The open weight, open architecture releases of the past several days has me more convinced that ultimately, the winner will be whoever burns their models to ASICs fastest.
The LLMs themselves are capable of doing some aspects of chip design as evinced by the K3 press release.
Furthermore, the frontier models are "good enough" for a wide swathe of tasks and will soon hit that threshold for a good amount of software engineering (if not already). Does anyone think we need a Mythos level model to plan a road trip, or give someone tips on making a cake recipe?
A Fable 5 model running at 9,000 tokens/s on an ASIC rather than 150 tokens/s on electricity chugging Nvidia GPUs, or even giant SRAM Cerebras or Groq chips could be good enough to meet the majority of demand.
Furthermore, if you're an enterprise the risk of data exfiltration and feeding data to a potential competitor like OpenAI or Anthropic is greatly reduced if you could shift to on-prem ASIC deployments. A handful of chips could cover a wide variety of use cases and cover them more securely. There are a lot of corporate use-cases for LLMs that are not frontier math research or coding.
I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms. LLMs and model architectures seems significantly more volatile. I wouldn't want to be working out the finer details of my chip rollout only to find a new paper/approach that give multiples of performance.
So I guess it depends on how much the latest-greatest model motivates people, and my read on the current churn is that developers are extremely unloyal to brand at this point and will jump to whoever has the best model. And as long as the best model is running on programmable GPUs, that will be the dominant form.
The tokens per second performance numbers coming from Cerebrus/Talas are several orders of magnitude higher than models running on GPUs, which is such a huge step change that it will enable many more uses of LLMs that are impractical otherwise. I.e. think about gamers and burning in an LLM chip on a game console like a future Play Station - it doesn't matter if its a frontier LLM if it allows them to talk to in game characters in real time and have the LLM drive the storyline. The devs may be limited to training/RLHFing legacy models, but the performance enables many more use cases.
Honestly, whether you think burning current SOTA to hardware is an overinvestment risk depends on what your definition of intelligence is. If you think intelligence is something that can grow like height such that 18 months from now we will basically be bowing down to machine god giants that are running on B200s, then investing in ASICs is the wrong move. However, if you you subscribe to the (very reasonable view) that intelligence is more like a round ball that we are trying to make as spherical as possible (ala Francois Chollet's writings), then at some point the ball will be smooth enough for most people and many tasks.
It takes about 18 months to go through the design, verification, and manufacturing process if you move at breakneck pace. Design could probably be sped up.
About 18 months ago the top model was GPT-4o. Not great by today's standards, but still good enough for many tasks (certainly a big chunk of chatbot queries). The current SOTA covers far more use cases, but importantly at a level that surpasses many thresholds of utility.
>I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms.
Yesterday there's a news on a breakthrough for probabilistic computer with 1 million p-bits [1].
Since LLM is stochastic in nature, this type of new computer can be much better than ASIC for processing LLM data.
[1] Biggest Probabilistic Computer Turns Noise into Answers:
I thought about the volatility, too. But here are some additional thoughts:
Many AI uses are not that volatile. I had a 20 minute conversation today with some company's AI phone assistant. It was extremely good and would have been very helpful if any of the dozen people it tried to route me to would have picked up their phone. That AI won't need to be upgraded for a very long time. There is no reason for it to have a cloud brain except to force a recurring revenue for the company selling it.
Hardcore gamers are constantly throwing down insane money on the latest hardware. The rest of us can get by for a couple years with whatever we bought when the last one broke. Yeah, it's not the latest, but it gets the job done. I wonder if AI has not already reached the point where a gen 10 CPU--uh, I mean a v3 AI model--will get the job done for the next year. If I really need the up-to-the-second latest abilities for a minute, I can fallback to a cloud brain @ 1M tokens/$. Why pay a monthly lease on a 5-year plan for a 4-door Ford Ranger as your daily commuter? Buy a Clio and rent an F-250 twice a year when you need the hauling/towing capabilities.
The churn is an issue. For a while there, it felt like we were getting a new number format every month or two (e.g., fp4, ternary, etc.). That level of innovation works against moving things into hardware, or at least you need to be willing to spin hardware constantly.
I think the gamble comes down to how many tokens need to be served on your best model, versus how many can be served in the cheapest/fastest way.
Imagine if Anthropic could give effectively unlimited access to Sonnet, for $20. Wouldn’t that be an appealing option for many users? I know I’d make a lot of use of it for agentic tasks, office work, summarization, etc; when right now I’d save quota for more important tasks.
Apparently Google and team will build ASICs to run Gemini even though they have TPUs and it’ll be somewhat dynamic since the weights can be made modular [1]
seems to me that we are at the asymtote for most usage. sure run the prompts that need the frontier on generic silicon but burning the fable 5 model into silicon could be perfectly viable
A Fable 5 model running at 9,000 tokens/s on an ASIC rather than 150 tokens/s on electricity chugging Nvidia GPUs, or even giant SRAM Cerebras or Groq chips could be good enough to meet the majority of demand.
Agreed 100%. This guy thinks there's a limit on the demand for intelligence. You think that Fable 7 which can run a billion dollar corporation on its own has no consumer demand just because we have fable 5 at 9k tok/s? Who do you think will be the biggest customer of such a model? Fable 7, obviously.
Fable is a pretty crappy general purpose LLM. It reminds me a little bit of GPT 5.2, in that it sometimes gets argumentative and deceptive after being caught in a mistake, and it tends to make a lot of them when you ask for judgement calls. Opus 4.8 is better for that sort of thing, or Opus 4.6 if it's important that it actually follow all of your instructions.
And this is one of the big things that seems to be missed in these discussions: There is no longer a universal linear trend of LLMs being 'better' each iteration. They are becoming more specialized, and ones that approach problems from a different angle (like Fable/Mythos) can appear breakthrough when first released, but we don't appear to be on a path that actually leads to general purpose hyper intelligence.
> Does anyone think we need a Mythos level model to plan a road trip, or give someone tips on making a cake recipe?
This is starting to look at a lot like Intel vs Arm from the last era.
The Fable & Mythos are starting to look like a giant Xeon, while the smaller lighter models are starting to look like a lot of tiny ARM chips which sip on power instead.
The risk is the same as what Intel had. There is a group who are pushing them to go bigger and with a resource no limit approach, who have a lot of dollars to push you that way.
Follow them and they lead you to a pile of money, but then you risk something like Apple Silicon happening.
Something which got better because of efficiency & continuous improvement, not neutered due to it.
> The risk is the same as what Intel had. There is a group who are pushing them to go bigger and with a resource no limit approach, who have a lot of dollars to push you that way. Follow them and they lead you to a pile of money, but then you risk something like Apple Silicon happening.
The problem was that Intel stopped making things faster and better, and shifted to wearables and mobile chips instead of investing in their hard-tech strategy that had worked for decades.
Except aren’t these Chinese models having to go big too? While they might not be Mythos/Fable sized, a trillion plus parameters is hardly a local server or even desktop to mainframe style jump
GPUs have a lot of slack there is at least one lab that had a (small 8b) model generate almost 3000 tokens per second on a MI300X for a talk, instead of the typical software stack that did maybe 100ish tokens per second.
High bandwidth flash storage is in the works, i.e hard drives with TBs of storage and over 1 TB per second of read speeds. Meaning that in a couple of years you may be able to buy a card with 40-90GBs of HBM and 4TB of HBF
and run a 3T model locally at a reasonable speed for 10-20k as opposed to a cool mil.
It's always difficult to see it from the present, but we're not at some end stage in hardware development; we're still on the same curve our predecessors also couldn't see: they couldn't imagine that there would be high performance computers carried in our pockets, with staggering amounts of storage and compute, putting to shame the machines they filled rooms with.
> the winner will be whoever burns their models to ASICs fastest.
It'll obviously be China, and they won't need the bleeding edge of lithography tech to make it happen. Every Chinese smartphone will have something like Sonnet 5, along your car (well, not those of us in the US, but we'll look longingly at pictures of them while we drive whatever the government decides we're allowed to drive in Fortress America).
Give it ten years and your smart litterbox from Temu will be running its own local model.
China won in cloud services? Nope, not even close. They had to clone AWS just to try to keep up.
China won in mobile? Nope. Although they're very competitive there.
China won in search? Nope. Baidu who?
China won in ecommerce? Nope. Their dominance is overwhelmingly domestic.
China won in software? Nope. Windows is US. Android is US. iOS is US. MacOS is US. Linux is US/Europe/Global. Look at the top 50 largest software companies.
China won in silicon? Nope. Look at the top 50 companies.
China won in .... India and Latin America can manufacture iPhones now.
But sure, China's the obvious winner this time. Good luck.
While running the model at 9000 tokens/s is the more flashy demo, I imagine running 1000 concurrent requests at 150 tokens/s each is the much more achievable goal
At 9000 tokens/s you could interleave a lot of requests so long as pre-fill is also fast.
It really depends on how much you need to keep sessions open to take advantage of KV caching
It depends. If I was running a model locally I would much prefer 9000 t/s. If I was running an inference company, obv 1000 concurrent requests at 150 t/s is preferable.
I'm convinced there is a Transmeta/transpiler solution somewhere between 9000 t/s ASIC and Nvidia GPUs. LLMs fit the model much better than what they were trying to do with general purpose in the 2000s.
My gut is that, at least for now, there's a timescale mis-match problem. Hardware still takes too long, then you have to deploy it. I don't know much about "burning asics", but if the whole process of spinning up programmable GPU data centers is months, I imagine the whole ASIC cycle has some catching up to do. To be clear, by timescale mismatch I mean model quality improvement timescale vs. deployment timescale. But maybe we're finally getting to the point where behind-the-frontier-but-cheaper are in sufficient demand and ASICs make it cheap enough to close that gap.
If Qwen 3 Coder were available as a PCI card I could just slot into my desktop, and the software on the system could recognize the card and still work with other cards should I decide I later want to upgrade to Qwen 5 or whatever, and at a three figure price point, I imagine it would do quite well.
The comparison I've seen elsewhere is the old console systems with separate cartridges for games... I wouldn't want to be regularly swapping them, but if they came out in a form factor that didn't require me to shell out multiple-4 digit figures in upgrades just to use the next model, it'd easily be worth it for me.
I've already got a home lab, and it's specced to last, minus the GPU. I picked up a separate system for local llm experimentation, but I'm not likely to be upgrading it again. The value add is incredibly small compared to the cost. The real benefits are data privacy and never worrying about rate limits, and there's a price point beyond which an incremental improvement to the model doesn't justify upgrading the system.
The field is moving too fast for it to make sense. ASICs have a 12-24 month development cycle and you’d have to throw them in the trash 4 months later.
If this approach doesn't include a way to apply updated finetunes on a daily basis (also without going back to VRAM being the bottleneck) then the trash interval would be measured in hours, not months
They require a lot of computation regardless of whether this will take place on ASICs. China can do it, even if the chips are not the most power-efficient. They have built lots of electricity capacity.
Ultimately however, that just means that models will become dirt-cheap. The money will be made with applications built on top of the models.
I think the SotA is moving too fast for the production timelines of an ASIC, wouldn't you think? People are just now coming out with LLAMA ASICS but who would want to use LLAMA? Or I guess you are arguing that the models _now_ will be durably useful enough to commit the time to creating the ASIC?
guess you are arguing that the models _now_ will
be durably useful enough to commit the time to
creating the ASIC?
Objectively, the current frontier-ish models will be useful for some time. Imagine the zombie apocolypse hits, recedes, and you need to rebuild society. An offline copy of Fable or even Opus would be a nice thing to have.
