“Railway mania” in the UK in the 1840s and 1860s “involved capital investments of 15% to 20% of GDP”. US GDP is around $30 trillion. [0]
Between 1900 and 1929 the total capital invested in rail stocks and bonds in the US grew from $10.8bn (against GDP of $21.2bn) to $21.4bn [1] (1929 GDP was $104.6 billion). [2]
So the current AI capex doesn’t really seem too far fetched by comparison.
Apple’s revenue went from $13.9bn in 2005 to $391bn in 2024.
Google’s revenue went from $6.1bn to $348bn over the same period.
Microsoft’s revenue was $197.5m in 1986, $345.9m in 1987, $591m in 1988, $804.5m in 1989, $1183m in 1990, $1843m in 1991, $14.5bn by 1998 and $19.75bn in 1999.
So revenue 10x’d from ‘86 to ‘91 and again from ‘91 to ‘99.
Those are all nominal unadjusted figures, but all three companies are arguably “category defining”.
For OpenAI to go from public launch to what looks like ~$12 billion projected annualised revenue so quickly says quite a lot. If it only follows the Microsoft trajectory that would be $120bn by 2030.
But going back to the railways point: the impact of railways wasn’t measured in the revenue of just one company, but rather across the whole physical supply chain.
If you assume that AI could be as transformative to the digital supply chain (and everything it touches) then you could argue that investments of 20% of global GDP wouldn’t be crazy.
(Though, railway mania, 1929 crash, etc…)
[0] https://www-users.cse.umn.edu/~odlyzko/doc/mania18.pdf
[1] https://www.researchgate.net/figure/Composition-of-capital-r...
[2] https://www.measuringworth.com/datasets/usgdp/result.php?use...
I think the fibre optic analogy is a bad one. The key reason supply massively outstripped demand was that optical equipment massively improved in efficiency.
We are not seeing that (currently) with GPUs. Perf/watt has basically completely stalled out recently while tokens per user has easily increased in many use cases has went up 100x+ (take Claude code usage vs normal chat usage). It's very very unlikely we will get breakthroughs in compute efficiency in the same way we did in the late 90s/2000s for fiber optic capacity.
Secondly, I'm not convinced the capex has increased that much. From some brief research the major tech firms (hyperscalers + meta) were spending something like $10-15bn a month in capex in 2019. Now if we assume that spend has all been rebadged AI, and adjust for inflation it's a big ramp but not quite as big as it seems, especially when you consider construction inflation has been horrendous virtually everywhere post covid.
What I really think is going on is some sort of prisoners dilemma with capex. If you don't build then you are at serious risk of shortages assuming demand does continue in even the short and medium term. This then potentially means you start churning major non AI workloads along with the AI work from eg AWS. So everyone is booking up all the capacity they can get, and let's keep in mind a small fraction of these giant trillion dollar numbers being thrown around from especially OpenAI are actually hard commitments.
To be honest if it wasn't for Claude code I would be extremely skeptical of the demand story but given I now get through millions of tokens a day, if even a small percentage of knowledge workers globally adopt similar tooling it's sort of a given we are in for a very large shortage of compute. I'm sure there will be various market corrections along the way, but I do think we are going to require a shedload more data centres.
I believe Google has earned the most revenue of any business ever [1]
So if the idea is to unseat Google, and make LLMs that are monetized by ads -- well that would be a lot of revenue!
The problem is obviously that Google knows this, and they made huge investments in AI before anyone else
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I guess someone wants to do to Google what Apple did to Microsoft in the mobile era -- take over the operating system that matters by building something new (mobile), not by directly trying to unseat Microsoft
The problem seems to be that no one has figured out what the network effect in LLMs is. Google has a few network effects, but the bidder / ad buyer network is very strong -- they can afford to pay out a bigger rev share than anybody else
Google also had very few competitors early on -- Yahoo was the most credible competitor for a long time. And employees didn't leave to start competitors. Whereas OpenAI has splintered into 5 or more companies, fairly early in its life
[1] at least according to the Acquired podcast, which is reputable
edit: oops, it was profit, not revenue
https://www.acquired.fm/episodes/google
Google with this business model makes more profits than any other company, ergo tautologically, is the most magical business model ever discovered.
There are two main threads I keep going back to when thinking about long term AI and why so many investors/statespeople are all in:
1) the labor angle: it’s been stated plainly by many execs that the goal is to replace double percent digits of their workforce with AI of some sort. Human wages being what they are, the savings there are meaningful and seemingly worth the gamble.
2) the military angle: the future of warfare seems to be autonomous weapons/vehicles of all sorts. Given the winner takes all nature of warfare, any edge you can get there is worth it. If not investing enough in AI means the US gets steamrolled by China in the Pacific (and other countries getting steamrolled by whomever China wants to sell/lend its tech to), then it seems to justify most any investment, no matter how ridiculous the current returns seem.
> This is one of those rather surreal situations where everyone senior in this ecosystem knows that the math doesn’t work, but they don’t know that everyone else also knows this. They thought that they were the foolish ones, who simply didn’t get it.
I don’t know if it’s that surreal or unexpected. There’s a reason “The Emperors Clothes” is such a classic, enduring, fable. It’s happened before. It’ll happen again.
Not shading the article. All good points, just was surprised the author threw this bit in.
Buy more tulips.
