5 ms·

> a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. Yes of course it’s not going to increase exponenti

> a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. Yes of course it’s not going to increase exponentially forever. The point is, why predict that the growth rate is going to slow exactly now? What evidence are you going to look at? It’s possible to make informed predictions (eg “Moore’s law can’t get you further than 1nm with silicon due to fundamental physical limits”). But most commenters aren’t basing their predictions in anything as rigorous as that. And note, there are good reasons to predict a speedup, too; as models get more intelligent, they will be able to accelerate the R&D process. So quality per-researcher is now proportional to the exponential intelligence curve, AND quantity of researchers scales with number of GPUs (rather than population growth which is much slower).

Inufu 1y ago
Yeah exactly! It’s likely that it will slow down at some point, but the highest likelihood scenario for the near future is that scaling will continue.
LegionMammal978 1y ago
> The point is, why predict that the growth rate is going to slow exactly now? What evidence are you going to look at? Why predict that the (absolute) growth rate is going to keep accelerating past exactly now? Exponential growth always assumes a constant relative growth rate, which works in the fiction of economics, but is otherwise far from an inevitability. People like to point to Moore's law ad nauseam, but other things like "the human population" or "single-core performance" keep accelerating until they start cooling off. > And note, there are good reasons to predict a speedup, too; as models get more intelligent, they will be able to accelerate the R&D process. And if heaven forbid, R&D ever turns out to start taking more work for the same marginal returns on "ability to accelerate the process", then you no longer have an exponential curve. Or for that matter, even if some parts can be accelerated to an amazing extent, other parts may get strung up on Amdahl's law. It's fine to predict continued growth, and it's even fine to predict that a true inflection point won't come any time soon, but exponential growth is something else entirely.
- theptip 1y ago
  > Why predict that the (absolute) growth rate is going to keep accelerating past exactly now? By following this logic you should have predicted Moore’s law would halt every year for the last five decades. I hope you see why this is a flawed argument. You prove too much. But I will answer your “why”: plenty of exponential curves exist in reality, and empirically, they can last for a long time. This is just how technology works; some exponential process kicks off, then eventually is rate-limited, then if we are lucky another S-curve stacks on top of it, and the process repeats for a while. Reality has inertia. My hunch is you should apply some heuristic like “the longer a curve has existed, the longer you should bet it will persist”. So I wouldn’t bet on exponential growth in AI capabilities for the next 10 years, but I would consider it very foolish to use pure induction to bet on growth stopping within 1 year. And to be clear, I think these heuristics are weak and should be trumped by actual physical models of rate-limiters where available.
  - LegionMammal978 1y ago
    > By following this logic you should have predicted Moore’s law would halt every year for the last five decades. I hope you see why this is a flawed argument. You prove too much. I do think it's continually amazing that Moore's law has continued in some capacity for decades. But before trumpeting the age of exponential growth, I'd love to see plenty of examples that aren't named "Moore's law": as it stands, one easy hypothesis is that "ability to cram transistors into mass-produced boards" lends itself particularly well to newly-discovered strategies. > So I wouldn’t bet on exponential growth in AI capabilities for the next 10 years, but I would consider it very foolish to use pure induction to bet on growth stopping within 1 year. Great, we both agree that it's foolish to bet on growth stopping within 1 year. What I'm saying that "growth doesn't stop" ≠ "growth is exponential". A theory of "inertia" could just as well support linear growth: it's only because we stare at relative growth rates that we treat exponential growth as a "constant" that will continue in the absence of explicit barriers.
    - theptip 1y ago
