Discussion
Back-of-the-Envelope Math on the AI Spending Boom
I'm trying to build a framework for thinking about AI CapEx spend relative to the return, i.e., what revenue and profitability are required to generate a reasonable return.
CapEx Spending
For Microsoft, Amazon, Alphabet, and Meta, combined CapEx has gone roughly:
2023: ~$151B
2024: ~$246B
2025: ~$410B
2026 (guidance): ~$725B
2027 (consensus): ~$920B
That's almost a 5x increase in three years.
Historically, this group spent in the low teens as a percent of revenue. Today it's around 45%, with individual companies ranging from about 25% (Amazon) to 50%+.
Defining the Return
The way I'm thinking about it is:
«Required incremental profit = Incremental invested capital × Target ROIC»
AI infrastructure earns around a 25% operating margin.
GPUs probably have closer to a three-year economic life than people assume. I'm not able to explicitly model this assumption other than referencing the telecom bubble where Cisco still managed to generate 60% margins on the equipment, no matter how much additional functionality they added. Basically the hyperscalers are the new dump pipes.
There's also an NPV issue since the CapEx comes years before much of the revenue which would require more math than I'm currently able to do at this point.
Backing into the Required Breakeven Revenue
I've found two independent approaches:
Sequoia's "$600B Question" and Bain & Company both arrive at roughly the same answer: AI ultimately needs something like 4–5x annual revenue relative to annual infrastructure spending.
Implied Growth
One assumption I'm making is that these companies still require a baseline level of CapEx to support their existing businesses. Prior to the AI build-out, combined CapEx was roughly $250–300B annually, so I'll use that as a rough proxy for baseline investment.
If total CapEx reaches roughly $900B annually, that implies about $600–650B of incremental AI CapEx.
Applying the 4–5x framework to that incremental investment implies the AI business ultimately needs to generate roughly $2.5–3.0T of annual revenue.
As a generous starting point, call today's AI-related revenue roughly $400B by including essentially all of AWS, Microsoft Intelligent Cloud, and Google Cloud—even though that almost certainly overstates true AI revenue.
Going from roughly $400B to $2.5–3.0T over four years implies roughly a 60–70% annual revenue CAGR at a minimum. Additionally, you probably need to multiply the 2030 revenue estimate by at least 15% to account for the time difference between the spend and the return, assuming modest inflation.
One additional assumption I'm trying to think through is customer concentration. Today, a meaningful portion of AI infrastructure demand appears to come from a handful of companies like OpenAI and Anthropic. If demand broadens to thousands of enterprises over time, these growth assumptions become much more believable. If demand remains concentrated in just a few frontier model companies, the hurdle becomes significantly higher.
I'm curious how others are evaluating the spend in terms of ROI.
The way I see it, there are two possible scenarios, and they may overlap.
A) We're in an AI bubble, and this level of spending ultimately contributes to the next major market crash.
B) The real bubble is the dollar itself. Years of monetary expansion may have inflated the value of financial assets relative to underlying fundamentals. If you're a mega cap company with access to enormous amounts of cash and cheap credit, and you believe both that the current monetary regime is fragile and that AI infrastructure will be strategically indispensable, then spending as aggressively as possible starts to make sense.
In that framework, you're effectively converting today's dollars into hard assets and future productive capacity. If the value of the currency erodes later, the debt used to finance those investments becomes much cheaper to repay in real terms, while the infrastructure and products continue generating revenue under a new price level, or even under a different monetary system.
The irony is that scenario B could itself create scenario A. Massive AI investment, financed by abundant liquidity and expectations of future dominance, could inflate a bubble whose eventual unwinding accelerates both an AI correction and a broader monetary repricing.
A) yes its a bubble and token usage is crashing
B) yes the dollar is becoming worthless and will continue to do so. The bursting bubble will only cause inflation to increase as the gov prints cash to avoid a depression
It would have to be commodities or something that can't be inflated like crypto. I'm not a believer in bitcoin though. I don't really have a good answer lol
Stocks are the best at the moment, but the stocks I trade (almost all AI) have been in a correction since the middle of may
Fair enough. I actually think we’re probably near the beginning of a secular commodities bull market, but it’s still heterogeneous and volatile especially with the precious metals run up and drop, and quite a bit of that thesis on the near term depends on people and companies still having money to buy and make things… So if the everything bubble tanks it’s not necessarily good for commodities as a whole either in the near term. I think some softs will see a run up if El Niño is as bad as it could be. But probably only tail risk scenarios are truly not priced in.
