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> 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

I doubt that. In my tiny town in India, office workers use the 1800 INR (20$) plans. 1800 INR/month is like... 4% of the salary these guys get. And since this is mostly MS-office and windows explorer and chrome based stuff in very small, non-tech companies, it pays for itself in literally a day. If they _could_ pay less they would. After all they pirate MS office. But the cheap chinese plans dont have any distribution, whereas OpenAI seems to have effectively marketed to them via IPL ads and such. No one there even knows about deepseek. It also doesn't help deepseek and such are focused on the coding market. No multimodal, office plugins, etc.

For personal stuff, people there use Meta AI - mostly because it is directly available in whatsapp and these days they are pushing it by adding a dedicated button for it right on the home page. Regardless, they still call it "chatgpt". OpenAI brand is unmatched.

3 comments

Remember that 20$ is a subsidized rate openai is currently willing to provide. Once that goes down you think these guys would be willing to pay token based billing charges ?

It is not going to go away. Rather they have doubled down and opened 399 INR/mo (4$/mo) plans that as of late have GPT 5.6 Luna.

The reason this works is that you get lesser inference time compute used for queries on these cheaper plans which makes it sustainable and this is enough for the tasks these guys do.

And some local telcos are bundling this subscription as well, so most people just get it for "free". For example, I get Google AI Pro for free with my 350 INR/mo telco plan.

These companies have spent billions of investor dollars and they will need to recoup that cost soon. And then show year over year growth on top of that.

Unless they can massively scale down training and inference cost or implement AGI I don't know what their plan is. Just provide a subsidized plan for the next 10 or 20 years? Their costs are directly proportional to the amount of tokens the LLM produces. How is a monthly subscription plan supposed to account for such costs?

> These companies have spent billions of investor dollars and they will need to recoup that cost soon. And then show year over year growth on top of that.

Or else?

> Or else?

Their investors will throw the CEO under the bus and hire a new one that will enshittify it enough to make them money.

Oh, I didn't know it was that easy to make trillions of dollars in profit. You say there fella that they just need to shittify themselves and all will work out splendid, you say? That sounds like magic.

They dont need to scale down anything. AGI is a red herring.

Even Deepseek at its absurd prices is a very healthy business. Regarding their return on capex multiple, their CEO said they make a six-fold profit on their compute capex with 10 month recuperation. Because of this, all of them are spending aggressively on compute. Apart from that, user acquisition and data labelling are the major costs that are preventing net profitability right now. High quality data labelling is said to not have a cost advantage in china etc as well and they pay global market prices for this. I can confirm this is true in india too the model companies I know pay global market rates for high quality data.

> Their costs are directly proportional to the amount of tokens the LLM produces. How is a monthly subscription plan supposed to account for such costs?

By limiting the number of tokens you use per month? per week, per hour? And by limiting the inference time compute dedicated to each turn in each session.

> need to recoup

the world economy has shown itself capable of handling decade-scale recouping easily

The main obstacle today in the inference business is the high variability in usefulness/token. This does not need to be solved, but rather only quantified. Innovation is needed to be able to reasonably bound this variance for a reasonable subset of tasks. And we are making progress on this. Naturally though, tasks on the frontier of current capabilities have very high variance. The last couple of years has followed the pattern where tasks no longer on the frontier have reduced variance, but I am not claiming this will continue to be the case generally as the frontier improves.

> Even Deepseek at its absurd prices is a very healthy business. Regarding their return on capex multiple, their CEO said they make a six-fold profit on their compute capex with 10 month recuperation.

That's "theoretical profit" - in some imaginary world where the free subscribers would pay the top tier cost.

https://techcrunch.com/2025/03/01/deepseek-claims-theoretica...

I am not talkiong about that, I explicitly said a interview by the CEO. Which is here:

https://archive.is/NLuG9

> their return on capex multiple, their CEO said they make a six-fold profit on their compute capex with 10 month recuperation

I am not familiar with chineese model companies as much as I am with US based ones so I don't have much to say beyond that the CEO is incentivced to pump up those numbers.

> By limiting the number of tokens you use per month? per week, per hour? And by limiting the inference time compute dedicated to each turn in each session.

If this was so simple I don't know why GitHub copilot went to token based billing at my company.

> This does not need to be solved, but rather only quantified. Innovation is needed to be able to reasonably bound this variance for a reasonable subset of tasks

It's much better to make a business case for them after finding this bound right? Currently I can't use copilot for anything serious since I cannot predict how many credits one request is going to consume.

