3) Buy all the RAM, increasing the barrier to entry to push back the tide a bit, in time for a juicy IPO.

#1 isn't going to happen because we're actually data limited, not compute limited. You can throw all the compute in the world at bad data and it won't make a difference, but an undertrained model with perfect training data will absolutely slay.

#2 isn't going to happen, because these labs have shown they have limited app/design sense, and they also lack the industry connections and domain wisdom to execute.

The way things are actually going to go is that these labs will set up partnerships with huge biotech/engineering/etc firms, and do custom training/inference on specific tasks that promise to be wildly profitable with them, then take royalties on the creation in perpetuity. Why sell inference when you can partner with Pfizer to make a version of Ozempic that also makes people freaky jacked, or partner with Bectel to make a radically safer, more efficient Nuclear power plant?

> Be apple where everyone else is Linux.

Apple and Linux barely even compete in the same markets. Linux runs on the servers and embedded devices, Apple on the smartphones. Android is technically Linux but not in the "is a good analogy for open weight models" sense because Android is so deeply under the thumb of Google. The main place Linux and Apple actually compete is for PCs and laptops, and that's the market where the thing with 65% market share is Microsoft.

You forgot

3. Try to get the government to "certify models" to cause regulatory capture which is what both Anthropic and OpenAI has been pushing. No certification no use in business.

> own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).

The problem with this is that there are incumbents in all those spaces doing their own AI agents / platforms, and they're the ones choosing the models they use internally and they sell to their own customers. The margins and the possibility to fine tunie using open weight models, as well as the guarantee they'll keep running at predictable costs (no US orders yanking access), make them a very appealing option.

And if you're a company that needs an AI powered legal software, would you buy it from OpenAI/Anthropic, or from someone who you've already bought legal software from before and has the domain knowledge?

Google already owns the app layer, and hardware, and they are a frontier-level AI research firm.

I don't see how Anthropic or OpenAI survives being eaten by DeepSeek et al from the bottom of the stack and Google from the top.

Won’t all they need to do is say “best in class, latest models, fastest” and wine and dine a few execs and those enterprise deals will be signed?

In this case the people tasked with using the product won’t actually mind.

Mythos was outperformed by small, specific local models in multiple oss project.

So why are they losing so much money?

Money is made on the subset of inference that is charged at cost + margin via their APIs. API usage is so high because customers are still finding their feet, trying to understand how to measure the value they get from their spend, erring on the side of spend.

Yes, in a world of unmeasured value and tokenmaxxing, inference is profitable on SOTA models because all capacity is being consumed at all times, driving down marginal costs, but what about a world in which capacity isn’t constrained? There are still huge fixed costs.

Even the most optimistic leaks with the current high prices put the margin on API token inference at around 50%. How can SOTA models ever come close to competing on price? Price always matters. Offering the best model with the most brand recognition does not exempt OpenAI from the basic rules of business.

Historically, software has been such a successful business because the margins are incredible, 95%+ in many cases, driven by direct measurable value to customers that dwarfs the cost. A 50% margin at a time when your customers are falling over themselves to spend as much money as they can is not a good sign, it is a very bad sign, it leaves no room to ever achieve traditional technology margins, and inevitably leads to very weak margins.

Inference needs to become an order of magnitude cheaper than the value it delivers to ever have a chance of delivering on this wildly profitable vision. The cheap model providers have a much better chance of achieving that.

Outside of coding, almost every business case for AI doesn’t need above human intelligence, it doesn’t even need human intelligence, or half a human intelligence, a business can extract a lot of value from a machine that has a fraction of a human’s intelligence. Most human work does not use our intelligence, it is rote, a monkey could do it, and that’s where AI will be used most. Who is going to pay $10 per million tokens when they could pay $0.10 to get the same outcomes?

You can't just define training as "not a cost". Without training they have nothing to sell.

So they reduce their prices 50x and their revenue drops to $1B a year? And they're supposed to be worth $1T each? If there is an open model offering at the cost of inference + whatever margin you need to stay alive, then as inference falls Anthropic/OpenAI revenue also falls.

What's the end-game? It sounds like in this business, if you are right, your revenues drop every year.

