A company can hire staff, outsource support, or call a model to handle the same customer ticket. The prices are easy to compare. The promises are less obvious. An employee can take on follow-up work within agreed limits; a model charge usually covers the service just supplied. When something goes wrong, who fixes it, and who pays for the extra work? Those questions tell us more about the purchase than the token price does.
Five quotes, five allocations of risk
Imagine a company preparing to handle 100,000 customer-service inquiries. Its finance lead receives five proposals: hire employees, contract an outsourced support desk, pay freelancers by the case, call a model directly, or buy an agent service priced per resolution. This is a thought experiment, not a description of a real company, and it does not assume which quote is cheapest.
The support manager cares about service quality; finance watches the cost. Employees want reliable pay and training, vendors need a profit, and customers want an answer that works. Each sees a different part of the job. The supplier knows its product, the buyer knows its business exceptions, and a customer may discover the problem remains only after the ticket has closed.
If inquiries are standardized, answers are clear, and mistakes are easy to reverse, model calls may be economical. If a refund dispute depends on years of history, customer trust, and internal permissions, a cheap automated answer may create expensive follow-up work. Both outcomes fit the same technology. Distinguishing them requires real contracts, repeat-ticket records, human takeover time, and compensation data.
Start by separating the billing unit from the promised delivery, then read how the contract assigns the risks it leaves open. Monthly software is still software, and piece-rate pay can exist within formal employment. Piecework, employment, high salaries, and tokens describe different things: a way to pay, an organizational relationship, a pay level, and a service measure. Treating them as four successive eras muddles the comparison.
One purchasing question applies to all of them: when the original plan fails, who must keep working, who may charge more, and who has the authority to change how the work is done?
Piecework: who defines a unit of work?
Frederick Taylor did not invent piece-rate pay. Shop Management discussed existing daily wages, piece rates, and bonuses. Taylor tried to combine time studies, standard tasks, and differential pay so managers could specify a reasonable output and method in advance. Its production gains are the reformer’s own reports, not independent experiments. See Taylor, Shop Management.
From Adam Smith’s division of labor to scientific management, organizations recorded and scheduled more production knowledge. The Wealth of Nations used a pin factory to explain how specialization can raise output. Taylor asked a further set of questions: how should a task be decomposed, how long should it take, and what should meeting the standard pay? See Smith, 1776, Book I, Chapter I.
Once a worker’s know-how becomes a standard procedure, a firm can use it to train newcomers, compare output, or renegotiate rates. The knowledge changes hands, and so does some bargaining power. A worker may earn more while losing control over the pace of work.
Taylor also described workers’ fear that piece rates would be cut. If today’s extra effort merely gives management the basis for raising tomorrow’s quota, holding back some capacity becomes rational. This is a commitment problem: management promises to share efficiency gains, but workers cannot know how long that promise will last. See the relevant discussion in Shop Management.
Breaking knowledge work into prompts, tool calls, tests, and retries is another way to set standards. The analogy goes only so far: machines have no living needs or labor rights, and generated text can be harder to inspect than a manufactured part. What carries over is the problem of unfinished work. Someone must handle what the standard leaves out.
Employment preserves room to adapt
If every task could be fully specified, inspected, and bought instantly, perhaps a company would need little more than a purchasing office. Ronald Coase’s 1937 answer was that markets themselves have costs: finding a counterparty, bargaining, contracting, and rearranging the agreement when conditions change. Coordination inside a firm has costs too; the boundary of the firm depends on their comparison. See Coase, “The Nature of the Firm”.
Herbert Simon gave employment a more formal treatment in 1951. Within an acceptable set of actions, an employee allows the employer to decide later which action will be required. This helps explain why firms pay for work that is not yet fully specified. It is not a complete legal definition of every employment contract, and it does not imply unlimited obedience. See Simon, “A Formal Theory of the Employment Relationship”.
A support team cannot know in January what the next product failure will be, or which departments a refund dispute will involve. Keeping an internal team lets a firm reassign work within agreed limits. It also means paying for training and for staff time when demand is low. Employees still face dismissal, performance reviews, and uncertain careers. A salary keeps some uncertainty inside the organization.
Fixed salaries can also be a response to complex cooperation. Sharing knowledge, spotting a problem early, or helping a colleague may be difficult to price separately yet decisive for team performance. Forcing each contribution into an individual piece rate leaves important work without an owner.
People also coordinate outside both spot markets and managerial commands, using repeated dealings, reputation, and reciprocity. Walter Powell’s work on network organizations examines these relationships. Trust in professional services, supplier partnerships, and research teams can coordinate what prices and hierarchy cannot. See Powell, 1990. Agent interfaces and logs may help such arrangements operate, but a communication protocol cannot manufacture trust, capacity to compensate, or a long-term commitment.
From Ford to Big Tech: what does high pay buy?
