"It costs OpenAI less money to serve GPT-5.5 than GPT-4." does it though? do you have the numbers? Or you just making stuff up?

It's just like saying every dependency is a ticking bomb. In a very strict sense, it's true. But it really doesn't matter for most businesses (and absolutely doesn't matter for early stage startups.)

Very much agree - efficiency improvements are very real both on model and hardware side. The reliance on proprietary OpenAI/anthropic APIs is a problem though, one that will naturally resolve itself in the favour of self-hosted/open models.

Where do you work?

moores law ftw

> sad dark HN loser path

Assertion assertion assertion wishful thinking assertion.

Show, don't tell. Show us that we're wrong and this isn't a VC black hole. The CEO of Enron as late as September 2001 could've called every critic a sad dark loser with nobody challenging him publicly. Jim Cramer famously yelled anyone pulling their money from Bear Sterns in 2008 was "silly, do not be silly" exactly 8 days before their collapse and a -92% stock drop. In COVID, calling everyone paranoid and sensationalist about some mythical new flu was popular in December 2019 and gone by March 2020. How about Uber, the seeming go-to for how VCs can turn a money hole into a profitable business? The average price increase is now 18% per year and still going up, with an over 60% increase in 5 years. Does anyone still talk about the "sad dark HN loser path" of those who doubted VR in 2018, proven right by 2026? How's your VR startup doing?

> within a few years we will be running local models as good as today’s frontier models with almost no cost burden

Based on what? The RAM requirements alone are extraordinary.

No, running large models on shared, dedicated hosted hardware at full utilization is going to be vastly more cost-efficient for the foreseeable future.

Or put another way, the frontier models are very quickly deprecating assets, because of the competition in the market.

They have to keep getting better to stay ahead of each other and open weight.

Which means it's the opposite of a timebomb, the article has it completely backwards, tokens at current level of reasoning will continue to get cheaper.

I'm not sure 'local' will be the end state, as hardware needs are high. But certainly competitive forces tend to push profit margins toward zero.

Extended discussion on this topic:

https://corecursive.com/the-pre-training-wall-and-the-treadm...

> within a few years we will be running local models as good as today’s frontier models

I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.

The economics of local AI just doesn’t make sense. A model like Opus is - supposedly - something like 5T parameters, which is likely something like 3TB of GPU memory.

Local models never reach the % utilization that cloud providers have (80%+), and they’re always going to be much better than local models for this reason.

I've spent the last month bringing in a small demo of what the future could be like, running Qwen, Gemma, and Deepseek, behind LiteLLM so we can monitor token usage, and instead of some dumb ass "tokenmaxxing" we're actively trying to get the cost of inference both down, and in-house.

Boss is happy, very happy. We're rolling it out more widely now.

But this is the future.

> within a few years we will be running local models as good as today’s frontier

Unless there isn't some important breakthrough in hw production or in models architecture, it's quite the opposite: bigger, more expensive and more energy-intensive hw is needed today compared to 1 or 2 years ago.

Hard agree - the benefits of local/self-hosted models are not just hardware/cost (it might be more expensive at the moment), but what you get in exchange is unnerfed/unstupified models, full cost/usage transparency, optimized/specialized models, privacy/security, etc.

>within a few years

Eventually, we'll see. Frontier models still need some pretty serious hardware which will slowly come down in cost. Smaller models are becoming more capable, which will presumably continue to improve.

I think there's still a pretty big gap, though. Claude estimates Opus 4.6 and GLM-5 need about 1.5Ti VRAM. It puts gpt-5.5 around 3-6Ti of VRAM.

That's 8x Nvidia H200 @ ~$30k USD each. Still need some big efficiency improvements and big hardware cost reduction.

I think this is a good under-represented point. Again and again things that could only run on a mainframe get ported to the personal device level. However it looks like the campaign to eliminate the PC (by pre-buying all RAM) is the counter-stroke.

If that’s true, then it will be even cheaper to provide them as a subscription. Following your logic, every company would be running their own data centers instead of using cloud providers.

I disagree. No one will want to use second rate models when the frontier models reach a specific level of capability. Enterprise will keep paying.

This is wrong because local models are very expensive, just as expensive as the frontier.

It would cost me $300 in normal deepseek v4 pricing (non discounted) PER DAY, but I get it all for $500 worth of subscriptions.

People who are this certain of their predictions should be forced to put real money on them on Kalshi or Polymarket instead of drive-by blowharding on HN.

There's still going to be plenty of use-case and demand for frontier models running across hundreds or thousands of GPUs. It's just not going to be in the current shape - certainly not accessed by the general public for rote business tasks.

You'd have a point if Cloud ^tm didnt take off into a multi billion dollar industry.

