ivanovm

this is a fun comparison, so I ran the numbers - in the US the cancelations alone are as big as Europe's entire planned buildout (~11 GW), or almost 3x of what is currently under construction (~3.8 GW)

You can’t buy gpus at reasonable prices because US frontier labs are spending nearly a trillion dollars this year on data center buildout, not because some mid tier European insurance co needs to serve deepseek

It's a total delusion to think that the key to reverse-engineering the brain or producing energy from fusion is policy.

It also happens to be the favorite pretext for people to seize more political power and launder more money through nonprofits though.

RL environment (instruction, stateful container, reward function) is the training data product being bought

No, it isn't. The private data is largely private data, created by highly-specialized, highly-paid contracted teams of experts for domains finance, swe, consulting, etc.

Reddit data is just not that interesting, that deal is worth like $60m/year. Labs spend 10x as much on computer-use RL environments.

labs invest multiple billion dollars a year each in private data, and that number is growing. internet training data is not where frontier capabilities come from, this view is outdated

I don't condone the practice, and I don't want to run afoul of anthropic or risk my reputation for it

it is certainly possible and being done all over the place. there's a black market that chinese labs use to buy frontier american llm trajectories by the millions through US intermediaries. they're not even particularly shy about it, i have been offered $0.7 per opus 4.8 call

there's also a market for chinese labs sending checkpoints to US companies to be trained on US compute and sent back

i'm surprised that so many people take chinese tech reports about how they train their models at face value tbh

The benchmarks are now the equivalents of SAT/ACT/other standardized exams for humans. They are directionally quite predictive, but with plenty of outcome variance on the margins

Yes, they did. You could also find this information easily. A company like Andon creates value by exposing interesting AI failure modes, so it makes perfect sense for them to move on to harder problems when the previous ones get saturated. I think you're just being overly cynical.

You could just look it up on their website leaderboard? The newest Claude model makes over $10k profit over a simulated year of operation, after starting with $500

I don't think this was a simple assumption. LLMs used to be much dumber! GPT-3 era LLMS were not good at grep, they were not that good at recovering from errors, and they were not good at making followup queries over multiple turns of search. Multiple breakthroughs in code generation, tool use, and reasoning had to happen on the model side to make vector-based RAG look like unnecessary complexity

Kinda funny how we went full circle with you calling me ignorant and illiterate on the basis of not using your preferred terminology, as opposed to the actual, obvious meaning of the subject