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TL;DR
Useful life is the number of years a company assumes a computer will keep earning its keep, and it is an estimate rather than a measurement. Meta, Amazon, Microsoft, Alphabet, and Oracle each publish this number in their annual filings with the US Securities and Exchange Commission. The number decides how much of the purchase price is charged against profit each year.
Meta extended the assumed useful life of most of its servers and network equipment to five and a half years effective 1 January 2025 and expected the change to cut that year’s wear-and-tear charge by about $2.9 billion. Amazon moved the opposite way on the same date, shortening the assumed life of a portion of its servers and networking equipment from six years to five. Amazon gave the reason in its filing as the increased pace of technology development, particularly in artificial intelligence and machine learning.
The profits reported by the companies running artificial intelligence rest on an assumption about how long a computer keeps working, and the companies do not agree on the assumption. Amazon disclosed that its change raised 2025 depreciation and amortization expense by $1.4 billion and cut net income by $1.0 billion. Both the longer estimate and the shorter one are permitted under the same accounting rules.
Every computer you have ever owned got slower, then got replaced.
Nobody wrote the date down in advance.
The companies building artificial intelligence do write it down.
They are required to, and the number they choose moves how much money they appear to make.
That number is called useful life. It is the number of years a company assumes a machine will keep doing useful work before it is retired.
It sounds like a technical detail buried in a filing. Yet it is one of the largest open questions in the AI boom, because companies buying the same equipment have written down different answers.
Every one of them is guessing. That is the part worth sitting with.
Meta’s wear-and-tear bill on servers alone reached $13.36 billion last year
Start with what the number does.
When a company buys something that lasts for years, it spreads the cost across all the years it expects to use the thing, rather than charging the whole amount in the year it writes the check.
That spreading is called depreciation, and most people already hold the idea from cars.
A car bought for $30,000 and expected to last ten years loses value on a schedule.
If you assume it lasts five years instead, the schedule doubles, and every year of ownership looks more expensive.
Servers work the same way, at massive scale. In 2025, Meta reported $13.36 billion in depreciation on servers and network assets, up from $11.34 billion in 2024 and $7.32 billion in 2023.
That figure covers wear alone; on machines Meta had already bought and paid for.
It is the portion of that existing equipment the company counted as used up inside twelve months, on a single line of a single company’s accounts. In two years, it has grown close to double.
Change the assumed life and that line moves by billions without a single machine being bought, sold, or touched.
The thing most readers would get wrong here is treating wear as a physical fact. The machine ages at whatever rate the machine ages. The number on the page is a choice made in advance about a future nobody has seen yet.
Two companies, the same machines, the same January
On 1 January 2025, Meta and Amazon both changed their answer. They changed it in opposite directions.
Meta extended the estimated useful life of most of its servers and network assets to five and a half years, up from a prior range of four to five years.
The company expected the change to reduce its 2025 depreciation expense by roughly $2.9 billion.
Longer life, smaller annual charge, higher reported profit, and not one new machine required to produce the effect.
Amazon went the other way on the very same effective date.
It shortened the assumed useful life of a portion of its servers and networking equipment from six years back to five.
Amazon’s filing named the reason directly: the increased pace of technology development, particularly in artificial intelligence and machine learning.
In plain terms, Amazon told its shareholders that the hardware is going out of date faster than it used to.
The cost was disclosed too. For 2025, Amazon reported that the change increased depreciation and amortization by $1.4 billion and reduced net income by $1.0 billion, or $0.10 per share, primarily affecting Amazon Web Services.
Two companies. The same core supplier. The same technology cycle. The same calendar date. Opposite conclusions, both signed off by auditors, both entirely legal.
If you have been reading headlines about AI profits and taking them as measurements, this is the moment to notice how much of those figures is an estimate about time.
Amazon changed its own answer three times in four years
The disagreement runs inside the companies as well.
Amazon lengthened the assumed life of its servers from four years to five effective January 2022.
It lengthened them again, from five years to six, effective January 2024, and disclosed that the change cut depreciation and amortization by $3.2 billion and added $2.5 billion to net income for that year, twenty-three cents a share.
Twelve months later it reversed course and went back to five.
Read the three moves together and you have a company that stretched the number twice while the machines looked durable, then pulled it back when the pace of new chip releases picked up.
Here is the honest half of the story, and it cuts both ways.
Amazon’s reversal is the strongest public evidence that the longer schedules elsewhere are generous, and critics have made exactly that argument.
It is also true that older chips remain heavily used, particularly for running finished AI models rather than training new ones, so the case for a very short life can be overstated. No regulator has found any of these companies at fault.
There is no rule that names the correct answer, because the correct answer depends on how fast the technology moves next.
The misunderstanding worth clearing is that an accounting footnote is a small thing.
On spending this size, a footnote measured in years is one of the largest single levers on reported profit that a company controls.
The chip still works. A newer one does the job for less.
A chip that has been retired is almost always a chip that still works. That distinction is where most of the confusion lives.
A five-year-old phone still turns on. It gets replaced because the new one is better and because apps stop being built for the old one. AI chips retire for a version of the same reason.
NVIDIA, the company whose chips most of the AI boom runs on, has moved to releasing a major new generation roughly every year. Each generation does more work for each dollar of electricity.
The older chip keeps running. It simply costs more to get the same result out of it, and at some point, the electricity bill alone makes retirement the cheaper option.
That is why nobody can settle the question by testing the hardware.
The machine keeps running and gets out-competed anyway, on a schedule set by a supplier’s product calendar rather than by any physical limit.
It is also why the second-hand market gives no clean answer.
Prices for renting and buying previous-generation chips have swung hard in both directions over the past two years, moving on chip supply, memory shortages, and demand for running finished models.
There is no settled resale value to check the estimate against.
If you have assumed that somewhere inside these companies sits a measured number, what sits there instead is a market that has yet to decide.
Microsoft, Alphabet, and Oracle all stretched the number first
Amazon’s reversal stands out because it runs against the direction everyone else moved.
Microsoft told investors in 2022 that it was extending the depreciable life of server and network equipment in its cloud business from four years to six, with a disclosed benefit of roughly $3.7 billion in the following financial year.
Alphabet, the parent company of Google, announced a similar assessment in its January 2023 earnings release, moving servers from four years to six and certain network equipment from five to six.
The actual effect for the full year 2023 was a $3.9 billion reduction in depreciation expense and a $3.0 billion increase in net income, twenty-four cents per diluted share.
Oracle raised its own estimate for servers and networking equipment from five years to six, effective at the start of its 2025 financial year.
Four large companies stretched the number. One pulled it back. Every one of them buys from the same handful of suppliers and watches the same product releases.
The thing to carry out of this is simple. One of the biggest inputs into whether AI makes money is an open disagreement, and the people disagreeing are the ones closest to the machines.
Why this sits under the AI you use every day
Every AI answer you have ever received came off a machine sitting in a building, drawing power, wearing out on a clock nobody has agreed how to read.
The public argument about whether all this construction makes sense gets conducted in the language of power and enormous spending announcements.
Underneath all of it, holding up the profit figures those announcements are measured against, is a number written in years, chosen in advance, and revised whenever the companies change their minds about the future.
Meta wrote five and a half. Amazon wrote five. They wrote it on the same day, about the same machines, and the world will not know which of them was right for several more years.
The next time a headline tells you an AI business has turned profitable, part of that answer was set by an estimate of how long a computer lasts.
What is the oldest piece of technology you still use every day, and what finally makes you replace something rather than keep it running?