Subjectively, it's hard to say if people will pay for "a model from 18 months ago, but REALLY FAST AND CHEAP"
The speed difference suggests some use cases that might narrow the performance gap. With a > 50x performance delta you have some headroom to play with.
You can do many many fast iterations of ye olde "Ralph loops." You can also jack up the reasoning/effort level. And you could probably do some combination of both, while still running really fast ie 10x the number of iterations at 2x reasoning/effort.
So I think a hypothetical "50x faster Opus 4.8, but burned into ASICs" could be pretty competitive against the frontier models from 2027, 2028, 2029, and maybe beyond?
I dream of some kind of "adjustable" ASIC layers, that have the most "computation demanding" layers as ASICs plus a bunch of configurable R+W circuits (FPGAs?) that can be written to upgrade the models to some extent.
What's fascinating is that we are pushing the state of the art of hardware at this point.
> the winner will be whoever burns their models to ASICs fastest.
I don't understand how this works when the models are evolving so fast that your burned ASIC is outdated (or at least not top of the line) in a few weeks.
If llama 3 70b were available for $400 today, people would make it work. They would have 4 of them working side by side or 1 of them working with a GPU on another model.
To me, it's like imagining if Sonnet 3 was burned into an ASIC 8 years ago and then never changed. It would still be revolutionary, and today we would have an entire ecosystem of tools and services built around it, likely surpassing some of our current workflows.
The frontier is a different beast, but it would likely mean competing on price.
for many purposes they're good enough now. If I had an opus 4.8 class model on a box next to me that could produce tokens at rates like 5000/s, i don't know if i'd need a new one for a long time. I think we might be underestimating how powerful very very fast LLMs could be, since they could iterate on tons of small variations on tasks. paired with deterministic guardrails that gate "doneness", you could loop on tasks for a long time having the agent try different strategies until the goal was reached, in ways that are just impractical now (and very expensive)
I think it makes more sense if you think about an ASIC pipeline which is updated on an annual basis, like Apple's M or A-series chips.
Sure, an ASIC model will always be behind (12 months? 18 months?), and a hosted flagship will be significantly better. But as time marches on, would I use a flagship model from last year if it came on a PCI card and cost $1000? Without a shadow of a doubt.
Cerebras is not an ASIC. It is a large wafer scale chip that has a load of small SRAM modules paired with tiny compute models. The SRAM is basically a giant cache that is supposed to eliminate the bottleneck between shuttling and materializing big tensors between DRAM and cache, but the actual amount of SRAM is still nowhere near enough to server a frontier model on a single wafer. You still need dozens of wafers, which makes Cerebras rather cost intensive given that Nvidia can get dozens of GPUs out of a single wafer
Currently the setup is paged view in RAM shuttled to HBDRAM (VRAM) on the GPU, which in turn has to get materialized piece by piece onto cache SRAM on the GPU.
Cerebras tries to get around this by keeping everything on cache SRAM as much as possible, which it burns directly to the chip wafer itself and physically places that SRAM directly next to the tiny compute unit that does the actual math.
An ideal setup (not sure how easy this is to achieve in practice), is the burn the weights of the model directly to the chip as a sort of ROM, the actual math operations as actual digital circuits, and have SRAM, or even something akin to naked registers to directly compute off inference batch data. Cuts out 2-3 layers of abstraction and indirection.
You don't need memory because the output of the previous layer flows directly into the input gates of the next. So no I/O to/from HBM or similar
You still need some memory for the context, in flight answers, ... but not for the model weights and for the output of the intermediate layers. I found taalas demo here: https://chatjimmy.ai/
Last I saw they posted Deepseek R1 numbers in Feb of this year.
The challenge is rolling out a new one every 7-8 weeks as the weights change & cheap enough for a hyper scaler to afford to buy one and save enough on power over the next 8 weeks as a payoff.
not sure about that, but im actively working on designing ultra sparse models that i want to have perform competitively with stuff 100-10_000 times larger. ehich does yield similar throughput. time will tell id it works out
Yeah, I've heard of Taalas doing it. Not sure of others but I'm sure lots of companies are considering it, especially as we start to hit points of depreciating returns in training.
What precisely is an ASIC supposed to do that a more programmable accelerator can't? Memory latency is memory latency: doesn't matter whether it's embedded in a cache-line wait state or some flipflop state machine. ROM isn't going to be faster than RAM either.
Likewise, for compute, is the ASIC somehow going to beat a systolic array? You can't have one circuit per weight: the die area and electrical fan-out would be insane. I'm not seeing how an ASIC specialized for a specific model would actually help much.
I mean, sure, we can build more specific accelerators, e.g. for softmax, but these work fine in the context of a programmable pipeline.
Yes, there are more exotic things out there, like optical matrix multiplication systems. Those are different. But above, aren't you talking about just doing conventional digital linear algebra, but with a model-specific set of circuits?
No, if you burn your model into the silicon you don't need memory as the output of a layer flows through circuitry directly into the next one. No I/O to memory of any kind. You still need a bit of memory for in flight answers, but that's it.
I keep thinking about the Figma thing. If you're unaware, here's the google summary:
----
The Board Departure: Mike Krieger, Anthropic’s CPO and a co-founder of Instagram, sat on Figma’s board of directors. He resigned on April 14, just days before news of Claude Design broke. This sparked speculation over conflict of interest and the use of proprietary product strategy information.
Betrayal of Partnership: The launch aggravated the tech industry because Figma relied on Anthropic's models to power its own AI features, and even announced a joint "Code to Canvas" integration. Reports indicate Figma was blindsided by the depth and scope of Claude Design.
Market Reaction: The "SaaSpocalypse" thesis—fears that major AI foundation models will rapidly build application layers and cannibalize their own SaaS partners—was realized when the news broke. Figma’s stock saw an immediate 7% drop upon the announcement.
----
I would suggest to people using LLMs: you should be cautious about giving these companies data or relying on them. If you're building an AI startup, there's a very good chance they could decide to directly compete with you if your idea has traction. You're also at their mercy for API pricing etc.
Your last two sentences remind me of the olden days of people building applications on top of FB and Twitter APIs (and later, Reddit), only to to have the rug pulled out from under them in one way or another
Olden days being FB and Twitter APIs? Don't forget about Apple Sherlocking independent devs. This has been happening with almost any platform for a very long time.
even before that, by some accounts I hear the reason VCs over invested in dot com was primarily because they saw something that could not easily usurped by Microsoft by releasing their own sh*tty copy. so they all piled in and then bubble dynamics took over.
Back in the day, a contract company I worked for had entire teams of people dedicated to building FB apps. When that collapsed, it came down fast and hard.
I believe the coding tool revenue is an important factor right now but not the endgame. The AI companies have to become consumer products to justify their gigantic valuations. We always talked about the “super apps” - one app that fulfills most consumer needs, like
WeChat in China.
OpenAI is the company most visibly making a huge bet on becoming a “consumer super device”, even designing their own hardware to stop being dependent on Apple and Android.
The long term battle is not AI companies vs SaaS, but rather AI companies vs big consumer brands - Apple, Google, Meta, Amazon.
Imagine having the ultimate digital assistant that you can throw any task at. Under the hood it creates its own code and interfaces on the fly to help you accomplish your task. By using LLMs as coding assistants we are all training them to become really good at it. If the newcomers succeed to deliver the ultimate digital assistant first, then even a monster like Apple could become obsolete.
Our current incarnation of capitalism is all about monopolistic behaviors. If these LLM companies do get to the point of being able to replace employees I fully expect them to stop selling shovels and start producing the gold directly, anyone else be damned. And, frankly, this has always been the case. If a product is built on top of another service it has a limited lifespan. Either the product will be purchased or it will be replaced by the service it depends on. How many "killer apps" has Apple silently absorbed into iOS over the years?
That's very true. I realized it myself when I worked on my first app, I made a resume tailor last summer back when GPT 5 just came out.
Then, by the time GPT 5.5 came out you could already generate a flawless PDF and Word doc resume with the same formatting as your base resume. It really is amazing.
Trying to compete with these labs is a bad idea, you might work hard on your thin wrapper and then they come out and have their chatbot or harness do it way better than you.
But I also think this is really cool. It's a new era in software. Build something worthwhile and you won't need to worry about a lab doing the same thing with their chatbot.
Disagree: The era of "<Tech Giant> will eat your startup by throwing capital/devs at it" are over. Anthropic, OpenAI et al are under too much competitive pressure to chase every quixotic product idea.
You are vastly underestimating how much more profitable a 10% annual return on GPUs is than basically anything else you would use an LLM for.
They think whatever you are doing is cute and would very much like to ensure their models can do it even better in the future, but competing? Not even worth the time to think about
This has been a story, age as old as time. How many times have startup founders been told "you're a feature" or "Google/Meta/etc can copy you so fast". Operators just need to stay vigilant of their value add, or they never really had a moat in the first place and were living on borrowed time anyway.
> I would suggest to people using LLMs: you should be cautious about giving these companies data or relying on them. If you're building an AI startup, there's a very good chance they could decide to directly compete with you if your idea has traction. You're also at their mercy for API pricing etc.
LLM generated code is not copyrightable, so even if they do "steal" it - I don't think there's legal grounds to do anything about it.
It can't be stolen. You don't really own it.
You can try to lock it in a safe and hope no one ever gets a hold of it. You can lie and say you didn't use an LLM, but Anthropic and OpenAI et al probably have logs to disprove that.
This is a popular misreading of the state of the law.
Someone tried, as a bit of a stunt, to register a work for copyright with generative AI as the sole creator/author. That registration was rejected. This is quite different from a person using generative AI as a tool to create a work.
People have copyright in photos and videos they create, even if they used a camera. Same with images and code, even if they used an LLM.
I'm not your lawyer, but to the extent you have copyright in works you create, the fact that you used a tool doesn't diminish the copyright.
So, code is not the only data they potentially have access to, and it might not even be the most interesting data. There's also whatever other things you plug the AI into: slack, email, internal documentation, etc.
Also, a lot of people using LLMs are using it on code bases that were written before LLMs were common for coding. So in that context, you really are allowing them to train on your code for free.
> LLM generated code is not copyrightable, so even if they do "steal" it - I don't think there's legal grounds to do anything about it.
> It can't be stolen. You don't really own it.
At least in the UK computer generated code has been copyright-able for decades [1]
> (3)In the case of a literary, dramatic, musical or artistic work which is computer-generated, the author shall be taken to be the person by whom the arrangements necessary for the creation of the work are undertaken.
And the ToS of the major/minor AI providers as far as i can tell have no opinion on what you do with the code in terms of ownership.
For one, on the margin people are willing to pay a lot for slightly better models. I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs. I have no interest in optimizing that to get it slightly lower. There are a very vocal minority that optimizes this or companies whose LLM expense is marginal, but I think that's the minority (correct me if I'm wrong, curious what their customer base looks like)
Also the actual LLM is a tiny portion of the value added. Anyone that tried to build agentic solutions from LLM apis quickly realizes that a huge value is the Claude Code / Codex harness. There are open source implementations like OpenCode but they're not nearly as good.
Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them. Google sheets is probably 99% and available to everyone and better in a lot of regards. But the immense value spreadsheet software produces workers above the $100 or whatever a year makes it so that there is a real moat and no one bothers exploring alternatives.
> For one, on the margin people are willing to pay a lot for slightly better models. I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs. I have no interest in optimizing that to get it slightly lower. There are a very vocal minority that optimizes this or companies whose LLM expense is marginal, but I think that's the minority (correct me if I'm wrong, curious what their customer base looks like)
Enterprise is how Claude/OAI make their money. Enterprise companies absolutely optimize around costs for infra, it's like an entire job category.
