AI Capex Tracker Excel: Hyperscaler Spending Watchlist for Q1 2026 Earnings

AI capex tracker excel is the cleanest way to compare what the hyperscalers are spending on artificial intelligence infrastructure with what they actually earn back from it. Microsoft, Alphabet, Meta, and Amazon are all reporting Q1 2026 results inside a tight late-April window, and the single most-watched line on every call is capital expenditure. Investors want to know whether the trillion-dollar build-out for AI compute, networking, power, and data centers is converting into revenue, free cash flow, and durable operating margins. A single spreadsheet that lines up capex against revenue, free cash flow, and the underlying valuation for every hyperscaler and every direct AI supplier makes those questions much easier to answer than reading ten transcripts and a dozen earnings decks in isolation.

This guide explains how to build that workbook, which verified MarketXLS formulas power it, and how to use the template for educational scenario analysis instead of stock recommendations. The companion files at the bottom of the post give you both a static reference workbook and a live-formula version that refreshes whenever you open it inside Excel with MarketXLS installed.

Here is the watchlist that the template is organized around.

Why an AI capex tracker excel matters in Q1 2026

Hyperscaler capital expenditure has become the most consequential number in the entire technology sector. The four largest hyperscalers (Microsoft, Alphabet, Meta, and Amazon) collectively guided to spending levels for 2026 that are several times larger than what the same companies were spending only three years ago. The capex line is no longer a footnote on the cash flow statement. It is the single largest swing factor for free cash flow, the largest ongoing call on the balance sheet, and the largest input into how investors think about the long-term return profile of the AI infrastructure cycle.

There are three reasons this matters more in Q1 2026 than in earlier quarters. First, the absolute size of the spend is now large enough to move free cash flow at the company level. When capex grows from 10 percent of revenue to closer to 20 percent in some cases, free cash flow yield compresses even if revenue is still growing nicely. Second, the market has become more impatient about the conversion from capex to AI revenue. Cloud divisions, AI services, and search products are being scrutinized to see whether the capital deployed in 2024 and 2025 is now showing up as billings, run-rate revenue, or margin expansion. Third, there is a growing awareness that capex commitments tend to be sticky. Once a hyperscaler signs power contracts, leases data center shells, and books custom silicon, those commitments do not flex with quarterly demand. That is what makes the capex line so important to monitor: it is a forward-looking statement about how confident management teams remain in the AI demand curve.

A spreadsheet helps because the relevant ratios are not reported in a single press release. To compare hyperscalers and suppliers you need to pull capex, revenue, free cash flow, operating income, and market capitalization side by side, then derive ratios like capex intensity (capex divided by revenue), free cash flow yield (free cash flow divided by market cap), and operating margin. A workbook that pulls those numbers automatically with MarketXLS formulas makes earnings season a repeatable process instead of a transcription exercise.

What this AI capex tracker excel is designed to answer

A useful capex workbook should help you reason through a short list of practical questions during reporting season:

1. Which hyperscalers have the highest and lowest capex intensity right now?
2. How is each hyperscaler's free cash flow yield holding up against the new spending baseline?
3. Where do the AI compute and networking suppliers stand on revenue growth and operating margin compared with hyperscalers?
4. Which names look most resilient if capex pace accelerates further, and which look most exposed if it cools?
5. How should portfolio sizing change as the capex story evolves into the second half of 2026?

The reason the template uses six sheets instead of a single screener tab is that the five questions above do not fit on one view. You need a dashboard, but you also need a scenario sheet, a positioning sheet, an allocation sheet, and a comparison view. Each of those serves a different reasoning step.

The six-sheet workbook structure

Two files are linked below. They share the same layout.

Download the templates:

• - Pre-filled with current-style sample data and formula references for every cell
• - Live-updating formulas with no static market data in the core tables

Use the static file to read along with this post and to see exactly which formula sits behind each cell. Use the live template file when you have MarketXLS installed and want the dashboard to refresh on every open.

Here is what each sheet does.

Sheet 1: How To Use

The first tab is a short tutorial that explains the purpose of each sheet, the inputs that drive the rest of the workbook, the meaning of the sample versus template files, and the key MarketXLS formulas used throughout. It also lists the website and demo links so the workbook is fully self-contained the first time you open it.