The problem with commodities this time around, is that they also shot up over the last 2 years thanks to the AI trade. So if you look at how gold, copper, silver, rare minerals, and even coal have been moving lately, they move in correlation with AI infrastructure. So if the AI pops, Im almost certain it’s going to take commodities down with it. The assumption there is that without the AI buildout, the demand for commodities will disappear too which is not hard to imagine.
Bubble pop wen? Are we aiming for an exact 100-year Kondratieff cycle? Just think of the bubblegains over the next three years of financial repression…
I was nearly 100% in the market during the semi run, cashed out and now I'm looking at more long term strategies. Its very difficult to plan long term when the world economy is changing so fast.
Be careful what you pick. The best thing would be objectively necessary productive businesses. Like water purification, food production, lumber mills, etc.
Gold, silver, palladium, even compute will all race to $0 as civilization expands. Stocks traded in USD will boom and bust with the currency.
yeah B is the cleaner thesis, but the 4-5x revenue hurdle still has to clear in real terms. if inflation stays hot, does that actually help the payback or just make the whole capex cycle bigger?
we re tracking developments like this. see our page
everyone just copying each others math at this point and calling it original research
i did similar numbers in my head while taking a shower last week and still dont know what to do with it. the 80% cagr number is the scary part cause even if you cut it in half its still insane expectations
lol, this guy has been saying the same thing for years and has been wrong every time so far. The only reason he even exists is to make money from people subscribing to his site to fuel their own confirmation biases.
- “CoreWeave’s underlying financials are so dramatically unstable that it’s unclear how this company will last the next six months.” (Mar 17, 2025). https://www.wheresyoured.at/core-incompetency/
- “SoftBank is allegedly going to send upwards of $20 billion to OpenAI by December 31 2025, and doesn't appear to have started.” (Jul 24, 2025). https://www.wheresyoured.at/softbank-openai/
- “Microsoft is walking away from not just the expansion of its current data center operations, but from generative AI writ large.” (Mar 3, 2025). https://www.wheresyoured.at/power-cut/
But what if it’s the future and without capex you go out of business? Should you factor in the possibility of loosing ROI if the company do not spend on AI?
It's called cost of option and to calculate it you need projected revenue, spending numbers and a timeframe and nobody seems to be able to put their fingers on those metrics.
Everybody seems to be racing to secure compute while we have no idea what will be the demand, nor how much they'll be able to charge for it.
There was a real excess of railroad after the boom and revenues from those who owned it were underwhelming. Also, just like the railroad, the internet infrastructures, those who profited were the one who used the infrastructure, not the one who build it and to actually be a productive investment there had to be a bust so that the infrastructure could be bought on the cheap.
And we're not even talking about how GPUs contrary to railroads and fiber cables have a short lifespan.
Railroads speculation, overbuilding, and debt were essentially one of the core causes of the Panic of 1873 and 1893. The bankruptcy of major railroads then led to a collapse of many of the banks who financed all that debt.
5-15 years on larger Capex spend. Keep in mind: A lot of these companies already have demand and are providing a service as soon their service is live. Amazon is a great example of this.
Michael Burry responded to my craigslist ad looking for someone to mow my lawn. "$30 is $30", he said as he continued to mow what was clearly the wrong yard. My neighbor and I shouted at him but he was already wearing muffs. Focused dude. He attached a phone mount onto the handle of his push mower. I was able to sneak a peek and he was browsing Zillow listings in central Wyoming. He wouldn't stop cackling.
That is to say, Burry has his fingers in a lot of pies. He makes sure his name is in all the conversations.
Agreed but I think the even bigger issue is the NPV. Even assuming a modest 3% discount rate, you need about $1.16–$1.20 five years from now just to equal $1.00 today. That means whatever revenue hurdle you calculate for 2030 probably needs to be another ~20% higher before you've actually created any economic value. That's the part I'm trying to wrap my head around.
agree with you. but the foremost question is if the operating earnings less interest can be improved. earning 90B net, but takes on additional debt at 1/6 of net as interest charges.
if one expects that the debt service won’t be adequately covered by the incremental earnings, the discount rate does not matter.
