There's a few major problems with the article. The most obvious is that frontier labs are not charging remotely close to the cost of tokens; afaik most estimate north of 80% profit margins. As a reference, providers are profitably providing Kimi K2.6 for $4/1Mtok out. Is that as good as Opus? No, but it's probably at least Sonnet level, so that's ~4x cheaper than Sonnet while still being profitable to serve on the margin. So you aren't plausibly getting into actual subsidization territory until you're over 5:1 sub to nameplate token costs.

How many tokens can you realistically burn through in one chat session? Opus and many other frontier models do maybe 60tok/s, less 250k/hr out. In you can use more, but in most cases cache is 5-10:1 cheaper than new input. Say you average 500ktok in, 90% cache, per request. That amounts to 100-150ktok in new input-equivalent costs, which in most cases is ~20-30ktok in output-equivalent costs. Do a request every minute, that's a total of about 1.5-2Mtok/hr. At API prices that's $50/hr for Opus, but really it probably only costs Anthropic $10/hr to serve that.

That said, even if a developer is burning $50/hr, many, many employees at large companies cost more than $100k/yr to employ all costs considered, so making them say 20-30% more productive can easily make that worth it for most. If the labs shave their margins ultimately to more like 20-30%, you'd have ~$15/hr in costs to use the services, and nearly every white collar job is way over 30k/yr to employ. If your salary is 80k, you probably cost the company 200k all in, so making you 15% more productive offsets the $15/hr cost.

So first party providers are not in a horrifying position or anything from a subsidization standpoint. The people in bad shape are Cursor and Perplexity, who don't have frontier models and are dependent on the open source community, which is typicly 6-12 months behind the frontier. They have to pay full freight API costs at 80% margin for the big boys to serve their harnesses, which is indeed untenable, and they'll have to either force users to use open source models and/or in house models they can serve at-cost or they will have to charge vastly more.

Gemini, Claude, and ChatGPT first-party services like Antigravity, Codex, and Claude Code are not in serious trouble though.

It's not even a fixed cost per token (even though it's billed that way, and that's still miles better than a fixed-price all you can eat). You're incurring a cost that's proportional to generated tokens times the context for each (plus the prefill cost for any uncached input), so the expense grows quadratically with your average generated context.

This all becomes extremely visible when trying to do agentic coding with local language models - you quickly realize that controlling context length and model size is just as important as avoiding wasted effort. The real scam is not AI Q&A ala ChatGPT, that's actually quite viable - though marginally less so as conversations grow longer. It's agentic coding with SOTA models and huge contexts.

Isn't this akin to saying Big Pharma companies could easily make money if they just stopped doing expensive research? The massive R&D spend is the core of the business plan; it's the only reason they can demand high prices in the first place. Once OpenAI stops spending billions on training, their pricing power vanishes because users will just migrate to Anthropic or whoever releases the next frontier model. Would imply there'd be space for only one to outlast them all in some sort of war of attrition (perhaps similar to silicon industry).

Your math is pretty bad 50$/h is a yearly cost of going by swedish standards, 50$/h × 40h/week × 48 weeks / year = 96k$/year At that rate is a really shitty bargin for 30% increase in productivity. Even if you drop it to 20$/h and sort of break even, you are loosing competens building and teory building, decreasing the likeleyhood of making architectual progress and risk getting bogged down in a swamp.

> How many tokens can you realistically burn through in one chat session?

I've used single digit billions in a couple days, FWIW.

Problem with this math is it always assumes some ridiculous baseline compensation (or costs, in this case) as a matter of fact. There's an entire world of developers not costing 200k to their employers.

Truth of the matter in most companies large enough is if you make your devs 30% more productive, then that'd mean 30% more code going through "change management" hell for months. You're not even paying to stand still, you're just pushing even more down a bottleneck. The price most people are willing to pay to make things worse is close to zero.

> providers are profitably providing Kimi K2.6 for $4/1Mtok out.

Do you perchance have a source for this? Is the profitability assessment comprehensive, including hardware amortization? I've found it hard to track down actual hard numbers for the cost of inference.

> afaik most estimate north of 80% profit margins

This seems to be the lynchpin of your argument.

It makes me wonder if I have been living under a rock, because I have never heard of frontier labs making money. AFAIK all AI firms are simply burning money to acquire customers at this stage. Is this wrong?

lots of words.

do you think per token prices will go up or down in the long term? will the price per task trend down or up?

what about the price of human labor?

> That said, even if a developer is burning $50/hr, many, many employees at large companies cost more than $100k/yr to employ all costs considered, so making them say 20-30% more productive can easily make that worth it for most. If the labs shave their margins ultimately to more like 20-30%, you'd have ~$15/hr in costs to use the services, and nearly every white collar job is way over 30k/yr to employ. If your salary is 80k, you probably cost the company 200k all in, so making you 15% more productive offsets the $15/hr cost.

Nobody including the connected article is making the argument that this cannot be profitable ever. People are saying "there is no way this admittedly quite interesting tool is going to be able to make back all of this money" and I think they are completely right to say that.

You can absolutely make money with this stuff, just not at this scale. The buildout for this shit has been certifiably crazy and a number of the involved firms are overleveraged for tens and even hundreds of billions of dollars.

How in the sweet fuck are you paying that off, plus giving investors dividends, selling this at $15/hour/user??? That math does not math. A quick google says there are between 1.5 and 4.4 million developers in the US alone, let's say it's 5 million, to be generous, and each of them is subbed to this for 8 hours per day, continuously. That's 600 million per year in revenue. If you took ALL that revenue, and put it towards paying down this debt, not leaving any for employee salaries, upkeep, ongoing development, it would take DECADES to pay down what OpenAI already owes.

And yes I'm sticking directly to code, because that's the only thing I've seen it be really good at. Are we really proposing that every knowledge worker on earth and every manager of such workers is going to have an autonomous agent running all the time!? To do what, make sure they don't have to read or write email? Which even just that example is bringing in a fucking mess of legal, compliance, and security violations because LLMs are not intelligent and are not capable of being properly secured.

Like I'm sorry, I cannot take this industry seriously when even the most basic back-of-napkin math is saying, nay, screaming from the rooftops that they are FUCKED.