Sheet 2: Main Dashboard

The dashboard is the core of the workbook. The top of the sheet has four yellow input cells:

• Portfolio Size (default 100,000)
• Max Position Weight (default 18 percent)
• Capex Intensity Alert level (default 20 percent)
• Earnings Season Flag (1 during the season, 0 outside)

Below the inputs is a watchlist table for the ten tickers with eighteen columns of live data plus a derived score. Each row pulls live data with formulas like:

• =QM_Last(A10) for the snapshot price
• =PERatio(A10) for the trailing P/E multiple
• =Beta(A10) for the risk lens
• =MarketCapitalization(A10) for company size
• =HF_REVENUE(A10,"","","TTM") for trailing twelve-month revenue
• =HF_CAPITAL_EXPENDITURE(A10,"","","TTM") for trailing twelve-month capex
• =HF_FREE_CASH_FLOW(A10,"","","TTM") for trailing twelve-month free cash flow
• =HF_OPERATING_INCOME(A10,"","","TTM") for trailing twelve-month operating income
• =HF_REVENUE_GROWTH(A10,"","","TTM") for the trailing revenue growth rate
• =SimpleMovingAverage(A10,"50") for the medium-term trend reference
• =RelativeStrengthIndex(A10) for the momentum oscillator
• =FiftyTwoWeekHigh(A10) and =FiftyTwoWeekLow(A10) for the annual trading range

Capex intensity is computed in column K as =IFERROR(J10/I10,0), which divides capex TTM by revenue TTM. The Score column blends valuation (lower P/E is better), free cash flow yield (free cash flow divided by market cap is rewarded), revenue growth, operating margin, capex intensity (penalized for being too far from a 15 percent target), and a small trend factor.

A color scale is applied to both the Score column and the Capex Intensity column so you can see at a glance which names sit at the extremes. Conditional formatting on the score uses a red-yellow-green gradient. Conditional formatting on capex intensity uses the opposite gradient since extreme intensity is usually a free cash flow headwind in the near term.

Sheet 3: Capex Scenario Analysis

The scenario sheet is the heart of the educational workflow. It lays out five scenarios for capex pace and AI revenue conversion and shows the implied direction for free cash flow, valuation multiples, and watchlist bias.

These are scenario buckets, not predictions. The point is to think through how the watchlist bias would shift in each case so you have a framework ready before management teams start guiding capex on Q1 2026 calls.

Sheet 4: Strategy Positioning

The strategy sheet maps each ticker to a sub-group theme, then computes capex intensity, free cash flow yield, and operating margin for each name. A simple rule classifies each name as Core or Satellite using the formula =IF(AND(E5>0.03,F5>0.2),"Core","Satellite"), which marks a position Core when free cash flow yield is above 3 percent and operating margin is above 20 percent. The thresholds are intentionally simple. Adjust them inside the sheet to match your own framework.

Sheet 5: Portfolio Allocation

The allocation sheet sizes positions using the dashboard score and the portfolio inputs. Target weights are normalized to sum to 100 percent of the watchlist, then capped by the Max Position Weight input. Dollar allocation flows from the Portfolio Size input. A capex intensity column and a weighted free cash flow contribution column give two extra views on the resulting portfolio shape.

Sheet 6: Capex Comparison

The comparison sheet is a clean, single-screen view of the spending side of the story. It shows revenue TTM, capex TTM, capex over revenue, free cash flow TTM, free cash flow over capex, operating income TTM, operating margin, and revenue growth in one nine-column table. The free cash flow over capex column is particularly useful since values below one suggest a company is currently spending more on capex than it is generating in free cash flow, which is the literal definition of an investment-heavy phase.

How the AI capex tracker excel reads each sub-group

Different sub-groups in the watchlist respond to different questions during Q1 2026 reporting.

Hyperscalers (MSFT, GOOGL, META, AMZN)

The four hyperscalers are the demand center for the entire AI infrastructure cycle. Capex at these four companies is the single most-watched cluster of numbers in Q1 2026. The template lets you compare capex intensity (column K on the dashboard) and free cash flow yield (free cash flow TTM divided by market cap, computed inside the score formula) across all four. Even with similar absolute capex dollars, the four companies have very different revenue bases, so the ratios tell the more useful story than the headline number alone.

