I know a lot of people at companies where the marching orders changed on a dime end of Q1/start of Q2. These are shops that were fully on the "use AI or die (because we will fire you)" train.
Now there's monitoring, reporting, alerting not just on overall cost but on "over-use" of best/priciest models based on total-or-percent tokens/dollars, etc. All of this comes with direct developer engagement & standardized management escalation for holding it wrong.
To me this customer behavior does not smell like a product you can 10x the pricing on to get profitable. We have exited the exploration phase and now ROI matters.
I work at a Fortune 200 company. At first, it was the Wild West. Need an LLM? You got it. Need to or want to build an army of agents? Done and done. We literally had everything at the tips of fingers for about 3 months. Teams were building their own internal tools, the team I work on canceled contracts with several software vendors because teams were building the same tools for what they thought was nothing.
Then they signed contracts with Anthropic and Google because I would assume they saw the token usage was through the roof. One month later? They completely cut off access to everybody for both Claude and Gemini. If you wanted access? Suddenly it was several forms, along with several approvals and a rock solid business case why you needed it. And before you got to the forms? You were added to a waiting list that was thousands of people long.
The entire company is now in damage control after trying to get the genie back in the bottle. I'm guessing someone saw how much we would be paying for the tokens we'd been using and decided to shut the party down so to speak.
Myself and several other devs were laughing about the whole thing. The company was so amped about what AI could do they never even bothered collecting any analytics that would affirm or deny any of this had a positive impact. Even some of my team members were talking about the placebo effect AI has had on a lot of C-Suite folks.
Microsoft adding Deepseek support already as I recall?
That is - for any definition of "they are behind X months" then eventually they get to the point Claude was in January when the world freaked out, but at 1/10th the cost. A lot of firms are going to mandate that is good enough for their developers.
At least on a personal I feel like I’ve been getting the same amount of work done but I have to think harder rather than sitting back and prompting and waiting.
But that's because I never got on the "run three dozen agents in a ralph loop" trend or other high-token usage methods. The way I use AI is discrete and targeted and it seems that's how it will be for everyone once the economics settle.
I believe this hasn't been confirmed yet but I think it speaks to a bigger problem for the AI companies which is, if you give capable developers a good reasoning LLM, they can make it work like it was a really expensive model.
I believe we are 100% at the stage of good enough for the vast majority of tech companines. Fable and others will be more valuable for non-traditional tech companies.
I read somewhere that the chinese AI companies are sharing knowledge and it would not surprise me if the government is applying pressure by saying work together or else. If they work together, they can truly commoditize LLMs and with China ramping up hardware support for AI, I see the future being inference speed and hardware being the moat.
Which makes sense to me. Selling a chatbot interface/model access to the general public was never going to be a viable long term play. You still need developers to wrap the models into specialized tools. Queue the Jobs quote "It's a feature, not a product."
The only hiccup in that happening is will the US Gov let Anthropic and/or OpenAI fail when that time comes.
I built my career on Solaris and it got rugpulled by Linux.
That wasn’t because of software, it was because of hardware. Linux’s cost advantage existed because Sun hardware had huge margins, because their software was basically free.
AI will probably be a repeat of this. Whoever can come up with the hardware solution that minimizes the cost per token will win.
I believe the 5090 still holds this crown, but someone certainly knows better than I do.
And of course the C-suite will have unlimited access to Mythos tier models, which they'll use to summarize reports, while passing down mandates to rank and file to increase usage of less expensive models.
OpenRouter charges an extra 5.5%, Fireworks does not, Google is separate, but I doubt it will take 18 months. They are already aware they are losing business.
OpenRouter is the wrong abstraction for enterprise, we only need one model provider, not everyone in the world. Nor do we want to have to worry about failover going to providers we don't want.
Over the last month I have seen companies scrambling to measure deliverables against cost. Most of the back room talk is to the affect of giving devs a small allowance ($500 a month) and then making them prove their own productivity increases (again, based on deliverables, not LoC) before they either take it away or give them more.