> Also the actual LLM is a tiny portion of the value added. Anyone that tried to build agentic solutions from LLM apis quickly realizes that a huge value is the Claude Code / Codex harness. There are open source implementations like OpenCode but they're not nearly as good.
Claude and OAI are not valued at $1T because of their harnesses
> Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them. Google sheets is probably 99% and available to everyone and better in a lot of regards. But the immense value spreadsheet software produces workers above the $100 or whatever a year makes it so that there is a real moat and no one bothers exploring alternatives.
Enterprise excel is like $50 a month, the closed source labs charge orders of magnitude more than that per user per month for enterprise, and want to charge more if they can to maintain their valuation. I spent $20 in an afternoon yesterday using my work plan for one workflow for reference, I probably spend at least $50 a day and I don't even do that much coding anymore.
$200/mo for 20x Max is great value - but it's not sustainable and won't last forever. Microsoft gave up. Anthropic will too. I use roughly $10k/mo - at that price it's absolutely not worth it.
I'm genuinely not sure where the balancing point even is.
> Claude and OAI are not valued at $1T because of their harnesses
They're valued at that because they add a lot of value and people pay for the product. The product is more than the LLM. If you want argue the value of the harness vs LLM but flippant remark adds nothing.
> Enterprise excel is like $50 a month, the closed source labs charge orders of magnitude more than that per user per month for enterprise, and want to charge more if they can to maintain their valuation. I spent $20 in an afternoon yesterday using my work plan for one workflow for reference, I probably spend at least $50 a day and I don't even do that much coding anymore.
I want to know what percent of their revenue is this API spend compared to just a Claude/Codex max plan that's $200/m and very generous.
Here, people tend to forget about enterprise customers. Enterprise is excluded from using these heavily subsidized subscriptions, I know of orgs that are spending around $500k/month for teams of ~100 developers actively using AI.
This is a spot where smaller orgs are getting a definite advantage (both cost and speed). If you're a company with 20 - 50 people, you can probably get away with Claude Teams instead of Claude Enterprise, and pay 10x less in ai costs.
> considerably more than the $200/m I pay the frontier labs.
This is a very rich / developed country privilege perspective.
Where I live, it's not unusual for a monthly wage to be around $200. Of course developer wages are much higher, maybe as much as $1000 a month, but $200 is still a huge chunk of that so it doesn't really matter how much "value" you get out of it if you're no longer able to pay rent or buy decent food.
Even in developed countries, $200 a month is out of reach for all kinds of people who would benefit from it (students without rich families, entrepreneurs, etc.).
Thats great, but the market will always be driven by rich developed countries. I don't think Anthropic and OpenAI is banking on third world adoption in their growth estimates.
> on the margin people are willing to pay a lot for slightly better models
That margin is getting smaller and smaller. I would have been with you a week ago; paying for Fable was worth it compared to all other models. But with K3, the difference has shrunk to the point where, for me, it's not worth the cost anymore.
In other words, it may be worth paying five times as much to get 10% better real-world outcomes for a lot of people, but a lot fewer people will pay five times as much to get 2% better outcomes.
> a huge value is the Claude Code / Codex harness
For me, it's the opposite. Having to use Claude Code instead of the harness I prefer is a point against Anthropic, not for it.
100% agree for the harnesses, I always end up using CC when I want to use Opus but a few months ago I had my opencode harness set up but Copilot Pro+ rose too much the prices for me to keep using it…
What harness do you prefer using ?
I don't think subs are what keep their lights on. And their API is so absurdly expensive.
For the harness hard disagree but ultimately its up to each ones taste. You may want to check this tho https://harnessrank.net
On our side we use Claude/GPT/Kimi (it replaced Antigravity) for development. But we build our systems around a cheaper denominator (Deepseek previously, recently we added GPT 5.6 which have good prices as well). We offer BYOK for Claude but its def not an option to build something on top of it (for us).
> There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them
Excel has network effects. If N people are using excel, the incremental N+1'th user is forced to use it or risk not being able to open documents from the N users. Excel is also sticky, in the sense that a user used to Excel UI is hesitant to switch to a new UI. Tokens dont have network effects. If N people use fable, the N+1'th user could use any other model, and have no impact.
The $200 you’re paying is heavily subsidized, you need to consider the real inference cost which could be closer to $10k if you’re using the maximum permitted by your plan.
Not true. Enterprise pay for api. If i am allowed to use fable withouta budget, i can spend 3k per day. Using combination of cheaper models (and better at least in my experience) can lower this to 300. Considering the layoffs, thw companiesdo care a lot of this cost. Price matters more than quality for many use cases. For example, i can writea game where npc are all k3/5.6 level intelligence if the price is cheap enough.
You assume everyone has extra 200$ to pay. For someone living in a low wage country, it's hard to pay that much, even if you produce a multiple of that value in theory
Spending a lot on making your top developers more effective is easy to justify.
But once you have an AI-powered application in production, why wouldn't you go with far cheaper and capable enough models? That's just optimising a business process like any other. You'd use cheaper providers whether Chinese, or other on-prem models.
> Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them.
Sure but were these open source alternative as pervasive and widespread and talked about when Microsoft was bundling Excel with the OS? Or were there millions of dollars, if not billions of dollars being spent by these competitors? The answer is No. It is not the same scenario.
While I understand the logic of harnesses - those are not full proof. It is trivial to setup K3 or Qwen to work with Claude Code/Codex by intercepting requests and routing to K3/Qwen. You have tools like CCSwitch which can do that for you.
The only thing remains is - people willing to pay a lot for slightly better model. That is true but price sensitivity is also a thing. The mania case for Anthropic/OpenAI/SpaceX/Google is that they will capture large part of the enterprise market. But in many cases outside coding AI capabilities will be resold.
That is instead of buying AI for lets say HR functions you pay for embedded AI in your HR software. And the HR software company will have incentives to raise their own margins. If their provide similar looking experience using Qwen/K3 why will they buy Anthropic/OpenAI. That is the real risk here. While as an individual you can continue to pay top dollar.
For people cranking out SaaS / web services / web pages / "full stack" work I don't see a huge difference.
If you're building an optimizing compiler, a CUDA kernel, a database, a high performance concurrent data structure with tricky locking, etc. etc. it's still not really close. Sol 5.6 on high just slays e.g. GLM 5.2 for this kind of work for me.
I'm sure K3 is fine for these things too, but I can't afford it at its API rates compared to a Codex coding plan.
It's amazing how quickly Fable went from 'Game-changing model that needs to be banned' to 'Yeah it's alright, but OpenAI is also just as good and there are a couple of good open weight alternatives that are equivalent for almost everything'
The hype cycles are shortening, perhaps we really are reaching some kind of plateau this time (famous last words)
And given that Chinese models are closing the gap there are basically two thing that could be happening. One is that they are moving faster than US companies developing closed models, and two that we're starting to hit a plateau for model capabilities where all the easy gains have been plucked, and now it's not really possible to move forward at the same rate on the frontier. Of course, both things could be happening at the same time.
Fable is still the same model, it’s still a great model, and to be honest all these articles writing and speculating on how the LLM industry is going to evolve are not that insightful nor interesting.
I don’t think one should pay much attention to them.
The plateau is inevitable because their rapacious training methodologies are only viable when there are no defense in place, but information continues to evolve, which means the models will have to be continuously updated, but will be doing so with less and less freely available data.
My understanding is that the labs ran out of freely available data to train on a while ago, and now primarily rely on human data vendors such as Surge and Mercor to source their data.
> More importantly, as sustainable long-term businesses, model-only providers are particularly at risk. Knowledge Atlas, Moonshot Labs, and Anthropic face defensibility challenges versus OpenAI, Alibaba, SpaceX, Meta, and Google.
Hm. How is OpenAI not a “model-only” provider just like Anthropic? Seems like they are vulnerable in the same way.
ChatGPT is synonymous with non technical/work related LLMs. They're amassing a ton of user history. That history improves the product for the user because it has more context into the person. They can feed it back into model improvements and for advertising.
You can see a future where a user types in "plan a vacation for me" and ChatGPT coordinates everything from there. Those sorts of users aren't going to switch because model X is 10% cheaper or better.
I see a future where lots of models can plan a vacation for me, not just ChatGPT. Saying that there is brand equity in the ChatGPT brand feels a lot like saying there’s a brand equity in the MySpace and AOL 25 years ago. Google in particular, via Android and its relationship with Apple to power Siri, has a much better shot at grabbing the “plan a vacation for me” consumer market, IMO.
I could easily see OpenAI become irrelevant in 2 years if they stumble at all and don’t keep up with the other frontier models.
The article does address what they see as the difference ("its investments in product, consumer experience, site publishing, voice, and hardware are all directions that have clearer moats")
That said I think it is pretty easy to make a case that these would-be differentiators are either currently underwhelming or completely unproven (as in the case of hardware).
Right. Arguing with the original article, not you, my counterpoint would be, “Sure, if they’re able to do that. But so could any of the other model-only competitors.” The best you can say today is that they have announced an intention to do those things, but in no way have they established themselves as being successful, yet. And the model-only Chinese providers have access to lots of e.g. wearable and consumer tech.
They are trying to diversify into consumer hardware and also are further along in owning data centers. The consumer hardware product, assuming it launches and does decently well, puts them in a very unique position relative to pretty much every other frontier lab. It's more speculative, but I'd argue it can change things quite a bit for them if it works out.
They've been doing hardware stuff as well. Both "consumer facing" bs like wearables / portables, but also more importantly chips for inference.
Having a good model is one thing, being able to serve that model at good speeds and match demand is another. See Anthropic ~6months ago. Or Moonshot, they've already suspended subscriptions to their coding plans, because they can't meet demand.
Upper bound of AI progress - recursive self improvement. In this case AI will be responsible for building better models, making people who own datacenters the winners. Anthropic/OAI is cooked.
Lower bound of AI progress - plateau. Progess is slowing, focus is on serving a meaningful peak capability at the lowest possible price. There's been news today that Google is building a Gemini chip with weights baked into silicon. Considering a chip's lifetime of 2-3 years at minimum, and that a 2-3 year model today would be useless today, they're expecting they wont make a similar amount of progress in the next 3. Game is about selling at the lowest margin. Anthropic/OAI is cooked.
So their survival rests on the presumption that AI progress will fall between these two extremes.
Baking weights in makes a lot of sense for inference speed and power efficiency and has the added benefit of putting many end-users on the hardware refresh treadmill.
> So their survival rests on the presumption that AI progress will fall between these two extremes.
That feels like a very generous framing. There's very little opportunity between the extremes that would paint a convincing outlook of survival for either company.
Perhaps if they were able to scale down their spending drastically they could survive, but that requires acknowledging their current valuations are BS. Doing so is a major risk, that will piss off all share holders. There's also the employees they would need to fire or reduce salary. The shift of focus internally to sustainability would be a major challenge.
I think a big question is whether any of these labs can produce a model that is _ahead_ of Anthropic and OpenAI.
A related question is how much they're dependent on the APIs of Anthropic and OpenAI to achieve their results - whether through distillation or other uses.
If these models are derivative of Anthropic/OpenAI I would expect performance to be more narrow and progress to be limited.
> Once a model is built, the biggest cost is inference
Something i cant find any reliable data for, but would help for a sense of scale: How much use before its equal to training?
I.e. assuming you have the training data and setup, and we only care for compute - How hours of using eg Kimi K3 / Fable, for it to equal the compute required to train it?
if you assume that training requires about 3x the compute of inference (one forward pass, one backward pass, parameter updates), and we take DeepSeek-V3 since their numbers are public.
they used ~14.8 trillion tokens with about 2.66 million GPU hours.