Your point about non frontier tasks using less tokens makes sense. As you said, let's see if it holds up

Return on compute capex is tied mostly to gpu lifetimes so I don't think it will be different for the American companies, who also charge much more being closed source.

> If this was so simple...copilot...

Github copilot still has subscriptions. They moved away from request based accounting to token based accounting for the usage limits, as did cursor, and everybody else.

subsidized versus API pricing sure, but do we have concrete proof of how much it is subsidized versus the breakeven inference costs of those tokens?

I remember the piracy era, and it used to be a joke whether anyone could actually stop it.

Eventually MS dealt with piracy by going after firms, and finally by offering something cheap enough.

Between the legal aspects, and the fact that its MS office, paying Rs 1800 a month is possible.

I’ve been trawling every source I can find to understand what the story on the ground is, and when it comes to productivity it’s a huge mixed bag. The variance in outcomes between independent coders, frontier labs, someone in SV and someone in India is mind-bending right now.

Most firms which talk about their AI plans are not seeing traction, and the AI projects are going to the same place that the ML projects used to go to die.

It’s at the individual level that I am seeing productivity gains, however that isn’t something firms are happy to hear right now.

>Eventually MS dealt with piracy by going after firms, and finally by offering something cheap enough.

Sure, because they realized that selling software at any cost because software has extremely low overhead. Selling 100M Windows at 2 USD was easier and my guess is a wash at that price.

However, AI is not selling software, it's selling hardware usage which is not free and has real cost. If Rs 1800 which is 20 USD is not profitable, then they will not offer it.

First I'd like to understand what are these "small" non-tech companies in tiny towns 45k to its employees? The last labor survey had average Indian salary pegged at 21k.

> it pays for itself in literally a day

Most comments in the thread say this but don't explain the how. How does it pay for itself "literally" in a day? Through productivity/time savings? How exactly?

I can only assume people are talking about productivity gains. Have their been reports of increased productivity or layoffs in tiny town, small non-tech companies in India that I am unaware of? What is the source here? Without a concrete source this is mostly anecdotal evidence and not backed by data.

Today there is novelty factor of AI. Questions or tasks which required people to really plan out now can be tested through AI quickly. To many people that seems like progress and that AI is paying itself. But most of it is busy work and isn't adding value to the companies. Given that everyone is on the AI hype train they might not be looking at the expenses closely. With time they will start poring it over and cancelling contracts en-masse.

> Don't explain the how

They processed 10 cases in a day rather than 7. In some cases, new work took its place and the company makes a little bit more money, but this is a lagging effect naturally.

> Have their been reports of increased productivity or layoffs in tiny town, small non-tech companies in India that I am unaware of

PWC et al don't go around doing reports on these tiny companies bro. The only information you get is if you know people in that place and they tell you yourself, which is how I got to know.

> Given that everyone is on the AI hype train they might not be looking at the expenses closely

Yeah maybe F500 ceos exposed to LLM coding agents are on the hype train to some extent, but these M.S office pirating small companies are not on any hype train lol.

> The last labor survey had average Indian salary pegged at 21k.

Averaging across the whole of India gives you a useless number. In the richer regions, 21k is less than what a full time food delivery/quick commerce driver makes.

> PWC et al don't go around doing reports on these tiny companies bro. The only information you get is if you know people in that place and they tell you yourself, which is how I got to know.

I knew the post screamed "trust me bro" vibes and now I know for sure. Government does Labor force surveys. Here's one for last year. Point me out how the participation is changing through AI paying for itself. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2246009&re...

> Averaging across the whole of India gives you a useless number. In the richer regions, 21k is less than what a full time food delivery/quick commerce driver makes.

See the above survey for the figures. But then this is like someone using California to say how well America is doing.

> Government does Labor force surveys.

I am very aware of MOSPI. We collaborate in compiling the data for them! BTW there is an MCP server now: https://github.com/nso-india/esankhyiki-mcp.

What do you expect to glean out of PLFS though? Why would broad based labour force participation or other similar data change due to AI paying for itself? What?

If you meant to somehow glean some information out of the wages report, note that the wages survey was last done in 25. The quarterly and monthly bulletins don't contain wage data. Regardless, these are too macro to give any sense of any change at all happening in AI-accelerable knowledge work.

By "PWC report", I meant one where they went to firms and recorded using the same methodology some same notion of AI "paying for itself" or "increasing productivity".