why aren't all 11 resolved? a statement only gets an assessment when the public
record can support or contradict it. opinions and what-ifs never can, and 2 checkable
ones are still open, waiting for their date. predictions held up or didn't;
assertions are supported or contradicted. on every card:
▮▮▮▮▮ certainty ·
▮▮▮▮▮ debate potential. speakers are clickable
Prediction Open · timeframe Aug 2034
Narayanan: AI model scaling cycles are ending
“We're not gonna have too many more cycles, possibly zero more cycles, of a model that's almost an order of magnitude bigger in terms of the number of parameters than what came before, and thereby more powerful.”
Prediction Not checkable as stated
Narayanan: Skeptical GPT-5 will yield a leap comparable to GPT-4
“Are we going to see a GPT-V that's as big a leap over GPT-V as GPT-V was over GPT-V? I'm frankly skeptical.”
Prediction Not checkable as stated
Narayanan: Recursive AI scaling using synthetic data will not work
“And the other way to look at synthetic data is, okay, you take one trillion tokens, you train a model on it, and then you output 10 trillion tokens, so you get to the next bigger model, and then you use that to output a hundred trillion tokens. You know, I'll …”
Prediction Not checkable as stated
Narayanan: AI bots influencing elections with misinformation is not a real danger
“So people have been worried, for instance, about bots creating misinformation with AI and influence in elections and that sort of thing. We're very, very skeptical that that's going to be a real danger.”
Prediction Held up
Narayanan: Restricting global access to AI models will fail
“Even if one country decides that models should be closed, the odds of getting every country to enact that kind of, ah, ah, rule are, you know, just vanishingly small. So if our approach to safety with AI is going to be premised on ensuring that quote unquote b…”
Prediction Not checkable as stated
Narayanan: Enterprise AI deployment will be very slow
“It's got, you have to actually deploy AI to be able to get to certain types of learning, and I think that's gonna be very slow, and I think the a good analogy is self-driving cars, of which we had prototypes, you know, two or three decades ago, but for these t…”
Prediction Open · timeframe Aug 2029
Narayanan: Lower AI inference costs will increase total enterprise AI spend
“And I predict that we're going to see the same thing with models when models get cheaper. They're put into a lot more things, and so the total amount that companies are spending on inference is actually going to increase.”
Prediction Not checkable as stated
Narayanan: Exponential AI growth will flatten and models will commoditize
“So I think that's going to happen both with models as well as with these hardware cycles. You know, I can't predict how long that's going to take, but we are, I think, going to get to a world where models do get commoditized.”
Prediction Not checkable as stated
Narayanan: AI tools will not become the default way people learn anytime soon
“I'm very skeptical that these new kinds of learning are going to get to a point anytime soon where they're going to become the default way in which people learn.”
Prediction Not checkable as stated
Narayanan: AI proliferation will force people onto trusted news sources
“That's right. So that, that actually is our prediction. People we predict are going to be forced to rely much more on getting their news from trusted sources.”
Prediction Not checkable as stated
Narayanan: Near state-of-the-art AI on personal devices will accelerate
“We have you know, close to state of the art AI models that can already run on people's personal devices, and I think that trend is only going to accelerate.”