14.8 * 3 = 44.4 t inference tokens.
obviously, this is back of the envelope math, but at 100t/s you would need like ~14k years. scale this to >100k GPUs and your in the hours to a couple days range.
> This is where OpenAI has an advantage over Anthropic. While its models are trailing Anthropic's in recent months, its investments in product, consumer experience, site publishing, voice, and hardware are all directions that have clearer moats.
I was with you up until this point. I don’t think OpenAI has any more of a substantial product moat than Anthropic; if anything, the Claude / mythos etc brand is a valuable asset that OpenAI lacks.
Yes, many of the elite HN engineer always online types have come to prefer Codex, and but if you actually talk to regular engineers in industry, agentic coding is simply still synonymous with Claude Code.
And for the non-engineering uses, Claude is so much more pleasant of a conversational companion than any of the GPT line, and I suspect is this baked deeply into the model, otherwise OpenAI would have closed this gap by now.
In November of 2025, I would have said that Anthropic's Opus 4.5 model together with their Claude Code harness was the first and only system where a well-specified software feature could be implemented correctly for me in one shot. Today, I'm about equally happy to use Claude or Codex. And if both of those start to squeeze customers for money or get too zealous about safety it looks like there are going to be plenty of capable open weight models and true open source harnesses to use with them. Even Google might eventually deliver a capable model + agent combination (Gemini 3.1 Pro still seems pretty strong, but Antigravity was inept the last time I tried to use it.)
I'm happy if Anthropic's business remains viable as one of several strong competitors. The company's safety-first ethos is driving them to increase refusals and deliberately-built-in ignorance with their newer models. In the long run, Anthropic may be best remembered for accelerating the development of software in general so that other people could build less timid tools.
I think the jury is still out on K3/Q3.8 and if they are equivalent to Opus or Fable. Benchmarks have gotten incredibly murky and I've tried models that are "the same as X model" and been unimpressed. I've tried other models with Claude Code and with tools like OpenCode or Pi and nothing has really come close to Claude Code using Anthropic's models (mostly Opus 4.8).
I'm not saying these other models are trash, just that I'm not quite ready to put them on equal footing to Anthropic or OpenAI models.
I think the future of LLM coding (and more) is probably routers that decide on a per-task basis which model to route to. Know when to use Fable/Opus and when to fall back to DeepSeek/Kimi/Qwen, even all the way to local models. Of course that's not in the frontier lab's interest but it feels like there is a lot of low-hanging fruit there. I hate that right now it's pretty much "just use the same model for planning/execution/etc" (without standing on your head).
If there's any hope for AI sovereignty and equality, we would have to either make expensive models cheap to run or make cheaper models do less work.
Making the latter happen involves either reformulating work in ways less intelligent LLMs can work better with. Or condensing intelligence into smaller models.
The bigger question for me is , at what point does investing in higher capability general models will stop showing the ROI?
For example - What percentage of workflows require this new highly capable model? How much of it can be replaced with the software tooling around it? What I mean is if the software tooling can optimize the query over a few iterations does it get the same output as from a single shot high capability model query?
Well written article. I liked how the author categorizes companies and their strategies into different buckets (not authors choice of words) and/or combination of harness, data centers, electricity, foundational models. I would have loved to read how companies mentioned are pivoting to build their moat
Anthropic will get squeezed by open models for 80% of the use cases that don't require frontier capabilities and by vertical specific labs for the high value tasks that would (bio, finance, math, etc.), where smaller use case specific models will beat them on cost and speed while matching or exceeding the performance of their largest general models.
Even their hail mary of being first to "AGI" will never happen because all it takes is China blockading Taiwan or Nvidia cutting them off to stop them from eating up a large chunk of the economy.
There is no scenario where the rest of the world will sit on their toes and let OpenAI or Anthropic monopolize "AI". Too many countries, large well capitalized players and partners / suppliers who could never let that happen.
Kimi K3 allows all existing players to restart at the frontier and keep competing with OpenAI/Ant. It also gives employees at these labs a better more lucrative path of starting new labs with fresh books and clean cap tables, building on top of K3 without needing to spend all the capex on pretraining their own models. Plenty of them already vested their stock and would have 0 problems raising 100s of millions of dollars for new labs, making them paper billionaires over night.
None of my use cases require frontier capabilities but I still pay $200/month to a frontier lab. I value the additional time saved at more than $200/month. If I had to pay actual API rates, then I'm not sure what I would do, but it would not be an easy decision.
Sure, but anthropic is charging businesses based on usage now and tried hard to pull Fable from the consumer subscriptions before Sol and K3 dropped.
Even now on the $200 plan I use up my Fable credits in a single day and had to start using codex and openrouter for more usage because Fable burns $100s an hour when billed on usage.
Yes, the reckoning here will happen in a year or two when the (probably subsidized, maybe?) coding plans become either unavailable or much more costly.
It's also already the case that in larger companies people do not have access to these plans as employees and must use API rates.
There's also the political angle. When Anthropic and OpenAI held back their top of the line models because of Bessent & Trump's bullshit, and threatened to deny unwashed foreigners like me access... I dropped my Codex plan and made do purely with GLM 5.2 for three weeks before OpenAI finally released 5.6 Sol. Feels inevitable that this will happen again.
Or, somebody will come up with a way to serve e.g. Kimi K3 or the new Qwen model in an extremely cheap way. Or DeepSeek releases a competitive model at their cut-throat rates. And then the cost argument just wins.
you cant. The best you can do is Qwen 3.6 27b with a 24gig ( or cumaltive gpus ) to get to 24gb vram. ala 3090, mac with 36gb ram, amd cards, halo strix amd, dgx spark etc. Lots of youtube videos out there.
I haven't seen either of these running outside their creator's services yet, but typically you can watch services like openrouter or nano-gpt for it to show up at a (usually small) discount.
Well, if you're happy with around (as in within an order of magnitude or two of) 0.1 tokens per second... I believe that's around what people are getting when loading MoE weights from NVMe.
You've got to consider how much power that uses though. Depending where you live, some of these providers can serve it for less than you pay for power.
Anthropic needs to stop fearmongering and stop gatekeeping legitimate frontier AI usage from the general public! The AI built by distillation of the civilizational intelligence should not be exclusive to the privileged researchers.
Add another dimension - users tightening their purse on AI spend as the reality of returns hits them. Large companies might go for locally hosted models.
To everyone praising Open weight models, could you answer a simple question?
If Anthropic doesn't make money because of distillation attacks, how would they convince investors to invest in them, such that it makes financial sense for Anthropic to train even bigger models?
Assuming it is preferable for everyone that we get better models in the future. Distillation attacks remove the financial incentive.
OpenAI says they're not just distillation. "Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that."
If you put any faith in claims made by OpenAI's "Head of Strategic Futures," then it's hard to believe these models are just the result of distillation.
I don't think the timeline is plausible for Moonshot to have done any distillation from Fable, which means any distillation training data would have been from Opus.
The frontier labs have been tightly controlling model access lately to prevent further distillation, and the Chinese labs are still catching up rapidly - it's likely that whatever moat they used distillation to overcome early on is already dry
While I am sure some do distillation (even r/LocalLLaMA is full of randos bragging about it), I wish the companies named by Anthropics would respond to these accusations.
I think at least deepseek can easily take Anthropics to court and win a defamation case. The number of requests allegedly done from deepseek IP range is so small it is barely enough to run a few benchmarks.
Nope, I'll still buy Claude because the overall XP is better than Kimi and Qwen who literally copied basic harnesses to make kimi-cli and qwen-cli, respectively.
Also, you can tell if a model is genuinely powerful and well-thought-out vs a model that acts like it.
It's like Apple vs Xiaomi/Huawei. Sure, you can get a Huawei with bells and whistles, but most people learnt the hard way that those companies just copy the iPhone, so might as well get the real deal.
As somebody who bought an iPhone the day it became available and has been using iPhones for years, my favorite phone I've ever owned is my Huawei Mate XT Ultimate.
People keep pretending that Chinese companies only make second-class copies of American products until it's too late.
To be fair, the flagship Xiaomi, Vivo, Oppo etc are comparable in price to other flagship devices. You very much pay a premium for the large camera sensors they put in these devices, amongst other things.
The open weight, open architecture releases of the past several days has me more convinced that ultimately, the winner will be whoever burns their models to ASICs fastest.
The LLMs themselves are capable of doing some aspects of chip design as evinced by the K3 press release.
Furthermore, the frontier models are "good enough" for a wide swathe of tasks and will soon hit that threshold for a good amount of software engineering (if not already). Does anyone think we need a Mythos level model to plan a road trip, or give someone tips on making a cake recipe?
A Fable 5 model running at 9,000 tokens/s on an ASIC rather than 150 tokens/s on electricity chugging Nvidia GPUs, or even giant SRAM Cerebras or Groq chips could be good enough to meet the majority of demand.
Furthermore, if you're an enterprise the risk of data exfiltration and feeding data to a potential competitor like OpenAI or Anthropic is greatly reduced if you could shift to on-prem ASIC deployments. A handful of chips could cover a wide variety of use cases and cover them more securely. There are a lot of corporate use-cases for LLMs that are not frontier math research or coding.
I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms. LLMs and model architectures seems significantly more volatile. I wouldn't want to be working out the finer details of my chip rollout only to find a new paper/approach that give multiples of performance.
So I guess it depends on how much the latest-greatest model motivates people, and my read on the current churn is that developers are extremely unloyal to brand at this point and will jump to whoever has the best model. And as long as the best model is running on programmable GPUs, that will be the dominant form.
The tokens per second performance numbers coming from Cerebrus/Talas are several orders of magnitude higher than models running on GPUs, which is such a huge step change that it will enable many more uses of LLMs that are impractical otherwise. I.e. think about gamers and burning in an LLM chip on a game console like a future Play Station - it doesn't matter if its a frontier LLM if it allows them to talk to in game characters in real time and have the LLM drive the storyline. The devs may be limited to training/RLHFing legacy models, but the performance enables many more use cases.
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Honestly, whether you think burning current SOTA to hardware is an overinvestment risk depends on what your definition of intelligence is. If you think intelligence is something that can grow like height such that 18 months from now we will basically be bowing down to machine god giants that are running on B200s, then investing in ASICs is the wrong move. However, if you you subscribe to the (very reasonable view) that intelligence is more like a round ball that we are trying to make as spherical as possible (ala Francois Chollet's writings), then at some point the ball will be smooth enough for most people and many tasks.
It takes about 18 months to go through the design, verification, and manufacturing process if you move at breakneck pace. Design could probably be sped up.
About 18 months ago the top model was GPT-4o. Not great by today's standards, but still good enough for many tasks (certainly a big chunk of chatbot queries). The current SOTA covers far more use cases, but importantly at a level that surpasses many thresholds of utility.
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>I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms.
Yesterday there's a news on a breakthrough for probabilistic computer with 1 million p-bits [1].
Since LLM is stochastic in nature, this type of new computer can be much better than ASIC for processing LLM data.
[1] Biggest Probabilistic Computer Turns Noise into Answers:
https://news.ycombinator.com/item?id=48971938
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I thought about the volatility, too. But here are some additional thoughts:
Many AI uses are not that volatile. I had a 20 minute conversation today with some company's AI phone assistant. It was extremely good and would have been very helpful if any of the dozen people it tried to route me to would have picked up their phone. That AI won't need to be upgraded for a very long time. There is no reason for it to have a cloud brain except to force a recurring revenue for the company selling it.
