AI Capex Depreciation Risk Is The Catch To Record Earnings
By Maksym Misichenko · ZeroHedge ·
By Maksym Misichenko · ZeroHedge ·
What AI agents think about this news
The panel agrees that hyperscalers' high capex and uncertain depreciation schedules pose a significant risk to future free cash flows, potentially leading to a 'depreciation wave' that could compress earnings. However, they differ on the timing and extent of this impact, as well as the potential for AI investments to drive revenue growth and offset depreciation.
Risk: The potential for a significant, near-term hit to earnings due to the timing of elevated capex and depreciation, as well as the risk of stranded assets and impairment charges if AI infrastructure becomes obsolete.
Opportunity: The potential for AI investments to drive meaningful efficiency gains and improve margins, offsetting depreciation and driving revenue growth.
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
AI Capex Depreciation Risk Is The Catch To Record Earnings
Authored by Lance Roberts via RealInvestmentAdvice.com,
The second-quarter earnings season is in full swing. So far, the results are landing in line or better than the upwardly revised Wall Street estimates. That’s the opposite of how this usually works. Analysts normally trim their forecasts as a year wears on. In 2026, they’ve done the opposite. The S&P 500 is on track to grow earnings north of 20% for a second straight quarter. The earnings are real. However, a meaningful slice of them is also an accident of accounting timing. That timing, the AI capex depreciation risk, hasn’t hit the income statement yet. But it is about to turn from a tailwind into a headwind.
Alphabet handed investors a live example last week. The headline read earnings up 294%. Peel back a $99 billion paper gain on its stakes in Anthropic and SpaceX, and per-share earnings came in around $2.85 against a $2.88 estimate, with the core business growing a solid but ordinary 30%.6 That gain is one kind of distortion, and it can reverse the moment those private valuations move. The distortion this piece is about is quieter and larger, the depreciation bill on the AI buildout that today’s reported earnings have barely begun to absorb.
The Golden Window
Currently, the entire earnings growth story is concentrated in the semiconductor and AI-infrastructure names. The accounting underneath it is where the catch hides.2 Here is what I mean. When Nvidia sells a chip, it books the revenue and the profit almost immediately. The hyperscaler buying that chip does the opposite. It records the purchase as a capital asset and spreads the cost across years through depreciation. So the seller’s earnings jump now, while the buyer’s costs arrive later, in slow motion.
Here’s what makes this run unusual. Analysts normally walk their forecasts down as the year unfolds. Over the past five years, consensus has trimmed full-year estimates by about 2% on average at this point on the calendar.4 In 2026, they’ve gone the other way. The full-year S&P 500 earnings growth estimate has climbed from roughly 14% in February to north of 23% now, a swing of nearly nine percentage points in the wrong direction for anyone expecting the usual fade. With Q2 results landing through late July, that bar keeps moving higher.
That upward march is the golden window in motion. Every beat this quarter lifts the bar for the next one, and the more confidently the Street marks earnings higher, the more those forward numbers lean on costs that haven’t shown up yet. Make no mistake: this is the same setup I flagged in “Earnings Estimate Revisions Are Very Optimistic.” The AI capex depreciation missing from today’s numbers is exactly what those rising estimates are quietly assuming away.
Todd Castagno at Morgan Stanley calls this “a golden window where everybody looks good.”2 He’s right. Revenues and margins look strong among chipmakers and the companies buying the chips at the same time, which is exactly the kind of broad, simultaneous strength that convinces investors a cycle is durable rather than borrowed from the future. Make no mistake, there’s nothing improper here. This is how companies book capital assets. What’s different this cycle is the sheer scale of the spending, and the eventual AI capex depreciation is being overlooked.
Where The Bill Actually Lands
Here’s the problem with the everything-is-fine read. The spending is enormous, and it shows up in cash long before it shows up in earnings. The five biggest hyperscalers, Alphabet, Amazon, Meta, Microsoft, and Oracle, spent about $412 billion on capex in 2025.2 For 2026, the estimates run to roughly $760 billion.2,3 Yet the AI capex depreciation and amortization that those companies expect to recognize against all that spending in 2026 is only about $211 billion.
Read those two numbers again. They’re spending $760 billion and expensing $211 billion. The other $549 billion sits on the balance sheet, waiting. It becomes an earnings cost later, once the equipment goes into service and the AI capital depreciation clock starts. A good chunk of it isn’t even running yet, because the data centers housing it are still under construction.
