GPT-5 was shown as being on the costly end, surpassed by o3 at over $100/hr. I can't directly compare to METR's metrics, but a good proxy is the cost of the Artificial Analysis suite. GLM-5.1 is less than half the cost to complete the suite of GPT-5 and is dramatically more capable than both GPT-5 and o3.
So while their analysis is interesting, it points towards the frontier continuing to test the limits of acceptable pricing (as Mythos is clearly reinforcing) and the lagging 6-12 months of distillation and refinement continuing to bring the cost of comparable capabilities to much more reasonable levels.
But, sounds like Taalas is trying to strike an interesting balance where they can at least spin up ASICs for new models reasonably quickly with their modular design. It’s a really interesting bet, and might pay off.
For an 8B parameter model.
Opus is estimated at 500B-2T parameters. At that scale you’re past reticle limits and need HBM and multi-die packaging, which means you’ve essentially built an inference ASIC (like Groq or Etched) rather than something categorically cheaper than GPUs. The “burned into silicon” advantage mostly evaporates at frontier scale.
I rebuilt my house from the studs, did my own electrical and plumbing, etc. This took a significant amount of training and research back in the day. I worked under my father for a decade before making this attempt. My father is a journeyman electrician and carpenter. I think any able bodied human could soon forgo much of that and simply get a breakdown of actions to perform in a particular order and get similar results.
24365 = 8760 8760$35 = $306,600
Yeah, a human working non stop will run $300k.
Now you said, the "best" models. I personally reckon that 80-90% of most work don't need the best models. They need a good model, and good models are super cheap. i.e, the tiny gemma4 or qwen3.6 models will be sufficient for most of those work.
AI cloud usage cost goes up near linearly, but local cost doesn't. So say someone built an under $10k system, with perhaps dual RTX 5090. That same system will be able to easily run 20 parallel requests. The only cost is electricity. You can run it 24/7. For 1 year, that's ~$6million. 20 humans will also have overhead of electricity, real estate and other things which far exceed the cost of electricity for just AI.
The thing AI agents are lacking is agency and autonomy. As they get closer and closer, the majority of humans competing in the same sort of tasks will have no chance.
I dont see how you get anywhere close to $6M of tokens out of a pair of 5090s. The class of model they could run is fairly small and extremely cheap to run via API (my math says running Gemma4-31B for 24 hours costs less than $1 on OpenRouter). Even with 20x concurrent requests you are orders of magnitude away from $6M/yr.
Your range of 80-120 gives a yearly wage of like 155k-234k which is clearly far too high for an average.
You'd have to be an expert on labor law in every country you seek to honestly compare. The only clean way to simply compare earnings is freelance rates.
An AI only doing a task correctly 50% of the time may in-fact be better than your N% chance of hiring a highly capable human for that task, and especially for contracting a human to a 1-2 hour task.
But your successful use of AI is still predicated on a human who can judge output and break the work into smaller tasks that fit the skill ceiling of the AI, which is currently no more than tasks that take a skilled human 2 hours.
That raises a question: if practical-tier inference commoditizes, how does any company justify the ever-larger capex to push the frontier?
OpenAI's pitch is that their business model should "scale with the value intelligence delivers." Concretely, that means moving beyond API fees into licensing and outcome-based pricing in high-value R&D sectors like drug discovery and materials science, where a single breakthrough dwarfs compute cost. That's one possible answer, though it's unclear whether the mechanism will work in practice.
AGI. [waves hands at the infinite money machine]
I think you're overestimating, or oversimplifying. Maybe both.
Assuming you used o3, that would cost $58800 per week. That’s an expensive bet for only 50% odds in your favor.
Of course the agents are only that good on benchmarks, in reality your odds are worse. Maybe roulette instead?
> I think you're overestimating, or oversimplifying
Yeah if you only read comments on HN but not the actual linked article you will get oversimplified conclusion. Like, duh?
Curiously, for most submissions it's the opposite - comments are much more useful and nuanced than the source being discussed.
Frontier models get hyped for their maximum task horizon, but that's also where they're 10-30x more expensive per hour than their optimal range. You're paying a massive premium for the hardest tasks and still failing half the time.
Honestly the practical takeaway is pretty boring: just break your work into smaller chunks. Not because the models can't handle longer tasks, but because the economics at shorter task lengths are just way better. The labs are racing to push the horizon out; the smart move for anyone actually paying the bills is to stay near the sweet spot and orchestrate from there.
Generalist models have similar problems as generalist humans. The proverbial "Jack of all trades, master of none."
That said, I've made my career as a generalist :)
Maybe the future of the backend is specialized models but the future of what faces the user is what appears to be a generalist model. Maybe it does things itself, maybe it just knows how to route to the specialist models, but the UX of a generalist model will win.
I meant more automatic selection and negotiation of which model gets which task based on filtering criteria, etc. so happening under the hood as you say.