A few useful framings for the hyperscaler block:

• A rising capex intensity coupled with rising operating margin is the most constructive combination, because it suggests the AI investment is showing up in profitability.
• A rising capex intensity coupled with flat or falling operating margin is a more cautious read, because it suggests spend is running ahead of monetization.
• Free cash flow yield matters more here than P/E, since the capex line is what most directly affects free cash flow.

Cloud build-out (ORCL)

Oracle has emerged as a distinct case in the AI capex story. The template treats it as a separate sub-group because its capex intensity ramp pattern looks different from the four hyperscalers. The relevant lens is whether the multi-billion-dollar AI cloud backlog is showing up in revenue at the rate management has guided. The HF_REVENUE_GROWTH column on the dashboard is particularly useful here.

AI compute suppliers (NVDA, AMD)

The compute suppliers sit on the other side of the hyperscaler capex line. When capex grows, supplier revenue grows. When capex pauses, supplier revenue eventually slows. The template helps you compare revenue growth rates and operating margins for the two main merchant GPU and accelerator suppliers, then put those numbers next to the hyperscaler capex intensity to see the linkage.

AI networking and custom silicon (AVGO, ANET)

Networking has become an increasingly large share of every AI cluster build. Broadcom captures both custom ASIC revenue and AI networking switches, while Arista is the cleanest pure-play on AI cluster switching. Both names tend to have higher operating margins than the merchant compute suppliers, so the template's operating margin and free cash flow yield columns surface the quality difference quickly.

AI server integrators (DELL)

Server integrators sit at the lower end of the value chain. The relevant metrics are backlog conversion, revenue growth, and gross margin trend. The dashboard pulls revenue growth and operating income, and the comparison sheet shows operating margin so you can see why integrators trade at lower multiples than the chip and networking suppliers.

Building the workbook in Excel with MarketXLS

To build a similar workbook from scratch, the steps are straightforward.

1. Set up the watchlist column. Put each ticker in column A starting at the first data row. The rest of the workbook will reference these cells.
2. Add live snapshot quotes. Use =QM_Last(A10) for the price snapshot. The QM_ family of functions provides the most up-to-date market data in MarketXLS.
3. Pull the trailing twelve-month financials. The HF_ family of functions accepts (Symbol, Year, Quarter, TTM) parameters. Passing empty quotes for Year and Quarter and "TTM" in the fourth slot returns the trailing twelve-month value. Examples include =HF_REVENUE(A10,"","","TTM"), =HF_CAPITAL_EXPENDITURE(A10,"","","TTM"), =HF_FREE_CASH_FLOW(A10,"","","TTM"), =HF_OPERATING_INCOME(A10,"","","TTM"), and =HF_REVENUE_GROWTH(A10,"","","TTM").
4. Compute ratios in derived columns. Capex intensity is =IFERROR(J10/I10,0). Free cash flow yield is =IFERROR(L10/H10,0) where L is FCF TTM and H is market cap. Operating margin is =IFERROR(M10/I10,0) where M is operating income TTM and I is revenue TTM.
5. Layer in valuation and trend context. Use =PERatio(A10), =MarketCapitalization(A10), =Beta(A10), =SimpleMovingAverage(A10,"50"), =RelativeStrengthIndex(A10), =FiftyTwoWeekHigh(A10), and =FiftyTwoWeekLow(A10) for the standard equity context.
6. Score the watchlist. Combine the metrics with weights that match your own framework. The default scoring in the template gives 20 percent weight each to free cash flow yield, revenue growth, and operating margin, 15 percent each to valuation and capex intensity discipline, and 10 percent to trend.
7. Add scenario thinking. A scenario sheet keeps you honest. Even if you do not formally model probabilities, writing out what you would do under each capex outcome forces a clearer process.

For a deeper introduction to building stock workbooks in Excel, the MarketXLS website and the AI stock screener guide cover related workflows. The Mag 7 earnings tracker and semiconductor earnings tracker cover overlapping watchlists from different angles. The defensive sector rotation model and Fed pause sector rotation model provide complementary macro frameworks for thinking about how AI capex sits inside a broader portfolio.

Reading the dashboard during a real Q1 2026 call

The most useful way to read this dashboard during a real earnings call is to keep three columns in view: capex intensity (column K), free cash flow yield (computed inside the score, or the dedicated column on the comparison sheet), and revenue growth (column N). When a hyperscaler raises its full-year capex guide on the call, the capex intensity number will mechanically move higher, free cash flow yield will compress, and the score will reflect the trade-off automatically.

