Artificial intelligence has moved from a market narrative to a measurable economic force, and the good news for entrepreneurs and investors is that you do not need proprietary data to track it. A 2026 note from staff at the Federal Reserve Board identifies publicly available series—corporate capital expenditures, national equipment investment data, and aggregate earnings measures—that together reveal how much of the "AI buildout" is real spending rather than sentiment. Fidelity analysts frame the same question as a bubble test: five directional indicators, from earnings growth to the interest-rate cycle, that show whether AI-driven valuations rest on genuine productivity gains.

Morgan Stanley's research institute goes further, calling AI "an industrial buildout" with nearly $3 trillion in infrastructure spending still ahead as of its March 2026 analysis. This article assembles those threads into a monitoring framework you can update from public filings and government statistics.

Tracking the AI Buildout: Infrastructure and Investment

The first category to monitor is the physical buildout itself. The Federal Reserve note measures capital expenditure as total purchases of "property and equipment" as reported on companies' 10-Q filings, tracking the major technology firms—including Oracle and Coreweave, the latter of which went public in the first quarter of 2025—through U.S. Securities and Exchange Commission data. This gives a direct, quarterly read on what hyperscalers are actually spending on AI-related hardware and data centers.

The scale is substantial. Morgan Stanley Research estimates $2.9 trillion in global capital expenditure on data centers between 2025 and 2028, of which roughly $1.4 trillion would be covered by hyperscaler cash flows, with the remainder drawn from roughly $200 billion in corporate debt issuance, $150 billion in securitized credit issuance, about $800 billion in potential private credit via asset-based finance and joint-venture debt funding, and roughly $350 billion in other capital, primarily private equity, venture capital, and sovereign funds. For an investor, the composition of that financing stack matters as much as its size: who is funding the buildout, and with what kind of money.

Two caveats from the Fed note deserve attention. First, because hyperscalers increasingly lease data center capacity rather than own it, headline capex figures could progressively understate total AI-related infrastructure investment. Second, broader national investment data—such as Bureau of Economic Analysis figures on equipment investment—includes substantial non-AI activity, so any AI-specific component must be approximated, for instance by extrapolating a pre-2023 baseline, and that approximation may become less reliable over time. The Fed's own suggestion is to triangulate across these investment indicators rather than rely on any single series.

One structural observation frames the whole category: as of 2026, the Fed staff note that financial markets have been highly responsive to changes in the AI narrative, while aggregate output and labor market data show limited signs of broad-based transformation. The economy is reorganizing around the technology, but the real effects are concentrated in certain areas. Watching whether that gap narrows is itself an indicator.

Corporate Financial Health: Earnings and Productivity Signals

The second category moves from spending to returns. Fidelity's analysts identify aggregate earnings growth and aggregate earnings quality as two of their five core indicators, arguing that these help distinguish genuine AI-driven value creation from speculative market movements. The backdrop is encouraging on the growth side: based on FactSet data cited by Fidelity, the S&P 500 appeared on track for its tenth consecutive quarter of earnings growth, with analysts expecting a third straight year of double-digit earnings acceleration in 2026 and contributions from all eleven sectors.

Morgan Stanley's research adds a monetization lens. In its most recent mapping of 3,600 stocks for AI exposure, 21% of S&P 500 companies mentioned at least one AI benefit, up from 10% in 2024. But the firm's caution is pointed: the market is not paying for "AI mentions" alone. Earnings leverage from AI is coming into view, which elevates the value of actual monetization over narrative. For a monitoring framework, that translates into a concrete question for each earnings season: are the companies claiming AI benefits converting them into revenue and margin, or only into conference-call language?

Fidelity's own position, stated early in 2026, is that AI remains a multiyear investment theme that should drive increases in productivity, efficiency, and profits—while acknowledging that the degree to which the technology will drive a structural break from historical economic trends remains difficult to quantify. That epistemic honesty is the right posture for a monitor: track the earnings data, but do not assume a regime change until the aggregates show it.

One financial-health signal deserves special weight. To date, according to Fidelity, companies have funded their AI-related capital expenditures almost entirely from earnings rather than debt—a sign that the buildout is likely not extreme and is not increasing the risk of systemic financial strain. If that funding mix shifts toward leverage, the risk profile of the entire buildout changes, which makes capex funding sources a leading indicator worth watching alongside the spending totals.