Why would a firm that constantly seeks lower costs ever raise wages? Ford’s five-dollar day in 1914 makes the question concrete. The Henry Ford’s collection note says the company had paid $2.34 for a nine-hour day and proposed five dollars for eight hours amid severe turnover on the assembly line. The new treatment was conditional and included investigations into private life. See the Ford archive, 1914.
Higher pay can reduce departures and training losses, helping a firm maintain steady production. That is a plausible efficiency-wage mechanism, but the episode does not prove that a raise inevitably increases output. High pay and strict control coexisted at Ford, and an income improvement did not improve every working condition.
A technology company may pay well for an engineer who knows a complex internal system because that knowledge is hard to find, or because replacing the engineer would delay a project. Equity and deferred bonuses add reasons to stay. Saying “talent is valuable” misses those costs and does little to explain why the same person earns different amounts at different firms.
A study using matched U.S. employer–employee data by Song and coauthors shows that differences between firms and the sorting of high-income workers matter for understanding inequality. The widening gap between firms is not entirely a larger firm-specific pay premium; workforce composition matters too. The paper is not a compensation survey of technology companies. See “Firming Up Inequality,” 2019, institutional summary. Understanding high pay requires looking at individual ability, firm resources, and the division of gains between them.
The same engineering judgment can affect far more revenue inside an organization with many users, proprietary data, and mature distribution. Compensation cannot therefore be explained only by counting individual actions. Yet excess corporate profit does not guarantee a proportional employee share; bargaining power, competition, and labor institutions still matter.
If agents make routine execution cheaper, people who recognize the problem and understand the business may become more valuable. If those abilities become reliably reproducible too, the premium may fall. To judge whether a high salary will last, first identify the scarce capability the employer is paying for.
Platform labor as an early digital piece-rate experiment
Before agents, online platforms had already divided large volumes of work into tradable units. An ILO survey of about 3,500 workers across five microtask platforms and 75 countries examined pay, task availability, rejections, and unpaid time spent looking for tasks. The posted unit price did not show all of the time needed to obtain the work. See ILO, 2018.
Platforms lower the cost of finding work and transacting across borders. They can also shape the labor process through task allocation, ratings, and account rules. Freedom to decide when to log on is separate from control over pricing, evaluation, and appeals. The platforms and populations in one study cannot represent every freelancer.
Replacing a fixed role with per-task purchases can reduce a company’s cost when demand is weak. The worker may then bear the waiting time between jobs, equipment costs, and skill maintenance. This may be a genuine efficiency gain or merely a transfer of volatility. To tell the difference, calculate job search, waiting, rework, and dispute time alongside pay for completed orders.
AI production still relies on data labeling, review, and other human work. The ILO’s discussion of “invisible labor” is a reason to inspect this supply chain. See Rani and Dhir, 2024. Their work concerns human inputs across the production chain; it does not claim a person is secretly producing each automated answer in real time. An automated interface alone reveals little about labor upstream.
What is left for people after the easy tickets are automated? The remaining work may be harder, more urgent, and less predictable. It could support skilled exception-handling roles or leave gig workers absorbing more variation. Comparing task difficulty, total hours, income volatility, and rejections before and after automation would help tell the difference.
The distance between a token bill and a useful result
A model token is a sequence unit used to represent model inputs and outputs, and often to meter charges. It is not a fixed number of words or a universal unit of computing across models. Vendor credits are another billing layer; blockchain assets are another category again. Sharing a name does not give them the same economic character.
At the article’s source cutoff, Anthropic documented separate rates for input, output, caching, and related categories. Salesforce’s Agentforce page displayed alternatives involving Flex Credits per action, sessions, and user licenses. See Anthropic’s pricing documentation and the Agentforce pricing page. These pages establish that pricing models coexist, not that one has proved most effective.
The analogy between tokens and an electricity meter helps separate inputs from outputs, but it soon breaks down. Electricity has a standardized physical unit; a token’s computational cost and capability depend on the model and context. Two systems can consume the same number of tokens and deliver very different quality. A better system may need fewer calls for the same task.
A wage supports a labor relationship between a person and an organization, including pay, work arrangements, rights, and obligations. A model fee pays a company for a service. Software can receive a budget and execution authority, but the name of an agent does not supply recoverable assets or a promise to compensate losses. Saying that a company “pays an AI a salary” can imply that it bought a responsible organizational actor when it only bought a service.
A more useful denominator is the full cost per verified useful outcome. For a common period and quality requirement:
Full cost per useful outcome = (model and tool charges for all attempts + allocated orchestration and infrastructure + human review and exception handling + governance expense + estimated error losses) ÷ number of verified useful outcomes.
Charges for retries belong in “all attempts” and should not be added twice. Fixed investments need a stated allocation period. If there are no useful outcomes, the measure has no meaningful finite unit cost. Large losses that have not yet occurred are often difficult to estimate reliably and should appear as separate stress scenarios, not a precise decimal that hides uncertainty.