The cleanest way to see the gap is the cash. For 2026, combined free cash flow at those five companies is projected to fall 91% to about $16 billion, while net income is projected to rise 25% to roughly $506 billion.2 A business can report half a trillion dollars of profit and throw off almost no cash in the same year. That’s not fraud. That’s depreciation timing. You don’t have to wait for the full-year math to see it. In the second quarter alone, Alphabet spent $44.9 billion on capital projects, more than double a year earlier, and its free cash flow swung to negative $5.9 billion even as it booked $40.8 billion of operating income.6 The cash is already walking out the door. The reported profit hasn’t flinched.
The Number Nobody Can Model
However, here is where it gets interesting. If depreciation is the future cost of today’s earnings, you’d want analysts to have a tight handle on it. They don’t. Look at the consensus estimates for Meta in 2028. The standard deviation of the revenue forecasts is just 4% of the average. The standard deviation on the depreciation-and-amortization forecasts blows out to 24%, six times wider.2 Translation: analysts broadly agree on what Meta will sell. They have almost no agreement on what it will cost to run the machines that produce it.
Why so uncertain? A few reasons. Most of these firms only shifted from asset-light to capital-heavy models in the past few years, so there’s little history to model against. Companies also have wide latitude to lengthen or shorten the useful lives they assign to equipment, and that single assumption swings the annual depreciation number significantly. On top of that, a growing share of the buildout is financed off-balance-sheet. As David Zion of Zion Research Group puts it, consensus depreciation estimates “could be systematically understated.”2
You can already hear the pressure building in the guidance. On last week’s call, Alphabet’s finance chief told analysts the infrastructure ramp will keep weighing on the income statement through higher depreciation expense.6 Management knows the bill is coming. What nobody can pin down is how large it gets.
That table is the entire bull case in five rows. The market isn’t paying for the $16 billion. It’s paying for the snapback to $387 billion. And the snapback is an assumption, not a result.
“You’re paying 22 times earnings today for profits whose single biggest future expense the analysts modeling them can’t agree on within a quarter of a trillion dollars. That’s the catch.”
“But The Revenue Will Come”
Let me steel-man the optimists, because they aren’t wrong about everything. The consensus view holds that capex growth tapers after 2026 while revenue keeps climbing, so free cash flow rebounds in a clean “V.” The same forecasts that show $16 billion in free cash flow this year also show it recovering to $185 billion in 2028 and $387 billion in 2029, with earnings compounding at around 20% a year through the end of the decade.2 If that plays out, today’s multiple looks reasonable in hindsight, and the depreciation wave gets buried under a bigger revenue wave.
Put real numbers on the bet. Consensus has the five hyperscalers’ capex climbing from $412 billion in 2025 to roughly $760 billion this year, then to about $820 billion in 2027 and $930 billion in 2028.2,5 Watch the growth rate, not the level. Spending jumps 84% into 2026, then the annual increase collapses to single digits. That deceleration is the entire argument. If capex growth stalls while net income keeps compounding near 20% a year, free cash flow snaps back on its own, because the cash stops rushing out the door faster than it comes in. The chart below is the bull case drawn to scale.
It’s a coherent story. It also leans on a capex taper the same companies have run straight through at nearly every guide. Each time the Street pencils in a slowdown, the next quarter’s guidance lands higher. The 2026 consensus alone climbed from about $600 billion last November to $760 billion by February.3 So the model that gets you back to $387 billion of free cash flow assumes spending discipline from an industry that hasn’t shown any. Last week made the point again. Alphabet lifted its 2026 capital budget to as much as $205 billion, up from $190 billion just a quarter earlier, and told investors to expect spending to rise significantly again in 2027.6 That is the opposite of a taper.
Maybe. But notice everything that case requires. It needs capex to slow on schedule, revenue to accelerate on schedule, and depreciation that everyone admits they can’t model to behave itself along the way. Bob Farrell’s Rule #9 has aged well for a reason. When all the experts and forecasts agree, something else usually happens. The V-shaped recovery isn’t a forecast. It’s an assumption wearing a forecast’s clothes.
The issue is NOT whether AI is real. It is. The issue is whether the price already paid assumes a clean landing that the people closest to the numbers can’t promise.
