How much AI spend is your stock splurging? If the company doesn’t tell you, here are 5 ways to tell!

AI spend is surging faster than any other technological development in history. There are varying estimates of just how much and one of the reasons is that most refuse to disclose the number. Yes there are others such as numbers shifting quickly for those that do disclose.

But even though we’d all be rich if we had a dollar for everytime companies used phases like “AI investment”, “AI infrastructure”, “AI‑powered products”, or “AI‑enabled productivity”, the spend at an individual company level is harder to quantify.

When a company or CEO says the word AI
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For analysts, boards, and investors, this creates a simple problem: if management won’t tell you how much the firm is spending on AI, how do you work it out? Is it even possible? Well, it is possible to tell. AI spend shows up in capex, in operating expenses, in asset growth, in footnotes, and in the structural shape of the balance sheet. The trick is learning to read those signals. We think five methods are most reliable.

5 Ways to Quantify Your Company’s AI spend

1. Examine the cash flow statement for capex step‑ups

AI is capital‑intensive: datacenters, GPUs, networking, cooling, land, and power infrastructure. When a firm’s “Purchases of property & equipment” suddenly jumps, and when management commentary attributes the increase to “infrastructure” or “technology”, the simplest inference is that the delta is AI‑related. Hyperscalers now allocate 75–100% of incremental capex to AI infrastructure; mid‑market firms typically allocate 40–60%. The cash flow statement is therefore the cleanest starting point. It is also the most difficult for management to obscure, because capex must be recognised when cash leaves the business. If a firm is building AI infrastructure, the cash flow statement will show it.

2. Analyse operating expenses for AI‑driven growth

AI talent is expensive, and internal model development often sits inside R&D. When R&D rises faster than revenue, and when headcount disclosures show engineering or machine learning hiring, the increase is rarely coincidental. Infrastructure Opex also rises with AI workloads: power, cooling, networking, and cloud consumption. These appear in “cost of revenue – infrastructure” or “technology & operations”. Even enterprise AI tooling leaves a trace in SG&A or R&D, especially when firms adopt Copilot‑style licences across knowledge‑worker populations. The income statement is less definitive than the cash flow statement, but it reveals the operating side of AI spend: talent, tooling, and utilisation.

3. Track the balance sheet for structural changes

AI investment creates identifiable asset growth patterns: PP&E rises as datacenters and servers are built; intangible assets rise when firms acquire AI startups or capitalise internal software; lease liabilities rise when datacenter expansions rely on long‑term leases. Goodwill increases when acqui‑hires or AI‑related acquisitions occur. The balance sheet is slower to move than the income statement, but when it moves, it confirms the underlying capital intensity. A firm cannot build AI infrastructure without expanding PP&E, and it cannot acquire AI companies without increasing intangible assets or goodwill. The balance sheet therefore acts as the structural corroboration of the story told by capex and Opex.

4. Read the footnotes carefully

Firms often disclose AI‑related commitments indirectly: multi‑year GPU procurement contracts, datacenter expansion plans, capitalised software detail, segment‑level infrastructure commentary, or purchase obligations tied to compute. These disclosures rarely quantify AI spend directly, but they allow you to attribute portions of capex or opex with far greater confidence.

For example, when a firm discloses a multi‑year commitment to purchase A$5bn of compute capacity from a cloud provider, or when it reveals that capitalised software increased due to “AI platform development”, the attribution becomes straightforward. Footnotes are often overlooked, but they contain the most candid language in the entire filing. It can be time consuming the read them all, but you could just download a company’s financial statement and ask Claude or ChatGPT to examine the footnotes for anything there that could indicate higher AI spend.

5. Benchmark the firm against peers.

Industry ratios are surprisingly stable: hyperscalers spend 8–15% of revenue on AI; mid‑market firms spend 2–5%; AI typically represents 12–18% of the IT budget; per‑employee AI tooling spend sits around A$1,000–1,500 per knowledge worker. When your inferred estimate sits wildly outside these ranges, the model needs revisiting. When it sits inside them, the estimate is usually directionally correct. Benchmarking is not a substitute for analysis, but it is a powerful sanity check.

These five methods form a repeatable workflow: start with capex, move to Opex, confirm with the balance sheet, refine with footnotes, and validate with benchmarks. To show how this works in practice, consider Meta.

Case Study: Estimating Meta’s AI Spend

Let’s look at a real-life case study with Meta. Meta is one of the biggest AI players does not disclose its AI spend. It talks about “AI infrastructure”, “AI compute”, and “AI investment”, but it never provides a number. Still, it does provide other numbers which you can take plenty of hints from.

Begin with the cash flow statement. Meta’s 2025 capex was A$57.7bn (US$38bn), up from roughly A$35bn (US$23bn) in prior years. The A$22bn step‑up is substantial. Management commentary attributes the increase to “investments in AI infrastructure, including datacenters and servers”. Hyperscaler benchmarks suggest that 75–100% of incremental capex is AI‑related. Applying that ratio to the A$22bn increase yields an AI‑attributable capex of A$16.5–22bn. A midpoint of A$19bn is defensible. This is the first anchor: AI infrastructure is driving the capex surge.

Move to the income statement. Meta’s R&D rose from A$44bn (US$29bn) to A$53bn (US$35bn). The A$9bn increase aligns with AI talent, model development, and platform engineering. Industry ratios suggest that 30–40% of R&D growth in AI‑heavy firms is directly attributable to AI. That implies A$2.7–3.6bn of incremental AI operating spend. Infrastructure Opex also rose by roughly A$3bn; applying a 20–30% AI attribution yields A$600–900m. Enterprise AI tooling adds another A$67–100m. A midpoint of A$4bn for operating AI spend is reasonable. This is the second anchor: AI talent, tooling, and utilisation are driving Opex growth.

Check the balance sheet. PP&E increased from A$92bn (US$61bn) to A$112bn (US$74bn). The A$20bn increase mirrors the capex step‑up. Intangible assets rose modestly, goodwill was flat, and lease liabilities increased by A$3bn, consistent with datacenter expansion. The balance sheet therefore corroborates the inference that the capex surge is AI‑driven. This is the third anchor: the structural footprint matches the spending pattern.

Triangulate the estimate: AI capex of ~A$19bn plus AI operating spend of ~A$4bn yields a total AI spend of ~A$23bn. Meta’s 2025 revenue was A$201bn (US$133bn), implying AI spend of roughly 11–12% of revenue. This ratio sits comfortably inside hyperscaler benchmarks. It is also consistent with the broader industry trend: firms with large consumer platforms and advertising businesses are now allocating double‑digit percentages of revenue to AI infrastructure and model development.

Bottom Line

Even when a company’s management refuses to disclose AI spend, the financial statements reveal it. Capex shows the infrastructure footprint; Opex shows the talent and tooling; the balance sheet shows the structural buildout; footnotes show the commitments; benchmarks show whether the estimate is plausible. Meta’s ~A$23bn inferred AI spend is not a disclosed number, but it is a defensible one.

The broader implication is that investors can rarely rely on management guidance to understand AI investment – if any guidance is given, and even then it may not be reliable (at least not for long). They must learn to read the financial footprint. AI spend is now one of the largest capital allocation decisions in corporate history.

It affects free cash flow, capital intensity, margin structure, and long‑term competitiveness. It determines whether a firm can build proprietary models, whether it can deploy AI at scale, and whether it can compete in markets where AI‑driven productivity becomes the baseline rather than the differentiator. And also…whether or not they will emerge from this period as Amazon emerged from the dot com bubble, or if they’ll emerge as Pets.com did.