But the question is how much are people willing to pay for AI.

I use AI every day at work, but I only pay $20. That's the most I will ever want to pay. And, so far, it gives me everything I need.

If OpenAI or Anthropic suddenly said "Sorry, the game is up. You'll have to pay $100/month now", I would 100% look into cheaper Chinese solutions.

I suspect the AI subscription (or API) economy is whale economy. You have a small, small percentage of users that are happy to pay whatever it takes - while the vast majority will either use the free tier, and then the next group will pay for the cheapest or next cheapest subscription.

EDIT: And I'll echo what another user wrote here. The VAST majority of office users around the world don't work for tech companies flush in cash. Even adding something like $20 - $100 subscriptions to every user is a serious enough financial obligation that it needs to go through budget planning / boards. Even more so for all the government workers around the world.

And I know very few white collar workers who are, beyond "I guess Google Search is AI now". Each their own bubble, I guess.

AI has some value in some scenarios. Most models still fail badly enough often enough that I personally would not pay $80 for it. I would make use of it if it were free, or $10 / month, but it would have to significantly improve if I were to ask my employer to pay $150 / month for it - let alone $1500 / month.

The evidence is obvious: everyone and their dog has access to highly subsided AI agents right now. Literally every single company is pushing its employees to trial AI - some even forcing AI use. So why aren't we seeing a giant white-collar productivity boom yet?

The idea that every single white-collar worker is going to get a $800 / month AI subscription is ludicrous. The big question is, can the AI companies survive off everyone getting a $5 / month subscription, with a handful of workers getting a $500 / month one?

> Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.

Uber has a fairly large moat: the regulatory mess they’ve waded through and the collection of drivers willing to drive for them. Sure, it’s probably easier to start a competitor now than 20 years ago, but there’s still a substantial network effect — a transportation service needs a lot of cars to provide good service, and they need a lot of users to keep those cars busy.

An AI service like chatgpt.com or claude.ai costs literally billions of dollars to develop, except that several Chinese companies are currently happy to spend that money and release the models and weights for free. And even a small company can rent the datacenter capacity they need, at least at moderate scale, and they only need enough scale to reliably fill up the batches on a handful of servers. There is no obvious network effect — a tiny Uber with a total of two cars in a city is useless, but a tiny AI provider that only services 2000 users is just fine as long as someone else provides the models weights.

And the costs to run these services keep coming down. The numbers people are reporting for unquantized DeepSeek v4 Flash 0731 on 2x RTX 6000 Pro [0] using open weight models and open source inference stacks are astounding, and one single copy of this could probably cover the entire inference usage of a decent sized business doing the kind of desk work that OpenAI and Anthropic could need to monetize to cover their expenses. (Using bespoke models has value but may reduce available usage of a single inference machine. Quantizing can make everything cheaper and is often good enough.)

This is not to say that these companies won’t make it on the strength of their enterprise offerings (never underestimate the willingness of enterprises to overpay for services they’re already using or by becoming the next Googles and finding other revenue sources beyond just inference.

[0] I admit to some skepticism that the pile of sketchy, obviously AI-generated patches to SGLang that allegedly achieve this are doing what the say they’re doing, but the point stands.

The real question is not whether there will be a market for AI, it is obvious that there will be one (as it was obvious the Internet was a gigantic thing in the dotcom boom). It is rather whether the value will be in the models (and if so in which?) or the infrastructure or somewhere else.

Interesting thoughts from Dwarkesh Patel on the future of models [1]. In summary, no moat if a client can switch to a competitor from a dropdown when all models are similar-ish (so models are commodities), but to really compete with humans, AI needs to start "learning on the job", i.e. accumulating experience like an employee (as opposed to the training / inference approach where the model isn't learning after it is released). If your model does that, then you have vendor lock-in as you can't replace an experienced model with a competitor. Then the value is in the model.

As in the 90s, this could go in all sort of different directions, hard to make predictions. And remember the 90s: the value was in the browser (you had to purchase it, eg Netscape), then the browser came for free with the OS (no value in the browser), then the browser was the most important thing in the world as it was control over the default search engine, everyone thought portals was the most important thing to control, so ISPs had their own applications accessing the internet from their own portal, etc - none of those people were stupid.

[1] https://www.youtube.com/watch?v=iewm45atodE

    Google funds Anthropic
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          v
 Anthropic promises to rent Google's TPUs
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          v
 Google guarantees the infrastructure
 needed to fulfil Anthropic's promise
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          v
 Wall Street lends against Google's guarantee
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          v
 Borrowed money buys Google-designed TPUs
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          v
 The TPU purchases "prove" demand for Google TPUs
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          v
 Anthropic's compute capacity and valuation rise
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          v
 Google's investment in Anthropic rises in value
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          v
 Higher valuations justify still more financing
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          +--------------------------------------+
                                                 |
                                                 v
                                           DO IT AGAIN

$80 per month in a 5,000 people Enterprise is 400k per month which translates to ~5m per year. That is easily in the 8-16% of their total IT budget. I don’t know you, but companies don’t like to increase recurring cost. Is AI good? Yes. Is it clear how it generates enough ROI to justify a material IT cost increase? No. This is the problem now, not how much white collar workers use AI, but what is the productivity narrative of it. Because AI is so generic, there is no clear specialized use case, like when Salesforce invented the CRM for example.

That’s not the argument though. The argument is that conversion rates as per OpenAI are about 5% and to serve the other 95% you need to build significant infrastructure that costs hundreds of billions annually. So for every white collar worker that you know and uses it and pays for it there are 20 free loaders. And mind you, 5% conversion rate is extremely high already as a number per se. It won't magically reach 20% in a couple of years. Chances are it will fall particularly for the enterprise segment due to the advent of the Chinese models.