I don't get why ads are never mentioned in the article. The current use cases of GenAI (chatbots, generate [art] for me,...) have extremely obvious monetization angles through ads, and then there's a positive chance that they can bring in revenue through more ways than that (they already do in the form of subscriptions, eg). It might be that the economics still don't work out, but at least it should be considered?
In current system if you do not do actual straight up criminal fraud you get charged for you get to keep all the money you got on the way. So even if math never makes sense there is money to be earned for the time being. And then when it fails, well there is always the next scheme. And next round of people who believe they can extract their share on the way.
On one of the latest Odd lots episodes finally an analyst had an investment thesis that made sense to me:
They think they are building an AI god.
If you think of it in religious terms it suddenly makes sense. Expected rate of return? One scenario has has infinite expected return (some kind of pascals wager/mugging)!
Of course there will be no AGI. Just a planet we'll have to live on where those deluded idiots wasted our resources on some boondoggle. Maybe this kind of concentration of power is a bad thing? I think we are going to get to those kind of questions once the party is over.
Wow, weird to see Kuppy on this platform. Thought it was a mistake haha.
"the industry is spending over $30 billion a month (approximately $400 billion for 2025) and only receiving a bit more than a billion a month back in revenue."
That's called a "bubble". Obviously, this time it is different until it isn't.
I own several books of trig and other tables, three slide rules and a couple of calculators, a working Commodore 64 and an IT consultancy company.
We are fiddling with LLMs as yet another tool. We are getting some great results but not earth shattering.
Tulips are very pretty flowers. I have several dozen in my garden. I have some plants that are way more valuable than tulips in my garden too.
It's an interesting thought experiment, but not sure it's the entire story.
Imagine at the start of the electrification era people went "We'd need to build loads of cables and power plants and stuff that's expensive, lets just stick to steam power".
It's not a bet on this making sense via pedestrian business economics but rather that it'll be a game changer.
...whether that pans out is a technological and societal question, not an economic one in my mind
I've said it before and I'll said it again:
The people investing in AI companies (and the big players spending in AI) are seeking Artificial General Intelligence (AGI). It's the only way they get a return on their capital.
They are investing so they can get there first. Money basically becomes meaningless at that point, whoever owns the AGI owns the world. That's the only way to get a return on that investment.
> As you can imagine, when you’re the vendor, the customer and the investor in a company, there’s a strong incentive to artificially inflate the numbers by signing preferable contracts that use very large numbers, and then round-trip the capital.
That about sums it up.
I generally get frustrated about this type of framing because it’s myopic and narrow.
We are in geopolitically fraught times. Money alone is not capital.
We have been living in an era where financial capital has dominated.
We are entering an era where computing capital, intellectual capital, and military capital will dominate.
The people in control of those when the game changes are the ones writing the rules.
> the industry is spending over $30 billion a month (approximately $400 billion for 2025) and only receiving a bit more than a billion a month back in revenue.
I suspect that this revenue number is a vast underestimation, even today, ignoring the reality of untapped revenue streams like ChatGPT's 800M advertising eyeballs.
1. Google has stated that Gemini is processing 1.3 quadrillion tokens per month. Its hard to convert this into raw revenue; its spread across different models, much of it is likely internal usage, or usage more tied to a workspace subscription rather than per-token API billing. But to give a sense of this scale, this is what that annualized revenue looks like priced at per-token API pricing for their different models, assuming a 50/50 input/output: Gemini 2.5 Flash Lite: ~$9B/year, Gemini 2.5 Flash: ~$22.8B/year, Gemini 2.5 Pro: ~$110B/year.
2. ChatGPT has 800M weekly active users. If 10% of these users are on the paid plan, this is $19.2B/year. Adjust this value depending on what percentage of users you believe pay for ChatGPT. Sam has announced that they're processing 6B API tokens per minute, which, again depending on the model, puts their annualized API revenue between $1B-$31B.
3. Anthropic has directly stated that their annualized revenue, as of August, was $5B [2]. Given their growth, and the success of Claude 4.5, its likely this number is more around $6B-$7B right now.
So, just with these three companies, which are the three biggest involved in infrastructure rollouts, we're likely somewhere in the realm of ~$30B/year? Very fuzzy and hard, but at the very least I think its weird to guess that the number is closer to like $12B. Its possible the article is basing its estimates on numbers from earlier in 2025, but to be frank: If you're not refreshing your knowledge on this stuff every week, you're out of date. Its moving so fast.
[1] https://www.reddit.com/r/Bard/comments/1o3ex1v/gemini_is_pro...
[2] https://www.anthropic.com/news/anthropic-raises-series-f-at-...
The comparison to railroad infrastructure is interesting.
I think the author is wrong on this point however: > Today’s tech just cannot do what will be required of it (AI shouldn’t be dispensing medication when it can’t even count to 7).
The failures of AI are thought provoking, and more so when considered together with other results where AI performs at near expert level on challenging benchmarks. However, perfect reasoning is hardly a requirement. Most humans are not particularly good at reasoning, and most jobs do not need it. Both humans and AI can use calculators and other tools. All that's needed is that the AI is more or less as good as a human, while requiring much less pay.
A good exercise to appreciate the current state of AI might be to ask AI to write an essay about this topic ("how much revenue is needed to justify current AI spend, and draw parallels to the dotcom boom and building the transcontinental railroad"). Try it with two different models, using the deep research mode. I expect the results would be humbling.