771–780 of 1001 posts
Earlier quoted context omitted.
I don’t understand the issue, cannot you ignore his commentary and just look at the numbers?
I'm not financially literate enough to trust my own analysis of the numbers. Ideally I'd like commentary from someone like Bloomberg's Matt Levine, a genuine expert in financial matters who is also extremely good at explaining them in terms non-finance-professionals like me can understand.
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What do you mean by "take down Bear Stearns"? Btw., projections are just that - projections and I am not sure Acemoglu proved things mathematical (as in a mathematical proof) but rather within the context of a model/assumptions. That financial markets/innovation can outpace the actual innovation is also not some new insight, but that alone doesn't necessarily make for a useful prediction.
Bear Stearns was the first bank to collapse in the 2007-2008 subprime mortgage crisis, also known as the housing bubble. That bubble was also manufactured by reckless financial engineers. And those who warned early were ridiculed: https://markets.businessinsider.com/news/stocks/who-is-nouri... "When he spoke of an impending housing crash at the International Monetary Fund that year, the audience chuckled, the New Yor…
Not everyone who spoke about house price risk was ridiculed, btw.
One thing I’m observing in these comments is a willingness of folks to project their own predictions onto Ed’s statements when validating their plausibility. Eg. “I think he’s wrong about the timing but I do expect AI companies to go to zero.” You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning. Those differ from Dan’s essay, which engage…
> August 2025 https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ : "These models have clearly hit a wall where training is hitting diminishing returns"
> Wrong
It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference. See eg. https://www.tobyord.com/writing/mostly-inference-scaling . And in fact in the quoted and linked article https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ Zitron comes up with something which looks like a recognisable explanation of this:
> Because model developers hit a wall of diminishing returns, and the only way to make their models do more was to make them burn more tokens to generate a more accurate response (this is a very simple way of describing reasoning, a thing that OpenAI launched in September 2024 and others followed).
> As a result, all the "gains" from "powerful new models" come from burning more and more tokens.
AFAICT the other drivers of recent progress in LLMs have been: ploughing in lots and lots of specialised training data custom-made at piecework websites https://www.youtube.com/watch?v=4pG3SJQPAwk ; and work on harnesses and the like. AFAICT neither of those makes false the claim that "[t]hese models have clearly hit a wall where training is hitting diminishing returns" either. Similarly, even if some big new advance does cause training or post-training to start scaling like gangbusters again in 2027 or 2028 that wouldn't make the quoted statement clearly wrong: Zitron would clearly like you to infer that there won't be any further big advances soon in LLM training, but the quoted statement doesn't clearly make that claim. (Even if he had made that claim, and it did turn out to be wrong, it would be a relatively forgivable error, more on the "cloudy crystal ball" than "misstates currently known facts" end of the spectrum.)
So: it seems that Luu took a fairly specific, objectively judgeable claim from Ed Zitron; and that claim was ... correct?; and Luu instead rated it "Wrong" without further elaboration. It seems that Luu interpreted the quoted claim as saying something like "model progress has ceased"; but it seems that's not what that specific claim (as opposed to whatever other things Zitron has said at other times and places) said.
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The doctor said I was sick...but I did not die on Tuesday, so checkmate! You are confusing an imprecise prognosis with a false diagnosis. And The funniest part is that "he keeps writing, therefore he was proven wrong" contains no actual proof that he is wrong.
Imprecise and incorrect are the same thing when it comes to an exact science. His predictions include dates and that makes them binary. You can only be right or wrong. Being close is still incorrect, and he's not even close.
He wasn’t being imprecise, he wasn’t “correct in spirit”, he was DEAD WRONG.
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RIM is dying now -> useful for stock trading RIM is dying in the next four years -> useful for life planning. Anyway, i just wanna get on record that i predict an ai bubble pop event in the next 12 months.
> Anyway, i just wanna get on record that i predict an ai bubble pop event in the next 12 months. If it happens on Sep 2nd 2027 you'd be wrong though.
One thing I’m observing in these comments is a willingness of folks to project their own predictions onto Ed’s statements when validating their plausibility. Eg. “I think he’s wrong about the timing but I do expect AI companies to go to zero.” You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning. Those differ from Dan’s essay, which engage…
Depends if you care about the "prediction" part or if you care about the assessment of the situation (regardless of date).
If someone in 2000 said "the subprime mortgages market is a bubble and will blow no later than 2003", they got the prediction wrong, but their assessment would be right.
Earlier quoted context omitted.
Bear Stearns was the first bank to collapse in the 2007-2008 subprime mortgage crisis, also known as the housing bubble. That bubble was also manufactured by reckless financial engineers. And those who warned early were ridiculed: https://markets.businessinsider.com/news/stocks/who-is-nouri... "When he spoke of an impending housing crash at the International Monetary Fund that year, the audience chuckled, the New Yor…
I am fully aware of the GFC, but not sure what "take down" should mean there in relation to Bear. Not everyone who spoke about house price risk was ridiculed, btw.
It seems unambiguous in this context.
One thing I’m observing in these comments is a willingness of folks to project their own predictions onto Ed’s statements when validating their plausibility. Eg. “I think he’s wrong about the timing but I do expect AI companies to go to zero.” You can do that, but then you’re no longer discussing his predictions. You’re discussing your predictions, and your own positioning. Those differ from Dan’s essay, which engage…
In general I have about zero enthusiasm for trying to find defensible interpretations of things that Ed Zitron said, and I generally agree that the name of Zitron just largely needs to stop coming up in anti- and anti-anti-AI arguments since, it seems, he's just not a particularly insightful or reliable voice on the subject. That said, one or two of the specific assessments in Luu's article seem dubious as well, espe…
>> Wrong
>It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference.
I'm not an expert either, but while I do think for a bit it looked like ~all the improvement was inference-time scaling, it hasn't stayed that way. Mythos/Fable is likely a very large model (ex: it knows many things without searching) and this is probably part of its high level of capability, and the companies have started doing very large amounts of RL (which in OpenAI's case led to the HF attack).