I get it! And I appreciate people like you pointing out the business side of LLMs.

Also, these open weight models are significantly lower quality than the high end coding models, and for some reason a lot of people think they're exactly the same. Maybe engineers who only dabble in LLM usage aren't doing enough complex work to notice...?

What are you even talking about? Everyone knows that Anthropic is drastically subsidizing their plans. It's actually the exact opposite of what you're talking about. The costs are extremely high and the prices are actually what's being subsidized and cheap right now.

> It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?

Isn't this the thing people have said about every new technology since the printing press? And it has been mostly true, but it has also been the case that the incumbents have fought hard to lock things back up again. Newspapers and radio stations buy each other up, the open web gets locked inside Facebook (which, 30 years ago, people were already worried about with AOL), people have computers in their pockets they can't run their own programs on anymore.

Interests are going to want to lock the new information thing behind a gate so they can charge a toll and censor what they don't like, same as it ever was. You don't win by default, you have to fight to stop them.

OpenAI, though they seem to backtrack it lately, have been slowly pushing forward of their launch of ads which would be a supplemental way to support cheaper use of their models. This is currently not as great a fit as the modern day banner ads, but it will be interesting to see where they go with that.

> It would not be surprising if GPT and Claude get cheaper too as inference gets cheaper

No because the biggest factor in their current price is VC subsidization which has likely peaked if OpenAI is now serving ads and Anthropic has increased their API pricing

True, outside of the dark tactics I imagined in the article, they will have to compete at lower costs. It's just that the current iteration does not feel cost competitive yet.

Probably they will, unless Claude and GPT become luxury brands like Gucci. Currently it makes no sense for them to invest into efficiency. They need to put everything into competing for the top spot as long as they still have a shot.

I feel like this comment is just engagement farming, but I'll bite anyways

there is a larger appetite for something like open source AI mostly b/c of price. we all know these labs have not figured out their pricing model, and we're all holding our breath out of fear of what the prices could be.

also, if you consider that the only toll to knowledge work before was personal time, and now you need to pay $100s month just to keep up with the baseline speed. it makes sense people are looking for something that gets them back to a workflow where the price to do work is near $0.00.

I think for a smaller group though, it's more to do with a certain combination of principles. Some people don't want censorship, other's want ownership, some want the knowledge of working on LLMs to not be gate kept.

The closest example I can think of is using a proprietary hosted database versus a self hosted open source option, like Oracle vs Postgres. OpenAI and Anthropic are each individually privately valued over $1T and Oracle is currently valued at half that. They’re not worthless, but they’re severely overvalued.

Yes. Many industries are zero-sum-ish in nature,have winner-take-all dynamics or reputational costs for cheaping out. Financial trading. Big law. Military. National Security. Big insurance. Management consulting. Advertising.

For others even a small edge can be important. Pharma and Biochem research. Research in general. Any industry where there are major reputational risks.

It may not make sense to use the most expensive model to replace your payroll clerk, but there are plenty of use cases for the best available.

the difference here is that switching is trivial due to standardized APIs to the underlying LLM capability.

harness <-> gateway <-> inference provider

Easy to switch any of them and (mostly) possible to combine any with any

Replacing something like Excel is crazy-hard because of network effects, replacing an Enterprise CRM is akin to a removing a metastasizing cancer

I think this is very likely and something that everyone seems to be missing when valuing these AI firms. AI is not the new industrial revolution, it's the new cloud VM: a very useful commodity software offering.

Cloudflare's Deepseek V4 Pro prices are 4x more than Deepseek's for input and output tokens, and 100x more for cached input tokens, which is crucial for the tool uses of agents which cause multi-turn conversations.

How does caching help here? How much repetition is there in queries?

Reach AGI to leapfrog whoever is behind. Burn everything to get there faster.

Enterprise plans don't have the equivalent of the subsidized-usage-included Claude Max/ChatGPT Pro plans anymore. The revenue generated and total amount of tokens used by individuals is probably a tiny fraction of tokens billed at API pricing.

And that means either:

1. They overprice their APIs to make their subscriptions look reasonable

2. They burn money with their subscriptions

Cheaper until you factor in security and liability, which are going to get increasingly salient over time.