      Sorry, to be clear I was making the stronger claim: I would consider it very foolish to use pure induction to bet on _exponential_ growth stopping within 1 year. I think you can easily find plenty of other long-lasting exponential curves. A good starting point would be: https://en.m.wikipedia.org/wiki/Progress_studies https://en.m.wikipedia.org/wiki/Progress_studies With perhaps the optimistic case as https://en.m.wikipedia.org/wiki/Accelerating_change https://en.m.wikipedia.org/wiki/Accelerating_change This is where I’d really like to be able to point to our respective Manifold predictions on the subject; we could circle back in a year’s time and review who was in fact correct. I wager internet points it will be me :) Concretely, https://manifold.markets/JoshYou/best-ai-time-horizon-by-august-2026 https://manifold.markets/JoshYou/best-ai-time-horizon-by-aug...
rmunn 1y ago
NOTE IN ADVANCE: I'm generalizing, naturally, because talking about specifics would require an essay and I'm trying to write a comment. Why predict that the growth rate is going to slow now? Simple. Because current models have already been trained on pretty much the entire meaningful part of the Internet. Where are they going to get more data? The exponential growth part of the curve was largely based on being able to fit more and more training data into the models. Now that all the meaningful training data has been fed in, further growth will come from one of two things: generating training data from one LLM to feed into another one (dangerous, highly likely to lead to "down the rabbit hole forever" hallucinations, and weeding those out is a LOT of work and will therefore contribute to slower growth), or else finding better ways to tweak the models to make better use of the available training data (which will produce growth, but much slower than what "Hey, we can slurp up the entire Internet now!" was producing in terms of rate of growth). And yes, there is more training data available because the Internet is not static: the Internet of 2025 has more meaningful, human-generated content than the Internet of 2024. But it also has a lot more AI-generated content, which will lead into the rabbit-hole problem where one AI's hallucinations get baked into the next one's training, so the extra data that can be harvested from the 2025 Internet is almost certainly going to produce slower growth in meaningful results (as opposed to hallucinated results).
- theptip 1y ago
  > Where are they going to get more data? This is a great question, but note that folks were freaking out about this a year or so ago and we seem to be doing fine. We seem to be making progress with some combination of synthetic training datasets on coding/math tasks, textbooks authored by paid experts, and new tokens (plus preference signals) generated by users of the LLM systems. It wouldn’t surprise me if coding/math turned out to have a dense-enough loss-landscape to produce enough synthetic data to get to AGI - though I wouldn’t bet on this as a highly likely outcome. I have been wanting to read/do some more rigorous analysis here though. This sort of analysis would count as the kind of rigorous prediction that I’m asking for above. E2A: initial exploration on this: https://chatgpt.com/share/68d96124-a6f4-8006-8a87-bfa7ee4ea3c7 https://chatgpt.com/share/68d96124-a6f4-8006-8a87-bfa7ee4ea3... Gives some relevant papers such as https://arxiv.org/html/2211.04325v2#:~:text=3.1%20AI https://arxiv.org/html/2211.04325v2#:~:text=3.1%20AI
danlitt 1y ago
> why predict that the growth rate is going to slow exactly now? why predict that it will continue? Nobody ever actually makes an argument that growth is likely to continue, they just extrapolate from existing trends and make a guess, with no consideration of the underlying mechanics. Oh, go on then, I'll give a reason: this bubble is inflated primarily by venture capital, and is not profitable. The venture capital is starting to run out, and there is no convincing evidence that the businesses will become profitable.
mmcnl 1y ago
It has already been trained on all the data. The other obvious next step is to increase context window, but that's apparently very hard/costly.
- theptip 1y ago
  I don’t think this is true. See https://arxiv.org/html/2211.04325v2 https://arxiv.org/html/2211.04325v2 for example.
  - mmcnl 1y ago
    There are diminishing returns.
kryptiskt 1y ago
I think progress per dollar spent has actually slowed dramatically over the last three years. The models are better, but AI spending has increased by several orders of magnitude during the same time, from hundreds of millions to hundreds of billions. You can only paper over the lack of fundamental progress by spending on more compute for so long. And even if you manage to keep up the current capex, there certainly isn't enough capital in the world to accelerate spending for very long.
skybrian 1y ago
Yes, nobody knows the future of AI, but sometimes people use curve fitting to try convince themselves or others that they know what’s going to happen.

