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AI hardware has no single, established “half-life.” A server can keep running after it loses competitive appeal, and an accounting estimate of its useful life is not a measurement of when it becomes obsolete. The real capital-timing problem is that companies must pay for land, power, buildings, servers, networking and accelerators before those assets are ready to generate revenue—and the different pieces have very different economic and accounting lives.
What does “capital half-life” mean for AI hardware?
Here, “capital half-life” is a metaphor for how the economic usefulness and competitive value of an AI investment can change over time. It is not a recognized accounting measure, a measured physical lifespan, or a standard number of years that applies to GPUs.
“How long does AI hardware last?” can mean at least three things: how long it remains operational, how long a company expects it to provide accounting benefits, or how long it can compete economically for a particular workload. Those answers can differ. Public disclosures from Meta, Alphabet, Amazon and Microsoft give useful evidence about investment timing and company assumptions, but they do not establish a universal lifespan for AI accelerators.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhy does the spending arrive before the revenue?
AI capacity is not just a shipment of chips. A company may need to secure land, build or fit out a data center, arrange power, install networking and servers, and then bring the equipment into service. Cash outlay, completion, depreciation and revenue therefore need not begin at the same time.
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Amazon CEO Andy Jassy described the timing gap this way: “we have to lay out capital and cash in advance of when we can monetize it.” In Amazon’s 2026 published interview summary, he said some infrastructure outlays precede monetization by about six months and some by about two years. These are his descriptions of Amazon’s investment timing, not a universal schedule for every project or company.
The lag matters because a company commits capital while demand, utilization and the relative performance of available hardware can still change. If capacity takes longer to enter service or earn revenue than expected, the investment’s economic value may develop differently from the original plan. The cited company materials do not quantify that risk with a common GPU-level measure.
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How do company spending figures compare?
The reported figures show the scale of investment, but they cover different companies, reporting periods and descriptions of capital spending. Actual results should not be confused with forecasts, and these amounts should not be added as if they were a harmonized industry total.
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|---|---|---|
| Meta, 2025 Form 10-K (2026) | $69.69 billion in 2025; approximately $115–135 billion expected in 2026 | 2025 purchases of property and equipment; 2026 company capital-expenditure guidance, not realized spending. |
| Alphabet, 2025 Form 10-K (2026) | $91.4 billion in 2025 | Reported capital expenditures for the year. |
| Microsoft, FY2026 Q3 earnings call (2026) | Approximately $190 billion expected for calendar 2026 | Forecast capital expenditures stated on the call; approximately $25 billion was attributed to higher component pricing. Both amounts are forecasts. |
These figures are not directly interchangeable: company disclosures can differ in scope and in how they describe capital outlays, and Microsoft’s stated forecast is for calendar 2026 while its source is an FY2026 Q3 call. The numbers show the size and continuing growth of planned investment, not how quickly any specific accelerator will pay for itself.
How long do servers, networks and data centers last?
Companies report useful-life estimates for broad asset categories, not necessarily for an individual GPU generation. The available statements also differ in status: Meta’s figure is an accounting estimate in a filing, while Amazon’s figures are an executive description of the company’s assets.
| Company and asset category | Reported period | What it does—and does not—tell you |
|---|---|---|
| Meta: most server and network assets | Estimated useful lives of 5.5 years, effective January 1, 2025 | Meta’s accounting estimate for most assets in these categories; it is not GPU-only and does not specify when equipment becomes uncompetitive. |
| Amazon: hardware and networking | About six years | Jassy’s 2026 description of Amazon’s assets, not a cross-industry standard or a GPU-specific estimate. |
| Amazon: data-center assets | 30-plus years | Jassy’s description of data-center assets, which should not be treated as the life of the servers or accelerators inside them. |
A long-lived building can support successive generations of shorter-lived computing and network equipment. Treating the building, power infrastructure, networking and accelerators as one asset with one lifespan would obscure the investment’s different replacement and accounting timelines.
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Why is useful life different from obsolescence?
An accounting useful-life estimate is the period over which a company expects an asset to provide benefits for accounting purposes. It is not proof that the asset will fail at the end of that period, nor proof that it will remain equally valuable until then. Physical operation, utilization, relative performance, resale value and ability to earn revenue are separate questions.
Alphabet says its estimated benefit periods can change in light of asset performance, expected technology advances and future deployment plans. The company reported $21.1 billion of property-and-equipment depreciation in 2025. That expense is an accounting recognition of cost, not a direct measure of equipment failure or competitive decline.
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Alphabet also says depreciation begins when property and equipment are ready for intended use. Data-center construction can take multiple years, leaving assets under construction or assembly before they enter service. This is why a purchase date alone does not tell a reader when depreciation begins or when the capacity starts supporting revenue.
A shorter estimated useful life generally means recognizing an asset’s cost over a shorter accounting period. The cited disclosures do not support assigning a specific earnings effect to a hypothetical change, or converting any company’s estimate into a universal prediction of AI hardware obsolescence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can—and can’t—you conclude about AI hardware becoming obsolete?
The evidence supports a careful answer rather than a single countdown. Large infrastructure bills precede monetization; companies use different useful-life estimates for different asset classes; and accounting assumptions can change as technology and deployment plans evolve. But the available company materials do not establish a standardized, GPU-only figure for how long AI hardware remains profitable or competitive.
- Physical life: whether equipment can continue operating.
- Accounting life: the period a company estimates it will benefit from an asset, as reflected in depreciation assumptions.
- Economic or competitive life: how long the asset can earn an acceptable return or perform competitively for a workload. The cited figures do not measure this on a comparable basis.
So “the half-life of AI hardware” is best understood as a capital-planning question: how quickly the value of a large, early commitment may change as projects enter service, demand develops and technology advances. It should not be mistaken for a sourced number of years before a GPU stops working—or stops making money.
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