I’m guessing the lost funds are bleeding out to energy concerns. Correct? Asides from brief construction contracts for data centers, I’m unsure how one makes such large sums of money disappear.
Still supposed to be valued 1.500.000 Million dollars. Lol.
deleted by creator
Goddamm… This could easily have housed every man, woman, and child in this country. But no, no free housing while we squander wealth with our stupid ideas.
Billion is officially the new million.
this goes in line with thier aggresive pandering to the govt to be used mass surveillance.
I’m doing my part by constantly asking chatgpt questions without ever logging in or paying them money. Eventually they’ll all go belly up and maybe we can ditch this shit.
80 billion could have bought a whole lot of Chinese EV’s to resale and they could have made a profit.
A billion here, a billion there. Over some time it might become quite a sum.
Repeating the DotCom burst.
but worst. because some of those sites provided actual value, “AI” does not, most heavily used by Palantir/AI as mass surveillance and profiling people for bombings or harrasment.
I’m totally spamming this across the thread with copy/vaste.
$8B real loss on $16B revenue. ($72B is a bondholder gain (accounting loss) from conversion). $9B in training costs not counted as loss, even though it is worthless whenever next model version is released.
$561B in forced spending on compute over next 10 years, with $750B total compute costs lined up. Structural losses are baked in for a few years.
even though it is worthless whenever next model version is released.
For most version releases I actually doubt that, I’m pretty sure they just keep training the model and release checkpoints every few months. Architectural changes, however, will indeed invalidate the previously trained models, and occasionally they have to do that to keep up with the competition.
But if you do architectural changes even just once a year, that means you’d have to depreciate those training costs over just that one year to be truthful. Meaning they might as well be losses rather than depreciating capital investments.
There is a genuine “training corpus” that is an accumulated asset and grows and feeds new models. The vast majority of training cost, that should be expensed, is the training time compute. Neither of these are expensed. There was about 13 major US model releases in September. At least 7 of these replaced a 1 month old previous model family. Open AI gpt sol went from 5.6 to 6 to 6.1 in about 2 months.
Fast model releases not only lock in structural losses, they juice revenue growth illusions. Model use rate generally drops by 66% 4 weeks after release, because of competition, and a surge of use on release for evaluation.
New models are trained on older models, so even if the older model is not longer at par, without it the new model couldn’t exists
But, when the word guessing program gains consciousness, it will be capable of doing the job of every worker in the world, and eager to do those jobs, and anybody who owns Anthropic shares will be a trillionaire in an economy that totally works and is healthy. So, better invest now!
Musk giving 1 share (about $160) to all US children with a Trump account, will make the next generation cheer for skynet genocide and oppression. “For a while, it added 10-20% to my net worth.”
I’m totally spamming this across the thread with copy/vaste.
No wonder they’re panicking so hard over open weight Chinese models
Good.
i wish them ruin, you made my day
Why haven’t they just asked Claude how to make money with their business model? /s
They have.
-
Wait for excited CEOs to fire all their staff.
-
Crank up the prices.
2 only works if regulatory capture in place to prevent cheaper competition from open models.
That’s why they keep trying to play the “AI is so dangerous” narrative and why you keep hearing about “rogue AI agents” which in reality someone had to instruct to break the law, and they probably used some of their no-guardrails internal-use models (so Claude Mythos rather than Fable).
Claude Mythos and Fable are the exact same model, the branding only reflects which one has more restrictive classifiers in front of it during deployment. But the weights are identical.
-