Measuring Claude 4.7's tokenizer costs - https://news.ycombinator.com/item?id=47807006 (309 comments)
All I can say is: the motivation letters don't look like they're written by AI anymore.
Basically, claude can solve issues for you where it requires the implementation of existing code or a combination of existing patterns, but novel it cannot do.
Writing maintainable code that scales.
Where the long-term payoff still seems speculative, is for companies doing training rather than just inference.
What I'm curious about are what about the other stuff out there such as the ARM and tensor chips.
So: I buy that the cost of frontier performance is going up exponentially, but that doesn't mean there is a fundamental link. We also know that benchmark performance of much smaller/cheaper models has been increasing (as far as I know METR only looks at frontier models), so that makes me wonder if the exponential cost/time horizon relationship is only for the frontier models.
Do we? Because elsewhere in the thread there's people claiming they are profitable in API billing and might be at least close to break even on subscription, given that many people don't use all of their allowance.
Step 1) Bubble callers will be proven wrong in 2026 if not already (no excess capacity)
Step 2) Models are not profitable are proven wrong (When Anthropic files their S1)
Step 3) FOMO and actual bubble (say around 2028/29)
I have no data to support this, but I think they just about break even on API usage and take overall loss on subscriptions/free plans.
You have (limited) 100 Coke cans to sell (that you bought for say $1)
There are two large lines being formed for that. One line is offering an average $3 per bottle and another line is offering an average $2 per bottle.
Tell me which line they would throttle/starve even though they make a profit out of it.
Also, when the lines were formed you had no idea of the average price, but now you are getting a clear picture. Would you change your strategy / pricing or stick with your original "give the bottle to everyone for the same initial $1 price"
I have access to that article
https://www.saastr.com/have-ai-gross-margins-really-turned-t...
Like I said, majority of people (including smart ones) are going to be surprised by the profit margins of AI labs and there will be a mad rush to buy AI stocks till it reaches bubble proportions.
2025 was merely a 1996 "Irrational Exuberance" moment. We haven't seen the late 1999 mania yet
Difference is that the current prices have a lot of subsidies from OPM
Once the narrative changes to something more realistic, I can see prices increase across the board, I mean forget $200/month for codex pro, expect $1000/month or something similar.
So its a race between new supply of hardware with new paradigm shifts that can hit market vs tide going out in the financial markets.
For inference, there is already a 10x improvement possible over a setup based on NVIDIA server GPUs, but volume production, etc... will take a while to catch up.
During inference the model weights are static, so they can be stored in High Bandwidth Flash (HBF) instead of High Bandwidth Memory (HBM). Flash chips are being made with over 300 layers and they use a fraction of the power compared to DRAM.
NVIDIA GPUs are general purpose. Sure, they have "tensor cores", but that's a fraction of the die area. Google's TPUs are much more efficient for inference because they're mostly tensor cores by area, which is why Gemini's pricing is undercutting everybody else despite being a frontier model.
New silicon process nodes are coming from TSMC, Intel, and Samsung that should roughly double the transistor density.
There's also algorithmic improvements like the recently announced Google TurboQuant.
Not to mention that pure inference doesn't need the crazy fast networking that training does, or the storage, or pretty much anything other than the tensor units and a relatively small host server that can send a bit of text back and forth.
Isn't reading from flash significantly more power intensive than reading DRAM? Anyway, the overhead of keeping weights in memory becomes negligible at scale because you're running large batches and sharding a single model over large amounts of GPU's. (And that needs the crazy fast networking to make it work, you get too much latency otherwise.)
> becomes negligible at scale
Nothing is negligible at scale! Both the cost and power draw of the HBMs is a limiting factor for the hyperscalers, to the point that Sam Altman (famously!) cornered the market and locked in something like 40% of global RAM production, driving up prices for everyone.
> sharding a single model over large amounts of GPUs
A single host server typically has 4-16 GPUs directly connected to the motherboard.
A part of the reason for sharding models between multiple GPUs is because their weights don't fit into the memory of any one card! HBF could be used to give each GPU/TPU well over a terabyte of capacity for weights.
Last but not least, the context cache needs to be stored somewhere "close" to the GPUs. Across millions of users, that's a lot of unique data with a high churn rate. HBF would allow the GPUs to keep that "warm" and ready to go for the next prompt at a much lower cost than keeping it around in DRAM and having to constantly refresh it.
128GB is all you need.
A few more generations of hardware and open models will find people pretty happy doing whatever they need to on their laptop locally with big SOTA models left for special purposes. There will be a pretty big bubble burst when there aren't enough customers for $1000/month per seat needed to sustain the enormous datacenter models.
Apple will win this battle and nvidia will be second when their goals shift to workstations instead of servers.
My guy, look around.
They are coming for personal compute.