Thanks for calling that out. I went through and extracted a good handful of those. It’s not a short list. It’s a handful.

“”” The subsidy era is not winding down gracefully. It is showing cracks everywhere. … the question is not whether they got a good deal. The question is how long that deal survives. … A developer running three or four concurrent coding agents is not consuming 3x or 4x the tokens of a chat conversation. It is an order of magnitude more … These are not experiments anymore. They are load-bearing workflows. … That is not a rounding error. That is a line item that needs its own budget code. “””

I guess the good news may be that if/when there is a major pricing correction, that many of the people using free or $20/mo subscriptions to generate social media commentary may balk at the real cost and go back to writing it themselves.

One can at least hope.

It's not metaphorical. It's load-bearing.

Or describing something as “the unlock”.

I hate it. This article starts off well! There is data and it seems well argued, but then halfway through, there it is: example of trend. Another example. Third example. It’s not just X – it’s Y.

It’s as jarring as getting halfway into a well written article, clicking a link to a source, and getting rickrolled.

It’s all you can do to not let it distract you from the fact that in 1998, The Undertaker threw Mankind off Hell In A Cell, and plummeted 16 ft through an announcer's table.

please stop giving hints on LLM writing markers, let's not do their adversarial training for free

I have been saying this and viscerally reacting to this “contrastive language” for months. it… “hits different”…

“Load-bearing” is a new one for me though, yuck.

X is the adjective framing.

load bearing has snuck into my vocabulary, but I work with construction workers so it's slightly more intuitive I guess? :/

or the word 'canonical'

Managers in my org love using it in "their" Slack messages.

It is not human language. It's AI slop!

Isn't that always the case in the early stages of new technology adoption? It becomes less and less true as the new technology becomes more and more integrated.

In the first few years after electric motors became a thing, one could have said the same thing. We would have just gone back to steam. If you tried to "do without them" now, society would collapse.

So the question is not if we can do without them now, it's if we can do without them in 5 to 10 years (or however long it takes for them to be fully integrated)

In my opinion, that's likely a large part of why it's being pushed so hard. Not to drive honest revenue, but to get AI products so deeply embedded that 'just removing AI' won't be seen as an option, even when keeping it has higher and higher costs, up to and beyond airline-style bailouts from the government. An entirely new layer of wealth-extracting intermediary, being sold under false promises.

It's always weird when people are suggesting to use some AI tool for the most mundane and generic kind of task. Like it's some kind of pet that will die if it's not used every once in a while.

It's like witnessing a rocket using the most powerful engine on Earth then once it escaped orbit turn off the engine and said "It is flying without power!".

Yes, sure, right now it is ... but that's NOT how it got here.

There are trillions invested to recoup and at most billions in sales. It doesn't add up to tokens making a profit any time soon.

Tokens can be sold at profit, but 70% of compute expenditure goes to R&D and model training[0]. Inference needs to cover all of that as well as being profitable in a vacuum.

[0] https://epoch.ai/data-insights/openai-compute-spend

They aren't being sold at a loss but they aren't being sold at enough to cover the current losses and the costs. The losses are being passed around in some fucked up circular funding mess which will inevitably collapse into a debt crisis at some point.

In other words, AI companies have positive earnings before expenses

Do you think it will be the case for the Claude Code/Codex tokens as well? I think those are heavily subsidized, but they're the only ones I find real value in.

That isn't enough. Over time the need for growth and increasing profits will squeeze existing margins.

Is sounds very much like "trust me bro" ...

Obviously I, like basically everyone else here, don't have access to Open AI or Anthropic books so it's just guessing based on public available evidences, but "tokens aren't being sold at a loss" does not imply there is any profit.

And, even if there is some profit, it needs to be big enough to at least pay back the capex spendings and finance the next model iteration.

Brad Gerstner might need a primer in asset depreciation.

This is the sort of uncritical thinking that inflates bubbles in the aggregate.

He's an interested party. His investments are worth a lot more if he says that tokens are sold at a profit. I don't understand how anyone would trust him?

If tokens weren't being sold at a loss, Anthropic would be screaming about it from the rooftops. They've been desparately trying to make themselves not look like a money furnace lately, but it's not really working.

They might be sold at-compute-cost, but that of course ignores training, salaries, and everything else.

They are kinda trying to replace figma. That could be valuable.

They are available. Seats for team or enterprise plans cost more than the retail prices, but they are fixed prices with resetting usage limits. You can assign seats to members that are the equivalent of $20/$100/$200/mo plans.

You can also do everything metered. There are multiple ways to buy.

Subs are absolutely available to businesses. There’s metered plans for ghe equivalent consumer plan.