Obviously this won’t be on an individual basis but some kind of unit.
Either way, with how much I see these companies cutting back I have no idea how the big AI companies are going to be profitable.
Sure, you can use AI to potentially replace software engineers, but the F500 are also terrified of not having accountability or making mistakes. They won't be firing any engineers. In that scenario, there's just no room for AI usage. If you have to be responsible for all the code, then... AI has to either manage it completely autonomously (which even Fable can't) or... humans have to be in the loop which means they still have to understand the code. The best way to understand the code is to write the code yourself. So there's no productivity gain to be had.
I'm pro-AI, but I think we're due for a big crash next year.
I'm not sure that's something to rely on. I would be Fable 5 will be phased out and the bleeding edge will be priced up.
It's more about the level of abstraction. If AI handles 80% of the grunt work and I spend my time on architecture and reviews that's still a win
Consider the people younger than you. Who are literally shutting their brain off so AI can cheat on their essays and exams. They aren't going to be good architects or code reviewers.
Rational takeaway is to step back and analyse what's really happening here.
- Are we really in for a crash?
- What does it say about the culture and people's mental models that we have two radically opposing viewpoints on AI costs and people still arrive at same conclusion?
Neither Anthropic nor OpenAI are subsidizing enterprise customers. Neither Anthropic nor OpenAI allow Business nor Enterprise customers access to the high value $200/mo plan. Both organizations have moved to a "cheaper plan per user + API Pricing after that" (e.g. $20/mo + usage). The $100/$200/mo plans are for individuals only (of course, many individuals use these plans at work, but that's beside the point; they aren't selling this plan to enterprises).
> SemiAnalysis also analyzed the platform's gross margins, implausibly assuming that tokens were priced at 4 times the cost of generating them and: With the current subsidies, all it takes for a user to have a gross margin of at best negative 25% is for them to use as little as 25% of their rate limit.
The article's source for this claim is not SemiAnalysis; its Zitron. But once you dig through his article, Zitron links to a SemiAnalysis tweet [1] where they, as the paragraph states, implausibly assume gross margins of 75% to come up with their weird analysis of the subscription plans. Citing this for anything is weird, because afaik that 75% number is a total shot in the dark. We have no clue what their margins are. My take is that the only reason that 75% number is implausible is because it may underestimate the inference margins of Ant/OAI's API pricing.
[1] https://x.com/SemiAnalysis_/status/2064815045767213400?ref=w...
Only reason deepseek is so cheap is because well I don't know, but actual pricing should be around their initial price which was 4x, at that price you have a healthy 25-50% margin based on occupancy, given the deepseek v4 is a very sparse moe model.
GLM 5.2 for example doesn't have more than 30-50% margins that's assuming old pricing for GPUs, current inflated GPU pricing well I am certain the margins must be lower. Ofc you can host for cheaper with quantization, and if you have very consistent capacity/utilization, which is not the norm with AI workloads.
Overall for large models like GPT 5.5 or Opus there must be healthier margins of around 50-70% assuming GPU pricing didn't increase for these companies. Even if it did 30-40% margin should be possible, even in worst case assuming all GPU they had saw a jump in pricing.
For smaller models it's hard to say, I would guess 20% but these models might be much smaller than I suspect, then it might be double that.
Note the issue is less intelligent tokens don't linearly scale down in memory usage, which is the biggest pain point of serving models. Context sizes have fucked us all.
Also anyone claiming OAI makes less margins on APIs or stuff might be wrong given they are on much lower context size, 1M context definitely is a lot more expensive to serve especially with smaller models like sonnet.
If true then why are neither Anthropic or OpenAI dropping their API pricing to gain market share when both are clearly doing all sorts of political and PR maneuvering to compete in a cutthroat market?
