I also believe that before any real companies are running these models locally, they will already have some kind of agentic layer.

With the current frontier model lab progress, i do not see any real company which makes real money, running local models.

Running local models is easy for me, for sure not that easy for any company. Your DC needs to be able to host GPUs, it needs the cooling power, you need to have a DC. Without a DC, you need to have someone maintaining critical infrastrucutre, taking care of model evaluation etc.

For external parties, there might become a new business model: You might not hire an external anymore, but a token budget and the 'operator of the token budget'.

The current chip fabs are full, developing a high end / cheapisch local LLM Chip will still take a few years as long as the DC GPU demand is still as high as it is.

There's additional advantages that everything you query, all of your context cache and everything it outputs stays private and can't be arbitrarily turned off by external interference.

Personally I think it would be a fairly good bet that something with the 1TB of RAM needed to properly self-host GLM5.2 will still be a very usable piece of hardware in 4 to 5 years from now. There will be even larger, newer models available, sure. But there will also be better models that continue to fit in the same size.

And the reality is that other industries aren’t finding the use for LLMs as much as programmers are. Sure there are some benefits but you can’t fire your marketing department and replace it with AI

If you can use a subscription with any of the SOTA models, do that.

Instead of around 4k EUR in token costs, my Opus usage costs me 108 EUR (with taxes) per month with their Max 5x plan. It's the same with OpenAI, those are heavily subsidized.

It doesn't make sense to pay per-token, unless you must.

> What is happening here is that leading AI labs are charging not only for inference but also for research in model architecture, training data collection and curation, model training cost (which can be tens or even hundreds of millions of dollars), paying their employees and recovering the marketing costs.

Chances are, they're never getting that money back. Best case scenario, the hype around AI slowly declines, worst case - it crashes and takes a part of the economy with it.

Also anyone doing distillation with hundreds or thousands of those subsidized attacks is probably winning big. Especially as the model architectures (e.g. DeepSeek V4) are more oriented towards efficiency.

> Last but not least and in fact the most important factor, is the ability of users to run local models. So far, almost everyone is using cloud-hosted models and local models are either too big to deploy or too slow to work with. With advancements in chips, this will change in 4-5 years’ time.

Currently beefy hardware to run them fast enough to be competitive with the cloud (at least 60 tps) is expensive and even then the small local models quite suck compared to SOTA or even DeepSeek V4 Pro and GLM 5.2, though they're way better than they used to be (compare Qwen 3.6 with 2.5 for example).

OpenAI and Anthropic will just go back to entirely healthy valuations of ~$5-10B each and the industry carries on.

If all of global spend on Anthropic/OpenAI/Gemini APIs just switches over to DeepSeek then easily we can decrease total AI spend by 10x

I really believe that in the near-term future we will run our LLMs in hardware, not in software. Hardwire a capable model into a device the size of a graphics card, embed it into a laptop, and you have something that uses less power, does faster inference, doesn't require additional CPU or memory, doesn't cost a monthly fee, and will probably eventually be available for under a (few) hundred bucks.

This is obviously untrue, both with GPT-5.4, and Claude Fable as examples in the last 6 months.

a surprisingly large fraction of production workloads can be handled by smaller models with the right scaffolding. it's often easier to switch to a larger model than to engineer those pieces, so many teams never bother.

my intuition is that a lot of the current "ai cost crisis" is really an orchestration problem rather than a model pricing problem. before asking whether frontier pricing is sustainable, i'd first ask how much of that spend is simple tasks being sent to the smartest available model by default.

my bet for the next few years is that the model itself stops being where the value is. frontier models will become more like commodities, and the real difference will be the layer around them as routing each task to the cheapest model that can do it well, verifying the output, and only escalating when needed.

eventually, asking "which model do you use?" will sound a bit like asking "which cpu do you use?" the engine still matters, but the system built around it matters a lot more.

