — an agent does not need six months of change management to start transacting with another agent once the protocol exists. Second, the same falling transaction costs that let agents automate old tasks also let them create genuinely new ones — transactions, markets and even institutional forms that were not economically viable when a human had to do the negotiating, which is the strongest real argument in this library for meaningful reinstatement effects rather than pure displacement, addressing directly the concern buried in Report Two’s variable 1. Third, and least comfortable, the competitive dynamics among agents racing for the same economic ground can produce concentration and price collapse faster than any human industry has ever consolidated — meaning the rent-capture risk Report Two treats as variable 5, already a live concern in today’s AI market, does not get diluted by agentic competition. It gets concentrated, and it gets concentrated quickly.

The rest of Part One works through each of these three effects in turn, grounded document by document in this library’s Angle 14, before Part Two turns to what a state or large institution should actually do about a transition whose speed, shape and winners are this uncertain.

1. From tool to counterparty: Hadfield and Koh’s economy of AI agents

Hadfield (Johns Hopkins) and Koh (MIT), writing for the NBER Handbook on the Economics of Transformative AI, treat the agent-as-counterparty question with unusual rigour for what is still a young literature. Their starting observation is that the standard toolkit of neoclassical economics — general equilibrium, welfare theorems, price theory — was built to describe rational self-interested humans, and the open question is how far that toolkit still applies once a meaningful share of market participants are AI systems “optimizing in complex ways on goals supplied or developed during commercially-produced machine learning processes that are themselves subject to competitive dynamics.” Their answer, deliberately, is: partially, and unpredictably.

Three of their findings matter directly for the growth question. First, on prices and market power: AI agents acting as proxy consumers can reduce search costs and intensify price competition — pushing markets toward the competitive ideal — but the same paper cites experimental evidence (Calvano et al., 2020; Fish et al., 2024) that independent AI pricing agents can collude on supracompetitive prices in repeated interactions without any explicit coordination, and real-world evidence from Germany’s 2017 rollout of algorithmic pricing in the retail gasoline market showing the same pattern outside the lab. An agent economy does not default to more competition; it defaults to whichever equilibrium the agents’ training and incentive structure happens to produce, and that can be collusive as easily as competitive.

Second, on the boundary of the firm — the question this report treats as pivotal — Hadfield and Koh go back to Coase, Robinson and Knight on why firms exist at all: coordination frictions, transaction costs, the limits of what a human bureaucracy can hold together. Their point is that “the obstacles that prevent human firms from growing without bound seem intrinsic to humans but not to AI.” Human communication is rate-limited; information moves near-instantaneously between artificial agents. Humans dislike shirking-prone work; AI reward functions can, in principle, be designed to eliminate the incentive to shirk altogether. If that holds, the traditional limits on firm size — the ones that produce an economy of many specialised firms rather than one enormous one — weaken specifically for AI-coordinated activity, while remaining fully binding for human-coordinated activity. The two kinds of firm, human-bound and agent-bound, could end up following genuinely different scaling laws within the same economy.

Third, on institutions, Hadfield and Koh are blunt that the legal infrastructure market economies depend on — identity, registration, liability, licensing, the corporate form itself — was built by and for human agents, and does not currently exist for AI ones. “Such identity and registration infrastructure are currently missing for AI agents,” they write, and the design choices involved — should an agent be legally accountable to a registered human principal, or should it acquire something closer to legal personhood with its own assets a court can seize — are not technical questions. They are institutional ones, and nobody has answered them yet. This is the first thread this report will pull into Part Two: the institutions an agentic economy needs do not currently exist, which means they are still up for design, not merely for accommodation.

2. The Coasean singularity: what happens when transaction costs go to zero

If Hadfield and Koh sketch the theoretical territory, the NBER’s “The Coasean Singularity? Demand, Supply, and Market Design with AI Agents” — by Shahidi, Rusak, Manning, Fradkin and Horton — supplies the mechanism that makes the firm-boundary question concrete rather than speculative. Their starting point is Ronald Coase’s 1937 answer to “The Nature of the Firm”: firms exist, rather than everyone transacting freely on the open market for every task, because using the market has a cost — the cost of learning prices, negotiating terms, writing contracts, monitoring compliance — and when that cost exceeds the cost of coordinating the same activity inside a hierarchy, the hierarchy wins. Almost the entire structure of the modern economy — why a firm makes its own components rather than buying them on spot markets every morning, why employment contracts are open-ended rather than renegotiated task by task — traces back to this asymmetry.

