@gabriel_zucman@JeffBezos He is making a point about the percentage of government receipts that come from the lower 50% - you are responding with data about the percentage of income that individuals pay - your observation isn’t relevant to his argument.
@Jason@RoKhanna It means employees are now obligated to produce $25 an hour + a consumer surplus margin for the employer, or they are economically infeasible to hire. Harms entry level and early-skills people, creates barriers to entry.
@stevesi@Jason If the belief is that an increasing velocity of labor displacement will negatively impact employment, and if we have already seen an increasing velocity of labor displacement, we should also see a negative response in overall employment - but we aren’t seeing this. why?
@Jason Thanks - I think you bring up reasonable points here, but consider that the increasing speed of deployment will also correlate with an increased speed of deploying new products and services in domains where humans are still required.
Jason - how do you reconcile this position with historical observations on tech breakthroughs that drive inflection points in automation? The Industrial Revolution, electrification, the personal computer, the internet, etc. In each of these transitional periods jobs were displaced, but the productivity effects associated with capital displacing labor meant that new products and services were accessible and new jobs were created. Most importantly, overall employment went up, not down. Is your position that “it’s somehow different now?”
#1 is not a misleading claim, as you described. It’s a relatively fair and consistent explanation of how LLMs work. LLM training is a process of inferior the distributions that underlies the training data, and inference is a process of sampling from this distribution. What is to argue with here?
@paulg@SteveStuWill Much lower sample size for the primary education study - if they had a comparable sample size of 80k+ you’d probably see a regression toward the mean