> It's a winner-takes-all market and everyone wants to be the next Google
absolutely isn't! if billed per token, there is no reason to be married to a single model family provider at all. the models have very different strengths and weaknesses, you should be taking advantage of this at all times.
people used to say this about search engines and web browsers, as well
regardless, eventually Google became the universal default for both. When it comes to software, the average person doesn't shop around for the technologically optimal choice, they just use what everyone else is using.
If they shut down all training today they’d be absolutely printing money for the next couple quarters and then die with a bang once the other lab releases the next frontier to the public.
How? They're already burning $2 bills to make $1, court documents shown that Anthropic has already been lying around revenue (claimed to have made $19 billion when it's actually $5 billion to date [1]).
Not hard to believe they're lying about other things when they've been lying about the capability of their products since inception.
That is not what the article says, it says $19B ARR.
I don’t necessarily see a contradiction. $19B run rate, achieved very recently, is actually consistent with $5B lifetime earnings, because their growth curve is so sharp. Zitron is not good at math.
This is not lying, that is just what run rate revenue means! It makes sense to use as a metric when a company’s user base is growing as fast as Anthropic’s is.
I'd be surprised if they're making money on inference just from that. There's no way someone paying $20 p/m and using it all day is not spending way more on even just the electricity for tokens, let alone the capex.
I don't really get the last bit. It's hard to imagine what a new fangled "frontier model" could do that would blow anyone out of the water. Like what does this look like? Really good benchmarks? Who cares about that anymore?
Anthropic isn’t the best by any reasonable measure. They’re the best in some areas and get pwned in others.
In general AI is very much like human intelligence in the regard that no two models are the same just like no two people are the same. IOW if you are a single model shop you might even not have any idea that you’re falling behind.
there are examples of files and tapes which veracity isn't in question in a much bigger and more important country than Slovenia regarding much more important people than the Slovenian prime minister and... it doesn't matter much, apparently
Still have 4 brand new ones in my storage unit. Just in case these moments.
Joke aside (I do have them tho!), I don't think Optane is that much use (not to mention it is only 256GiB for my unit). It is useful legacy crutch if you have legacy software that is not designed to issue multiple reads / writes in parallel. If you do, it is really not faster than NVMe, especially these modern ones.
It's not about being faster (except for small reads where latency dominates, which is actually relevant when reading a handful of expert-layers immediately after routing), it's the wearout resistance which opens up the possibility of storing KV-cache (including the "linear" KV-cache of recent Qwen, which is not append-only as it was with the pure attention model) and maybe even per-layer activations - though this has the least use given how ephemeral these are.
Yes, their NAND division has been sold, it is now mostly under solidigm. Maybe solidigm could bring it back, but it seems unlikely (given the previous commercial failure).
100M tokens should be enough to put all but the absolutely biggest code bases into a single context. It’s probably also about as much as a single average person in the West reads in a lifetime (make of that what you will philosophically); all x86 manuals should fit nicely with room to spare.
absolutely isn't! if billed per token, there is no reason to be married to a single model family provider at all. the models have very different strengths and weaknesses, you should be taking advantage of this at all times.
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