Hardcore gamers are constantly throwing down insane money on the latest hardware. The rest of us can get by for a couple years with whatever we bought when the last one broke. Yeah, it's not the latest, but it gets the job done. I wonder if AI has not already reached the point where a gen 10 CPU--uh, I mean a v3 AI model--will get the job done for the next year. If I really need the up-to-the-second latest abilities for a minute, I can fallback to a cloud brain @ 1M tokens/$. Why pay a monthly lease on a 5-year plan for a 4-door Ford Ranger as your daily commuter? Buy a Clio and rent an F-250 twice a year when you need the hauling/towing capabilities.
The churn is an issue. For a while there, it felt like we were getting a new number format every month or two (e.g., fp4, ternary, etc.). That level of innovation works against moving things into hardware, or at least you need to be willing to spin hardware constantly.
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I think the gamble comes down to how many tokens need to be served on your best model, versus how many can be served in the cheapest/fastest way.
Imagine if Anthropic could give effectively unlimited access to Sonnet, for $20. Wouldn’t that be an appealing option for many users? I know I’d make a lot of use of it for agentic tasks, office work, summarization, etc; when right now I’d save quota for more important tasks.
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Apparently Google and team will build ASICs to run Gemini even though they have TPUs and it’ll be somewhat dynamic since the weights can be made modular [1]
[1] https://finance.yahoo.com/technology/ai/articles/google-plan...
seems to me that we are at the asymtote for most usage. sure run the prompts that need the frontier on generic silicon but burning the fable 5 model into silicon could be perfectly viable
640K ought to be enough for anybody.
Agreed 100%. This guy thinks there's a limit on the demand for intelligence. You think that Fable 7 which can run a billion dollar corporation on its own has no consumer demand just because we have fable 5 at 9k tok/s? Who do you think will be the biggest customer of such a model? Fable 7, obviously.
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I am really confused about the point you're trying to make. 150tok/s is slow, but so is 9000tok/s? Or they're both fast? Or 150tok/s should be enough?
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> 640K ought to be enough for anybody.
I get this reference!
> Does anyone think we need a Mythos level model to plan a road trip
Yes. The latest OpenAI and Anthropic models are terrible at planning roadtrips.
This is something I try to use them for frequently. They constantly get things completely wrong.
I’d say that about half of the stops they suggest fail to follow whatever filters I’ve asked for.
Fable is a pretty crappy general purpose LLM. It reminds me a little bit of GPT 5.2, in that it sometimes gets argumentative and deceptive after being caught in a mistake, and it tends to make a lot of them when you ask for judgement calls. Opus 4.8 is better for that sort of thing, or Opus 4.6 if it's important that it actually follow all of your instructions.
And this is one of the big things that seems to be missed in these discussions: There is no longer a universal linear trend of LLMs being 'better' each iteration. They are becoming more specialized, and ones that approach problems from a different angle (like Fable/Mythos) can appear breakthrough when first released, but we don't appear to be on a path that actually leads to general purpose hyper intelligence.
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> Does anyone think we need a Mythos level model to plan a road trip, or give someone tips on making a cake recipe?
This is starting to look at a lot like Intel vs Arm from the last era.
The Fable & Mythos are starting to look like a giant Xeon, while the smaller lighter models are starting to look like a lot of tiny ARM chips which sip on power instead.
The risk is the same as what Intel had. There is a group who are pushing them to go bigger and with a resource no limit approach, who have a lot of dollars to push you that way.
Follow them and they lead you to a pile of money, but then you risk something like Apple Silicon happening.
Something which got better because of efficiency & continuous improvement, not neutered due to it.
> The risk is the same as what Intel had. There is a group who are pushing them to go bigger and with a resource no limit approach, who have a lot of dollars to push you that way. Follow them and they lead you to a pile of money, but then you risk something like Apple Silicon happening.
The problem was that Intel stopped making things faster and better, and shifted to wearables and mobile chips instead of investing in their hard-tech strategy that had worked for decades.
Except aren’t these Chinese models having to go big too? While they might not be Mythos/Fable sized, a trillion plus parameters is hardly a local server or even desktop to mainframe style jump
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> whoever burns their models to ASICs fastest.
There is already custom hardware see cerebras.
GPUs have a lot of slack there is at least one lab that had a (small 8b) model generate almost 3000 tokens per second on a MI300X for a talk, instead of the typical software stack that did maybe 100ish tokens per second.
High bandwidth flash storage is in the works, i.e hard drives with TBs of storage and over 1 TB per second of read speeds. Meaning that in a couple of years you may be able to buy a card with 40-90GBs of HBM and 4TB of HBF and run a 3T model locally at a reasonable speed for 10-20k as opposed to a cool mil.
> Meaning that in a couple of years you may
There is no "may" here. You will see this.
It's always difficult to see it from the present, but we're not at some end stage in hardware development; we're still on the same curve our predecessors also couldn't see: they couldn't imagine that there would be high performance computers carried in our pockets, with staggering amounts of storage and compute, putting to shame the machines they filled rooms with.
https://taalas.com/
> the winner will be whoever burns their models to ASICs fastest.
It'll obviously be China, and they won't need the bleeding edge of lithography tech to make it happen. Every Chinese smartphone will have something like Sonnet 5, along your car (well, not those of us in the US, but we'll look longingly at pictures of them while we drive whatever the government decides we're allowed to drive in Fortress America).
Give it ten years and your smart litterbox from Temu will be running its own local model.
China won in desktop PCs? Nope.
China won in cloud services? Nope, not even close. They had to clone AWS just to try to keep up.
China won in mobile? Nope. Although they're very competitive there.
China won in search? Nope. Baidu who?
China won in ecommerce? Nope. Their dominance is overwhelmingly domestic.
China won in software? Nope. Windows is US. Android is US. iOS is US. MacOS is US. Linux is US/Europe/Global. Look at the top 50 largest software companies.
China won in silicon? Nope. Look at the top 50 companies.
China won in .... India and Latin America can manufacture iPhones now.
But sure, China's the obvious winner this time. Good luck.
While running the model at 9000 tokens/s is the more flashy demo, I imagine running 1000 concurrent requests at 150 tokens/s each is the much more achievable goal
At 9000 tokens/s you could interleave a lot of requests so long as pre-fill is also fast. It really depends on how much you need to keep sessions open to take advantage of KV caching
It depends. If I was running a model locally I would much prefer 9000 t/s. If I was running an inference company, obv 1000 concurrent requests at 150 t/s is preferable.
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I'm convinced there is a Transmeta/transpiler solution somewhere between 9000 t/s ASIC and Nvidia GPUs. LLMs fit the model much better than what they were trying to do with general purpose in the 2000s.
My gut is that, at least for now, there's a timescale mis-match problem. Hardware still takes too long, then you have to deploy it. I don't know much about "burning asics", but if the whole process of spinning up programmable GPU data centers is months, I imagine the whole ASIC cycle has some catching up to do. To be clear, by timescale mismatch I mean model quality improvement timescale vs. deployment timescale. But maybe we're finally getting to the point where behind-the-frontier-but-cheaper are in sufficient demand and ASICs make it cheap enough to close that gap.
If Qwen 3 Coder were available as a PCI card I could just slot into my desktop, and the software on the system could recognize the card and still work with other cards should I decide I later want to upgrade to Qwen 5 or whatever, and at a three figure price point, I imagine it would do quite well.
The comparison I've seen elsewhere is the old console systems with separate cartridges for games... I wouldn't want to be regularly swapping them, but if they came out in a form factor that didn't require me to shell out multiple-4 digit figures in upgrades just to use the next model, it'd easily be worth it for me.
I've already got a home lab, and it's specced to last, minus the GPU. I picked up a separate system for local llm experimentation, but I'm not likely to be upgrading it again. The value add is incredibly small compared to the cost. The real benefits are data privacy and never worrying about rate limits, and there's a price point beyond which an incremental improvement to the model doesn't justify upgrading the system.
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The field is moving too fast for it to make sense. ASICs have a 12-24 month development cycle and you’d have to throw them in the trash 4 months later.
If this approach doesn't include a way to apply updated finetunes on a daily basis (also without going back to VRAM being the bottleneck) then the trash interval would be measured in hours, not months
We're getting new models every three months. They'd have to work quickly to get those chips made and installed.
> the winner will be whoever burns their models to ASICs fastest
As of today, that appears to be Google!
https://news.ycombinator.com/item?id=48986351
Yeah, I have been thinking exactly the same. Makes all the sense in the world and is inevitable as far as I can see.
https://news.ycombinator.com/item?id=48464958
They require a lot of computation regardless of whether this will take place on ASICs. China can do it, even if the chips are not the most power-efficient. They have built lots of electricity capacity.
Ultimately however, that just means that models will become dirt-cheap. The money will be made with applications built on top of the models.
Fun as the asic play would be it has a giant hole - context storage. Raw speed only gets you so far if you can’t store and cache
I think the SotA is moving too fast for the production timelines of an ASIC, wouldn't you think? People are just now coming out with LLAMA ASICS but who would want to use LLAMA? Or I guess you are arguing that the models _now_ will be durably useful enough to commit the time to creating the ASIC?
Objectively, the current frontier-ish models will be useful for some time. Imagine the zombie apocolypse hits, recedes, and you need to rebuild society. An offline copy of Fable or even Opus would be a nice thing to have.
Subjectively, it's hard to say if people will pay for "a model from 18 months ago, but REALLY FAST AND CHEAP"
The speed difference suggests some use cases that might narrow the performance gap. With a > 50x performance delta you have some headroom to play with.
You can do many many fast iterations of ye olde "Ralph loops." You can also jack up the reasoning/effort level. And you could probably do some combination of both, while still running really fast ie 10x the number of iterations at 2x reasoning/effort.
So I think a hypothetical "50x faster Opus 4.8, but burned into ASICs" could be pretty competitive against the frontier models from 2027, 2028, 2029, and maybe beyond?
I dream of some kind of "adjustable" ASIC layers, that have the most "computation demanding" layers as ASICs plus a bunch of configurable R+W circuits (FPGAs?) that can be written to upgrade the models to some extent.
What's fascinating is that we are pushing the state of the art of hardware at this point.
> the winner will be whoever burns their models to ASICs fastest.
I don't understand how this works when the models are evolving so fast that your burned ASIC is outdated (or at least not top of the line) in a few weeks.
If llama 3 70b were available for $400 today, people would make it work. They would have 4 of them working side by side or 1 of them working with a GPU on another model.
To me, it's like imagining if Sonnet 3 was burned into an ASIC 8 years ago and then never changed. It would still be revolutionary, and today we would have an entire ecosystem of tools and services built around it, likely surpassing some of our current workflows.
The frontier is a different beast, but it would likely mean competing on price.
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for many purposes they're good enough now. If I had an opus 4.8 class model on a box next to me that could produce tokens at rates like 5000/s, i don't know if i'd need a new one for a long time. I think we might be underestimating how powerful very very fast LLMs could be, since they could iterate on tons of small variations on tasks. paired with deterministic guardrails that gate "doneness", you could loop on tasks for a long time having the agent try different strategies until the goal was reached, in ways that are just impractical now (and very expensive)
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I think it makes more sense if you think about an ASIC pipeline which is updated on an annual basis, like Apple's M or A-series chips.
Sure, an ASIC model will always be behind (12 months? 18 months?), and a hosted flagship will be significantly better. But as time marches on, would I use a flagship model from last year if it came on a PCI card and cost $1000? Without a shadow of a doubt.
Sol is running on Cerebras right?
