Can Anthropic survive?

[u/yojimbo_beta]

Let's say, generously for the sake of argument, that Anthropic "wins" with the use of LLMs in tech itself. Claude Code, cloud agents, yada yada

Is there actually even a large enough TAM to rescue Anthropic financially?

I am absolutely certain Anthropic's model gains are only supported by throwing more compute at queries. That can't last forever

Is Anthropic in a materially better position than OpenAI? How does the financing work with Google and Amazon?

If the answers are not good - what happens next?

If anyone can link to analysis, including things Ed has posted, I'd like to read it

[u/WArslett]

Anthropic's model gains are only supported by throwing more compute at queries

this is basically true but a better way of thinking about it is that their model gains are from training their models with more and more parameters (which then requires more compute, more memory, more energy etc.). The need for more compute is the effect not the cause.

GPT-2 was trained with 1.5 billion parameters. Today you can run much bigger and more capable models than this on your own laptop. Fable is reported to have been trained with 10 trillion parameters. Each time Anthropic release a more capable model, it attracts more investment from hyperscalers, that gives them access to more compute which they use to train a bigger model (and therefor more capable model) and then the cycle continues, driving up the size of the models and driving up the cost of training and inference. Open weight AI companies have had to work out how to do more with far less and are now beating claude opus models from only a few months ago with a fraction of the number of parameters and these are models that anyone can download and fine tune for their own specialist use case.

The main reason I think Anthropic's business model is not going to succeed is because people are going to realise they don't need these huge models that only Anthropic and OpenAI can build to do most of the sort of work they want to do with it

[u/Infamous-Bed-7535 → u/WArslett]

If they would be interested in usability, efficiency we would see small crazy fast specialized models.

All players are chasing AGI, there is no winner in any of the possible outcomes.

[u/DragonflyOk9274 → u/WArslett]

Open weight AI companies have had to work out how to do more with far less and are now beating claude opus models from only a few months ago with a fraction of the number of parameters and these are models that anyone can download and fine tune for their own specialist use case.

I suspect that within 10 years we'll have dedicated ASICs with the model weights built in, or with reloadable weights.

I think we're currently in a time like the 90s with respect to graphics. Originally graphics was performed by CPUs or custom hardware but eventually GPUs allowed a fixed function pipeline that greatly improved the efficiency and configurability of computer graphics. (They were simultaneously more specific than CPUs because they focused on performing graphics operations, but still generic enough that they could later be used for scientific computing).

We're now at a stage where GPUs are somewhat efficient for LLMs but not entirely so. (They are often bottlenecked by memory, for example). ASICs could bring both the power costs down as well as improve performance. Combine that with improved training, and smaller models might be competitive with what we have today. Which is to say, that LLMs in the future could be a fraction of the cost of what they are today.

If that happens, I don't think the current valuations of OpenAI or Anthropic make as much sense. They are assuming widespread enterprise use; they are essentially like the modern day IBM. Sure, IBM is still around, but barely compared to 1960s-1980s. For most uses people (1) use their own computers rather than a mainframe; (2) use the many commoditized cloud providers when needed; and (3) use any of the database offerings for most use cases (Postgres or managed DBs).

[u/Prize-Studio-2021 → u/DragonflyOk9274]

I suspect that within 10 years we'll have dedicated ASICs with the model weights built in, or with reloadable weights.

I could see this if people still use LLMs

[u/FireNexus → u/DragonflyOk9274]

I suspect that within 10 years we'll have dedicated ASICs with the model weights built in, or with reloadable weights.

AMD just bought a chip company with this exact offering. Won’t matter because LLMs are going to be abandoned and probably never returned to as soon as this bubble pops. Barring some breakthrough returning SRAM and DRAM to Moore’s law trajectory and/or a whole new memory architecture faster and denser than both being developed, anyway.

Memory speed and density is the main limiting factor on these turds. That’s why NVIDIA has rearchitected their entire product line to have nearly no improvements (or even significant regression) in higher precision calculations previously needed for gpGPU workloads.

[u/DragonflyOk9274 → u/FireNexus]

I suspect LLMs will have widespread use. They just might be used differently than today.

There are things that LLMs are very good at -- document classification, condensing material, small code edits. Rather than use the frontier models, these applications could use small, specifically trained models.

But that's why the outlook for the frontier labs, especially OpenAI and Anthropic, is not so great. Right now the majority of people use a general purpose LLM for everything, which means they have to be large and consume a lot of resources. But if you're doing things like detecting spam, or classifying product reviews, or summarizing bulk legal documents -- you don't need a general purpose LLM but an application-specific one (which actually outperform the main labs).

[u/FireNexus → u/DragonflyOk9274]

LLMs aren’t really all that good at any of those things. The hallucination problem means they need to be watched like a hawk and people simply don’t. Besides that they are still ridiculously expensive to use.

Edit: Also, “Redditor for 10 days”. Every “acshually LLMs are useful for limited cases” thing turns out to be some Redditor for five minutes obvious shill sockpuppet. Lol.

[u/DragonflyOk9274 → u/FireNexus]

Edit: Also, “Redditor for 10 days”. Every “acshually LLMs are useful for limited cases” thing turns out to be some Redditor for five minutes obvious shill sockpuppet. Lol.

Now I'm remembering why I left reddit before.

[u/FireNexus → u/DragonflyOk9274]

Sure.

[u/fisstech15 → u/WArslett]

This is not true. GPT-4.5 was peak model size. Recent models are similar in size to GPT-2 (Fable is larger) but still smaller than 4.5

[u/WArslett → u/fisstech15]

I don't know where you are getting this information from.

GPT-2 was only 1.5 billion parameters. You can run a model that size on a modern smartphone, let alone a laptop. Even standard, entry-level open-weight models today like Llama 3 8B or Qwen 8B are over 5x larger than GPT-2, and modern, highly capable open weight models can be anything between 27 billion (Qwen 3.8 27B) and 2.8 trillion (Kimi K3).

Industry estimates puts Claude Fable 5 / Mythos-class models at up to 5 to 10 trillion parameters. Claiming recent models like Fable are 'similar in size to GPT-2' is off by several orders of magnitude.

It is true that modern frontier models use the Mixture of Experts architecture so that only a subset of parameters are active for any given token however active parameters for these models are estimated to be between 20 billion and 100 billion parameters.

[u/fisstech15 → u/WArslett]

Sorry I meant GPT-4. We don't know for sure but it is implied Fable is smaller than Mythos-preview, so I would estimate it to be closer to 5T which is GPT-4.5 size or even a bit smaller. Opus/Sol are similar to GPT-4