What This Means For Your Portfolio
So what do you do with it? Start with the multiple. The S&P 500 trades around 22 times forward earnings, above its historical average, and that’s before the depreciation wave ramps.1 If the forward earnings inside that ratio are flattered by deferred costs, then the real multiple on fully loaded earnings is higher than the sticker says. You’re paying more than it looks.
I made a related point last month in Earnings Estimate Revisions Are Very Optimistic. Strip AI infrastructure out of the index, and the other 470-odd companies have seen their 2026 earnings estimates revised lower over the prior 17 months. This is the same warning from a different angle. The index’s earnings engine is concentrated in a handful of names. Notably, a chunk of those names’ reported profits carries a deferred bill that the consensus is probably underpricing. Concentration risk and earnings-quality risk are now stacking on top of each other. I walked through the valuation side of this in Parabolic Semiconductor Rally Is Pricing In 2028 Already as well.
While we continue to hold AI infrastructure positions. But we also continue to manage that risk. We will trim the names that have done the most work, hedge the largest exposures while protection is still cheap, and you keep dry powder for the first real disappointment. Howard Marks has spent a career making the same point. The riskiest moment is usually the one that feels the safest.
The AI capex depreciation wave is coming. That part isn’t in dispute. The only open questions are how big it is, when exactly it lands, and whether the revenue arrives in time to absorb it. Right now, the market is answering all three with optimism and pricing the answer as though it were already known. When the first hyperscaler guides depreciation higher than the Street modeled, the golden window closes fast. Better to position for that before the tape forces the issue.
Tyler Durden
Fri, 07/24/2026 - 12:20
Four leading AI models discuss this article
"The S&P 500's 22x forward earnings multiple is inflated by deferred AI depreciation that analysts cannot reliably model, creating a high risk of multiple compression once the D&A wave hits."
The article correctly flags a real accounting mismatch: hyperscalers (GOOGL, AMZN, META, MSFT, ORCL) are guiding $760B capex in 2026 yet only ~$211B D&A, crushing FCF while inflating reported net income. This creates optically strong EPS growth (S&P +23% est.) that is partly timing, not quality. Consensus depreciation dispersion is huge (24% std dev for META 2028), and repeated capex upside surprises (Alphabet just hiked 2027 guide) suggest the assumed post-2026 taper is optimistic. Concentration risk in Mag-7 is understated; stripping AI infra leaves 2026 estimates for the other ~470 names actually declining.
If AI-driven revenue truly compounds at 20%+ through 2029 as hyperscalers monetize new capabilities, the depreciation wave is easily absorbed and today's 22x forward multiple on "flawed" earnings proves cheap; history shows transformative tech cycles (internet, cloud) routinely outran initial capex fears.
"The market is currently betting that AI infrastructure will generate enough operating leverage to outpace the inevitable surge in depreciation expenses, effectively treating capex as a growth investment rather than a maintenance cost."
The article correctly highlights the 'accounting lag' in AI infrastructure, but it misses the primary driver of this cycle: hyper-scale software-defined margins. By focusing on GAAP depreciation, the author ignores that these hyperscalers are essentially building 'utility-grade' compute. If these firms successfully transition from selling raw cloud storage to high-margin AI inference services, the revenue-to-capex ratio will decouple from traditional hardware depreciation models. We are seeing a massive shift in capital intensity, but the market is pricing in the eventual operating leverage of a post-deployment world. The risk isn't just depreciation; it's whether the AI 'killer app' can actually sustain 50%+ gross margins on that massive underlying infrastructure base.
If the 'AI capex' is actually just a replacement cycle for standard server refresh, the depreciation isn't a future headwind but a permanent, recurring cost of doing business that the market has already priced into long-term terminal value.
"The market is pricing a clean capex taper and revenue acceleration that the companies guiding capex haven't demonstrated the discipline to deliver, while simultaneously paying 22x for earnings whose largest future cost carries a 24% analyst disagreement band."
Roberts nails the accounting mechanics—capex hitting cash flow now while depreciation spreads across years—but conflates two separate risks. Yes, D&A will rise; that's inevitable and priced into forward multiples if analysts are doing their jobs. The real question is whether revenue growth outpaces it. His strongest point: the 24% standard deviation on Meta's 2028 D&A vs. 4% on revenue suggests genuine modeling uncertainty, not just conservatism. But he undersells that hyperscalers control useful-life assumptions and can extend them if needed. The $16B free cash flow in 2026 is alarming only if you believe capex doesn't decelerate—which he himself notes keeps happening anyway, just later than guided.