Since they aren't dropping the API usage prices (and are in fact raising them in a lot of subtle ways) then one of these options almost has to be true: they are still subsidizing inference, training costs are so ridiculously high that they need to make huge profits off inference or collapse in on themselves, or they are price fixing.
Maybe because they're trying to IPO this year, and their IPO prospects will be a lot worse if their S-1s show them to be losing money on inference as opposed to making a healthy profit.
The market for open weight model hosting gives you an idea of the profitable price floor, it's pretty clear there's markup baked into OAI/Anthropic's APIs.
They are? In the before times of 2025, Opus 4.1 was $75 per million tokens. Opus 4.8 is $25, and Fable is/was $50.
> Neither Anthropic nor OpenAI allow Business nor Enterprise customers access to the high value $200/mo plan.
> Neither Anthropic nor OpenAI allow Business nor Enterprise customers access to the high value $200/mo plan. Both organizations have moved to a "cheaper plan per user + API Pricing after that" (e.g. $20/mo + usage).
I actually think that even the API pricing of OpenAI and Anthropic are still subsidized. I don't think they make any profit on inference when you factor in depreciation. They likely still operate that at a loss.
It's no coincidence that Anthropic only had a "profitable" EBITDA with not paying Elon for compute for a bit of time, and when EBITDA curiously ignores depreciation. Models grow stale over time, as knowledge is not static.
It's irrelevant how big their models may or may not be. Depreciation needs to be taken into account, so does actual compute expenses. Training those models is not cheap, and you will never reach a point where a model is "final". You will always need to train the next one.
Eventually the bill has to be paid. Money and resources are finite still.
Chinese models and open model providers are, indeed, competing on price, and the difference shows.
Edit: to the commenter below . It was widely reported that these companies were unprofitable 1 from last year. I am asking question to this specefic comment because they made a very specific claim about part of plan thats profitable . something only an insider would know.
1. https://www.wsj.com/tech/ai/openai-anthropic-profitability-e...
I do hope that a day will come where you can buy the nvidia spark thingy for 5k that can run the equivalent of Opus 4.6 or 4.5 locally and that would be a massive thing.
How?
* Moores Law is almost over. The 5090 improves over the 4090 mostly because of quant improvements.
* even if the hardware improves, there’s a huge incentive to slow roll the next generation. Nobody wants to end up like Sun Microsystems. Sun’s used hardware was faster than its new hardware, once you considered price. Sun ended up competing with its own used equipment.
The most obvious place for improvement is RAM, network and storage.
If someone can bring more RAM onto the market, that will unstick things.
There is significant room to make more specialized neural network accelerators with new compute-in-memory architectures.
If the brain can run 86 billion neurons on 30W it must be possible.
There isn't one AI intelligence S curve, there are thousands of them, and they're mostly invisible in the major benchmarks, but for someone trying to do work in that specific area of capability, the progress is transformative.
Once moat is achieved, you don't have to compete on price. Of course it'll be academic because the AI will probably destroy all of us.
Btw, some Chinese corporates have already seen this and increased their price. Zhipu AI & Tencent for example. Alibaba, Baidu, and Tencent also announced multiple price increases for their AI services.
And, even with the price increases, Z.ai and Tencent are still much cheaper than Anthropic or OpenAI models. I think there's an efficiency focus among the Chinese models that is absent at OpenAI and Anthropic, and in the end I suspect efficiency will be the winning feature. Google seems to understand that. Gemini 3.5 Flash is pretty competitive with the big guys, and it's small enough for Google to run it profitably (I assume) for a price that's much less than the frontier models. Gemma 4 models are showing off a bunch of efficiency techniques (MTP, QAT, the 12B encoder-less vision model that soundly outperforms much larger vision models, DiffusionGemma), and I assume they have several more techniques that aren't published.
There are ~1.6M software engineers on the US [0], earning a bit under 150k/year on average [1]. If AI companies captured all of that spend, that amounts to about 250B/year. The article assumed that they need around 300B/year to keep up with their debt.
