Part One: A new source of work

A new source of work

Software has automated fixed processes for decades. Agents extend that ability: given a goal, they can choose steps, use tools, inspect the result and decide what to do next. A person can direct the task, or an event can start it under rules a person has set.

We call the resulting activity machine labor. It includes the attempts that fail. People remain responsible for the authority they give an agent and for deciding whether its work is good enough to use.

The change matters because software demand can grow beyond the number of people using it. One person can run several agents at once. A business can assign agents to processes that continue outside the working day. The limit becomes the supply of useful tasks, the cost of completing them and the infrastructure available to run them.

A person can use software directly, delegate a task to an agent, or set rules that let an event start the work. Each path uses infrastructure. The diagram shows how an agent can add steps and call other agents before the task is complete.

Cheaper work expands what gets attempted

For selected coding tasks, we estimate about $1 of model use against roughly $200 of professional time, before review and other costs. The comparison excludes review, retries, integration and the cost of running the finished software. It illustrates the change in direct production cost; it is not a measured reduction in the full cost of software development.

The more interesting effect is what becomes worth building. A spreadsheet can become a live dashboard. A weekly report can become a monitor. A checklist can become a process that carries out its own steps. People who could describe a problem but could not write software can now attempt a solution.

We expect much of the new work to come from tasks that were previously too expensive. An analyst can investigate more companies. A small business can test more ideas. A security team can examine more events. The outcome depends on whether the additional work is useful enough to justify its full cost.

Lower prices can expand use. Total spending rises only if the increase in use more than offsets the lower price. We think this is plausible in AI, but it will not happen automatically or equally in every market.

This illustration combines a 200-fold reduction in direct production cost with a 33-fold expansion in potential builders. Neither multiplier is a measured change in completed, useful software. The one-billion-person pool represents people who might build, not active developers or paying customers. Review, integration and operating costs still matter.

Count the time agents actually work

An agent that exists for a month may work for only a few minutes. An agent count therefore tells us little on its own.

A machine hour is one hour of active execution by one agent process. Ten agents working for an hour contribute ten machine hours. An agent waiting for an event contributes none. When a parent waits for a child agent, we count the child’s work without also counting the parent’s wait.

One instruction can produce many steps: retrieve information, call a model, query data, run a test, save a result and check it. The time spent on those steps is what we want to measure. A model call, a backend action and a completed task are different units.

Each hour also has a different infrastructure cost. Some agents run cheaply on a local machine. Others use a warehouse of business data or a large model. Part Three follows those differences into the bill.

An agent can plan a step, use a tool, inspect the result and continue. The loop may finish, fail or require human help. More steps create activity; accepted results establish usefulness.

Illustrative examples show work that could become economical as the cost of using agents falls. The task counts are assumptions. They explain the mechanism behind our demand thesis rather than measure adoption in 2026.

A single task can involve model calls, searches, code execution, database reads and stored results. The diagram is illustrative. Calls, tasks and machine hours are different units and should not be added together.

The evidence is strongest in software

Public GitHub activity gives us an early view. Merged code changes rose from roughly 3 million a month in 2023 to more than 18 million in September 2026. New repositories also grew sharply.

These are development events. A merged change does not establish AI authorship, quality or economic value. The series gives us a way to follow software production while better measures of agent use develop.

Company disclosures point in the same direction. Agents are writing code, creating databases and deploying applications. Other evidence is less encouraging: adoption remains uneven, review is costly, and some customers reduce usage after the first experiment. We keep both sides in the record.

Public GitHub merged pull requests rose from roughly 3 million a month in 2023 to more than 18 million in September 2026. Repository creation provides a second view of activity. These counts do not identify AI authorship or establish the quality, continued use or value of the software produced.

The record combines company adoption disclosures, pricing changes and capability tests with evidence of weak retention, incomplete deployment and costly review. Each observation carries its date and denominator. Salesforce’s conversation and action prices use different units; they do not establish a 95% price cut for the same task. METR (Model Evaluation and Threat Research) measures the horizon as the human time associated with tasks an agent completes at a stated success rate, not continuous autonomous runtime.

Two paths to 2032

Our scenarios differ mainly in adoption and autonomous work. Both include agents directed by people and agents that start tasks under standing instructions.

In the Agentic Workforce, adoption reaches 70% of knowledge workers by 2032, a base of about 1.2 billion people in our count (Appendix A5). Most agents remain human-directed, but the smaller autonomous group contributes a large share of the hours because it runs longer. About 17 billion agents work an average of three hours a day. That produces an annual rate of about 19 trillion machine hours.

In the Machine Economy, adoption reaches 95%. Businesses deploy many more autonomous agents, and a larger share works through most of the day. About 68 billion agents average 13.6 active hours a day. The annual rate reaches about 340 trillion hours, subject to the scenario’s compute assumptions.

Neither is a cautious case. From the 2026 starting point, the first requires machine hours to multiply about 3.4 times a year; the second requires about 5.5 times. Slower adoption, limited repeat use or inadequate compute can produce much smaller outcomes.

The Agentic Workforce reaches about 19 trillion annual hours in 2032; the Machine Economy reaches about 340 trillion under the stated capacity assumptions. Both are modeled scenarios. The two horizontal lines are all paid human work, seven trillion hours a year, and knowledge work, about 2.5 trillion. The vertical scale is logarithmic, so equal spacing represents equal multiples. Sources and method: machine hours model.

The prediction we are making

We estimate all paid human work in 2026 at about seven trillion hours a year. In our published scenarios, machine hours pass that benchmark in 2032 and 2030 respectively.

This is a comparison of time. A machine hour may contain a failed attempt, while a human hour may contain judgment an agent cannot provide. Passing the line would establish the scale of machine activity. Its usefulness and value must be measured separately.

Our central prediction is the crossing by the end of 2032. The much larger Machine Economy outcome is a stronger claim. It can fail even if the crossing occurs.

Annual growth required from the original 2026 forecast starting point through 2032. These are requirements of the forecast, not observed growth rates. The benchmark is seven trillion paid human working hours in 2026.