A cheap model that needs repeated calls and frequent human takeover may cost more than an expensive but stable system. Conversely, an elaborate model may be used for a task that only needed a lookup table. Procurement should match task difficulty to required reliability, then compare the full process cost.
Usage pricing can reduce what customers pay for idle capacity, but the provider still has to provision compute. Prepayment and minimum-use clauses can return some of that risk to the buyer. Read the usage commitment as well as the unit price.
Outcome pricing still needs an evaluator
If tokens do not measure value, is direct outcome pricing better? The problem moves from “how much was used?” to “what counts as complete?” At the source cutoff, Fin’s documentation distinguished customer-confirmed resolutions from inferred resolutions where the customer did not seek further help. If the customer returned to the same conversation, the original charge could be reversed. Certain configured handoffs could also count as billable outcomes. The word outcome therefore does not remove the need to read its definition. See Fin’s official outcome rules, checked September 19, 2026.
A closed ticket may qualify for payment even though the customer still needs help. Neither party has to be acting in bad faith; they may simply mean different things by “complete.” The contract needs an observation period and rules for reversing outcomes and handling disputes.
Holmström and Milgrom’s theory of multitask principal–agent problems offers a useful lens. When a job has several dimensions, strong incentives on an easily measured indicator can crowd out quality that is hard to measure. A weaker incentive on one metric can sometimes be preferable. See Holmström and Milgrom, 1991. Applying that mechanism to agent procurement is an extension in this essay; the paper did not study generative AI.
Consider another thought experiment. A company pays per “resolved ticket.” The supplier can improve the knowledge base or alter closing and escalation rules. Customers know whether their problem recurs, but the buyer mainly sees logs. One path genuinely reduces repeat contacts and benefits both parties. Another uses a short observation window or difficult escalation to improve the metric while worsening the experience. These are possible incentive branches, not allegations about a particular vendor.
To distinguish them, examine recurrence, human transfers, independent samples, and refunds after disputes. Then ask: Who defines the outcome, who can reverse it, and whose costs absorb failure? A supplier that accepts more execution risk may charge a premium, narrow the scope, or retain exclusions. Outcome pricing does not provide every safeguard for free.
Will agents make firms smaller or larger?
Coase’s framework permits two opposite predictions. If agents reduce the cost of finding services, coordinating interfaces, and inspecting deliveries, small teams may purchase more capability from the market. If agents mainly reduce internal communication, documentation, and management costs, large firms may operate more lines of business within one organization.
The outcome depends on how much market purchasing costs fall compared with internal coordination costs. Clear tasks, independent acceptance tests, and replaceable suppliers favor outsourcing. Sensitive context, frequent changes, and costly mistakes give firms reasons to keep an internal team. Price, capability, and the actual contract still decide the case.
Headcount, business scale, and market power should be separated when discussing firm boundaries. Fewer employees do not necessarily mean fewer controlled assets, customers, or transactions. A company can become less labor-intensive and more concentrated at the same time. To decide whether it has really become smaller, examine headcount, revenue, outsourcing, and scope of control separately.
Protocol-based cooperation may lower switching costs only if identity, data, and work history are genuinely portable. If long-term memory, permissions, and evaluation are bound to one platform, a smooth interface can conceal how much must be rebuilt on exit. The number of open interfaces alone does not tell us whether leaving is easy.
Who receives the efficiency gains?
Efficiency gains do not automatically flow to any one party. They may appear as lower prices, better service, higher wages, or more free time. They may remain as corporate profit or platform fees. Technology influences how much new value exists; competition, bargaining, contracts, and institutions help determine its distribution.
Before putting an agent into a business process, test it on a bounded set of historical tickets or a limited live trial. Record the full cost and service quality. Set a spending limit and name the person who takes over exceptions. Those records are a better basis for changing roles or suppliers than the lowest token price multiplied by an ideal number of calls.
For HR teams and employees, changing tasks matter more than changing titles. Brynjolfsson, Li, and Raymond studied 5,172 customer-support agents after the introduction of an AI assistant. They reported an average increase of roughly 15 percent in issues resolved per hour, with larger gains among less experienced and lower-skilled workers. That is evidence of assistance in one setting. It does not establish that an autonomous agent can replace an entire job, much less determine a layoff rate. See “Generative AI at Work,” 2025.
If a company automates all junior tasks while still relying on experienced people for exceptions, where will future experts gain experience? AI may also help novices learn through explanation and feedback. A break in apprenticeship is therefore a risk to test, not a settled result. Track whether new workers can handle unfamiliar problems independently, whether they receive real feedback, and whether senior capability remains renewable over several years.
