> 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.
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.
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.
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).
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.
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.
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.
> I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs.
AFAIK the frontier labs make their money on enterprise, and they recently changed their terms so that most businesses can't use the $200/m plan anymore.
This is why companies like Uber - and mine :( - announced per employee AI budgets on the order of ~$1000/month.
When you're forced to pay API costs, the value prop of Kimi/Qwen becomes a lot more compelling.
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.
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.
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?
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
> 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.
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.
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.
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
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.
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.
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.
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).
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.
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.
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.
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 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.
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 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.
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)
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.
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.
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.
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
> 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.
> 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?
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?
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[ 1.2 ms ] story [ 1618 ms ] threadHm. How is OpenAI not a “model-only” provider just like Anthropic? Seems like they are vulnerable in the same way.
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.
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.
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.
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).
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.
Why should I pay for a frontier model that I can’t use?
AFAIK the frontier labs make their money on enterprise, and they recently changed their terms so that most businesses can't use the $200/m plan anymore.
This is why companies like Uber - and mine :( - announced per employee AI budgets on the order of ~$1000/month.
When you're forced to pay API costs, the value prop of Kimi/Qwen becomes a lot more compelling.
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.
----
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.
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
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...
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.
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.
And if they are, the point remains that Anthropic has a brittle product advantage that users and investors should be cautious about.
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?
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.
I dont care if that company dies. Or if OpenAI dies. I dont mind investors investing into other things either.
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).
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.
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.
Making the latter happen involves either reformulating work in ways less intelligent LLMs can work better with. Or condensing intelligence into smaller models.
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.
The hype cycles are shortening, perhaps we really are reaching some kind of plateau this time (famous last words)
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.
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.
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
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?
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?