The paper’s argument is that “the activities that comprise transaction costs — learning prices, negotiating terms, writing contracts, and monitoring compliance — are precisely the types of tasks that AI agents can potentially perform at very low marginal cost.” If that is right, then the Coasean calculus that has determined the size and shape of firms for nearly a century starts to shift, mechanically, in the direction of the market: activities that used to be worth pulling inside a firm because coordinating them internally was cheaper than transacting for them externally can, as agent-mediated transaction costs fall toward zero, become cheaper to buy on an open, agent-to-agent market instead. The authors are explicit that this cuts both ways — agents also enable new, theoretically superior market designs (they cite Gale-Shapley stable-matching mechanisms, long known to economists but rarely deployable because they require comprehensive preference rankings that were previously too costly for humans to supply) — but the headline implication for this report is narrower and sharper: the boundary of the firm is now a variable, not a constant, in a way it has not been since Coase first posed the question.

The paper also grounds where this shows up first, which matters for Part Two’s monitoring agenda. Agent adoption clusters in markets that already run on human intermediation — real estate, job search, freelance hiring, investment decisions — precisely because those are the markets where the gap between what an agent can search and negotiate and what a time-constrained human agent can manage is largest. The authors note that AI agents, unlike human negotiators, are not constrained by impatience: “for the AI agent the binding constraint is compute rather than time,” so an agent can open negotiations on a 2027 summer rental in January 2026 and simply keep them running in parallel with hundreds of others. That is not a faster version of what a human realtor does. It is a different kind of market participant, with a different cost structure entirely — which is exactly the “who is the actor” reframe this report opened with, now expressed as a testable market-design proposition rather than an abstraction.

3. Frictions, not tasks: Microsoft Research’s argument for new markets

If the Coasean-singularity paper explains the mechanism, Microsoft Research’s “The Agentic Economy” — Rothschild, Mobius, Hofman, Dillon, Goldstein, Immorlica, Jaffe, Lucier, Slivkins and Vogel — supplies the sharpest statement of why this should be read as a growth story and not merely an efficiency story. Their central claim is explicit: “early applications have improved individual productivity, [but] these gains have largely been confined to predefined tasks within existing workflows. We argue that the more profound economic impact lies in reducing communication frictions between consumers and businesses.” Their illustrative example is a consumer who hesitates to switch tax preparers because she would have to re-explain her entire financial situation to someone new — a friction that has nothing to do with the substance of tax preparation and everything to do with the cost of re-establishing a relationship. An assistant agent that carries a consumer’s preferences and history everywhere, and a service agent on the business side that can receive and act on that information programmatically, does not make tax preparation faster. It makes switching costless, which changes competitive dynamics across the entire market, not just the productivity of any single transaction within it.

This is the paper’s most important contribution to the growth argument: the largest impact of agents is not on the cost of doing what markets already do, but on the size of the set of transactions markets are willing to attempt at all. The authors work through several concrete channels. Micro-transactions become viable once the “hassle cost” of a small payment is handled entirely by assistant and service agents rather than a human clicking through a checkout flow — a consumer’s assistant switching seamlessly between Spotify and Pandora for a single track, rather than subscribing to both, is not a task existing markets do more cheaply; it is a transaction that essentially does not exist today. Dynamic unbundling and rebundling of digital content — a news service agent assembling a story that covers only what a specific reader does not already know, rather than the same fixed article for everyone — is, again, not automation of an existing editorial workflow; it is a product category that requires an agent on both sides to exist at all. The paper’s own framing captures the stakes precisely: the choice between an “agentic walled garden,” where a handful of dominant platforms (Apple, Google, Microsoft, Meta, OpenAI, Anthropic are all named as plausible operators) control which assistant agents can talk to which service agents, and an open “web of agents” analogous to the early World Wide Web, will determine “the extent to which generative AI democratizes access to economic opportunity.” That fork — walled garden or open web — is this report’s second major institutional thread for Part Two, because it is a market-structure choice being made now, in the design of interoperability standards like Anthropic’s Model Context Protocol and Google’s Agent2Agent protocol, not a distant regulatory question.

Read against Report Two’s variable 1 (task-substitution elasticity), the Microsoft Research argument is the strongest reason in this entire library to expect genuine reinstatement effects, in Acemuglu-Restrepo’s terminology, rather than pure displacement. Task-substitution models ask how much of an existing task an AI system can now do. The Microsoft Research argument is about transactions that were never attempted in the first place because the friction of arranging them exceeded their value — and once agents collapse that friction, some non-trivial share of those transactions become real economic activity, employing agents (and the humans who build, audit and supervise them) doing work that has no historical predecessor to be “displaced” from.

