As their scale also scales the rewards to attacks and as their responses get worse, the attacks become more frequent. That leads to more false positives, and a skepticism that any given case could be one of them. In practice, claims like Zuckerberg’s that only the biggest companies like Meta can invest the resources to do good content moderation are clearly false, because scale reliably makes content moderation worse.
Dan Luu makes a very real and serious contribution to the literature on scaling and the big tech companies, going further than anyone I’ve ever seen to argue that the big 5 might be overvalued/not that powerful, but ultimately what he’s doing is listing helpful arguments that chip away at the capabilities of the big 5, and then depicts his piece as overwhelming proof that they’re doomed bloated incompetent husks that can’t do anything anymore.
Lots of the arguments are great, but not all are created equal; for example, it’s pretty well known that actually-well-targeted ads scare off customers and that user retention is the priority for predictive analytics (since the competitor platforms’ decisions to use predictive analytics to steal user time are not predictable decisions), but Luu just did the usual thing where he eyeballs the ads and assumes that tells us everything we need to know, and doesn’t notice anything wrong with this. There’s some pretty easy math here (sufficiently large and diverse pools of data make it easier to find people/cases that help predict a specific target’s thoughts/behavior/reaction to stimuli), and either Luu failed to pass the low bar of understanding it, or the higher bar of listing and grokking the real world applications and implications.
Ultimately, I’d consider it a must-read for anyone interested in Earth’s most important industrial community (and scaling in general), but it’s worth keeping in mind that the critical mass of talent (and all kinds of other resources and capabilities) accumulated within the biggest companies is obviously a pretty major factor, and although he goes a long way to chip away at it (e.g. attack surface for data poisoning), Luu doesn’t actually totally debunk it like he says he does.
Dan Luu makes a very real and serious contribution to the literature on scaling and the big tech companies, going further than anyone I’ve ever seen to argue that the big 5 might be overvalued/not that powerful, but ultimately what he’s doing is listing helpful arguments that chip away at the capabilities of the big 5, and then depicts his piece as overwhelming proof that they’re doomed bloated incompetent husks that can’t do anything anymore.
Lots of the arguments are great, but not all are created equal; for example, it’s pretty well known that actually-well-targeted ads scare off customers and that user retention is the priority for predictive analytics (since the competitor platforms’ decisions to use predictive analytics to steal user time are not predictable decisions), but Luu just did the usual thing where he eyeballs the ads and assumes that tells us everything we need to know, and doesn’t notice anything wrong with this. There’s some pretty easy math here (sufficiently large and diverse pools of data make it easier to find people/cases that help predict a specific target’s thoughts/behavior/reaction to stimuli), and either Luu failed to pass the low bar of understanding it, or the higher bar of listing and grokking the real world applications and implications.
Ultimately, I’d consider it a must-read for anyone interested in Earth’s most important industrial community (and scaling in general), but it’s worth keeping in mind that the critical mass of talent (and all kinds of other resources and capabilities) accumulated within the biggest companies is obviously a pretty major factor, and although he goes a long way to chip away at it (e.g. attack surface for data poisoning), Luu doesn’t actually totally debunk it like he says he does.