For flexible workers, an agent may let one professional deliver a project that once needed a small team. It may also depress the price of standardized services. People who retain the customer relationship, domain judgment, and delivery responsibility may face a different market from those selling generic actions. Measure income after subscriptions, customer acquisition, rework, and waiting—not a single impressive delivery speed.
For model and platform vendors, products, distribution, and credible delivery stand between input cost and customer willingness to pay. This essay’s working hypothesis is that tokens will remain a low-level cost measure while some customer contracts move toward service levels and outcomes. Work with weak verifiability or exploratory requirements may remain better suited to usage or subscription pricing. Long-run coexistence of several models fits the mechanisms above better than total replacement by one.
Easily reproduced capability may push down some service prices. Training, compute, and customer acquisition still cost money, so a cheap call need not mean a free service. How much of the saving reaches customers, workers, or platforms depends on competition and bargaining.
For society, task productivity, corporate profits, and public welfare must also be measured separately. David Autor’s historical discussion of automation emphasizes that technology replaces some tasks and complements others, while demand and job redesign shape employment. See Autor, 2015. If income increasingly comes from several projects and platforms, benefits and training may need to follow the person. Renaming workers “independent nodes” cannot resolve that institutional question.
What remains scarce when intelligence is commoditized?
Pieceworkers worry about rising quotas. Platform workers pay for time spent waiting for jobs. Firms buying agents have to handle the exceptions models leave behind. Each arrangement can leave work outside the quoted unit price.
Even if model calls keep getting cheaper, a firm must decide what to do, judge whether the result is trustworthy, and answer to customers when it fails. People with business knowledge, reliable data, and sound judgment may become more valuable. Their pay will still depend on how hard they are to replace and how much bargaining power they retain.
Return to the support purchase order. Alongside “price per case,” add a line explaining who handles a problem that remains unresolved and how that work is charged. Without it, the low-cost option may save money only on the quote.
Sources and further reading
The sources below are ordered by their role in the essay. Historical texts help explain mechanisms; surveys and empirical papers retain their sample limits; vendor pages document public pricing and outcome rules at the review date. All seven diagrams are original conceptual models and do not estimate measured costs or distribution shares.
- Adam Smith, 1776, An Inquiry into the Nature and Causes of the Wealth of Nations, Book I, Chapter I. Public text. The classic treatment of the division of labor.
- Frederick Winslow Taylor, Shop Management, first published in 1903; the linked electronic text identifies the 1911 edition. Full text. Time study, piece rates, and labor–management incentives; cases are the author’s own reports.
- Ronald H. Coase, 1937, “The Nature of the Firm,” Economica 4(16): 386–405. DOI. Market transactions compared with coordination inside firms.
- Herbert A. Simon, 1951, “A Formal Theory of the Employment Relationship,” Econometrica 19(3): 293–305. Paper. Authority and the acceptable set of actions in employment.
- Walter W. Powell, 1990, “Neither Market Nor Hierarchy: Network Forms of Organization,” Research in Organizational Behavior 12: 295–336. Author-hosted text. Relationships, reciprocity, and network coordination.
- The Henry Ford, collection note on the 1914 five-dollar day. Archive. Wage, turnover, and qualification context.
- Jae Song, David J. Price, Fatih Guvenen, Nicholas Bloom, and Till von Wachter, 2019, “Firming Up Inequality,” Quarterly Journal of Economics 134(1): 1–50. DOI; institutional summary. U.S. firm and worker sorting, not a technology-industry compensation census.
- ILO, 2018, Digital Labour Platforms and the Future of Work: Towards Decent Work in the Online World. Report. A multinational survey across five microtask platforms.
- Uma Rani and Rishabh Kumar Dhir, 2024, “The Artificial Intelligence Illusion: How Invisible Workers Fuel the ‘Automated’ Economy.” ILO article. Human labor in the AI supply chain.
- Anthropic, “Pricing.” Official documentation. Input, output, caching, and related categories; checked September 19, 2026.
- Salesforce, “Agentforce Pricing.” Official page. Action credits, sessions, and user licenses; checked September 19, 2026.
- Intercom, “Fin AI Agent Outcomes.” Official rules. Outcome definitions, inferred resolution, and subsequent reversals; checked September 19, 2026.
- Bengt Holmström and Paul Milgrom, 1991, “Multitask Principal–Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design,” Journal of Law, Economics, & Organization 7, special issue: 24–52. Journal page. Multidimensional tasks and incentive distortion.
- Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, 2025, “Generative AI at Work,” Quarterly Journal of Economics 140(2): 889–942. DOI; institutional summary. Productivity and heterogeneous effects of a customer-support assistant, using the journal version.
- David H. Autor, 2015, “Why Are There Still So Many Jobs? The History and Future of Workplace Automation,” Journal of Economic Perspectives 29(3): 3–30. Paper. Task replacement, complementarity, and labor-market adjustment.