> AI Cannot Afford To Slow Down — It Needs $3 Trillion Or More In Revenue By End Of 2030 To Sustain Its Existence

Is this true? With the total 2024 wages being 11.7 trillion USD [0], and nonfarm payrolls totaling 158,000 in the same year [1], it's an order of magnitude higher than my back of the napkin guesses I've made that AI needs to take or create 1/20 jobs minimum to break even.

[0] https://fred.stlouisfed.org/series/BA06RC1A027NBEA [1] https://fred.stlouisfed.org/series/PAYEMS

Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.

Consumer revenue is only a smallish share of the puzzle, but still:

If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?

Assume Google will have similar tools in their phones, and Google search will continue to have the offering it does.

In short, where is the evidence that once Apple's tech exists, consumer AI is worth, to Anthropic or OpenAI, anything noticeably more than that $1B a year?

Maybe OpenAI strikes a deal to put something in Samsung phones. Let's say Samsung is ten times as desperate as Apple (which is how it looks, often). Still only $10B a year?

2026 consumer revenue projections from OpenAI are pitched at $14-15 billion, apparently. If they get that, it's the only year they will get that, because by late this year, everyone with an iPhone will have something useful built in.

Ed Zitron is a mouthy British rabble-rouser, but I think he is probably mostly on the money.

BTW, one thing for sure he is right about are the economics, as of today there is no way these massive investments are gone be paid.

Who makes consumer devices? Google

Who makes operating systems? Google

Who makes browsers? Google

Who makes the world’s most popular websites? Google

By the time 90% of average internet users get to chatgpt.com or whatever, they already went through several Google chokepoints, each layer is one more place Google can answer their questions.

And that’s not even getting into the chips, the data centers, the data, the talent, the consumer apps, the enterprise apps, the cloud platform, the brand, and of course the biggest cash printing machine in human history.

You would honestly have to be insane to bet against G.

At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.

I interpret the exact same evidence in the opposite direction. A year ago the idea that a company would spend $1,500/month/employee on AI tooling felt absurd, what could people possible want to do with AI that would cost that much?

Then coding agents (and, increasingly, general purpose agents) happened and suddenly companies are having to set limits because otherwise the demand from their employees is too high.

The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?

I think a much more reasoned critique of AI is that of Tyler Cowen, whose argument is basically that most processes aren't constrained by lack of intelligence but by organizational and social factors which mean for AI to be useful you have to redesign organizations and work to take advantage of what AI is good at. Since most organizations are fairly bureaucratic that takes a while, especially in the large industries that are the most economically important.

Ed's criticism of the large AI companies seems particularly misguided to me since they are the ones actually advancing the technology and seem to have real moats given their access to large amounts of training data from their users. I don't see any possible future in which 5 or 10 years from now there is less AI than we have now and I would expect usage to be much higher.

We are only five or six years into the leap LLMs represent. For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up. It took so long not because of the physics of radio waves required time to mature and improve, but because everything else needed to profit from it did require time.

To me, LLMs are not so much AI as it is a building block. Radiowaves maybe, or the equivalent of transistors. We are already seeing that it's possible to chain LLMs into agents. Currently, price is a strict limiting factor for coding and agents.It's probably fine-ish if all you want is Claude Code or Codex, but there are many other possible compositions of LLMs that most people don't dare to experiment with. For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost. Were prices of inference hardware go down and inference algorithms keep improving, I'm convinced (and afraid) we would see things very difficult to imagine today.

As an example of obvious wrong things, I remember a tweet of his where he was mocking people talking about agents and agentic coding. He was kind of saying that he was going crazy as agents weren't a thing really and people talking about them like they were real. Something like "agents?! what agents?! these guys hear themselves?!". The answers were full of hundreds of people patiently explaining how they were actually _using_ agents. This wasn't in 2023, it was a couple of months ago.

He just has an audience and an engagement target. His objective is clicks, not informing.

Edit:

> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.

Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.

Apple themselves have said there is usage limits, with a subscription upgrade for more usage. So clearly AI Labs are directly competing on that front, it's just a normal default/chosen decision. Considering there are defaults and still successful competitors (eg. safari v chrome), there's no reason to think that competition can't handle this too.

Edit: I want to add that Google is also probably willing to give the model away at a discount to its true value in exchange for guaranteeing that their primary competition (who has tons of cash) won’t have an economic incentive to enter the foundation model training arms race.

Most users who actually want these features for anything more serious than summarization and style updates will probably find value in a modest subscription or ad-supported tier of higher quality models, even if just for occasional usage. Apple can provide this, but once you're comparing features, for many Gemini/Claude/ChatGPT may be a better fit.