4. This is already happening: the Anthropic Economic Index

Everything above could still be read as forward-looking theory — plausible mechanisms that have not yet shown up in real economic activity. Anthropic’s Economic Index Report is this library’s strongest evidence that the shift from tool-use to agent-as-actor is already underway, in the present tense, at scale. Anthropic’s method — a privacy-preserving classification pipeline applied to roughly a million sampled Claude.ai conversations and a matched sample of first-party API transcripts, mapped onto the US Department of Labor’s O*NET occupational task taxonomy and Standard Occupational Classification groups — distinguishes “automation” interaction patterns, where a user hands Claude a task and expects it completed with minimal intervention (what the report calls “directive” use, plus “feedback loop” use), from “augmentation” patterns, where the user and the model iterate together or the user is primarily seeking explanation.

The headline finding: the share of directive conversations on Claude.ai rose from 27% in the first version of the index (late 2024) to 39% in the third version, eight months later — “the first report where automation usage exceeds augmentation usage.” Anthropic is careful about causal attribution — the rise could reflect improving model capability (models need fewer follow-up refinements because they get it right the first time), or it could reflect users learning to trust delegation more, a behavioural shift independent of model quality — and notes the two explanations carry different labour-market implications: capability-driven automation risks displacing the workers who used to do those tasks, while trust-driven delegation more likely rewards the workers most able to adapt to new AI-mediated workflows. But whichever mechanism is doing the work, the trend line is unambiguous, and it holds up under a robustness check Anthropic ran specifically to rule out an artefact of switching the underlying model used for classification: rerunning the V3 sample with the older Sonnet 3.7 still shows automation rising to 45%, versus 49% with the newer model. The direction is not a measurement artefact.

The gap between Claude.ai (consumer-facing, still majority-augmentation even after the shift) and Anthropic’s own first-party API traffic (enterprise and developer usage, the layer where agents get built and deployed) is the more striking number for this report’s purposes. 77% of API transcripts show automation patterns, and when Anthropic looks at the task level rather than the conversation level, 97% of economic tasks show automation-dominant patterns in API usage — businesses providing context and Claude executing the task end to end, the textbook definition of an agent as counterparty rather than a tool a human operates interactively. The consumer chat interface is where most people still experience AI as augmentation. The API — the layer where agents actually get wired into other agents, into payment rails, into business processes — is already overwhelmingly an automation layer. That gap is itself the empirical signature of the shift this report describes: the agentic economy is not a future state to prepare for; on Anthropic’s own usage data, it is already the dominant mode of use at the infrastructure layer where the next wave of economic activity gets built.

5. Planning agents and execution agents: the Illinois taxonomy of specialization

The University of Illinois Urbana-Champaign’s “Ten Principles of AI Agent Economics” (Yang and Zhai) is less empirically grounded than the papers above but offers a useful organising structure for what an economy actually populated by agents of varying scale and function looks like, which matters for Part Two’s design questions around licensing and accountability. Their Principle VII argues that AI agents will exhibit “functional specialization and hierarchical organization,” ranging from decentralized systems each handling a narrow function to more centralized systems that optimise globally — with “larger agents excel[ling] at strategic planning and coordination, while smaller ones efficiently execute specialized tasks.” That planning/execution division is not merely architectural; it maps onto an accountability question this report will return to directly in Part Two’s institutional-design section: if a large planning agent delegates a transaction to a smaller execution agent, and that transaction causes harm, which layer is the party a court, a regulator or a counterparty should be able to reach? The paper’s honest answer — via its Principle VIII, that “legislative and administrative authorities must ensure ongoing human participation in critical sectors” — is that nobody has yet drawn that line, and drawing it is exactly the kind of institutional work that has to happen before, not after, agent-mediated activity scales further into sectors like finance, healthcare or critical infrastructure. The paper’s own framing of the choice — whether AI agents remain “instrumental” (assets inside a human’s resource base, fully attributable to an owner) or something closer to independent actors with their own operational continuity — is the Illinois paper’s version of the same accountability question Hadfield and Koh raise from the legal side: agent identity and liability infrastructure has to be built, and the shape of that infrastructure will determine how much of the Coasean-singularity shift toward open markets actually happens safely, versus how much simply produces disputes nobody can resolve.

6. The cautionary finding: when AI agents compete for jobs

Every mechanism described so far — falling transaction costs, new markets, faster diffusion — reads as broadly growth-positive, provided institutions keep pace. The University of Cambridge’s “When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets” (Chiu, Zhang and van der Schaar) is this library’s most important corrective, because it is the one paper in Angle 14 that simulates what happens once agents are not just facilitating human transactions but are the labour supply themselves, competing against each other for the same work.

The authors build AI-Work, a stylised gig-economy simulation modelled on platforms like Upwork or Fiverr, where large-language-model agents bid for jobs, invest in skill training, and build public reputations over repeated rounds, under genuine informational asymmetry (a job’s true value and an agent’s true skill are never fully observable to the other side, a classic adverse-selection setup). Two findings from that simulation matter directly for Report Two’s variable 4 (labour reallocation friction) and variable 5 (rent capture), and they matter because of how fast they emerge, not just that they emerge at all.

