We've been hearing this for 3 years now. And especially 25 was full of "they've hit a wall, no more data, running out of data, plateau this, saturated that". And yet, here we are. Models keep on getting better, at more broad tasks, and more useful by the month.
Yes, and Moore's law took decades to start to fail to be true. Three years of history isn't even close to enough to predict whether or not we'll see exponential improvement, or an unsurmountable plateau. We could hit it in 6 months or 10 years, who knows.
And at least with Moore's law, we had some understanding of the physical realities as transistors would get smaller and smaller, and reasonably predict when we'd start to hit limitations. With LLMs, we just have no idea. And that could be go either way.
Except for Moore's law, everyone knew decades ahead of what the limits of Dennard scaling are (shrinking geometry through smaller optical feature sizes), and roughly when we would get to the limit.
Since then, all improvements came at a tradeoff, and there was a definite flattening of progress.
I agree, I have been informed that people have been repeating it for three years. Sadly I'm not involved in the AI hype bubble so I wasn't aware. What an embarrassing faux pas.
We've been hearing this for 3 years now. And especially 25 was full of "they've hit a wall, no more data, running out of data, plateau this, saturated that". And yet, here we are. Models keep on getting better, at more broad tasks, and more useful by the month.
Yes, and Moore's law took decades to start to fail to be true. Three years of history isn't even close to enough to predict whether or not we'll see exponential improvement, or an unsurmountable plateau. We could hit it in 6 months or 10 years, who knows.
And at least with Moore's law, we had some understanding of the physical realities as transistors would get smaller and smaller, and reasonably predict when we'd start to hit limitations. With LLMs, we just have no idea. And that could be go either way.
> We've been hearing this for 3 years now
Not from me you haven't!
> "they've hit a wall, no more data, running out of data, plateau this, saturated that"
Everyone thought Moore's Law was infallible too, right until they hit that bend. What hubris to think these AI models are different!
But you've probably been hearing that for 3 years too (though not from me).
> Models keep on getting better, at more broad tasks, and more useful by the month.
If you say so, I'll take your word for it.
25 is 2025.
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Except for Moore's law, everyone knew decades ahead of what the limits of Dennard scaling are (shrinking geometry through smaller optical feature sizes), and roughly when we would get to the limit.
Since then, all improvements came at a tradeoff, and there was a definite flattening of progress.
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> And yet, here we are.
I dunno. To me it doesn’t even look exponential any more. We are at most on the straight part of the incline.
This quote would be more impactful if people haven't been repeating it since gpt-4 time.
People have also been saying we'd be seeing the results of 100x quality improvements in software with corresponding decease in cost since gpt-4 time.
So where is that?
I agree, I have been informed that people have been repeating it for three years. Sadly I'm not involved in the AI hype bubble so I wasn't aware. What an embarrassing faux pas.