Posted by Jowsey 20 hours ago
When the frontier is pacing itself in its speed in terms of adding new capabilities (notice how fast opus 5.5 got released after Astra), then that also means that the open weights models which were just behind are accelerating in their speed just as much as well.
Good news for us because the models which used to feel 6 months behind now feel like they are lesser.
Double threat to Anthropic and OAI because if open weights models become good enough at the current level of Sota while being cheaper (which time will tell if this model fits the bill or not), then people will use those models instead of OAI/Anthropic provided it is "good enough"
Though it has to be said that only real world use case can be trusted but I remain optimistic and hope that this turns out to be good in real world use cases as well.
But in my opinion, all of this is really good for us end users!
The only reason people aren't doing this is that hardware prices are high, thanks to, well, OpenAI and Anthropic :D
I wonder when will this madness end
So interested to see what the Kimi 3.1 has to offer.
Still get the model out there.
I wonder when the Artificial Analysis bench will be out?
Mostly it's datacenter build out and circular financing magic when the same dollars being commited multiple times by multiple companies passing hot potato to each other. They basically commit capacities 2-3 years ahead.
a lot of those money is the commitment from US AI labs that's not yet spend until the data center is roll out.
you are conflating.
Now, if you tell me American businesses are turning cheap Chinese tokens into goods and services of high value, for Americans and people all over the world, then obviously I would have a different outlook on what's happening here.
Zooming out, I think that build out still makes sense. There will be losers in it, but think about how ridiculous the amount of compute we're using has increased from 4 years ago. If I had to guess 60-70% of developers and engineers everywhere in the world are now burning thousands in compute individually every month, whereas it was previously approximately 0.
But what I’m hearing is this is a negligible cost compared to inference buildouts. Fair enough.