Inufu 1y ago

Yeah exactly! It’s likely that it will slow down at some point, but the highest likelihood scenario for the near future is that scaling will continue.

LegionMammal978 1y ago

> The point is, why predict that the growth rate is going to slow exactly now? What evidence are you going to look at? Why predict that the (absolute) growth rate is going to keep accelerating past exactly now? Exponential growth always assumes a constant relative growth rate, which works in the fiction of economics, but is otherwise far from an inevitability. People like to point to Moore's law ad nauseam, but other things like "the human population" or "single-core performance" keep accelerating until they start cooling off. > And note, there are good reasons to predict a speedup, too; as models get more intelligent, they will be able to accelerate the R&D process. And if heaven forbid, R&D ever turns out to start taking more work for the same marginal returns on "ability to accelerate the process", then you no longer have an exponential curve. Or for that matter, even if some parts can be accelerated to an amazing extent, other parts may get strung up on Amdahl's law. It's fine to predict continued growth, and it's even fine to predict that a true inflection point won't come any time soon, but exponential growth is something else entirely.

theptip 1y ago
> Why predict that the (absolute) growth rate is going to keep accelerating past exactly now? By following this logic you should have predicted Moore’s law would halt every year for the last five decades. I hope you see why this is a flawed argument. You prove too much. But I will answer your “why”: plenty of exponential curves exist in reality, and empirically, they can last for a long time. This is just how technology works; some exponential process kicks off, then eventually is rate-limited, then if we are lucky another S-curve stacks on top of it, and the process repeats for a while. Reality has inertia. My hunch is you should apply some heuristic like “the longer a curve has existed, the longer you should bet it will persist”. So I wouldn’t bet on exponential growth in AI capabilities for the next 10 years, but I would consider it very foolish to use pure induction to bet on growth stopping within 1 year. And to be clear, I think these heuristics are weak and should be trumped by actual physical models of rate-limiters where available.
- LegionMammal978 1y ago
  > By following this logic you should have predicted Moore’s law would halt every year for the last five decades. I hope you see why this is a flawed argument. You prove too much. I do think it's continually amazing that Moore's law has continued in some capacity for decades. But before trumpeting the age of exponential growth, I'd love to see plenty of examples that aren't named "Moore's law": as it stands, one easy hypothesis is that "ability to cram transistors into mass-produced boards" lends itself particularly well to newly-discovered strategies. > So I wouldn’t bet on exponential growth in AI capabilities for the next 10 years, but I would consider it very foolish to use pure induction to bet on growth stopping within 1 year. Great, we both agree that it's foolish to bet on growth stopping within 1 year. What I'm saying that "growth doesn't stop" ≠ "growth is exponential". A theory of "inertia" could just as well support linear growth: it's only because we stare at relative growth rates that we treat exponential growth as a "constant" that will continue in the absence of explicit barriers.
  - theptip 1y ago
    Sorry, to be clear I was making the stronger claim: I would consider it very foolish to use pure induction to bet on _exponential_ growth stopping within 1 year. I think you can easily find plenty of other long-lasting exponential curves. A good starting point would be: https://en.m.wikipedia.org/wiki/Progress_studies https://en.m.wikipedia.org/wiki/Progress_studies With perhaps the optimistic case as https://en.m.wikipedia.org/wiki/Accelerating_change https://en.m.wikipedia.org/wiki/Accelerating_change This is where I’d really like to be able to point to our respective Manifold predictions on the subject; we could circle back in a year’s time and review who was in fact correct. I wager internet points it will be me :) Concretely, https://manifold.markets/JoshYou/best-ai-time-horizon-by-august-2026 https://manifold.markets/JoshYou/best-ai-time-horizon-by-aug...

rmunn 1y ago

NOTE IN ADVANCE: I'm generalizing, naturally, because talking about specifics would require an essay and I'm trying to write a comment. Why predict that the growth rate is going to slow now? Simple. Because current models have already been trained on pretty much the entire meaningful part of the Internet. Where are they going to get more data? The exponential growth part of the curve was largely based on being able to fit more and more training data into the models. Now that all the meaningful training data has been fed in, further growth will come from one of two things: generating training data from one LLM to feed into another one (dangerous, highly likely to lead to "down the rabbit hole forever" hallucinations, and weeding those out is a LOT of work and will therefore contribute to slower growth), or else finding better ways to tweak the models to make better use of the available training data (which will produce growth, but much slower than what "Hey, we can slurp up the entire Internet now!" was producing in terms of rate of growth). And yes, there is more training data available because the Internet is not static: the Internet of 2025 has more meaningful, human-generated content than the Internet of 2024. But it also has a lot more AI-generated content, which will lead into the rabbit-hole problem where one AI's hallucinations get baked into the next one's training, so the extra data that can be harvested from the 2025 Internet is almost certainly going to produce slower growth in meaningful results (as opposed to hallucinated results).

theptip 1y ago
> Where are they going to get more data? This is a great question, but note that folks were freaking out about this a year or so ago and we seem to be doing fine. We seem to be making progress with some combination of synthetic training datasets on coding/math tasks, textbooks authored by paid experts, and new tokens (plus preference signals) generated by users of the LLM systems. It wouldn’t surprise me if coding/math turned out to have a dense-enough loss-landscape to produce enough synthetic data to get to AGI - though I wouldn’t bet on this as a highly likely outcome. I have been wanting to read/do some more rigorous analysis here though. This sort of analysis would count as the kind of rigorous prediction that I’m asking for above. E2A: initial exploration on this: https://chatgpt.com/share/68d96124-a6f4-8006-8a87-bfa7ee4ea3c7 https://chatgpt.com/share/68d96124-a6f4-8006-8a87-bfa7ee4ea3... Gives some relevant papers such as https://arxiv.org/html/2211.04325v2#:~:text=3.1%20AI https://arxiv.org/html/2211.04325v2#:~:text=3.1%20AI

danlitt 1y ago

> why predict that the growth rate is going to slow exactly now? why predict that it will continue? Nobody ever actually makes an argument that growth is likely to continue, they just extrapolate from existing trends and make a guess, with no consideration of the underlying mechanics. Oh, go on then, I'll give a reason: this bubble is inflated primarily by venture capital, and is not profitable. The venture capital is starting to run out, and there is no convincing evidence that the businesses will become profitable.

mmcnl 1y ago

It has already been trained on all the data. The other obvious next step is to increase context window, but that's apparently very hard/costly.

theptip 1y ago
I don’t think this is true. See https://arxiv.org/html/2211.04325v2 https://arxiv.org/html/2211.04325v2 for example.
- mmcnl 1y ago
  There are diminishing returns.

kryptiskt 1y ago

I think progress per dollar spent has actually slowed dramatically over the last three years. The models are better, but AI spending has increased by several orders of magnitude during the same time, from hundreds of millions to hundreds of billions. You can only paper over the lack of fundamental progress by spending on more compute for so long. And even if you manage to keep up the current capex, there certainly isn't enough capital in the world to accelerate spending for very long.

skybrian 1y ago

Yes, nobody knows the future of AI, but sometimes people use curve fitting to try convince themselves or others that they know what’s going to happen.