the part thats missing from this whole discussion is that the cost of running frontier models is heavily subsidized right now. companies are making "ai efficiency" decisions based on pricing that doesnt reflect actual compute costs. when inference and r&d prices normalize (and they will) the roi math that justified cutting your engineering team just stops working. but the people are already gone
also the nber finding is wild - 80% of firms report zero productivity impact from ai. zero. but layoffs attributed to ai are 12x what they were two years ago. its not ai displacement, its a market correction using ai as cause. the resume.org survey where 60% of hiring managers admitted they played up the ai angle because it sounds better than "we need to cut costs" pretty much says it out loud
the other thing nobody talks about is what happens to the junior-to-senior pipeline and tacit knowledge transfer. its not just that fewer juniors get hired. the ones who do get hired learn in an environment where the hard cognitive work is outsourced to a model. they never build the mental models you need to actually understand systems deeply. shen & tamkin literally measured this - ai assistance impairs conceptual understanding. so you end up with a generation that can prompt but cant debug from first principles and the seniors who couldve mentored them are already out the door
the article is right that reversals wont happen. id just add that even if they tried, the knowledge of what "bringing people back" means will have left with the people they cut
also the nber finding is wild - 80% of firms report zero productivity impact from ai. zero. but layoffs attributed to ai are 12x what they were two years ago. its not ai displacement, its a market correction using ai as cause. the resume.org survey where 60% of hiring managers admitted they played up the ai angle because it sounds better than "we need to cut costs" pretty much says it out loud
the other thing nobody talks about is what happens to the junior-to-senior pipeline and tacit knowledge transfer. its not just that fewer juniors get hired. the ones who do get hired learn in an environment where the hard cognitive work is outsourced to a model. they never build the mental models you need to actually understand systems deeply. shen & tamkin literally measured this - ai assistance impairs conceptual understanding. so you end up with a generation that can prompt but cant debug from first principles and the seniors who couldve mentored them are already out the door
the article is right that reversals wont happen. id just add that even if they tried, the knowledge of what "bringing people back" means will have left with the people they cut