Part Four: How we will test the thesis
Research commentary from Skycatcher, LLC, an exempt reporting adviser; not an offer, solicitation, recommendation or investment advice. The index returns in this part are equal-weight baskets defined in 2026 and applied back to 2022: hypothetical, hindsight-selected and not investable. The revenue paths and valuation arithmetic for Cloudflare, MongoDB, Snowflake and Datadog are our estimates and illustrations of the scenarios, not company guidance and not price targets, and the companies 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.
How we will test the thesis
The machine-hours prediction and the financial scenarios answer different questions. The first asks how much agents work. The second asks what selected companies earn from that work. A rising share price does not verify either one.
We begin with what markets have rewarded and what current prices assume. We then examine what our revenue scenarios require, how they could fail and the evidence that would settle the argument. Finally, we apply those tests company by company. Share-price performance has its own grading rule; it cannot substitute for operating evidence.
What has the market rewarded so far?
The market has rewarded much of the hardware and data-center build ahead of the backend. Our research baskets show that sequence. They are hypothetical baskets assembled in 2026 and applied to earlier prices, so they contain hindsight and do not represent an investable track record.
The backend’s average experience also hides large differences. Palantir materially changes the index result. Company size, revenue growth and valuation vary widely. The tracker shows those differences rather than asking one average to represent them all.
We compare price performance with reported revenue growth to see how market returns and business results differ.
Equal-weight, adjusted-price comparisons put the backend groups beside the Nasdaq 100 benchmark. The company roster was selected in 2026 and applied to history from January 2022, which introduces hindsight. The baskets are hypothetical and exclude fees, taxes and trading costs. Sources and method: Appendix C1.
The series compares trailing revenue growth for the covered companies. A quarter is shown only when at least 10 comparable companies report (50 for the Nasdaq 100), so coverage can differ from the full 32-company roster because not every company reports comparable quarterly figures. Company fiscal periods are not perfectly aligned. Sources and method: Appendix C1.
What do today’s prices assume?
The next question is what current prices require from the businesses. A revenue multiple compares a company’s value with its revenue. Growth, profitability, dilution and risk all influence it.
Our valuation tool asks what revenue growth could support a given multiple under stated cash-flow assumptions. The standard case holds growth for five years, then fades it toward 3%, with a 25% free-cash-flow margin and a 10% discount rate.
On those assumptions, the backend’s 6.6-times forward-revenue median at October 29, 2026, the latest close in the daily series, corresponds to roughly 15% annual growth for the first five years; the figure and this sentence update each trading day. Figure 4.3 shows the trailing-revenue multiple, a different basis. The four detailed case studies generally carried higher expectations. Each should be compared with its own scenario, on the same dates and revenue basis.
This is a sensitivity exercise. A multiple does not reveal a unique market forecast, and the result changes when margins or the discount rate change.
Median enterprise value to trailing revenue provides a common valuation reference for each group. Enterprise values and revenue inputs retain their dates and coverage. The median describes the selected companies; it does not adjust for every difference in margins, growth, dilution or business quality.
The reverse valuation calculation asks what revenue path supports the observed market value under the chosen margin, discount-rate and terminal-value assumptions. A different assumption changes the required growth. The result is a conditional calculation, not a market forecast or price target.
What must the businesses deliver?
Our four company cases make the revenue assumptions explicit. Together, the companies reported about $12.7 billion of combined revenue in the model’s 2025 base. Our scenarios reach approximately $40 billion to $49 billion in 2029, against the analyst-consensus snapshot of $32.7 billion registered on September 8, 2026; the October 7, 2026 consensus is $33.1 billion.
Most of that difference comes from the growth we assume for the existing businesses. Modeled agent revenue is only about 2% to 3% of the 2029 totals. The near-term forecast therefore depends on reported growth lasting longer or accelerating more than the consensus snapshot assumed.
