Post #2580744

2026-05-15 12:50 UTC

In other Scott of Siskind news, he just posted an entirely unnecessary amount of words to aggressively push back against the adage that “all exponentials sooner or later turn into sigmoids” as if it was by itself a load bearing claim of the side arguing against the direct imminence of the machine god. It’s just a bunch of arguing by analogy ( “helping you build intuition” ) and you-can’t-really-knows while implying AI 2027 was very science much rigorous, but it also feels kind of desperate, like why are you bothering with this overperformative setting-the-record-straight thing, have you been feeling inadequate as an AI-curious stats fondler of note lately?

Replies (2)

@lurker@awful.systems 2026-05-15 20:13

The idea of “the exponential curve goes up forever” has always been silly and an idea rooted in capitalism for me (“no bro you don’t get it we’re gonna get infinite money forever”). Limited resources exist, and people are already very fed up with the ludicrous amounts of water and electricity data centres take up. Making bigger models that need to run for longer is also probably going to take an exponential amount of resources (and also make people hate you more).

Open ##2580743
@scruiser@awful.systems 2026-05-15 21:33

he just posted an entirely unnecessary amount of words taking a quick look at it… it’s actually short by Scott’s standards, but still overly long, given that the only point he makes is claiming Lindy’s Law is applicable to predicting AI progress in absence of other information. Edit: glancing at it again… its not that short, I kinda skimmed until I got to Scott’s actual point my first time glancing at it. You can’t blame me for not reading it. you-can’t-really-knows Yeah, he straw-mans AI critics/skeptics as trying to make an argument from ignorance, then tries to argue against that strawman using Lindy’s Law (which assumes ignorance and a pareto distribution). He completely ignores that AI critics are actually making detailed arguments about LLM companies consuming all the good and novel training data, hitting the limits on what compute costs they can afford, running into problems of the long lead time for building datacenters, etc. Which is pretty ironic given his AI 2027 makes a nominal claim to accounting for all that stuff (in actuality it basically all rests on METR’s task horizons, and distorts even that already questionable dataset).

Open ##2620091