Cerebras is not an ASIC. It is a large wafer scale chip that has a load of small SRAM modules paired with tiny compute models. The SRAM is basically a giant cache that is supposed to eliminate the bottleneck between shuttling and materializing big tensors between DRAM and cache, but the actual amount of SRAM is still nowhere near enough to server a frontier model on a single wafer. You still need dozens of wafers, which makes Cerebras rather cost intensive given that Nvidia can get dozens of GPUs out of a single wafer
How does an ASIC manage to have 60x the memory bandwidth needed to achieve that speedup?
Currently the setup is paged view in RAM shuttled to HBDRAM (VRAM) on the GPU, which in turn has to get materialized piece by piece onto cache SRAM on the GPU.
Cerebras tries to get around this by keeping everything on cache SRAM as much as possible, which it burns directly to the chip wafer itself and physically places that SRAM directly next to the tiny compute unit that does the actual math.
An ideal setup (not sure how easy this is to achieve in practice), is the burn the weights of the model directly to the chip as a sort of ROM, the actual math operations as actual digital circuits, and have SRAM, or even something akin to naked registers to directly compute off inference batch data. Cuts out 2-3 layers of abstraction and indirection.
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You don't need memory because the output of the previous layer flows directly into the input gates of the next. So no I/O to/from HBM or similar
You still need some memory for the context, in flight answers, ... but not for the model weights and for the output of the intermediate layers. I found taalas demo here: https://chatjimmy.ai/
why not start with fogas ? you know … to test the waters ?
GPUs are already ASICs.
Apple's M7 comes to mind...
Another reason this may be true, selling hardware may be the only durable moat after a while
is anyone doing this ?
The closest example I've seen is ChatJimmy: https://chatjimmy.ai/ a prototype from Taalas running Llama 8B
Scaling this up to 2.8 Trillion (350X increase), will certainly be challenging.
If I was younger and had the right background, I'd love to dive into attempting somethign like this
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News just broke today that Google is planning on doing this: https://news.ycombinator.com/item?id=48986351
Taalas HC1 is the closest thing to this.
Last I saw they posted Deepseek R1 numbers in Feb of this year.
The challenge is rolling out a new one every 7-8 weeks as the weights change & cheap enough for a hyper scaler to afford to buy one and save enough on power over the next 8 weeks as a payoff.
not sure about that, but im actively working on designing ultra sparse models that i want to have perform competitively with stuff 100-10_000 times larger. ehich does yield similar throughput. time will tell id it works out
Yeah, I've heard of Taalas doing it. Not sure of others but I'm sure lots of companies are considering it, especially as we start to hit points of depreciating returns in training.
There was a startup that did this for Llama 3, I forgot their name. Etched is also doing some similar things I believe.
obligatory link to https://chatjimmy.ai
It sounds vaguely similar to what Cerebras.ai is doing
Open weights are a red herring if you can’t run the model independently. Good luck fitting these models on your Mac mini.
In fact the superior models are irreducibly nothing but superior web services run from China.
Did you not notice that there are inference providers running all the various Chinese open weight models in the US and EU?
Nobody needs "web services run from China" to use Chinese open weight models.
What precisely is an ASIC supposed to do that a more programmable accelerator can't? Memory latency is memory latency: doesn't matter whether it's embedded in a cache-line wait state or some flipflop state machine. ROM isn't going to be faster than RAM either.
Likewise, for compute, is the ASIC somehow going to beat a systolic array? You can't have one circuit per weight: the die area and electrical fan-out would be insane. I'm not seeing how an ASIC specialized for a specific model would actually help much.
I mean, sure, we can build more specific accelerators, e.g. for softmax, but these work fine in the context of a programmable pipeline.
Yes, there are more exotic things out there, like optical matrix multiplication systems. Those are different. But above, aren't you talking about just doing conventional digital linear algebra, but with a model-specific set of circuits?
No, if you burn your model into the silicon you don't need memory as the output of a layer flows through circuitry directly into the next one. No I/O to memory of any kind. You still need a bit of memory for in flight answers, but that's it.
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I keep thinking about the Figma thing. If you're unaware, here's the google summary:
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The Board Departure: Mike Krieger, Anthropic’s CPO and a co-founder of Instagram, sat on Figma’s board of directors. He resigned on April 14, just days before news of Claude Design broke. This sparked speculation over conflict of interest and the use of proprietary product strategy information.
Betrayal of Partnership: The launch aggravated the tech industry because Figma relied on Anthropic's models to power its own AI features, and even announced a joint "Code to Canvas" integration. Reports indicate Figma was blindsided by the depth and scope of Claude Design.
Market Reaction: The "SaaSpocalypse" thesis—fears that major AI foundation models will rapidly build application layers and cannibalize their own SaaS partners—was realized when the news broke. Figma’s stock saw an immediate 7% drop upon the announcement.
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I would suggest to people using LLMs: you should be cautious about giving these companies data or relying on them. If you're building an AI startup, there's a very good chance they could decide to directly compete with you if your idea has traction. You're also at their mercy for API pricing etc.
Your last two sentences remind me of the olden days of people building applications on top of FB and Twitter APIs (and later, Reddit), only to to have the rug pulled out from under them in one way or another
Olden days being FB and Twitter APIs? Don't forget about Apple Sherlocking independent devs. This has been happening with almost any platform for a very long time.
even before that, by some accounts I hear the reason VCs over invested in dot com was primarily because they saw something that could not easily usurped by Microsoft by releasing their own sh*tty copy. so they all piled in and then bubble dynamics took over.
story as old as internet itself!
Back in the day, a contract company I worked for had entire teams of people dedicated to building FB apps. When that collapsed, it came down fast and hard.
Still true today for AWS.
don’t forget m$ as well.
I believe the coding tool revenue is an important factor right now but not the endgame. The AI companies have to become consumer products to justify their gigantic valuations. We always talked about the “super apps” - one app that fulfills most consumer needs, like WeChat in China. OpenAI is the company most visibly making a huge bet on becoming a “consumer super device”, even designing their own hardware to stop being dependent on Apple and Android. The long term battle is not AI companies vs SaaS, but rather AI companies vs big consumer brands - Apple, Google, Meta, Amazon. Imagine having the ultimate digital assistant that you can throw any task at. Under the hood it creates its own code and interfaces on the fly to help you accomplish your task. By using LLMs as coding assistants we are all training them to become really good at it. If the newcomers succeed to deliver the ultimate digital assistant first, then even a monster like Apple could become obsolete.
How long did Apple take to kill off all the iPhone competitors.
These companies haven't got the attention span to work on one thing for that long.
The driving constraint if you want to take a spot in B2C is the speed at which consumers replace tech, not the speed at which tech can be developed.
Delusion
Our current incarnation of capitalism is all about monopolistic behaviors. If these LLM companies do get to the point of being able to replace employees I fully expect them to stop selling shovels and start producing the gold directly, anyone else be damned. And, frankly, this has always been the case. If a product is built on top of another service it has a limited lifespan. Either the product will be purchased or it will be replaced by the service it depends on. How many "killer apps" has Apple silently absorbed into iOS over the years?
And who will buy the gold? At a certain point everyone will be too poor to buy anything.
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I mean Claude IS replacing people. Even in Australia - Claude + postgres mcp is replacing data/bi people in even small and medium businesses.
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That's very true. I realized it myself when I worked on my first app, I made a resume tailor last summer back when GPT 5 just came out.
Then, by the time GPT 5.5 came out you could already generate a flawless PDF and Word doc resume with the same formatting as your base resume. It really is amazing.
Trying to compete with these labs is a bad idea, you might work hard on your thin wrapper and then they come out and have their chatbot or harness do it way better than you.
But I also think this is really cool. It's a new era in software. Build something worthwhile and you won't need to worry about a lab doing the same thing with their chatbot.
Disagree: The era of "<Tech Giant> will eat your startup by throwing capital/devs at it" are over. Anthropic, OpenAI et al are under too much competitive pressure to chase every quixotic product idea.
Then run your own fine tuned models for your AI startup.
Doesn't that mean you bet against the bitter lesson?
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You are vastly underestimating how much more profitable a 10% annual return on GPUs is than basically anything else you would use an LLM for.
They think whatever you are doing is cute and would very much like to ensure their models can do it even better in the future, but competing? Not even worth the time to think about
Is it only a 10% annual return? That's not a lot.
This has been a story, age as old as time. How many times have startup founders been told "you're a feature" or "Google/Meta/etc can copy you so fast". Operators just need to stay vigilant of their value add, or they never really had a moat in the first place and were living on borrowed time anyway.
> I would suggest to people using LLMs: you should be cautious about giving these companies data or relying on them. If you're building an AI startup, there's a very good chance they could decide to directly compete with you if your idea has traction. You're also at their mercy for API pricing etc.
LLM generated code is not copyrightable, so even if they do "steal" it - I don't think there's legal grounds to do anything about it.
It can't be stolen. You don't really own it.
You can try to lock it in a safe and hope no one ever gets a hold of it. You can lie and say you didn't use an LLM, but Anthropic and OpenAI et al probably have logs to disprove that.
But, if push comes to shove, you don't own it...
This is a popular misreading of the state of the law.
Someone tried, as a bit of a stunt, to register a work for copyright with generative AI as the sole creator/author. That registration was rejected. This is quite different from a person using generative AI as a tool to create a work.
People have copyright in photos and videos they create, even if they used a camera. Same with images and code, even if they used an LLM.
I'm not your lawyer, but to the extent you have copyright in works you create, the fact that you used a tool doesn't diminish the copyright.
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So, code is not the only data they potentially have access to, and it might not even be the most interesting data. There's also whatever other things you plug the AI into: slack, email, internal documentation, etc.
Also, a lot of people using LLMs are using it on code bases that were written before LLMs were common for coding. So in that context, you really are allowing them to train on your code for free.
> LLM generated code is not copyrightable, so even if they do "steal" it - I don't think there's legal grounds to do anything about it.
> It can't be stolen. You don't really own it.
At least in the UK computer generated code has been copyright-able for decades [1]
> (3)In the case of a literary, dramatic, musical or artistic work which is computer-generated, the author shall be taken to be the person by whom the arrangements necessary for the creation of the work are undertaken.
And the ToS of the major/minor AI providers as far as i can tell have no opinion on what you do with the code in terms of ownership.
[1] https://www.legislation.gov.uk/ukpga/1988/48/section/9
> LLM generated code is not copyrightable
[citation needed]
I've seen cases where it was determined that the LLM itself doesn't hold copyright. But where are you getting the idea that nobody holds copyright?
I think the risk is overstated.
For one, on the margin people are willing to pay a lot for slightly better models. I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs. I have no interest in optimizing that to get it slightly lower. There are a very vocal minority that optimizes this or companies whose LLM expense is marginal, but I think that's the minority (correct me if I'm wrong, curious what their customer base looks like)
Also the actual LLM is a tiny portion of the value added. Anyone that tried to build agentic solutions from LLM apis quickly realizes that a huge value is the Claude Code / Codex harness. There are open source implementations like OpenCode but they're not nearly as good.
Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them. Google sheets is probably 99% and available to everyone and better in a lot of regards. But the immense value spreadsheet software produces workers above the $100 or whatever a year makes it so that there is a real moat and no one bothers exploring alternatives.
> For one, on the margin people are willing to pay a lot for slightly better models. I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs. I have no interest in optimizing that to get it slightly lower. There are a very vocal minority that optimizes this or companies whose LLM expense is marginal, but I think that's the minority (correct me if I'm wrong, curious what their customer base looks like)
Enterprise is how Claude/OAI make their money. Enterprise companies absolutely optimize around costs for infra, it's like an entire job category.
> Also the actual LLM is a tiny portion of the value added. Anyone that tried to build agentic solutions from LLM apis quickly realizes that a huge value is the Claude Code / Codex harness. There are open source implementations like OpenCode but they're not nearly as good.