Where are you going to get these 128GBs? Aquaman? [0]
The ones who make RAM are inexplicably attaching their fate to the future being all LLMs only everywhere.
What's rising exponentially is the price of the most ambitious thing cutting edge agents can do.
But to answer whether the cost of AI agents is rising in general, you would take a fixed set of problems, and for each of them, ask "once it's solvable, how does the price change?"
For that latter question, there isn't a lot of data in these charts because there aren't enough curves for models of the same family over time, but it does look like there are a number of points where newer models solve the same problems at lower prices. Look at GPT5 vs. the older GPT models--the curve for GPT5 is shifted left.
The author performs a non sequitur by muddling two concepts of time. They say costs are getting “unsustainable” which is not a conclusion that follows.
What is true is that at a given point in time, cost to perform a task is exponentially related to the human time taken. But it does not mean it will remain that way.. far from it.
Happy to run it on your repos for a free report: hi@repogauge.org
This way - AI work is like a slot machine - will this work or not? Either way - casino gets paid and casino always wins.
Nevertheless - if the idea or product is very good (filling high market pain) and not that difficult to build - it can enable non-coders to "gamble" for the outcome with AI for $.
Sadly - from by experiences hiring Devs - hiring people is also a gamble...
This is the weirdest example of "gambling" I have seen in my life. If you'd've written "unprotected sex" I'd see the gambling part, but "extramartial sex" covers so much more than the tiny subset of "whose baby is it" (how many people are there having sex to gamble on who will be the father of a baby? 10?).
This made my day.
If they can do a task that takes 1 unit of computation for 1 dollar they will cost 100 dollars for a 10 unit task and 10,000 for a 100 unit task.
Project costs from Claude Code bear this out in the real world.
the first model to outcompete its competitors while using less compute would be purchased more than anything else
that depends on the ability to produce supply at a saturation rate.
It did work for internet backhaul links - ala, those dark fibres. However, i reckon those fibres are easier to manufacture than silicon chips.
I wonder if saturation is possible for ai capable chips.
It’s true that at a given point in time the cost to achieve a certain task follows exponential curve against time taken by a human. But.. so what?
- Smaller chunks make review much easier and more effective at finding bugs, as we've known since long before LLMs.
- Greater certainty provides a better development experience. I've heard people talk about how LLM development can be tiring. One way that happens, I think, is the win-or-lose drama of feeding in huge tasks with a substantial chance of failure. I think if you're succeeding 95% of the time instead of 70%, and the 5% are easier to deal with (smaller chunks to debug), it's a better experience.
- Everything is harder about real-world tasks because they aren't clean verifiable-reward benchmarks. Developers have context that models don't, so it's common that a problem traces to an detail not in the spec where the model guessed wrong. For real-world tasks "failures" are also sometimes "that UI is bad" or "that way of coding it is hard to maintain." And it's possible to have problems the dev simply doesn't notice. The benchmarks' fully computer-checkable outcomes are 'easy mode' compared to the real world.
- Fixing agents' mess becomes more work as task sizes increase. (Like the certainty thing, but about cost in hours than the experience.) Again, if the model has spat out 1000 lines and stumped itself debugging a failure, it'll take you some time to figure out: more time than debugging 250-line patch, and the larger patch is more likely to have bugs. And if an issue bug makes it out to peer review, you can add communication and context-switching cost (point out bug, fix, re-review) on top of that.
- Bugs that reach prod are really expensive. More of a problem when a prod bug can lose you customers vs., say, on most hobby things. Ord's post gestures at it: there are "cases where failure is much worse than not having tried at all." That magnifies how important it is the review be good, and how much of a problem bugs that sneak through are, which points towards doing smaller chunks.
How significant each factor is depends on details: how easy the task is to verify, how well-specified it is (and more generally how much it's in the models' wheelhouse, and how much in mine), how bad a bug would be (fun thing? internal tool? user facing? can lose data?).
I think the dynamics above apply across a range of model strengths, but that doesn't mean the changes from say Sonnet 3.7 to Opus 4.5 didn't mean anything; the machine getting better at getting the info it needs and checking itself still helps at shorter task lengths. Harness improvements can help, e.g. they could help keep models of the 'too much context, model got silly' zone (may be less severe than it once was, but I suspect will remain a thing), build better context, and clean up code as well as spitting results out.
Besides taking more of your time up front, involving yourself more also tends to drift towards you making more of the lower-level decisions about how the code will look, which I find double-edged. You have better broad context, and you know what you find maintainable. But the implementer, model or another person, is closer to the code, which helps it make some mid-to-low-level decisions well.
Plan modes and Spec-Kit type things can help with the balance of getting involved but letting the model do its thing. I've liked asking the LLM to ask a lot of questions and surface doubts. A colleague messed with Spec-Kit so it would pick one change on its fine-grained to-do list at a time, which is a neat hack I'd like to try sometime.