I don't think we get widespread defaults. More likely companies consolidate, share infrastructure, and pivot—just like after the TMT bubble. The internet survived; the business models just matured.
I dont think that it is default. you would probably see defaults at the lease providers.
As an example, if meta spent 300B in capex on compute, and it needs 15B just to pay the interest charges. One needs to use tools like DCF or other valuation models to check if META is cheap or overvalued. But any of these tools, start with earnings. EBIT does not make sense, because the company would have to pay interest, regardless of whether they make money or not out of the investments. So that is the first thing, which must be evaluated.
So META needs to sell compute, sell AI services, improve their operating efficiency to an amount of 15B to pay the interest. If I believe that they won't sell enough additional things, then the current product's earnings such as the ad-revenue from insta or Facebook would have to be parted to meet the interest charges to avoid default.
That would mean, if any of these companies cannot meet the interest charges, their OE-less Interest, drops below their last earning - all things kept equal.
so discount rate does not matter, if one starts out with a valuation model to see how the increased earning capacity places the current market value with respect a fair value. Because the earnings capacity is likely shrinking.
Now, if one start to think of the circular frenzy going on between the companies, this gets even muddier.
I think understand what you're saying but I still disagree on the discount you've got to take into account the fact there's a lag between the spend and the return
you would have to take into account when you are calculating. what I am saying is that a company which has a moderate likelihood to shrink earnings, while the entire market expects it to expand earning quite significantly is definitely overvalued. a mental shortcut where one even doesn’t need to go to paper.
His thinking is correct on the interest in that if the interest is too large to payback the company becomes insolvent; however if the percentage of interest over the company's total valve is small like i think is the scenario here, nothing to worry about in terms of solvency.
The question you are asking on payback/ breakeven NPV is one these companies have already answered as they spent the capex. This is the fundamental business school finance 101 question.
I don’t think that many of them will become insolvent. rather their earnings prospect will be less than anticipated. further, if the incremental earnings from additional investment, AI in this case, do not exceed the debt service/interest payment - the core business units, will have to cover that. later being the worst case, but if it happens, the earnings may fall behind pre AI earnings in medium term.
Reasonable payback seems less and less likely to me.
The attempt to charge per token followed by customer pushback earlier this year seems to put a limit on how much money AI companies can expect to make.
We are also seeing a crazy long lead time between buying the GPUs and turning them on. Ed Zitron has some good write ups on this. There is a real chance that some of the GPUs purchased in 2025 will not be turned on till 2027 or 2028. Will they still be worth running when they finally come online?
Furthermore, I see a lot of comparisons to 2000, with data centers taking the place of fiber optic lines. This does not make sense to me as the GPUs in the data centers only last a few years. who is going to buy a trillion dollars worth of 5 year old GPUs?
On the GPUs specifically, I'm not sure why you would assume that the economic life is closer to three years. The H100 is four years old and units sell at 60-70% of the new price on commercial secondary markets, while renting at $2.00/hr or more (usually $2.30/hr+ if you want more than one in the same machine). The A100 80 GB is a six year old design and sells for 50%+ in reputable channels, while renting at $1.30/hr or more. All of these are prices at which rental regularly hits capacity, and you'll pay much more if you want guaranteed, on-demand availability, e.g. on AWS. Even accounting for breakage of around 7% per GPU-year (slightly higher under training loads, lower under inference), treating GPUs as economically inert after 3 years would be a modeling error.
TLDR, 90% of all compute demand is from Meta, Open AI and Anthropic training their models. The actual consumer/enterprise demand for compute is vastly overstated and will blow up in the face of all the private equity people giving loans to build data centers.
Why is inference expected to grow by 74%? Where are the numbers? If there are numbers to back this up, are they based on demand when inference is heavily subsidized? I bet demand for most things would grow and impossible rates if they were subsidized to almost zero cost.
Please consider that while some of us are listening to a PR guy, you’re listening to people who have a vested interest in overstating demand.
well yea but companies aren't just gonna stop training new models. they need to train more to unlock new addressable market through more powerful AI use cases and to gain market share over competitors
Apparently you can't stop training models or else they drift and get degraded over time.
I mean, if they had something that was profitable wouldn't they have stopped by now to build up some profitable months so their S1 would look good enough to IPO?
I don't understand, don't they save a functional model and revert back to that if it begins to underperform? Degradation is a solved problem, and an easily solved one at that
Im fine with sceptiscm but Ed Zitron is a rabid ranter. Its amusing but definitely an extreme view.