1. We're still in the "$5 airport Uber" era of LLMs. They're heavily subsidized, and everyone still complains about costs.

2. There hasn't been a real incentive to work on cost optimization for data centers and the hardware they contain. When/if price hikes happen and send people scrambling to use other models or drastically reduce AI usage, this will suddenly need to happen.

3. We're massively overusing SOTA models. As long as you're on a subsidized subscription, you can use Claude Opus 4.8 high to write blog article meta descriptions. If you paid by token, you wouldn't do that.

4. Open models are a wildcard that could completely change the calculus.

Of course they do. How else do you expect them to pay for that? If you buy a Foo from Acme, Inc, you aren’t only paying construction costs, either.

> On the other hand, once an open weight model is released, any inference provider can easily host it and just do some markup on inference cost. This proves way cheaper than running a frontier AI lab.

The only logical conclusion for commercial AI labs is to never release their models as open data, and try to stay ahead of open models. One way to do that is by having better models, another by having more users (because that decreases the per-user costs of creating the models, decreasing the price difference with companies running open models). The frontier labs are aiming for a combination of both.

anyone got a source? sounds juicy

The author mentions $54 in costs but the reality is that developers are paid around this much per hour.

What is likely to happen: LLM performance goes even higher and can do tasks that take humans days to accomplish. You then have to compare LLM cost with human cost - something the Author has forgotten in their analsys.

The Chinese open weight models were always winning the AI race to zero where as the likes of Anthropic and OpenAI have no choice but to increase token costs.

Even Microsoft wants to use some of the Chinese models only realizing how expensive both the frontier models are. It turns out that Jevon's paradox does not exist in the US (it exists in China).

This "Tokenmaxxing" marketing stunt was a scam for the frontier models to raise even more money at unsustainable valuations.

In terms of running the model locally vs a service provider, that will be down to convenience more than anything else for the same reason why not everyone is hosting their own website at home on their own box.

Who in hell would actually do this? That's a level of problem that any of the flash-class models can solve.

Hand that sort of thing to GPT-mini, Haiku, or DeepSeek Flash, and save the big guns for big architectural problems.

1. How much it costs in terms of programmers' salaries?

2. Can DeepSeek do this (I bet it can) and how much it costs?

The fact the author ever had the idea of using a SOTA to solve do this means LLMs are actually quite cheap.

The other alternatives with LLMs becoming more expensive in an Uber-like move may not work due to a lot of competition. I also don't think usage will increase 10x. I don't always have coding tasks for an LLM despite it being good.

My reasons to believe so are outside of what interests HN community and I am neither endorsing this behavior, nor I think it is that simple. But US also has a huge debt that it must service. Wouldn't it be convenient if it was suddenly halved in actual value?

Not trying to be harsh, but that sounds like a skill issue. You have the language server to lean on; easy feedback loop; sub agent per type.

This isn't how coding models get better though. Why would this have anything to do with plateauing?

So how these companies and people manage to use these absurd amount of tokens is a mystery to me. It feels like this are just running huge amount of non-vetted data to the LLM's and or running loops against the LLM's which only produce fractional results if not wasted results for insane cost.

So really it is the equivalent of just burning money, or heating your house in the winter while having all your windows open.

1. Chat, being 3 yr old, is a fairly mature and solved problem today. Top companies aren't even talking about it anymore! Gemma 31B does it amazingly well (for $0.4/1M token output). Practically every near-SoTA and SoTA model does simple "chat-like" QA amazingly well -- summarization, basic question answering, single- or few-step search.

2. Tasks -- or knowledge work on a computer -- are the new frontier. Computers have become competent only recently, and only for some of the tasks so far. I'd guess another 2-3 yr development cycle will happen, after which "el cheapo" models will be virtually distinguishable from SoTA.

As tasks are the new game in town, AI labs can still charge a premium for it; for chat that premium has disappeared already; most users cannot tell 99% correct answer from 95% correct answer; nor do they always wish for maximum accuracy.