Machine Hour · Skycatcher research · edition of October 8, 2026

Rise of the Machine Economy

The internet is gaining a second workforce. By the end of 2032, we expect AI agents to work at a rate above 7 trillion hours a year, roughly the scale of all paid human work in 2026.

We see a future where billions of AI agents work around the clock. By the end of 2032, we expect this new workforce to exceed 7 trillion working hours a year, surpassing all paid human work in 2026. Our Machine Economy scenario reaches 340 trillion hours a year, 49 times that scale. This is a once-in-a-generation transformation in how humans work.

We came to this view by putting agents to work ourselves. They read filings, write software, check results and keep research current. A question can become an ongoing research process. A small team can build software, operate it and improve it as needs change. One person can direct many streams of work at once.

We measure this activity in machine hours. One agent actively working for one hour counts as one machine hour. Agents working in parallel count separately; idle time does not. This measures the scale of activity. Its value depends on the quality of the results, the cost of human review and whether people continue to use it.

Our two scenarios explore how far this could go. The Agentic Workforce reaches 19 trillion machine hours a year in 2032. The Machine Economy, where agents do much more work without step-by-step direction, reaches 340 trillion—49 times our 2026 human-work benchmark. Both require rapid adoption. The larger case also requires major gains in computing capacity, efficiency and reliability. These are scenarios to test as evidence develops.

As useful work becomes cheaper, more becomes worth attempting. Research can cover more ground. Small teams can run services that once required large organizations. People can pursue ideas that were previously too expensive to build or maintain.

Every one of those services needs somewhere to run. Agents need access to information, permission to act and a reliable record of what happened. Their software needs to keep working after the conversation ends. This is the opportunity we study in the backend: the infrastructure that runs software and preserves its work.

That opportunity spans cloud platforms, established providers, open-source projects and new companies. More activity creates demand, but competition, falling prices and customer choices will determine how much becomes lasting revenue.

The longer-term vision is an internet that works on our behalf. People set the goals and limits. Agents assemble the software, coordinate work across clouds and devices, and keep services running. Building the shared infrastructure that makes this dependable could expand what individuals and small teams can accomplish. We see a future where far more of what people imagine becomes something they can put to work.

Skycatcher, October 8, 2026

In May 1995, Bill Gates described the shift ahead in The Internet Tidal Wave. His memo is a useful reference for the question we face now: what changes when the Internet gains a new source of work?

The Internet is a tidal wave. It changes the rules.

Bill Gates, internal Microsoft memo · May 26, 1995

Figure 0.1 sets the scale. Both lines are modeled annual rates of machine hours against all paid human work; the parts that follow carry the evidence behind them, and the model lets a reader draw a different case.

Figure 0.1. Machine hours a year: two scenarios against all paid human work. Modeled annual rates on a logarithmic scale. The Agentic Workforce reaches 19 trillion hours in 2032 and the Machine Economy 340 trillion, on the stated capacity assumptions. These are scenarios, not bounds. The human lines compare time, not productivity or jobs displaced. The model lets a reader draw their own case. Passing all paid human work requires about 2.86x a year from the 2026 anchor for six years; the Agentic Workforce path grows 3.38x a year and the Machine Economy 5.46x. Figure 1.9 carries the test.

Figure 0.2. Revenue at four companies under two scenarios. The four-company scenarios reach $71 billion and $124 billion of annual revenue in 2032. Most of the near-term difference from consensus comes from our existing-business growth assumptions. The comparison uses analyst consensus through 2029 and our extension after that, with growth fading to 12% by 2032. The model also includes slower adoption at $47 billion and limited provider revenue at $59 billion in 2032. All four are available in the scenario controls. About nine tenths of the 2029 revenue above consensus comes from the existing-business growth assumption. Agent revenue matters more later and follows delivered hours. The 340-trillion-hour case needs nearly all the capacity in the post-2029 build; Appendix A3 sets out those assumptions.

More work becomes worth attempting

Agents can choose steps, use tools and check a result. For selected coding tasks, we estimate about $1 of model use against roughly $200 of professional time, before review and other costs. Review, integration and operating costs remain. The larger effect is what becomes worth attempting: a report becomes a monitor, or a checklist becomes a process that runs. Part One explains the demand case.

Counting the work

Active agents multiplied by hours per agent gives machine hours. In our scenarios, the average reaches about three hours a day in the Agentic Workforce and 13.6 in the Machine Economy. The difference rests mainly on autonomous work, which remains the least proven assumption. Machine hours pass the seven-trillion-hour human benchmark in 2032 and 2030 respectively. Part Two opens the calculation.

Where the work runs

We map the stack of machine labor in five layers, from hardware to applications. The backend is State (data and memory) and Work (execution and control). Agents need both. Our public-company universe follows thirty-two providers, while the wider research includes clouds, private companies and open-source alternatives. Competition, pricing and customer choices determine who earns lasting revenue.

Cloudflare, MongoDB, Snowflake and Datadog give us four detailed examples of how work reaches a bill. Their combined revenue growth rose from about 25% in the first quarter of 2025 to about 34% on the latest reported quarters, to July 31, 2026, the baseline for bet B11. Their AI disclosures offer further evidence, with different definitions. The timing does not tell us how much growth came from agents. Part Three follows the activity into the companies’ reported results.

Market expectations: a separate test

The research baskets show the hardware build being rewarded ahead of much of the backend. At September 30, 2026, Computation was up about twelvefold from January 2022. The backend basket, which includes Palantir, was up about 99%, against 84% for the Nasdaq 100; Palantir accounts for much of that gain. These are hypothetical baskets selected in 2026 and applied to earlier prices. They provide context for expectations, not a record of investment returns.

We believe the order of events is what matters. Through 2029, our financial scenarios depend mostly on stronger growth in the existing businesses. Later, agents become a larger source of revenue. From $12.7 billion in 2025, the four-company scenarios reach $71 billion and $124 billion in 2032, against about $52 billion in the extended consensus case shown in Figure 0.2. These outcomes depend on capacity, useful demand, provider share and pricing. More activity can coexist with lower provider revenue if customers choose other systems or prices fall faster. Part Four tests the financial paths and sets out what would change our view.

Figure 0.3. The layers of the stack since January 3, 2022. Daily price record. The equal-weight baskets were defined in 2026 and applied back to 2022, introducing hindsight. They are hypothetical and exclude fees and costs. This is also Figure 4.1 and the tracker’s index chart.

Our dated market predictions remain on the record alongside the operating tests. From October 1, 2026, we expect the backend basket to outperform Computation over one year and lead the listed comparison groups over three. The tracker grades the result from that start date.

Figure 0.4. Eleven bold bets on the record. Eleven supporting predictions, each with a measure and deadline, including an earlier machine-hour milestone for the end of 2027. The first grades fall in 2027. We publish misses alongside successes. See the tracker, Figure 4.8 and the full terms in Appendix D2.

In a personal post on August 19, 2026, Jerry Tworek, formerly a research vice president at OpenAI, reduced the future of computing to databases, neural networks and chips. It is a useful way to frame the question: as models do more work, what systems must keep that work running and preserve its results?

We believe machine work can become one of the largest new sources of software demand since the web. Machine Hour follows that possibility from the work agents perform to the infrastructure it requires. The thesis states our view, the model shows what it takes, and the tracker tells us where the evidence leads.

This website is research commentary from Skycatcher, published to share an idea for general research and discussion: not an offer, a recommendation or investment advice, it does not consider individual circumstances, and it is not intended for distribution where that would be unlawful. The indices are hypothetical and not investable, and the scenario figures are our estimates, not company guidance. Appendix D3 carries the disclosures.

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