The reason uber didn't loose that much business is because it replaced the established businesses and left most consumers with little alternative.

That's not the case with AI - unless the frontier labs achieve AGI or some sort of super intelligence that lets them create infinite economic value (at which point, any further discussion is pointless for obvious reasons), the average worker can do most of their tasks with 5% of the api costs using an open source model from China and achieve the same results.

The problem is the value you speak doesn't seem to be materializing. I barely know anyone who sings the praises of saving time with AI, but I know plenty of people who complain about peers doing a shoddy job because they used AI to do their work, or having to clean up someone else's AI generated slop.

But pay for who? $50 for a cheaper competitor? $20 for a chinese one? Or just buy hardware and host/co-host for privacy? And to balance the insane spending even your $60-80 example would be hardly enough from that white collar audience.

People may pay less than they are willing to pay as competition drives the price down. I paid $20/mo a while which I was happy with but I found I could get similar for free so pay $0 now.

At my $day_job we are forced to use a shared virtual machine for all work activities. The Windows instance has 64gb of RAM, 8 virtual cores of old EPYC CPU and no GPU acceleration. It is also shared between 6 users.

They will not spend extra 20$ per user per month to add compute and increase productivity.

The question is not whether LLMs have value; the question is who will earn how much money off them. I don't see any scenario where the companies that put hundreds of billions into their models make that money back.

If you need fewer workers due to the increased productivity, then the number of white collar workers (the TAM) won't be as large as anticipated.

Why $60/mo when you can pay $5? Why $5 when you can pay $1. Unless you happen to be in one of the industries OpenAI focuses on you would always just use a Chinese model. Even now, but especially in the next 1-5 years.

> To think that people won't pay $60-$80/mo to continue using it is wild to me.

I'm fine paying $200 per month. Companies seem to be fine at $2000 per month but I would balk at that. Companies are already balking at $20,000 per month.

So, we KNOW what the numbers are. If corporations are only willing to pay $2000 per month per developer for all the developers in the US, the amount of VC investment already exceeds what they can get back from 10 years of revenue.

Your base premise is flawed. I know plenty of white collar workers who do not use AI for their job.

I agree that it's working good enough and that it's here to stay: mostly everybody around me uses AI too but...

> Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.

The competition from open-weights models is already putting pressure on how much this can be billed. That's today, with stuff like OpenAI announcing 80% cuts on terra and luna. Tomorrow, in addition to the pressure from open-weights models, there's going to be pressure from chinese hardware: Tencent, Alibaba, etc. are all already working on their own AI chips.

What good would, say, a SOTA open-weights model running on chinese chips (chinese chips located in China or elsewhere btw) do to, say, Meta or Oracle's insane AI investements?

And it doesn't even have to be chinese chips: what good AMD AI chips doing inference by having weights etched on silicon will do to OpenAI and Anthropic?

All the people, especially here on HN, who were explaining six months ago that OpenAI and Anthropic models where so good for coding that coding was solved once and for all can now run better open-weights models than the proprietary ones from six months ago. For a fraction of the price.

If people, instead of paying $20 per month pay $80 per month for their AI subscriptions, what makes you believe that money is going to go to one of these companies participating in this $2 trillion debt?

And really, in which new domain have we seen the first movers being able to keep a lifetime grip on the market without getting their arse handed to them by the competition? Things moves quickly today: I'm not sure OpenAI and Anthropic are the Ford equivalent for AI.

For all I know OpenAI is "La Mancelle":

https://fr.wikipedia.org/wiki/La_Mancelle

And Anthropic is Panhard.

That AI is here to stay is a given. That people may be willing to pay more is likely (even though IMO they won't pay 4x more for something only marginally better). But that the current players are going to stay on top even though the competition closed the gaps to weeks?

I don't buy it.

I avoid subscriptions like the plague and I was getting enough value from Claude that I was paying for the Max plan for a couple months. I don't know how much Anthropic needs to make to be sustainable but I think it's worth north of $50 or $100 per month for many knowledge workers.

Currently, a $20/month openai subscription gives roughly $400 in codex api credits. That doesn’t count what you can get from the web chatbot.

So it better be that api tokens are seriously overpriced. There is value at $20/month. I am not so sure if it’s $400/month or more.

A bit of napkin math.

How many are the white collar workers? 300 mln? even with $100 subscription each, the monthly revenue for your addressable market rises to $30 bln. If we increase the figure to half of humanity and they are 3 bn, you multiply it upto 10 and $300 bn monthly, $3.5 tn annually.

Now start cutting it down keeping in mind that not everyone is an office worker, does not need $100 plan, has access to cheaper models, could get ai through someone elses subscription or local models. I'm not sure how down it will get, but it can get down a lot and will have to be split among few players. At least for me it does not seem unrealistic that the revenue won't be enough for everyone.

After using it a bit I’m convinced it’s not, as a whole, a bubble.

Bubbles exist around it. Data center construction may end up overbuilding, much like the dark fiber thing during dot.com, since better chips and models will shrink power and space requirements over time.

But the core tech is the most powerful new thing I have seen since discovering the Internet, at least. Maybe more.

using how?

Pays for itself how? Customers don't care if a business saved time on bullshit paperwork. It needs to actually convert to increased competetiveness which leads to increased profit to pay for itself. But if everyone is doing it anyway businesses will quickly realise they're just paying an AI tax and look for ways to eliminate it. Maybe by just not doing the bullshit they got AI to do.

Mods, can you explain what happened here?

This story had 49 points and 81 comments in about four hours and was on the front page. Then it seems to have disappeared from the top stories feed completely.