Yeah, I was confused about why it was talking about subscriptions for enterprise. The company I work at is billed on API usage.

Looks more like AI slop with paragraphs like these; > The pattern is identical across the board. Price for adoption, not for economics. Lock organizations in. Make AI a load-bearing part of every team's daily workflow. Worry about the bill later.

Not only that, but the API rate amounts being pearl clutched over in the article are still relatively trivial. 10k a month is not nothing, but when 10k a month enables a team of ~10-20 engineers, that's pretty good leverage.

Yes actually. After zirp ended, cloud costs got materially more expensive for enough enterprises that there was a good year or so of celebrated "we're moving back on-prem" stories on hn, where companies were announcing savings in the several to tens of millions per year.

I would expand this to any dumped product or service. Whenever the real cost isn't paid now it will sometime in future or will collapse. Just look at how extractive food delivery and taxis are. Start with dumping. Then be last one to survive and fleece all the sides you can.

No and yes.

>Your company becomes reliant on another party that does not share your interests or values, and can stop working for you on a whim for any reason

That's the same as human workers. In both cases there are contracts/money to help align interests

If only there was a way to think beyond direct substitution.

Not SMBs and SMEs. Big Enterprises would generally be using API buckets or Enterprise-specific consumption models via sales teams and contracts, but most companies would default to subscription tiers - either due to shadow IT paying out of pocket for subscriptions to duck corporate IT, or because they’re too small to negotiate rates and API buckets, or because their IT teams lack the skills needed for the same.

Remember that enthusiasts leaning on API keys and large enterprises are the exception, not the norm, and even some large customers may lean on subscriptions for at-scale adoption and wait for teams to report hitting usage caps before buying more token buckets. Subscriptions are predictable, reliable, and above all else a contractable way to acquire service.

Truth be told, this has been my red flag in orgs and with peers elsewhere for several years, now. Those orgs leaning on subscriptions are in for a nasty surprise within a year or two (like the author, I predict sooner than later), especially if those subscriptions power internal processes instead of AI buckets.

Hell, this is why I think there’s a sudden focus on the “Forward Deployed Engineer” nonsense role: helping organizations migrate from subscriptions to token buckets for processes so the bill shock doesn’t send them running away screaming.

Inference at per-token pricing is profitable.

The article's point is that if you're relying on flat fee subscriptions, a rude awakening may be coming. That seems plausible to me. Issues around token quotas are a frequent topic on HN.

So? How does it change the equation?

Nobody is going to charge "inference price" for model usage.

Just to clarify, value in this context is economic value through output. Not considering environmental impact etc.

You could use "git clone" or Wikipedia for free. If you mean the value of propagandizing gullible people, yes, there is "value".

> increase the pressure to use cheaper / free models

Not necessarily. Many factors go into what models are available at enterprise level. If you look around, not many companies (everywhere around the world) use DeepSeek models even though they are significantly cheaper.

It feels like they just pointed an AI model at Ed Zitron’s blog and asked it to make a super engaging and viral post.

The price for a given level of capability will fall, but the frontier has recently been getting more expensive. If you compare GPT-5 to GPT-5.5 on the Artificial Analysis benchmark, it's ~4x more expensive, but achieves a higher score. Claude 4.7 is also more expensive than predecessors because of a tokenizer change.

As the AI labs become more reliant on enterprise adoption, it makes sense to push capabilities at a cost that makes sense for businesses. Even if it prices out consumers or hobbyists.

Exactly.

Competitive pressure prevents a rug pull.

In a competitive race, each breakthrough gets copied or illicitly distilled or whatever. That means the frontier models are deprecating assets and the mark up tokens should get smaller and smaller.

Now bigger models are more expensive to run inference on, but today's models, or equivalent ability and size models, shouldn't go up in price.

5.5 is 4x the price, but 5.4 still exists, so its not rug pull, but a big more expensive to run and hopefully more valuable model.

I agree.

Between: more efficient models - tuned for the task at hand, the ability to run those models in-house, or even at the edges, plus Google and Microsoft are well positioned to stay ambivalent as they’ve got lots of products to sell and whether or not LLMs are part of the portfolio mix is completely dependent on enterprise customer demand.

Anthropic/OpenAI have a number of aggressive downward pressures on their pricing.

Again, GitHub Copilot is moving to usage-based billing June 1st.

https://github.blog/news-insights/company-news/github-copilo...

We popularized the term "enshittification" so we wouldn't have to keep explaining this.

After reading the third "rounding error" phrase I quit.

I think a solution could be local hardware acceleration the diffecult thing to achieve is not leaking dmodel data, since yeah that is obviously a nogo for antropic, openai, etc