Claude and OAI are not valued at $1T because of their harnesses
> Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them. Google sheets is probably 99% and available to everyone and better in a lot of regards. But the immense value spreadsheet software produces workers above the $100 or whatever a year makes it so that there is a real moat and no one bothers exploring alternatives.
Enterprise excel is like $50 a month, the closed source labs charge orders of magnitude more than that per user per month for enterprise, and want to charge more if they can to maintain their valuation. I spent $20 in an afternoon yesterday using my work plan for one workflow for reference, I probably spend at least $50 a day and I don't even do that much coding anymore.
$200/mo for 20x Max is great value - but it's not sustainable and won't last forever. Microsoft gave up. Anthropic will too. I use roughly $10k/mo - at that price it's absolutely not worth it.
I'm genuinely not sure where the balancing point even is.
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> Claude and OAI are not valued at $1T because of their harnesses
They're valued at that because they add a lot of value and people pay for the product. The product is more than the LLM. If you want argue the value of the harness vs LLM but flippant remark adds nothing.
> Enterprise excel is like $50 a month, the closed source labs charge orders of magnitude more than that per user per month for enterprise, and want to charge more if they can to maintain their valuation. I spent $20 in an afternoon yesterday using my work plan for one workflow for reference, I probably spend at least $50 a day and I don't even do that much coding anymore.
I want to know what percent of their revenue is this API spend compared to just a Claude/Codex max plan that's $200/m and very generous.
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Here, people tend to forget about enterprise customers. Enterprise is excluded from using these heavily subsidized subscriptions, I know of orgs that are spending around $500k/month for teams of ~100 developers actively using AI.
This is a spot where smaller orgs are getting a definite advantage (both cost and speed). If you're a company with 20 - 50 people, you can probably get away with Claude Teams instead of Claude Enterprise, and pay 10x less in ai costs.
> considerably more than the $200/m I pay the frontier labs.
This is a very rich / developed country privilege perspective.
Where I live, it's not unusual for a monthly wage to be around $200. Of course developer wages are much higher, maybe as much as $1000 a month, but $200 is still a huge chunk of that so it doesn't really matter how much "value" you get out of it if you're no longer able to pay rent or buy decent food.
Even in developed countries, $200 a month is out of reach for all kinds of people who would benefit from it (students without rich families, entrepreneurs, etc.).
Thats great, but the market will always be driven by rich developed countries. I don't think Anthropic and OpenAI is banking on third world adoption in their growth estimates.
> a huge value is the […] Codex harness. There are open source implementations
Like Codex https://github.com/openai/codex
If you think Codex is some neutral harness designed to work equally as well for all LLMs, I got a bridge to sell you...
> on the margin people are willing to pay a lot for slightly better models
That margin is getting smaller and smaller. I would have been with you a week ago; paying for Fable was worth it compared to all other models. But with K3, the difference has shrunk to the point where, for me, it's not worth the cost anymore.
In other words, it may be worth paying five times as much to get 10% better real-world outcomes for a lot of people, but a lot fewer people will pay five times as much to get 2% better outcomes.
> a huge value is the Claude Code / Codex harness
For me, it's the opposite. Having to use Claude Code instead of the harness I prefer is a point against Anthropic, not for it.
100% agree for the harnesses, I always end up using CC when I want to use Opus but a few months ago I had my opencode harness set up but Copilot Pro+ rose too much the prices for me to keep using it… What harness do you prefer using ?
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I don't think subs are what keep their lights on. And their API is so absurdly expensive. For the harness hard disagree but ultimately its up to each ones taste. You may want to check this tho https://harnessrank.net
On our side we use Claude/GPT/Kimi (it replaced Antigravity) for development. But we build our systems around a cheaper denominator (Deepseek previously, recently we added GPT 5.6 which have good prices as well). We offer BYOK for Claude but its def not an option to build something on top of it (for us).
> There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them
Excel has network effects. If N people are using excel, the incremental N+1'th user is forced to use it or risk not being able to open documents from the N users. Excel is also sticky, in the sense that a user used to Excel UI is hesitant to switch to a new UI. Tokens dont have network effects. If N people use fable, the N+1'th user could use any other model, and have no impact.
The $200 you’re paying is heavily subsidized, you need to consider the real inference cost which could be closer to $10k if you’re using the maximum permitted by your plan.
May I know the source for "closer to $10k"?
The "real" cost is what people pay, not what the AI companies claim.
Not true. Enterprise pay for api. If i am allowed to use fable withouta budget, i can spend 3k per day. Using combination of cheaper models (and better at least in my experience) can lower this to 300. Considering the layoffs, thw companiesdo care a lot of this cost. Price matters more than quality for many use cases. For example, i can writea game where npc are all k3/5.6 level intelligence if the price is cheap enough.
I'm not sure about the harness now: https://github.com/lidge-jun/opencodex
You assume everyone has extra 200$ to pay. For someone living in a low wage country, it's hard to pay that much, even if you produce a multiple of that value in theory
Spending a lot on making your top developers more effective is easy to justify.
But once you have an AI-powered application in production, why wouldn't you go with far cheaper and capable enough models? That's just optimising a business process like any other. You'd use cheaper providers whether Chinese, or other on-prem models.
This is a bad take.
> Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them.
Sure but were these open source alternative as pervasive and widespread and talked about when Microsoft was bundling Excel with the OS? Or were there millions of dollars, if not billions of dollars being spent by these competitors? The answer is No. It is not the same scenario.
While I understand the logic of harnesses - those are not full proof. It is trivial to setup K3 or Qwen to work with Claude Code/Codex by intercepting requests and routing to K3/Qwen. You have tools like CCSwitch which can do that for you.
The only thing remains is - people willing to pay a lot for slightly better model. That is true but price sensitivity is also a thing. The mania case for Anthropic/OpenAI/SpaceX/Google is that they will capture large part of the enterprise market. But in many cases outside coding AI capabilities will be resold.
That is instead of buying AI for lets say HR functions you pay for embedded AI in your HR software. And the HR software company will have incentives to raise their own margins. If their provide similar looking experience using Qwen/K3 why will they buy Anthropic/OpenAI. That is the real risk here. While as an individual you can continue to pay top dollar.
What is your workflow such that frontier models increases productivity considerably?
For people cranking out SaaS / web services / web pages / "full stack" work I don't see a huge difference.
If you're building an optimizing compiler, a CUDA kernel, a database, a high performance concurrent data structure with tricky locking, etc. etc. it's still not really close. Sol 5.6 on high just slays e.g. GLM 5.2 for this kind of work for me.
I'm sure K3 is fine for these things too, but I can't afford it at its API rates compared to a Codex coding plan.
For now.
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It's amazing how quickly Fable went from 'Game-changing model that needs to be banned' to 'Yeah it's alright, but OpenAI is also just as good and there are a couple of good open weight alternatives that are equivalent for almost everything'
The hype cycles are shortening, perhaps we really are reaching some kind of plateau this time (famous last words)
Open-weight models were lagging 4 months behind OpenAI/Anthropic at the beginning of the year. They are now just 4-6 weeks behind.
Kimi K3 is still worse than Fable and Fable was trained >4 months ago.
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And given that Chinese models are closing the gap there are basically two thing that could be happening. One is that they are moving faster than US companies developing closed models, and two that we're starting to hit a plateau for model capabilities where all the easy gains have been plucked, and now it's not really possible to move forward at the same rate on the frontier. Of course, both things could be happening at the same time.
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Fable is still the same model, it’s still a great model, and to be honest all these articles writing and speculating on how the LLM industry is going to evolve are not that insightful nor interesting.
I don’t think one should pay much attention to them.
The plateau is inevitable because their rapacious training methodologies are only viable when there are no defense in place, but information continues to evolve, which means the models will have to be continuously updated, but will be doing so with less and less freely available data.
> with less and less freely available data
My understanding is that the labs ran out of freely available data to train on a while ago, and now primarily rely on human data vendors such as Surge and Mercor to source their data.
We are only just starting to model the physical world. There will be more training on empirical data.
Almost like the company has no credibility with respect to its safety claims.
> More importantly, as sustainable long-term businesses, model-only providers are particularly at risk. Knowledge Atlas, Moonshot Labs, and Anthropic face defensibility challenges versus OpenAI, Alibaba, SpaceX, Meta, and Google.
Hm. How is OpenAI not a “model-only” provider just like Anthropic? Seems like they are vulnerable in the same way.
ChatGPT is synonymous with non technical/work related LLMs. They're amassing a ton of user history. That history improves the product for the user because it has more context into the person. They can feed it back into model improvements and for advertising.
You can see a future where a user types in "plan a vacation for me" and ChatGPT coordinates everything from there. Those sorts of users aren't going to switch because model X is 10% cheaper or better.
I see a future where lots of models can plan a vacation for me, not just ChatGPT. Saying that there is brand equity in the ChatGPT brand feels a lot like saying there’s a brand equity in the MySpace and AOL 25 years ago. Google in particular, via Android and its relationship with Apple to power Siri, has a much better shot at grabbing the “plan a vacation for me” consumer market, IMO.
I could easily see OpenAI become irrelevant in 2 years if they stumble at all and don’t keep up with the other frontier models.
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The article does address what they see as the difference ("its investments in product, consumer experience, site publishing, voice, and hardware are all directions that have clearer moats")
That said I think it is pretty easy to make a case that these would-be differentiators are either currently underwhelming or completely unproven (as in the case of hardware).
Right. Arguing with the original article, not you, my counterpoint would be, “Sure, if they’re able to do that. But so could any of the other model-only competitors.” The best you can say today is that they have announced an intention to do those things, but in no way have they established themselves as being successful, yet. And the model-only Chinese providers have access to lots of e.g. wearable and consumer tech.
They are trying to diversify into consumer hardware and also are further along in owning data centers. The consumer hardware product, assuming it launches and does decently well, puts them in a very unique position relative to pretty much every other frontier lab. It's more speculative, but I'd argue it can change things quite a bit for them if it works out.
They've been doing hardware stuff as well. Both "consumer facing" bs like wearables / portables, but also more importantly chips for inference.
Having a good model is one thing, being able to serve that model at good speeds and match demand is another. See Anthropic ~6months ago. Or Moonshot, they've already suspended subscriptions to their coding plans, because they can't meet demand.
Didn't the Apple lawsuit throw a monkey wrench into their hardware plans?
Upper bound of AI progress - recursive self improvement. In this case AI will be responsible for building better models, making people who own datacenters the winners. Anthropic/OAI is cooked.
Lower bound of AI progress - plateau. Progess is slowing, focus is on serving a meaningful peak capability at the lowest possible price. There's been news today that Google is building a Gemini chip with weights baked into silicon. Considering a chip's lifetime of 2-3 years at minimum, and that a 2-3 year model today would be useless today, they're expecting they wont make a similar amount of progress in the next 3. Game is about selling at the lowest margin. Anthropic/OAI is cooked.
So their survival rests on the presumption that AI progress will fall between these two extremes.
>. In this case AI will be responsible for building better models, making people who own datacenters the winners.
We need SETI@home for Open Weight models yesterday...
FWIW, I agree.
Baking weights in makes a lot of sense for inference speed and power efficiency and has the added benefit of putting many end-users on the hardware refresh treadmill.
Yes, but how much would be a static Sonnet 3.5 be worth today? Its just about 2 years old. I'm not even going to ask about 3yo models like GPT4.
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> So their survival rests on the presumption that AI progress will fall between these two extremes.
That feels like a very generous framing. There's very little opportunity between the extremes that would paint a convincing outlook of survival for either company.
