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.

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.

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.

That about sums it up.

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.

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.

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...

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.

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.

AI data centers have an impossibly short runway to achieve profitability

https://news.ycombinator.com/item?id=45546305

2) llm-provided tax/legal advice alone was worth hundreds of euros for me

3) there is no easy way to capture all this value. I still think edge intelligence/autonomy has a way to pay for the rest of the party, because those will be physical things. People eagerly part with money for things. Governments and enterprises will maybe pay for cloud services, if the price will be right

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.

There might be a slump.

But today's AI companies (both those you've heard of and those you haven't) are here to stay.

It'll be okay, don't panic.

Phase 1) Spend $400 bn / yr less a bit of income

Phase 2) ? - AGI aound 2030 - replace workforce or double output. Worth >1x world GDP or ~$100 tn ?

Phase 3) Profit!

(some discussion in "The case for AGI by 2030" https://80000hours.org/agi/guide/when-will-agi-arrive/)

Fund phase 1 by hyping it to investors

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

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.

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-...

At least for gaming, GPU performance per dollar has gotten a lot better in the last decade. It hasn't gotten much better in the past couple of years specifically, but I assume a lot of that is due to the increased demand for AI use driving up the price for consumers.

Why wouldn't Moore's Law continue?

A few thoughts:

1. The comparison to previous tech bubbles is apt - we're seeing massive capex without clear paths to profitability for many use cases.

2. The "build it and they will come" mentality might work for foundational models, but the application layer needs more concrete business cases.

3. Enterprise adoption is happening, but at a much slower pace than the investment would suggest. Most companies are still in pilot phases.

4. The real value might come from productivity gains rather than direct revenue - harder to measure but potentially more impactful long-term.

What's your take on which AI applications will actually generate enough value to justify the current spending levels?

First of all, warfare is not winner take all. That's a sort of video game naive conception. The famous quote "War is politics by other means" is much more accurate.

When armed conflicts happen, it's because the belligerents have specific objectives, and very rarely is that objective "the total obliteration of the enemy" vs something more specific and concrete like territory, access to natural resources, the creation of a vassal state that can be exploited, or sometimes purely ideological (nationalist notions growing into the idea that a people are entitled to an empire).

Anyhow, the point is warfare is not a winner takes all game of obliteration.

But also the idea that the future of warfare will be all autonomous weapons is massively overweighting on drone hype, and ignoring that a lot of the fundamentals haven't changed since the days of Bismark, despite the rise of drones, computer vision algorithms, etc.

A simple example is Ukraine, where the battlefield is essentially defined by the combination of traditional artillery, mines and similar fortifications, and simple observation drones that don't have any particularly complex AI. The combination creates a 20 km "no go zone" that has nothing really to do with autonomy.

In fact, the more AI centric loitering munitions provided by US/EU firms have performed quite poorly in Ukraine, which is why they're favoring much more simple implementations like using hobby FPV drone components, or remote piloting via GSM modems, etc.

Will these technologies play an increasing role in future conflicts? Of course. But they're not going to completely upend things, or obliviate more traditional platforms.

Heck, another example would be simple hand coded AIs have been better than humans in dogfights for decades now. And it matters exactly zero for real world conflicts, because what fighter pilots actually do isn't a Top Gun movie.

If those companies replace a low 2-digits percentages of the developers, and capture their entire salary, it's still not enough to reach the depreciation numbers on the article.

On 2, that could justify it... Except that we are talking about fucking LLMs. What do anybody expect LLMs to do in a war that will completely obliterate some country?

Even if we grant that this is possible, have any of these execs actually thought through what happens when their competitors also replace large chunks of their workforce with AI and then begin undercutting them on price? The idea that "our prices will stay exactly the same, but our salary costs will go to zero and become pure profit instead!" is delusional even if AI can actually replace large numbers of people, which itself is quite doubtful.

Oh, AI.

It is artificial but it is not intelligent. A LLM (int al) is a marvelous thing. I find sheer joy in conversing with a "gpt-oss-20b F16" that runs on a £600 GPU and a slack handful of CPU and RAM because so little gives so much.

OpenAI announced a few months ago that it had finally cracked $1B in monthly revenue (intriguingly, it did so twice, which makes me wonder how much fibbing there is in these statements).

I'll also say this: the fact that AI companies prefer to tout their usage numbers rather than their revenue numbers is a sign that their revenue numbers isn't stellar (especially given that several of the Big Tech companies have stopped reporting AI revenue as separate call-outs).

I wouldn't believe it if you told me even 1% of those users are paying. 10% is simply ridiculous.

Respectfully, the idea of sticking ads in LLMs is just copium. It's never going to work.

LLMs' unfixable inclination for hallucinations makes this an infinite lawsuit machine. Either the regulators will tear OpenAI to shreds over it, or the advertisers seeing their trademarks hijacked by scammers will do it in their stead. LLMs just cannot be controlled enough for this idea to make sense, even with RAG.

And if we step away from the idea of putting ads in the LLM response, we're left with "stick a banner ad on chatgpt dot com". The exact same scheme as the Dotcom Bubble. Worked real well that time, I hear. "Stick a banner ad on it" was a shit idea in 2000. It's not going to bail out AI in 2025.

The original content that LLMs paraphrase is itself struggling to support itself on ads. The idea that you can steal all those impressions through a service that is orders and orders of magnitude more expensive and somehow turn a profit on those very same ads is ludicrous.