Positive users agree AI token and compute costs doubling every 45 days yield only flat or minimal productivity gains because honest takes stress the need for clear ROI, while negative users respond with personal attacks on the poster.
Based on 16 visible X reactions from 37 accounts; directional sample.
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Great to finally hear less hype but more honest observations like this. Its been my feeling for some time that we should not be tokenmaxxing, why should we get paid to spend tokens to get stuff done thats meant to be our jobs but keep our comp. The only way this works is if someone can provably do more, e.g. we half their team size, or they deliver a 1 yr roadmap in 6 months. So far i have not seen anyone in the corporate world willing to take that deal. The story with tiny tech startups is different though, AI has made it possible for them to build their product without the funding. So no i dont expect AI to take out lots of jobs, in fact it is probably going to make engineers more useful, and i think there will an avalanche of new tech startups that are not selling "LLM wrappers", but selling software that the big boys cant build fast enough
@chamath Yes. It should be a force multiplier for your employees. Handle the grunt work for me while I supervise you, check work, and take care of big picture stuff.
@chamath No one is going to get lectured on governance and corporate sovereignty from someone using Chinese AI because they think it’s cheap
@chamath Seeing all this turn into a dumpster fire may be the best karmic event to date.
@chamath Chamath is ugly dude. Imagine if he’s broke. No girl will look at him.
@chamath You're a disgusting individual.
8090 works on production systems for large, often regulated, enterprises. Vibing isn’t tolerated because these are the systems that run western society - banking, power, healthcare, insurance etc. Over the last few quarters, the gains that we got from using frontier models inside of our Software Factory on these systems started to shrink but the costs kept doubling. This makes sense I guess, as in hindsight, we were initially asking the model to do mostly light work (generate basic PRs) and now we were asking it to do more complex work (mitigate dependencies across systems). Unless you grow context massively, be willing to run many A/B tests and iterate massively (ie use massively more tokens) complex tasks stay roughly unfinished by the model and requires the engineer to largely act alone. In other words, we find the last 5% (ie where a model is truly equivalent to a reasonable engineer) extremely difficult to achieve and extremely expensive to such a degree that the fully loaded cost of the model + the engineer will not pay for itself. So I asked our CTO to start thinking about other ways. We need our engineers to have access to the best tools BUT we also need to educate them to think even more for themselves - not less - in this last mile. At the same time, we need to find solutions that decrease our token costs by 90% - especially because these bleeding edge tokens are not nearly as cost effective as the tokens before it and are creating a big OpEx bill for us. I wonder how many engineers, in all orgs, are running amok right now by using the latest frontier models as a kind of slot machine. Increasingly turning their mind off, largely keeping productivity flat while their CEO and CFO deals with a massive token bill? My advice to you is that when you encounter this last 5% of very hard technical challenges in getting a complex system into production, be circumspect. The challenge of the last 5% is actually getting harder - especially as hundreds and thousands of code generation model runs run amok adding all kinds of random cruft into codebases that eventually need to be rationalized.
Positive users agree AI token and compute costs doubling every 45 days yield only flat or minimal productivity gains because honest takes stress the need for clear ROI, while negative users respond with personal attacks on the poster.
Based on 16 visible X reactions from 37 accounts; directional sample.
Ask a question below.
Published answers will appear here.
@chamath You're a disgusting individual.