Commitments and customer expansion offer evidence, but they do not secure several years of growth. We watch consumption, renewals and reported revenue each quarter. A renewed period of customer cost-cutting would weaken this part of the forecast long before the longer-term hours question is settled.
After 2029, the modeled agent contribution becomes much larger. By 2032 it is about a fifth of combined revenue in the Agentic Workforce and more than two fifths in the Machine Economy. That is when the financial outcome depends more heavily on agent hours arriving and reaching these providers.
Reported revenue, dated analyst consensus and the two Skycatcher scenarios are shown separately. The consensus extension after 2029 is our calculation, not a published analyst estimate. Each Skycatcher path combines existing-business growth with separately modeled agent revenue. The four companies do not represent the whole backend opportunity.
The separately modeled agent line contributes about 2.1% and 3.4% of combined revenue in 2029, rising to about 21.3% and 43.2% in 2032. Those shares belong to the Agentic Workforce and Machine Economy respectively. They are model outputs, not company-reported agent revenue. Existing-business revenue already includes some AI use. Sources and method: revenue model.
Can activity grow without much provider revenue?
Those revenue paths require more than agent adoption. The strongest commercial countercase accepts the machine-hours forecast. Agents work much more, but open-source systems, cloud bundles and internal software serve most of the growth. Prices also fall faster than our central assumptions.
We model that combination explicitly. With Machine Economy hours, flat provider share, steeper price declines and slower growth in the existing businesses, combined revenue is about $59 billion in 2032. Agent activity is enormous, while the modeled agent revenue at the four companies is only about $3 billion.
This is why the category view cannot be reduced to a claim about four stocks. New companies can succeed while established providers lose share. Customers may capture most of the economic benefit.
What would change our view
The risks span the whole chain, from repeat use to the cash providers keep.
- Repeat use remains low. Agents can finish impressive demonstrations but are rarely trusted with recurring work.
- Review remains too expensive. A low inference bill does not produce a low cost per accepted result.
- Capacity falls short. Power, hardware or efficiency improvements cannot support the hours demanded.
- New workloads choose other systems. Global activity rises while the providers we follow serve a smaller share.
- Prices fall faster than paid use grows. Customers do more work while spending less.
- Growth fails to become cash. Model costs, operating expenses or dilution absorb the revenue increase.
- The economic setting changes fundamentally. Our financial scenarios assume a functioning market economy and useful demand for the work performed.
The seven risks above, each paired with our counterpoint and the observation that would test it. A larger activity total is insufficient if work stays unreliable or providers fail to earn the expected revenue.
What evidence will settle the argument?
We turn these questions into dated predictions. The headline hours prediction sits alongside specific tests of capability, adoption, company revenue and market performance. Each has a baseline, a measure and a deadline.
The next readings concern adoption and company growth. We will publish outcomes against the original launch terms, whether predictions succeed or fail. A later forecast revision does not erase the original call.
We believe machine work can become a major source of infrastructure demand. The tracker is there to establish how quickly that is happening and who, if anyone, is earning durable revenue from it.
The register contains eleven supporting predictions with stated measures and deadlines, including a machine-hour milestone for the end of 2027. H1 separately records the main prediction that machine hours pass the human benchmark by the end of 2032. Original terms remain fixed. Outcomes are published whether the prediction succeeds or fails. Sources and method: Appendix D2.
How does the test differ by company?
The same tests can produce different answers for each provider. The company cards bring product exposure, reported evidence, scenario outcomes and the strongest countercase together. We judge each business on the paid work it serves and the evidence it reports.
The cases connect product exposure, reported financial evidence and conditional model outcomes. They illustrate how to investigate the revenue providers earn from the work. They do not establish a recommendation, a target price or that rising agent activity will favor every company equally.
The four cases illustrate the method. The wider opportunity includes new providers, open-source systems and cloud platforms. Part Five asks what they are building for agents and what a shared operating layer could make possible.