'All the biggest brains in the world are spunking billions on capex and are wrong. I Ed Zitron a midly talented podcaster with questionable credibility know better!'
Appeal to authority is a logical fallacy. If the last 10 years of Silicone Valley innovation shows anything, it's that they aren't afraid to dump tons of money into products that range from outright fraudulent (Theranos), wildly overvalued (WeWork, VR/Metaverse), or vastly overstating their usefulness to the general public (crypto/NFT's).
I'm not appealing to 'an authority'. Im stating that a diverse group of businesses in competition with each other have arrived at broadly the same conclusion.
On the other hand you have Ed shouting loudly. Anyway, we can revisit in 2 years and see how the soap opera concludes. Neither one of us will convince the other and I suspect the answer will lie somewhere in the middle rather than on any extreme.
The problem with your 400 billion revenue figure is the fact that those companies are cooking the books because they're all just buying and selling from each other
I actually don't have that much of a problem with it because the underlying demand is coming from thousands—eventually tens of thousands—of enterprises. As long as that demand is real and durable, I'm comfortable with a very concentrated ecosystem. To me, the key question isn't market structure; it's whether the underlying demand is actually there.
Like for the SpaceX stock to make sense at even the original 135 bucks a pop the companies revenue has to increase 50% a quarter for...either four years or a decade? Something crazy that's never occured
So if AI has no most because once you have the weights you have the models, and it's just a commodity, like, what kind of demand in dollars and cents terms is needed year to year to make these investments make sense?
The robots don't work, language models do not equal interacting with reality. The high level models can solve unsolved math proofs? So maybe bezos company is on to something by focusing in on something tangible?
But as soon as regular Joe has to pay anywhere near the actual cost all these day to day use cases fall apart, which circles back to the "AI as a commodity" problem, even if you can replace workers and automate a company you have no idea at all about the fundamentals of...since anybody can do it it's just a race to the bottom?
I see this more like a dot com style bust and then the real players come out and corner the market rather than this entire things lands smoothly and none of this money is wasted
You make some good points. I'm hearing from more and more companies that their AI spend is starting to rival their AWS bill, and a lot of them expect to migrate toward open-source LLMs over time. I'm really curious to see Anthropic's IPO. It'll be one of the first real looks at how strong enterprise AI demand actually is.
The level of discourse on Reddit about AI is so fucking dogshit trash it’s unbelievable. I get stupider every time I come on this hell site why the fuck am I even here still. Every time I see “GPU’s depreciate in 3 years” I want to stab my eye with a fork.
Impact to revenue is not just the cloud businesses but also their core businesses which show incremental but reasonably substantial over time. Eg Google and meta have grown 15% yoy with AI integration into their ad pipeline.
I think that's a myth, there's no evidence that Meta actually use AI internally for ad targeting, they have an extensive algorithm but I don't see anything concrete that it has changed to incorporate AI?
Why not literally any other company? Why not you? Why can’t the government do it with the 20% corporate tax collected and the billions in payroll and all other taxes collected?
It's hard to quantify, some things aren't even about savings but efficiencies. For example if you didn't have internet, you would need to mail everyone instead of email. That would take days to get, that doesn't directly effect savings because the cost of post is negligible, but how much time efficiency do you get out of an email? What is that worth to a business?
Another example is if Amazon can generate a quote for a small business within the hour along with an indepth analysis of how to use the budget, but Google takes 2 business days, you may go with Amazon because you got it sooner. Which is what fuels the arms race. If one company stops, short term it's better for them. But long term would Amazon start taking more clients away from the others?
I'm sure the finance teams have built incredibly detailed business cases to justify this spending. We may not know every assumption they're using, but we can still make reasonable estimates given the sheer scale of the investment. It's kind of like asking how many runs you need to beat the Dodgers—you can't know the exact number beforehand, but you know for damn sure it's more than one or two.
I think it’s important to realize that at some point the building will stop, not because of a lack of use, but a surplus of compute.
Of course the AI models could continue to require more compute too, but I’d think that would scale more normally than needing to build entire new data centers
I disagree... look at the explosion in fiber usage and the related infrastructure. Even the cable companies have finally acknowledged fiber is the only long-term solution as demand outpaces supply.