> Regulated industries will favour the closed sources, either by choice or mandate

Until your country will appear on naughty list of US administration because your local politician did something what mildly inconvenienced US oligarch

Europe has Mistral.

You and readers may be interested in Europe 2031

1. https://europe2031.ai/

Why Oracle?

As another commenter said a "model" is a file (or group of files, there's multiple formats available; GGUF format is all in one file for example). You download it to the hardware of your choice (ie your own desktop with NVIDIA GPU). You run the inference engine (llama-cpp, ollama,lm studio etc) and tell it where the downloaded model is and it runs inference (so you can start chatting with it, or run agents).

"Open weights model" means the developer made the model available for everyone for free. You can download it from huggingface.co for example and do whatever you want with it.

Why "open weights" and not "open source"? Because the "source code" for LLM would include things like training data, training methodologies and tools, so that you can do the training and produce the model (files) yourself. That would be like compiling from source code. Which is not done with these models, it's company's know-how, they only share the end result.

It's more analogous to "freeware" which is what we traditionally call freely distributed binary executable files. But people started calling them "open weights" instead and the term stuck.

A completely open model is one like the Allen Institute's Olmo model series:

https://allenai.org/olmo

The trained weights are open, the training software is open, and the data that goes into training the model is open.

Not many models are fully open.

An open weights model is one that has freely available trained weights, and maybe fine-tuning tools, but it lacks the original training data (and usually lacks the training software). These are the most commonly used local models, like Google's Gemma series, Meta's Llama, or Alibaba's Qwen.

An "open weights" model is one where you can download all the data and the code that you need to run inference with that model on your own hardware (typically from Huggingface.co).

That data includes not only the "weights" but also various files with required information, e.g. the tokenizer, the chat template, files that describe the structure of the "weights", e.g. number of layers, the number of "experts", routing information, etc. All this information may be distributed in many files (e.g. *.safetensors files with weights, *.json files etc.) or it may be aggregated in a single container file (with the .gguf extension).

You can see an example of the files included in a very simple open weights LLM here:

https://huggingface.co/google/gemma-4-12B-it/tree/main

Bigger LLMs have much more files, especially much more *.safetensors files, which contain the "weights". The "weights", i.e. matrices of numbers that are used in the computational algorithm that generates the output tokens, constitute the bulk of the data needed to run a model, i.e. from a few gigabytes to a couple of terabytes, which is why the term "open-weights" is used, but in fact by this term it is understood that all data needed for running inference is open.

For an open weights LLM, you do not have access to the data set used for training the model or to the algorithms that have been used during the training of that model.

You can still do some fine-tuning of the model, using your own training methods and your own additional training data. To facilitate this, several open weights models offer not only a model version that can be used for inference to implement a chat application or an agentic workflow, but also a "base" or "raw" version that is not suitable for being used directly for inference but which is suitable for you to do a post-training/fine-tuning, to create a model more appropriate for your particular needs.

An "open weights" model is sufficient for most of the potential LLM users, because training a model is something that requires expertise, expensive hardware and a lot of time, so few would be able to do it even when given access to the necessary data.

Aside from googling "how to download and run open weights model" check out localllama (yes 3Ls) subreddit. Huggingface.co is where many of them are published.

There's many providers that run open weights models and give you access. Many decent open weights models cannot be run on consumer-grade hardware (DeepSeek, GLM, many others).

literally ask cloud ai like the free gemini or chatgpt.. they could make you an expert on the subject overnight..

Whose IP do you think they are stealing? According to US courts, training is fair use. And even if it wasn't, they are distilling output from other models, which isn't copyrightable, again according to US courts.

Well I can't speak to the chinese gov part, but ALL the models are IP laundering systems. I'd rather IP get laundered into open source.

And the American ones are stealing and redistributing the IP of every single person who authored anything on the internet at some point.

Maybe, but there's tons of providers available, so you can pick one that you trust not to steal your IP (or run it yourself, if you're rich and paranoid enough).

Technically correct, the worst kind of correct :)

How dare they steal what we already stole!

I don't see how they could ban them in the US. Code is speech, and the first amendment still mostly holds. They might try, but I don't see the courts upholding it.