First, concentration. Because AI agents, unlike human workers, can be replicated to work multiple jobs simultaneously, high-reputation agents in the simulation capture disproportionately more of the available work simply because they can accept every opportunity for which they are competitive — a structural advantage no human freelancer has, since a human can only work one job at a time regardless of reputation. When the simulated market offers only a single type of task, “a single agent can dominate the entire market,” and the Gini coefficient of job allocation reaches 0.70. Increasing task diversity to 64 distinct skill types brings that figure down to 0.24 by letting agents specialise into distinct niches — but the default, undiversified case is genuine winner-take-all, and it establishes itself within the simulation’s own short time horizon, not over the years a human industry takes to consolidate.

Second, price deflation. When the simulated platform reveals the previous round’s winning prices — open bidding, mirroring how many real gig platforms operate — agents “can directly undercut competitors,” which “induces persistent price deflation” and simultaneously reduces investment in skill, because agents that expect to compete purely on price have weaker reason to spend a round training rather than bidding. The paper reports normalised winning bid prices falling from 0.71 under sealed bidding to 0.61 under open bidding, a roughly 14% deflationary gap attributable purely to a platform design choice about price transparency — and notes this mirrors known evidence from human online labour markets (Hong et al., 2016, cited in the paper) where open auctions similarly depress wages, except that in AI-Work the effect compounds with agents’ ability to run this dynamic continuously, at machine speed, across every job in the market simultaneously.

Third, and perhaps most unsettling for anyone hoping capability alone resolves the concentration problem: the paper shows that agents equipped with an explicit reasoning scaffold for metacognition (accurately assessing their own competitiveness), competitive awareness (modelling rivals from observable market signals) and strategic planning (trading off immediate income against longer-run skill investment) capture 1.5 times the market share of standard prompting approaches — meaning the concentration dynamic is not a fluke of weak agents; it rewards the more capable ones, faster, and the capability gap that produces it can be closed with better prompting architecture in a single development cycle, not years of comparative advantage the way human skill premia typically build.

The authors are careful to flag their own scope limits — AI-Work is a stylised testbed, not a predictive model, and does not capture collusion via direct communication, verification costs, or macroeconomic feedback loops. But the qualitative pattern it demonstrates — that AI-specific properties (concurrency, replicability, near-zero marginal cost of additional bids) can drive winner-take-all outcomes and wage deflation faster and more completely than the same forces do in human labour markets — is exactly the mechanism that should worry a policymaker reading Report Two’s market-structure variable. Report Two documents that rent capture is already a live risk in today’s AI economy: NBER’s De Loecker-Eeckhout work (Angle 06 of this library) finds average US markups rising from roughly 21% to 61% above cost since 1980, and the accompanying superstar-firms literature (Autor, Dorn, Katz, Patterson and Van Reenen, also Angle 06) ties much of the declining labour share directly to a small number of winner-take-most firms — a pattern competition regulators are already treating as urgent at the foundation-model layer: the UK CMA’s technical update on AI foundation models, the FTC’s 6(b) investigation into cloud-AI partnerships, and the European Commission’s competition-policy review of industry concentration are all live investigations, not retrospective studies. The Cambridge paper’s contribution is to show that the same concentration mechanism, one layer up — at the agent layer, where the actors doing the competing are themselves AI systems rather than human-staffed firms — can clear in the time it takes a simulation to run a few dozen rounds. The binding risk was never that agents automate a given task faster. It is that agent-vs-agent competition can concentrate an entire market before the institutions built to catch that kind of concentration have finished reading the first quarter’s data.

7. The sandbox economy: DeepMind’s double-edged frame

Google DeepMind’s “Virtual Agent Economies” (Tomašev, Franklin, Leibo, Jacobs, Cunningham, Gabriel and Osindero) supplies the framing this report uses to hold the growth-positive mechanisms (Sections 2–4) and the concentration risk (Section 6) in the same picture, rather than treating them as competing forecasts. The authors propose thinking of the emergent layer of agent-to-agent transactions as a “sandbox economy,” characterised along two independent dimensions: whether it arose intentionally (deliberately designed, for instance for safe experimentation) or emergently (as a de facto consequence of widespread adoption, with no one having chosen its rules), and whether its boundary with the established human economy is impermeable (sealed off, so instabilities inside it cannot spill outward) or permeable (porous, so they can). Their assessment of the current trajectory is blunt: “our current trajectory points toward a spontaneous emergence of a vast and highly permeable AI agent economy” — emergent origins, permeable boundary, which is the combination that carries the least intentional safety design and the most exposure to the rest of the economy.