Part Three: The infrastructure behind the work
Research commentary from Skycatcher, LLC, an exempt reporting adviser; not an offer, solicitation, recommendation or investment advice. The receipts, list prices and revenue paths in this part are our illustrations of the scenarios on stated assumptions, not company guidance and not price targets, and the companies named have not reviewed them. Skycatcher, its affiliates, principals and clients may hold long or short positions in any public company named here and may trade them without notice. Appendix D3 carries the full disclosures.
The infrastructure behind the work
Agents create demand while they work—and through the software they leave running. A task can create a database, an application or a recurring process that remains in use after the agent stops.
Lasting revenue depends on continued use and payment. We follow three steps: more agent activity, more infrastructure used, and more revenue earned by providers. Each step needs its own evidence.
The opportunity spans cloud platforms, established providers, open-source systems and new companies. Customers can choose bundled services or build their own. The four company cases below show particular ways activity can become revenue.
The sizing figure at the end of this part compares roughly $190 billion in selected backend categories with the $25 trillion to $35 trillion paid for knowledge work each year. The categories have different scopes and may overlap; the wage comparison establishes scale, not an addressable market.
Five layers of the system
Computation supplies chips, servers, networks and energy. Intelligence supplies the models. State stores data and memory. Work runs and coordinates software, controls access and monitors activity. Applications deliver the result to the user.
We call State and Work the backend. The labels help organize the research; products often cross the boundaries.
A model can propose an action. The systems around it must decide whether the action is allowed, apply it consistently and preserve a record. Better databases do not make an agent’s reasoning correct. They help contain mistakes, inspect what happened and recover.
The five-layer stack of machine labor
Skycatcher research framework · October 1, 2026. Counts are public pure plays in the stack roster, Appendix C1; the hyperscalers are counted once.
Our research map follows five layers: State, Work, Applications, Intelligence and Computation. Companies can operate across several layers. The categories describe what is sold; they do not establish which businesses will capture the most value. Sources and method: Appendix C1.
Who serves the new demand
Our public research universe contains thirty-two backend companies. The wider map includes hardware, models and applications for context. The universe is a research scope, not a portfolio or a complete list of potential winners.
Cloudflare, MongoDB, Snowflake and Datadog are four detailed case studies. They let us examine different ways agent activity can become paid usage. The largest cloud platforms, private companies and open-source alternatives remain part of the opportunity even where disclosure makes them harder to model.
The distinction between State and Work is economic as well as technical. Stored data and deployed applications can persist after an agent finishes. Execution and monitoring tend to vary with activity. Both can generate recurring spending, but neither guarantees it.
Model providers, applications, cloud platforms and independent infrastructure companies can each serve part of an agent’s work. Integration may shift spending between them. The map shows possible relationships, not fixed winners or market shares. Sources and method: Appendix E.
State retains value. Work earns through use.
Skycatcher research framework · September 8, 2026.
State services store the information an agent needs and the results it creates. Work services run tasks, move data and monitor execution. Both may benefit from more activity, but the financial outcome depends on pricing, utilization and whether the customer pays for the additional use.
Thirty-two companies provide a broad view of the category. Revenue observations retain their reporting periods; market values retain their quote dates. The roster is a research sample, with businesses of different sizes and exposure to agents. It is not a portfolio or a measure of the entire backend market.
Four ways an hour reaches a bill
Cloudflare serves network requests and runs code. Additional activity can fit inside an existing allowance before it creates an additional charge.
MongoDB supplies database capacity. An agent’s extra reads and writes may cost the customer little until they require a larger cluster or more resources.
Snowflake charges for warehouse compute and related services. Cost depends on the queries, the time resources stay active and the use of AI services.
Datadog charges for infrastructure monitoring and selected volumes of logs, traces and other records. Customers can change how much they collect, retain and index.
These contracts explain why activity and revenue can move at different times. More calls do not become more revenue one for one. Spare capacity, caching, volume discounts and alternative products all affect the bill.
Four meters on the machine hour
List prices from each company’s published price list on September 29, 2026; per-action costs derived. Cloudflare is the public bellwether for edge execution; execution on general-purpose servers is billed mostly by hyperscalers and private runtimes and has no public bellwether here. Volumes and revenue per unit are derived from company disclosures and are approximate.
Cloudflare, MongoDB, Snowflake and Datadog connect activity to revenue through different contracts and meters. The examples use public list prices checked on September 29, 2026. Customer discounts, commitments, included use and product mix can make realized bills materially different.