What matters then is the management commentary on AI revenue. If the company also raises its AI revenue commentary or cloud growth commentary, the market typically tolerates the higher capex. If the AI commentary stays unchanged or weakens while capex rises, the response is usually less generous. The workbook does not try to predict the market reaction. It tries to make the trade-off visible so that any decision you make is informed by the actual financial geometry rather than the headline.

Educational use, not investment advice

This workbook is built for educational scenario analysis. It is not a recommendation to buy or sell any of the names listed. The scoring framework, the input cells, and the scenario tab are all designed to be customized so you can adapt them to your own process and your own constraints. The default thresholds (3 percent free cash flow yield, 20 percent operating margin, 18 percent maximum position weight) are deliberately simple and should be revisited each quarter as the AI capex cycle evolves.

If you want to see MarketXLS in action with a structured walkthrough, the book a demo page is the fastest way to do it. The demo team can show you how the HF_ functions, QM_ functions, and the broader 1,100-plus formula library fit into workbooks like this one.

FAQ

What is an AI capex tracker excel? An AI capex tracker excel is a structured workbook that monitors capital expenditure, revenue, free cash flow, and valuation metrics for the largest hyperscalers and AI infrastructure suppliers. It compares spending pace against revenue conversion and free cash flow generation in one place, so you can read a sector-wide story instead of one company headline at a time.

Which companies belong in an AI capex tracker excel? The core watchlist is the four largest hyperscalers (Microsoft, Alphabet, Meta, Amazon) plus a cloud build-out name (Oracle), the two main merchant compute suppliers (NVIDIA, AMD), the AI networking and custom silicon names (Broadcom, Arista), and a server integrator (Dell). The template in this post uses exactly these ten tickers but is easy to extend.

Which MarketXLS formulas drive the workbook? The dashboard uses QM_Last, PERatio, Beta, MarketCapitalization, HF_REVENUE, HF_CAPITAL_EXPENDITURE, HF_FREE_CASH_FLOW, HF_OPERATING_INCOME, HF_REVENUE_GROWTH, SimpleMovingAverage, RelativeStrengthIndex, FiftyTwoWeekHigh, FiftyTwoWeekLow, and Sector. Every formula was verified against the MarketXLS function documentation before being placed in the template.

How is capex intensity calculated? Capex intensity is the trailing twelve-month capital expenditure divided by trailing twelve-month revenue, written as =HF_CAPITAL_EXPENDITURE(A10,"","","TTM")/HF_REVENUE(A10,"","","TTM"). A higher number means the company is spending a larger share of its revenue on capex, which usually compresses near-term free cash flow.

Why does the score reward middle-of-the-pack capex intensity instead of the lowest? Very low capex intensity in this watchlist often indicates a supplier name (NVIDIA, AMD, Arista) where capex is structurally small. Very high intensity in the hyperscaler block can compress free cash flow during a capital-heavy phase. Rewarding intensity that is neither extreme high nor extreme low keeps the score from over-favoring one sub-group. The 15 percent target inside the score is editable.

How is free cash flow yield computed in the workbook? Free cash flow yield is trailing twelve-month free cash flow divided by market capitalization. Inside the dashboard score that is (L10/H10) where L is =HF_FREE_CASH_FLOW(A10,"","","TTM") and H is =MarketCapitalization(A10). The Capex Comparison sheet also shows a free cash flow over capex ratio, which is a more direct view of how much investment is being funded by current cash generation.

Can the watchlist be customized? Yes. The template structure is generic. Replacing the ticker list in column A propagates through every formula in every sheet automatically, since each formula references the cell in column A rather than hardcoding the symbol. The score weights and the Core/Satellite thresholds are also editable directly inside the sheets.

The bottom line

AI capex tracker excel turns one of the most important narratives in Q1 2026 reporting into a repeatable spreadsheet workflow. Hyperscaler capital expenditure is large enough now to move free cash flow at the company level, the conversion from capex to AI revenue is being scrutinized harder than in earlier quarters, and the supplier ecosystem (compute, networking, integrators) sits on the other side of every capex print. A workbook that lines up capex, revenue, free cash flow, operating margin, and valuation across hyperscalers and suppliers in one place is the cleanest way to keep the comparison honest.

Download the templates above to follow along with this post. To explore the broader MarketXLS function library and see how it fits into more complex models, visit marketxls.com or book a demo.