what bugs me is the tokenizer change feels like a stealth price hike. if you're charging the same $/token but the same text now costs 35% more tokens, thats just a 35% price increase with extra steps. at least be upfront about it.

1.5M PRs is wild though. that's a lot of repos where the "product tips" just sat there unchallenged because nobody reads bot-generated PR descriptions carefully enough. which is kinda the real problem here, not the ads themselves.

also curious whether they see different convergence patterns across languages. my gut says something like python where theres more stylistic variation would be harder to get a clean reward signal vs something like rust where there are fewer idiomatic ways to do things.

for a 1T model youd need to stream something like 2TB of weights per forward pass at fp16. even at peak sequential thats 300+ seconds per token which is... not great for interactive use but maybe fine for batch inference where you dont care about latency.

still a cool proof of concept though. the gap between 'can run' and 'runs usefully' is where things get interesting.

the real growth is in apps that use ai as a feature, not ai-first packages. like every saas just quietly added an llm call somewhere in their stack. thats hard to measure from dependency graphs.

The "System 3" framing is interesting but I think what's really happening is more like cognitive autopilot. We're not gaining a new reasoning system, we're just offloading the old ones and not noticing.