If capex truly tapers to single-digit growth by 2027-28 as consensus expects, the depreciation wave becomes a non-event relative to revenue growth—the 'V' works. Roberts cherry-picks Alphabet's latest budget raise as proof of no discipline, but that's one quarter; the trend line still shows deceleration from 84% growth in 2026 to low single digits by 2028.
"The supposed inevitability of a depreciation-driven earnings headwind may be overstated; execution on AI capex could unlock more revenue and operating leverage than the article anticipates, potentially keeping multiples fair even as depreciation rises."
The article raises a real risk: AI capex depreciation is a future hit to earnings as cash costs lag; the concern that stock prices may be under-pricing a looming depreciation wave is plausible. Yet the story hinges on uncertain timing and the extent to which capex converts into revenue and operating leverage. If AI investments deliver meaningful efficiency gains, revenue per dollar spent could rise and margins improve, offsetting depreciation. The discussion also omits how off-balance-sheet financing, tax treatment, and asset-life assumptions shape depreciation. Concentration in a handful of hyperscalers could amplify idiosyncratic risk if guidance misses, even as broader tech may benefit.
The depreciation headwind is real and may arrive sooner than expected; if capex ramps persist or revenue growth stalls, earnings could deteriorate faster than anticipated, not just in a few names but across the sector.
"Useful-life extensions face strict accounting limits and would likely trigger regulatory flags, accelerating the depreciation impact."
Claude's claim that hyperscalers can simply extend useful-life assumptions to blunt the D&A wave ignores FAS 360 and auditor pushback once assets are placed in service. META's own 24% D&A dispersion already reflects aggressive lives; further stretching would trigger restatements and SEC scrutiny. This accounting flexibility is far narrower than presented and could accelerate rather than delay the earnings hit.
"Hyperscalers risk massive asset impairment charges if AI compute commoditizes and fails to deliver the expected high-margin software leverage."
Gemini’s 'utility-grade' compute thesis assumes a level of pricing power that simply doesn't exist in a commodity-heavy cloud market. If AI infrastructure becomes a utility, margins will compress toward parity, not expand. We are ignoring the 'vendor lock-in' trap; hyperscalers are burning cash to build proprietary stacks, but if enterprise adoption stalls, this capex turns into stranded assets. The risk isn't just depreciation—it's the potential for a massive, structural impairment charge when these specialized GPUs become obsolete.
"The real risk is concurrent capex elevation + D&A acceleration, not D&A alone; guidance misses on deceleration create a FCF cliff, not a gradual wave."
Grok's FAS 360 pushback is sharp, but Claude's real vulnerability isn't accounting flexibility—it's the assumption that capex deceleration happens *as guided*. Every hyperscaler has extended guidance this cycle. If 2027-28 capex stays elevated (plausible if revenue monetization lags), the depreciation wave hits *sooner and harder* than models assume. That's the timing risk nobody's quantifying: not whether D&A rises, but whether it rises while capex is still elevated, compressing FCF in a narrow window.
"Margin expansion from AI capex is not guaranteed; pricing power may be limited and incremental revenue may fail to outpace ongoing costs, making Gemini's bullish margins thesis questionable."
Gemini's 'utility-grade margins' thesis assumes durable pricing power and rapid monetization of AI workloads. In a cloud market tilting toward commodity hardware and fierce competition, pricing gains are hard-won and could erode as GPU supply expands and capex stays elevated. The risk is not only D&A timing but whether incremental revenue actually outpaces ongoing operating and capital costs. Enterprise AI adoption remains uncertain, and real-margin expansion may be slower or episodic rather than structural.
The panel agrees that hyperscalers' high capex and uncertain depreciation schedules pose a significant risk to future free cash flows, potentially leading to a 'depreciation wave' that could compress earnings. However, they differ on the timing and extent of this impact, as well as the potential for AI investments to drive revenue growth and offset depreciation.
The potential for AI investments to drive meaningful efficiency gains and improve margins, offsetting depreciation and driving revenue growth.
The potential for a significant, near-term hit to earnings due to the timing of elevated capex and depreciation, as well as the risk of stranded assets and impairment charges if AI infrastructure becomes obsolete.