At least based on Meta's recent behavior, forcing 30-50% of developers to switch to data labeling, it looks like that is actually their game plan.
[0] https://en.wikipedia.org/wiki/Software_engineering_demograph...
[1] https://www.indeed.com/career/software-engineer/salaries
The drug dealer analogy has a darker side to it, however.
Once your dependent, they can drive up the price just because. It doesn't need to be for existential reasons.
This is the crisis point for vibe-coders. A developer can go back to writing code by hand, as horrible as that might sound. Someone who hasn't learned to code but builds with AI can't go back. They either pay or they stop. That will be an painful choice whichever way you fall.
Certainly, the best models have gotten better since then, but I wouldn't consider DeepSeek V4 Pro or GLM 5.2 to be a big enough downgrade to be worse than coding by hand. I'm willing to spend a premium for the best model for coding because it wastes less of my time with dumb stuff, so I've got a Claude subscription. But, there is a limit to how much of a premium I'll pay. 10x over Chinese models? OK, fine. Opus saves me enough time to make it worth a couple hundred bucks a month. But, 100x, or more? Nah. I'll go a little slower, review the PRs a little more carefully.
And, open weights models do keep improving. DeepSeek V4 Pro is a notable improvement over earlier DeepSeek models, and the first DeepSeek model to cross the "better to work with it than without it" threshold into Opus 4.5 (or better) territory. GLM 5.2 is somewhere in the ballpark of Opus 4.6 (though without vision, a notable limitation for anything that requires a UI).
If apparently the only way you can make money with your product this early is to dilute and adulterate it behind the scenes, it strongly suggests you want the customer to continue to believe they are getting value that you can't afford to supply.
More prosaically: if either of these firms could prove that they were even really close to profitable on inference, they would have bloomin' said so while they were trying to raise more money.
I would assume when price hikes happen either 1) less non technical people would vibecode as it doesnt impact the work that much 2) people use the cheaper chinese models 3)we're jamming ai into everything because were exploring. We will just niche down into use cases that provide high roi
It seems like this ideology has been corrupted into a short-sighted "Establish a monopoly position as soon as possible at all costs, don't worry about tomorrow."
It's ironic because monopolizing a sector by investing heavily and suppressing profits used to be a long term move but it has become a short term move.
Here's a concrete example. Does some random AI company make operating profit on inference? I.e. if you only kept marginal costs, would you make a profit?
Well, depends what you account as your costs. If you're using hand-me-down hardware from previous generation's training, how much do you charge yourself internally for it? Maybe you show less, so investors take solace in profitable inference, even if you're losing money overall. How exactly are you accounting for electricity costs between training and inference? Is your army of SREs mostly servicing training new models (R&D expenditure) or inference (operating cost)?
This even has a name, and is called the "big bath" approach. If investors expect one part of your business to be a fiscal black hole, just shove all your costs there. They are accepting of it, and you make the rest of the business look better.
I'm not accusing AI companies of cooking the books, rather I'm trying to highlight you could see all the cash flows and still not know how much money is made or lost where.
This is the video I watched that explained the shenanigans (from the guests' perspective, not illegal, obfuscated)
If AI was around in the early 2000s Countrywide.ai would have been a thing.
Considering how much they spend on sales, marketing and R&D that doesn't sound that absurd
So depending on how literally we interpret Darios comment, OpenAI & Anthropic need to get to Apple+Google+Meta revenue numbers in like single digit years?
If you zoom out to the year 2100, it becomes a little pimple on the economy that is ready to pop, but in the here and now it can cause a lot of damage to real people's wages and finances over the next 3 years.
The funniest comment here. Have you seen the prices of the technical shit for the past two years? Dang, GPUs are not getting any cheaper, but more expensive with each year.
Anyone know what they are spending this on? Can't remember seeing one OpenAI ad.. Is it just pr and influencers? Ads in the US?