Comment by armcat
1 day ago
Reinforces what Karpathy tweeted [1], that AI is effectively tackling 2-5% of work and the gap between those fully exploiting AI and barely touching it is growing. The question remains whether this AI focus on the small % of workers is actually intentional (by tech/labs), or a natural limit of where AI is right now.
I wonder how much of lagging AI adoption is simply due to people not trusting the existing AI vendors, or the architecture of deployment broadly speaking? If we somehow figured out how to get frontier-class models deployed easily locally (and I don't mean the bumbling demos of today - I mean with the sort of fluidity one experiences when using frontier models from frontier labs), would adoption be way higher for structural reasons?
Kind of...
Karpathy is also saying the quiet part out loud: they are making progress by brute force. It's a cost driven arbitrage market.
Do I need to spend high prices on 1000 agents to solve this today, or should I wait 1000 days (with compute increasing and prices falling) and hope that I get lucky and get the right answer sooner. Is your competitor half the price but I need 1500 agents to do it in one day cause it's just a bit dumber.
The market for brute force intelligence is infinite - and at some point demand will drive the cost to single digit margins.