Oh, and I think there is an unfortunate but real risk that once again, apple totally over-promises here, and their AI models that they ship end up being pretty poor, and that drives users further into subscriptions.

> If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year

Ok let me read the thing so I can make up my mind… start scrolling down and get slapped by some subscribe pop up.

That’s where I decided to just cut my losses and go do something else.

Also because we now have a massive demonstration that vastly more efficient hardware is desperately needed.

Similarly other effective efforts towards on-device AI like Nvidia RTX Spark PCs and 2bit quants of strong models like DS4.

So inevitably, significant investment will be going into vastly more efficient CIM efforts like Mythic AI and new FeFET devices etc. in order to make human-level and beyond AI at scale feasible. There is so much demand for this and the power requirements of current hardware are so excessive, it seems unlikely that the data center build-outs will be able to recoup their costs before the more efficient paradigms make it out of the lab and start scaling.

So when I see monthly budgets in the thousands for developers at some larger companies, I'm curious to learn how they are managing to spend that kind of figure: how much code/documentation are they feeding into their prompts, are they using agent orchestration systems to make the code factory run 24/7, and how much value is coming out the other end versus before?

And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?

Those are the kind of questions I'd love to ask - I just wonder how much stuff is truly cutting edge and how much might be wasteful?

Now that you can get Gemini, operated by Apple (with the Apple privacy features that come along with that), why would you ever consider going Android/Pixel (outside of running GrapheneOS, but I'm talking regular consumers here)?

Google isn't even making anything on the deal with Apple. They pay $20B/year to be the default search engine. This is Apple just giving a $1B a year discount to that to be able to license Gemini.

Anthropic is growing way faster than doubling yearly so don't think this is entirely implausible

Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.

The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.

For example,

https://news.ycombinator.com/item?id=48464333

The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today. To have tools that can understand, manipulate, and produce it is a massive leap forward.

Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.

This is the part that concerns me the most, AI companies will start bidding on increasingly contentious contracts out of desperation. In practice, this means building services that facilitate killing to anyone they're not legally prevented from doing business with... and even then, they may be able to do business with them still through intermediaries.

Anecdotally, $dayJob consumes Anthropic models via Azure subscriptions which lend themselves pretty neatly to the spending dashboards Ed mentions are missing from Anthropic themselves, and finance seems ok with the current usage, but there's no real hard incentives internally for AI usage either.

I guess Q3-4 are going to be interesting to see where this all goes.

They have ai glasses and integration into instagram and facebook as the other avenues. I don’t see ai glasses as compelling yet, and don’t know how much more ad revenue or user engagement they can squeeze out with llms baked into the IG of FB flows. They are spending a lot and not seeing any returns. Am I wrong in being pessimistic about meta with AI?

Maybe we'll get there, maybe not. These days I only hear of datacenter investments.

Who writes like this? When you lead with "everyone who doesn't agree with me is a lying cheat coward imbecile" I think we should just turn the volume down on you to zero.

This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.

I really love LLMs for debugging and rubber ducking, but I kinda want to write all my code.

LLMs tend to have a hard time understanding composition.

They are possibly in a winner take all death race against each other.

The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.

The sunk costs are irrelevant when it’s a question of survival.

Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.

Anthropic is winning this race by a country mile right now.

This is such an important future bet for these companies that the trillions must be spent because there’s no future or a greatly diminished future for some of them unless they have ownership of the technology.

Impressive.

[0]: https://www.wired.com/story/ai-pr-ed-zitron-profile/

How people take this seriously? Anthropic is at 45B ARR S-1 shows inference margin climbed to 70% (obviously could drop) So where that 200B number is coming from ?

Also, if Anthropic or OpenAI fail in their revenue requirements I believe they would be absorbed by the guys with the money printing machines (Google, Microsoft and Meta).

That said, I think his voice is useful as a counter to the mainstream opinion.

Given the amount of investments, approaching AI from the angle of economics seems correct.

We all have some level of personal experience using AI/LLMs, both chatbots and coding tools, and I personally enjoy using them, but I am sure this experience is relevant in this discussion.

I also enjoy luxury hotels, gourmet food, jet skis and helicopters, but this is not something I indulge in often because of the cost-utility ratio.

The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.