Market Valuation and Sustainability: Assessing AI's Price

The third category asks what the market is charging for all of this. Fidelity's analysts note that AI has helped drive valuations for both the S&P 500 and for technology stocks within the index above their historical averages—even though each remains below the extremes of the late-1990s dot-com bubble. That is the essential comparison for any bubble assessment: elevated, but not yet at the prior episode's peak excess.

Valuation monitoring connects directly back to the other categories. High valuations paired with weak earnings quality would suggest speculative behavior; sustained valuation premiums supported by improving aggregate earnings would be consistent with genuine economic transformation. Fidelity rounds out its five indicators with two more that condition the valuation picture: the affordability and sustainability of corporate capex spending, and the interest-rate cycle, which affects how long-duration growth expectations can be priced.

The question Fidelity reports as number one from its advisor clients in early 2026—whether increased AI investment is creating a bubble on par with the dot-com craze—cannot be answered by valuation alone. It requires the full framework: what is being built, whether it is being funded prudently, and whether earnings are following the spending.

AI Economic Impact Monitoring Framework

The table below consolidates the framework into a trackable reference. Each category maps a distinct theme of AI's economic influence to specific, publicly available data series, so you can update the picture quarterly from SEC filings, BEA statistics, and index-level earnings data rather than relying on headlines.

Five categories for monitoring AI's economic and market impact
Indicator Category Specific Indicator(s) What It Measures Primary Data Source(s)
AI Infrastructure Buildout Corporate capital expenditures (purchases of "property and equipment"); global data center capex and its funding mix Direct spending on AI-related hardware, software, and data centers—the tangible "buildout" phase; who is financing it (cash flows, debt, securitized credit, private capital) SEC 10-Q filings for major technology firms (including Oracle and Coreweave); Morgan Stanley Research estimates of 2025–2028 data center capex
Corporate Financial Health Aggregate earnings growth; aggregate earnings quality; share of S&P 500 companies reporting AI benefits Whether AI mentions convert into actual revenue, margin, and productivity gains rather than narrative alone FactSet earnings data; corporate financial reports; Morgan Stanley Research's mapping of 3,600 stocks for AI exposure
Capex Funding Sustainability Share of AI-related capex funded from earnings versus debt Whether the buildout risks systemic financial strain; a shift toward leverage would change the risk profile Corporate financial reports, as analyzed by Fidelity
Market Valuation S&P 500 and technology-stock valuations versus historical averages and late-1990s extremes The market's pricing of AI's future impact; elevated-but-below-dot-com levels signal premium pricing without prior-episode excess Index valuation data, as assessed by Fidelity
Macro Context Interest-rate cycle; gap between market responsiveness to AI narrative and aggregate output/labor market data Whether financing conditions support the buildout and whether broad economic transformation is appearing outside concentrated sectors Federal Reserve economic analysis; BEA national data (noting non-AI activity requires approximation, e.g., pre-2023 baseline extrapolation)

Two interpretive rules make the framework work. First, triangulate: the Fed's own guidance is that no single investment series captures the buildout, especially when leasing arrangements hide infrastructure spending from headline capex. Second, treat the earnings and valuation categories as a check on each other—monetization evidence should justify any valuation premium, and the funding mix should show the buildout is financed without building systemic risk.

Navigating AI's Economic Evolution

Entrepreneurs and investors should continuously monitor the identified indicators to adapt strategies, focusing on the tangible AI buildout and its financial implications rather than market sentiment alone. The framework above is deliberately built from public sources—SEC filings, BEA national accounts, index earnings data—so it can be refreshed each quarter without proprietary research. It is a monitoring tool, not a forecast: none of these indicators predicts short-term market performance, and this article is not financial advice. The analysis reflects publicly available data as of late 2026.

The concrete signal to watch for is this: sustained growth in corporate capital expenditures on data centers and AI-related equipment, coupled with improving aggregate earnings quality in AI-adopting sectors, would indicate a healthy, expanding AI economic impact. The reverse pairing—flattening capex, deteriorating earnings quality, or a funding shift from earnings toward debt—would be the early warning that the buildout and the narrative have diverged. Each earnings season and each BEA data release gives you a fresh reading on which of those two paths the economy is on.

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