What the companies have reported
At the October 7, 2026 reading, all four case studies grew revenue faster in their latest reported quarter than in the quarter before: approximately 36% at Cloudflare and Datadog, 35% at Snowflake and 30% at MongoDB.
Their AI disclosures are less comparable. Snowflake disclosed a $100 million AI revenue run rate in December 2025 and later discussed AI’s contribution to growth. Datadog has reported the contribution of customers whose businesses center on AI. MongoDB has described growing agent activity without providing a separate AI revenue figure. Cloudflare has reported rapid developer-platform and agent-traffic growth.
These are signs of demand, with different definitions. Revenue from an AI company is broader than revenue caused by agents. A count of customers using AI is broader still. We keep those distinctions in the tracker.
The counterevidence matters. On August 6, 2026, Datadog reported lower usage from its largest customer and included that reduction in its third-quarter and full-year guidance. The customers that adopt first can also be the first to build alternatives or negotiate lower prices.
Eight quarters of revenue sit alongside dated AI and agent disclosures. The financial series establishes company performance; the disclosures provide context. Their timing alone does not show how much growth came from agents.
Deferred revenue helps track customer commitments and revenue timing; the tracker shows deferred revenue alongside reported revenue growth. The series covers the broader business and is affected by contract length and billing terms. It is not a direct measurement of agent demand.
From working time to revenue
For each company, we estimate:
*Delivered machine hours × share using the provider × revenue per hour served.*
The share using a provider is an assumption. Most global agent hours do not reach any particular company in our four-company model. One hour can also use several providers, so their shares are not exclusive market shares.
Revenue per hour served depends on the workload and contract. Our illustrative bills range from fractions of a cent for some network or database uses to between $5 and $33 for a data-intensive warehouse workflow. They use published prices and stated activity assumptions, before negotiated discounts.
We then let the price per comparable unit fall as volume grows. In the published scenarios, a tenfold increase in volume leaves 50% to 65% of the earlier unit price, depending on company and scenario. These are assumptions to test, not measured laws.
Illustrative receipts show where a workload might create charges at the four companies. They use stated workload and price assumptions. They are not a production customer’s invoice, and they should not be summed into a universal cost per machine hour.
Illustrative revenue per hour using each provider, 2025–2032. Prices retain 50% to 65% of their earlier level after each tenfold increase in relevant volume; this is not an annual price decline. Method and assumptions: revenue model.
The opportunity beyond current software budgets
We use roughly $190 billion as an illustrative grouping of backend markets. Their scopes differ and may overlap, so this is not a precise total for our 32-company universe.
Databases and analytics make up the largest part: about $161 billion in Gartner’s 2026 database-management forecast, compared with a $119.7 billion market in 2024. The 2026 figure is a forecast, not reported revenue (Forecast: Database Management Systems, Worldwide, 2023 to 2029, 2025 Update, December 2025).
For Work, we estimate $25 billion to $30 billion for metered observability and security event management, combining Gartner’s observability-platform and security information and event management markets. We add $1 billion to $2 billion for agent runtime, our estimate informed by Menlo Ventures’ December 2025 figures of $750 million for agent platforms and $1.5 billion for AI infrastructure in 2025.
Agents could expand these categories by making more work affordable and creating software that would never have been built by hand.
Knowledge work pays roughly $25 trillion to $35 trillion a year, our estimate from the 2.4 trillion knowledge-work hours in Appendix A5 at $10 to $15 an hour, against about $58 trillion of all labor income (the ILO’s labor income share of 52.3% applied to the IMF’s 2024 world GDP). The sizing figure uses the upper end, which gives a sense of the economic activity involved. It is not a backend revenue forecast. Customers retain part of the benefit, useful work still requires review, and spending is divided among models, applications and infrastructure.
The financial model is narrower. It estimates the four case studies company by company. Their combined revenue reaches about $71 billion in the Agentic Workforce and $124 billion in the Machine Economy in 2032. Those totals include their existing businesses; modeled agent revenue contributes about $15 billion and $53 billion respectively.
We believe the category can become much larger. The work ahead is to establish how much activity becomes durable paid use, and where that spending lands.
The illustration compares up to $35 trillion of human labor value with about $190 billion of backend spending, on the definitions in the text as of September 2026. Each tier assumes agents take on the stated share of that work and that providers bill 10 to 20 cents per dollar of human work delivered; both are illustrative assumptions, not estimates. These pools overlap with other economic activity and are not directly interchangeable. The comparison frames the scale of the question; it is not a forecast that labor spending transfers to infrastructure providers.