Perhaps if they were able to scale down their spending drastically they could survive, but that requires acknowledging their current valuations are BS. Doing so is a major risk, that will piss off all share holders. There's also the employees they would need to fire or reduce salary. The shift of focus internally to sustainability would be a major challenge.
I think a big question is whether any of these labs can produce a model that is _ahead_ of Anthropic and OpenAI.
A related question is how much they're dependent on the APIs of Anthropic and OpenAI to achieve their results - whether through distillation or other uses.
If these models are derivative of Anthropic/OpenAI I would expect performance to be more narrow and progress to be limited.
They dont need to be ahead on performance alone.
Its value per unit of currency spent.
Financials will ultimately drive decision making.
We are already seeing that more intelligence does not correlate with more revenue, for the firm purchasing tokens.
If I was OAI/Anthropic I'd be brown and yellow in the boxers.
OAI feels far more cooked than anthropic.
One of them heading to IPO and the other opting to not show their books should tell you everything
Definitely feels like there’s a particular narrative being pushed on HN today.
Dario can reverse this by going on the podcast circuit again and threatening everyone with 75% job losses due to (his) AI this time
Ramp the number up to 85% if that doesn’t work
If it still doesn’t work, go nuclear and target 100% job losses language
> Once a model is built, the biggest cost is inference
Something i cant find any reliable data for, but would help for a sense of scale: How much use before its equal to training?
I.e. assuming you have the training data and setup, and we only care for compute - How hours of using eg Kimi K3 / Fable, for it to equal the compute required to train it?
if you assume that training requires about 3x the compute of inference (one forward pass, one backward pass, parameter updates), and we take DeepSeek-V3 since their numbers are public.
they used ~14.8 trillion tokens with about 2.66 million GPU hours. 14.8 * 3 = 44.4 t inference tokens.
obviously, this is back of the envelope math, but at 100t/s you would need like ~14k years. scale this to >100k GPUs and your in the hours to a couple days range.
> This is where OpenAI has an advantage over Anthropic. While its models are trailing Anthropic's in recent months, its investments in product, consumer experience, site publishing, voice, and hardware are all directions that have clearer moats.
I was with you up until this point. I don’t think OpenAI has any more of a substantial product moat than Anthropic; if anything, the Claude / mythos etc brand is a valuable asset that OpenAI lacks.
Yes, many of the elite HN engineer always online types have come to prefer Codex, and but if you actually talk to regular engineers in industry, agentic coding is simply still synonymous with Claude Code.
And for the non-engineering uses, Claude is so much more pleasant of a conversational companion than any of the GPT line, and I suspect is this baked deeply into the model, otherwise OpenAI would have closed this gap by now.
In November of 2025, I would have said that Anthropic's Opus 4.5 model together with their Claude Code harness was the first and only system where a well-specified software feature could be implemented correctly for me in one shot. Today, I'm about equally happy to use Claude or Codex. And if both of those start to squeeze customers for money or get too zealous about safety it looks like there are going to be plenty of capable open weight models and true open source harnesses to use with them. Even Google might eventually deliver a capable model + agent combination (Gemini 3.1 Pro still seems pretty strong, but Antigravity was inept the last time I tried to use it.)
I'm happy if Anthropic's business remains viable as one of several strong competitors. The company's safety-first ethos is driving them to increase refusals and deliberately-built-in ignorance with their newer models. In the long run, Anthropic may be best remembered for accelerating the development of software in general so that other people could build less timid tools.
I would rather have anthropic fizzle out and somebody else take their place.
I think the jury is still out on K3/Q3.8 and if they are equivalent to Opus or Fable. Benchmarks have gotten incredibly murky and I've tried models that are "the same as X model" and been unimpressed. I've tried other models with Claude Code and with tools like OpenCode or Pi and nothing has really come close to Claude Code using Anthropic's models (mostly Opus 4.8).
I'm not saying these other models are trash, just that I'm not quite ready to put them on equal footing to Anthropic or OpenAI models.
I think the future of LLM coding (and more) is probably routers that decide on a per-task basis which model to route to. Know when to use Fable/Opus and when to fall back to DeepSeek/Kimi/Qwen, even all the way to local models. Of course that's not in the frontier lab's interest but it feels like there is a lot of low-hanging fruit there. I hate that right now it's pretty much "just use the same model for planning/execution/etc" (without standing on your head).
If there's any hope for AI sovereignty and equality, we would have to either make expensive models cheap to run or make cheaper models do less work.
Making the latter happen involves either reformulating work in ways less intelligent LLMs can work better with. Or condensing intelligence into smaller models.
I think condensing intelligence into smaller models is the way to go.
Also, “smaller” can mean many different things. The cost is not in storing the weights on disk.
It is in the power required to do the inference with the “active parameters”.
There is increasingly more evidence that those two can be decoupled and more power to those who are pushing on that lever!
The bigger question for me is , at what point does investing in higher capability general models will stop showing the ROI?
For example - What percentage of workflows require this new highly capable model? How much of it can be replaced with the software tooling around it? What I mean is if the software tooling can optimize the query over a few iterations does it get the same output as from a single shot high capability model query?
What ROI? Nobody is currently making money outside of the hardware manufacturers and hyperscalers
Well written article. I liked how the author categorizes companies and their strategies into different buckets (not authors choice of words) and/or combination of harness, data centers, electricity, foundational models. I would have loved to read how companies mentioned are pivoting to build their moat
Anthropic will get squeezed by open models for 80% of the use cases that don't require frontier capabilities and by vertical specific labs for the high value tasks that would (bio, finance, math, etc.), where smaller use case specific models will beat them on cost and speed while matching or exceeding the performance of their largest general models.
Even their hail mary of being first to "AGI" will never happen because all it takes is China blockading Taiwan or Nvidia cutting them off to stop them from eating up a large chunk of the economy.
There is no scenario where the rest of the world will sit on their toes and let OpenAI or Anthropic monopolize "AI". Too many countries, large well capitalized players and partners / suppliers who could never let that happen.
Kimi K3 allows all existing players to restart at the frontier and keep competing with OpenAI/Ant. It also gives employees at these labs a better more lucrative path of starting new labs with fresh books and clean cap tables, building on top of K3 without needing to spend all the capex on pretraining their own models. Plenty of them already vested their stock and would have 0 problems raising 100s of millions of dollars for new labs, making them paper billionaires over night.
None of my use cases require frontier capabilities but I still pay $200/month to a frontier lab. I value the additional time saved at more than $200/month. If I had to pay actual API rates, then I'm not sure what I would do, but it would not be an easy decision.
Sure, but anthropic is charging businesses based on usage now and tried hard to pull Fable from the consumer subscriptions before Sol and K3 dropped.
Even now on the $200 plan I use up my Fable credits in a single day and had to start using codex and openrouter for more usage because Fable burns $100s an hour when billed on usage.
I thought similarly until I decided to try DeepSeek.
It became an easy decision, even the $200/month by Anthropic sounds like a bad deal.
Yes, the reckoning here will happen in a year or two when the (probably subsidized, maybe?) coding plans become either unavailable or much more costly.
It's also already the case that in larger companies people do not have access to these plans as employees and must use API rates.
There's also the political angle. When Anthropic and OpenAI held back their top of the line models because of Bessent & Trump's bullshit, and threatened to deny unwashed foreigners like me access... I dropped my Codex plan and made do purely with GLM 5.2 for three weeks before OpenAI finally released 5.6 Sol. Feels inevitable that this will happen again.
Or, somebody will come up with a way to serve e.g. Kimi K3 or the new Qwen model in an extremely cheap way. Or DeepSeek releases a competitive model at their cut-throat rates. And then the cost argument just wins.
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How much is Anthropic's price due to inference cost or extra margin they can get away with by having the best model?
So, what's the most affordable way for a pleb who doesn't own 17 H100s to use Kimi K3 or Qwen 3.8?
you cant. The best you can do is Qwen 3.6 27b with a 24gig ( or cumaltive gpus ) to get to 24gb vram. ala 3090, mac with 36gb ram, amd cards, halo strix amd, dgx spark etc. Lots of youtube videos out there.
I haven't seen either of these running outside their creator's services yet, but typically you can watch services like openrouter or nano-gpt for it to show up at a (usually small) discount.
Well, if you're happy with around (as in within an order of magnitude or two of) 0.1 tokens per second... I believe that's around what people are getting when loading MoE weights from NVMe.
You've got to consider how much power that uses though. Depending where you live, some of these providers can serve it for less than you pay for power.
There will be smaller versions in the 10-30B parameters range that can run on consumer GPUs.
Anthropic needs to stop fearmongering and stop gatekeeping legitimate frontier AI usage from the general public! The AI built by distillation of the civilizational intelligence should not be exclusive to the privileged researchers.
So many words to say so little... Don't bother reading this.
Add another dimension - users tightening their purse on AI spend as the reality of returns hits them. Large companies might go for locally hosted models.
To everyone praising Open weight models, could you answer a simple question?
If Anthropic doesn't make money because of distillation attacks, how would they convince investors to invest in them, such that it makes financial sense for Anthropic to train even bigger models?
Assuming it is preferable for everyone that we get better models in the future. Distillation attacks remove the financial incentive.
This assumes all the open models are just a result of distilling Anthropic models. Which remains to be proven.
And if they are, the point remains that Anthropic has a brittle product advantage that users and investors should be cautious about.
OpenAI says they're not just distillation. "Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that."
If you put any faith in claims made by OpenAI's "Head of Strategic Futures," then it's hard to believe these models are just the result of distillation.
> investors should be cautious about
if they are cautious, what would make them invest in newer bigger models without the expected return? generosity?
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I don't think the timeline is plausible for Moonshot to have done any distillation from Fable, which means any distillation training data would have been from Opus.
The frontier labs have been tightly controlling model access lately to prevent further distillation, and the Chinese labs are still catching up rapidly - it's likely that whatever moat they used distillation to overcome early on is already dry
[Pending evidence that Chinese models are only based on distillation]
While I am sure some do distillation (even r/LocalLLaMA is full of randos bragging about it), I wish the companies named by Anthropics would respond to these accusations.
I think at least deepseek can easily take Anthropics to court and win a defamation case. The number of requests allegedly done from deepseek IP range is so small it is barely enough to run a few benchmarks.
> f Anthropic doesn't make money because of distillation attacks, how would they convince investors to invest in them,
I dont care if that company dies. Or if OpenAI dies. I dont mind investors investing into other things either.
You can simplify your question even more:
To everyone praising free and open source software, if software companies don't make money, how are they and YOU going to get paid, PERIOD?
Commoditising complement
Nope, I'll still buy Claude because the overall XP is better than Kimi and Qwen who literally copied basic harnesses to make kimi-cli and qwen-cli, respectively.
Also, you can tell if a model is genuinely powerful and well-thought-out vs a model that acts like it.
It's like Apple vs Xiaomi/Huawei. Sure, you can get a Huawei with bells and whistles, but most people learnt the hard way that those companies just copy the iPhone, so might as well get the real deal.
As somebody who bought an iPhone the day it became available and has been using iPhones for years, my favorite phone I've ever owned is my Huawei Mate XT Ultimate.
People keep pretending that Chinese companies only make second-class copies of American products until it's too late.
people buy Apple because of the brand - in particular trust.
the vast majority of people do not care about like-for-like phones based purely on features.
Lol yet I've used Apple and Android phones extensively and would choose Android every single time.
> so might as well get the real deal.
Almost the same thing for twice the price, just for the pleasure of saying that you believe Apple was first?
To be fair, the flagship Xiaomi, Vivo, Oppo etc are comparable in price to other flagship devices. You very much pay a premium for the large camera sensors they put in these devices, amongst other things.