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Comments (198)

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  LarsDu88
  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.
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  overgard
  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.
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  bko
  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.
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  port3000
  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)
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  drob518
  > 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.
• davidpapermill
  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.
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  torginus
  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.
• a13n
  Definitely feels like there’s a particular narrative being pushed on HN today.
• piazz
  > 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.
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  yalogin
  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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  sim04ful
  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.
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  athrowaway3z
  > Once a model is built, the biggest cost is inferenceSomething 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?
• philipkglass
  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.
• joshstrange
  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).
• warm_soup
  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
• spaceman_2020
  Dario can reverse this by going on the podcast circuit again and threatening everyone with 75% job losses due to (his) AI this timeRamp the number up to 85% if that doesn’t workIf it still doesn’t work, go nuclear and target 100% job losses language
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  m_ke
  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.
• SubiculumCode
  How much is Anthropic's price due to inference cost or extra margin they can get away with by having the best model?
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  joey64
  So, what's the most affordable way for a pleb who doesn't own 17 H100s to use Kimi K3 or Qwen 3.8?
• zkmon
  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.
• chihwei
  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.
• IshKebab
  So many words to say so little... Don't bother reading this.
• anon
  undefined
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  simianwords
  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.
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  behnamoh
  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.