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Is he headline strictly about subsidized subscriptions? Anthropic announced their first profitable quarter.
Companies may state cash flow positive, operating profit, EBITDA positive, but this is not a true profit in aggregate, just when stripping out many other expenses.
If anyone has evidence to the contrary, please share. Once they go public it will all be free to review, at least.
That’s probably because only a handful of companies manufacture GPUs, and they’re still expensive. I think that will change over time as competition increases. LLMs are also still in a relatively early stage. We’re already seeing models become both smaller and more capable—for example, GPT-4 compared to Qwen3-30B, which can outperform GPT-4 in many tasks while using significantly less compute. So if this trend contin…
Not to mention the rise of Chinese chips. GPUs that can run everything from Crysis to CUDA are a harder engineering problem to solve than creating a chip that's optimized for inference. Not to mention that inference is an excellent first step towards a full, competitive GPU as well.
And it seems all of these advanced chips rely on the most advanced lithography which is tightly guarded and supply locked by a few companies.
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Yes this is what I"m waiting for. I do hope these cards will not come with any kind of vendor locking though.
What kind of vendor locking would be possible?
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The whole premise of what's being described here is to bake the weights into the silicon. That isn't what I'd describe as vendor lock-in, any more than I'd describe a CPU that can only execute ARM instructions as vendor locked. To answer my own question, I bet they could figure out a way to still bill you per-token, if they wanted to.
Portability between x86 and ARM is not a form of vendor lock? And of course they could bill per-token, same way cable PPV worked (the bits were already in your house). But the cost structure of weights in silicon means that competitors would be encouraged to compete on this per-token cost, as their marginal cost would be zero. I don’t see that being a durable business model, but I guess the counter argument is it’s a…
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Can you share some use cases of what you think will get companies to become hooked? I'm really failing to see a dependency link emerging either now or in the future. That combined with the relatively easy switching costs. It doesn't bode well for AI companies seeking to create a walled garden.
Teachers in public schools are being told to use AI in their curriculum. In some cases, this means students are being taught not to think (regardless of the intent of the lesson). When prices make this curriculum untenable but the kids already depend on it, that rugpull is going to severely harm a generation of kids whose education was already disturbed in 2020.
Input: $0.257
Output: $1.286
Cache read: $0.0257
Cache write: $0.322
If this is the true cost of AI then the future might be dedicated extension cards for computers that hardcode entire models + weights. Downside: you need to buy a new one for each model. Upside: insanely fast inference and zero subscription cost, only one time purchase cost. Once a certain open source model gets good enough this might become viable. Right now the landscape is still shifting too fast. State of the art…
That would fuse 3D graphics and AI accelerators into 1 and the same unit, as far as consumer hardware is concerned.
Someone or something is having an hallucination that would make an AI jealous
If this is the true cost of AI then the future might be dedicated extension cards for computers that hardcode entire models + weights. Downside: you need to buy a new one for each model. Upside: insanely fast inference and zero subscription cost, only one time purchase cost. Once a certain open source model gets good enough this might become viable. Right now the landscape is still shifting too fast. State of the art…