I have found agentic coding to be extremely useful for a bunch of small, middleware, very focused bits of software for small businesses:

* A company had a very specific scheduling need, they needed to move about 8-15 staff around with a bunch of different shifts, and have custom reports on who was working how many hours, and have the employees get a nice clean email summarizing their schedule

* A manager wanted a very simple "let me send a text to add a to-do to the group list" need

* A sales team of 3 wanted to be able to type pricing of raw goods into their phone, have it compared to other market sources, and have it text the other 2 salespeople and their manager when they were out in the field

All of these were coded with Codex in about 4 hours with further refinements over the next week of back-and-forth with the people using the tools.

I suppose yes we could have found some custom middleware solutions that did similar things, but it's nice to be able to make a web page or tiny mobile app that just does EXACTLY what the person wants.

It's hard to do that and then listen to someone who says it's all just garbage.

The angry polemic that goes on and on and on with cuss words used liberally is just meant to evoke emotion and cathartic resolution to the type of people mentioned above. Not truth.

The thing is, there are a lot of people that find comfort in what he’s writing - primarily because it’s a coping mechanism against how quickly things are moving and a way to deal with being left behind. When you spend time, years, building institutional knowledge and making a whole identity out of it, you obviously will feel bad with the threat of it being commoditised.

I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.

Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.

Internet continued to thrive and grow even after the stock market came and went, it took 13 years to roughly nasdaq to recover but the explosion of GDP from internet has been largely decoupled from the previous bubble boom and bust.

If you use the stock market as a yard stick to project new revolutionary technology we shouldn't have had trains, internet. In fact internet should've stopped with the bust of Nasdaq and everybody would've moved back to using paper but we didn't it gave rise to the next wave of economic output powered by this new tech.

I don't see AI to be any different.

That seems doable. Next generation architectures and the models they produce are accelerating progress. More capable with less data and compute, which ironically will drive more demand, aka Jevon's paradox.

> If you are someone in the executive team of any major tech company, know that your employees are, for the most part, completely and utterly miserable.

I agree this is a problem. Adopting too eagerly and too early, and not listening to feedback from the people who are using these tools is a recipe for disaster.

Ed is confused between whether AI is useful, and whether the current level of funding and valuations are sustainable. The following statements can both be true:

1. AI is already quite useful and will continue to be so. This is true even if AGI doesn’t happen.

2. The funding and valuations of many AI companies are too far ahead of their skis, and will probably roll back. Some may fail entirely.

About the “where’s the productivity in AI?” question: I think it’s entirely possible that the primary benefit of AI will not be top-line growth but reduced costs (through reduced human labor). Companies will need to reduce prices to prevent losing market share to existing or new competitors, meaning that GDP may not increase, but costs will.

A good analogy might be networking companies and infrastructure companies during the dot com bubble. It devalued a lot of companies but the internet stayed. A lot of dot com companies didn't make it. Much of the infrastructure investment did not go to waste, however. Nor did a the technology go away.

I think it will be the same with data centers, related infrastructure, GPU hardware, algorithms, OSS components, etc. for AI companies. More companies need that stuff than is currently available. The ones that don't make it will have a lot of assets that they can pass on to the one that still have a chance. I don't think a lot of that stuff will get decommissioned or will be underutilized. It might get a little hair cut in value though. And like during the dot com bubble, some companies actually survived and did quite well. Especially those in the business of selling shovels during a gold rush.

After the inevitable consolidation that follows the next logical stages in the hype cycle, I don't think AI will go away. It might be a bit of a bloodbath for some silicon valley investors that placed the wrong bets in the last few years. But that's the price of doing business over there. That doesn't mean it's all bad. And the smarter ones probably spread their risk enough that they still might come out looking alright.

And like with the dot com bubble, many financial types have no clue what is happening and are running around like headless chickens. Which is why they ended up sinking a lot of money in exactly the wrong things. You'd hope they would have learned something.

But articles like this suggest that that might be too much to hope. They still don't really get how technology tends to not stagnate and might continue to deliver potential for performance and cost optimization. The current level of investment is only unsustainable if that doesn't happen and nothing else changes. I don't think those kind of closed world assumptions are a safe bet at all.

> No matter how horny or flaccid you are

These analogies are great.

Some are still steadily increasing prices.

A 1TB NVME drive - a good one - cost about $70. Now it costs anywhere from $150 for shit-tier drives to $300+ for the higher end stuff that used to cost $100-120.

I don't know much about the economics side; TFA gives a barrage of stats that seem to make a compelling case for bubblehood. OTOH, the claims about the utility of LLMs being unmeasurable are weak (the same criticism applies to hiring programmers, or indeed most office workers) and the metal spider straw man is frankly embarrassing to anybody who has actually used recent frontier agents for programming and seen what they can do.

Bloomberg is interested in what he has to say

But not HN commenters

It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.

In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.