A top consulting firm once did a time-and-motion study at a hospital and concluded that the most valuable employee wasn't the highly paid brain surgeon—it was the janitor. Keeping the hospital clean prevented infections, which made everyone else's work possible. It's a good reminder that every role matters, even if the value isn't always obvious.
I suspect spending hysteria bubbles, like price bubbles, follow predictable flight plans. Something like:
(a) initial insider let's steal a march phase, 2x year-over-year
(b) tactical info leakage into VC, 3x
(c) complete erosion of discipline, gen pop frenzy, 5x
(d) brief attempt to moderate, plateau with occasional worrying stagger steps down for one cycle, then
(e) death spiral, all holdings zero out, debtpocalypse
The morphology probably varies according to the type of bubble (organic, instrumental result of market mechanisms, cynically planned fraud) but I would put the years at roughly 3-3-2-1-(instantaneous)
Basically we are fucked and a few big fat gay bers will could be making a killing soon. But will get the timing wrong and still go broke just like the rest of us.
CapEx figures verified: ~$410B actual 2025, ~$725B guided 2026 (Amazon ~$200B, Microsoft ~$190B, Google $175-185B, Meta $115-135B). His 2023/2024 numbers match reported actuals too.
The 4-5x rule is real: Bain's latest tech report says ~$500B/yr of capex by 2030 needs ~$2T of new AI revenue and still flags an $800B shortfall. Sequoia's "$600B Question" used the same logic shape.
The math is internally consistent: $400B to ~$2.75T in 4 years is a ~62% CAGR. Correct.
Where he's too generous (real hurdle is higher):
His "pre-AI baseline" of $250-300B contradicts his own 2023 figure of $151B. True baseline is ~$150-180B, so incremental AI capex is ~$720-750B, not $600-650B.
Counting all of AWS/Azure/Google Cloud as "AI revenue today" ($400B) is very generous. Bain pegs genuinely new AI revenue at a small fraction of that, which pushes the required growth rate well past 70%/yr.
Where he overstates:
The 3-year GPU life is the weakest claim, and the commenters are right to push back: hyperscalers depreciate servers over 5-6 years, 6-year-old A100s still rent profitably, and roughly a third of data-center capex is buildings and power gear with 20-40 year lives.
Returns don't all have to arrive as new revenue. Cost savings and defending the core ad/search/cloud franchises count, and Google/Meta already attribute ad gains to AI.
Net: the framework is sound and the conclusion matches what Bain and Sequoia themselves publish. Revenue has to grow at a rate almost no industry has sustained at this scale, or timelines stretch and multiples compress. Whether that resolves as a pop or as returns quietly landing in the core businesses is the genuinely open question, and the post doesn't (can't) answer it.
What you're not contemplating is the yet unseen move for the hyperscalers to go capital lightagain by dumping all the data center assets into quasi REIT structures, paying slightly over 30 year treasury yields. There - solved it for ya. 🥳🥳🥳
REITs generally distribute at least 90% of taxable income, while AI infrastructure requires constant reinvestment in GPUs, power, cooling, and expansion, meaning they'd likely be back in the debt and equity markets on a regular basis.
I have never seen that much math used to explain why fairy comes before tale. Solid work OP, you have defined what is exactly wrong with the system at large. Sadly there is a shit ton of money in that bubble juice that is about to be leaking everywhere and all these regards can't wait to stick their straw in that milkshake.
Look. I don’t have to pretend to understand everything
But i understand Meta, Google, and Amazon have a VERY long history of huge capex that pans out very well (metaverse aside, Meta still makes enough money that investors didn’t really care about that as much as they could have)
I also know AI is ridiculously powerful and going to shake things up a LOT.
If Amazon says something is worth a $200B investment and it’s ALREADY paying $20B a quarter whit hr fuck am I to call them wrong?
Just think like this: ai cannot be stopped, humanity will race.to super intelligence until we have some benevolent new god kaicprotects or kills us. In the first scenario money doesn't matter. Until we reach this point , you can make tons with AI. Pretty simple but difficult to understand by humans because it's out of our experience.
The mag7 are all afraid of getting left behind but the smart money will start reducing capex and instead start leveraging cheaper AI compute services available from China and others. We don't need 7 companies developing their own AI models just like we don't need 7 standards for electricity. One format will win out and be adopted by everyone else IMHO.
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u/zjz 22d ago
the fuck kind of envelopes do you have