why do you think s-curves happen at all? My understanding is that it’s because there’s some hard problem that takes multiple steps to solve, and when the last step falls (or a solution is in sight), it’s finally worthwhile to toss increasing amounts of investment to actually realize and implement the solution.
I think S-curves are not, in general, caused by increases in investment. They’re mainly the result of how the performance of a technology changes in response to changes in the design/methods/principles behind it. For example, with particle accelerators, switching from Van der Graaff generators to cyclotrons might give you a few orders of magnitude once the new method is mature. But it takes several iterations to actually squeeze out all the benefits of the improved approach, and the first few and last few iterations give less of an improvement than the ones in the middle.
This isn’t to say that the marginal return on investment doesn’t factor in. Once you’ve worked out some of the kinks with the first couple cyclotrons, it makes more sense to invest in a larger one. This probably makes S-curves more S-like (or more step like). But I think you’ll get them even with steadily increasing investment that’s independent of the marginal return.
So Swanson’s law is an observation for solar panel cost, where each increase in production volume results in lower cost and it is driving an S curve.
It seems like there would be 2 separate effects running here: the S curve like technology improvement to photovoltaic cells, and as production volume increases, greater and greater automation is justified.
Note also you would expect that for silicon PV we are well into the diminishing returns area of the curve, yet costs continue to decline.
Eyeballing the plot..well....it actually looks kinda flat. Like increased solar cell volume is leading to scaling r&d investment and leading to almost linear efficiency improvements with time.
I would argue that AI inference hardware production would be an example of something that should benefit from the learning effect and lead to a similar S curve adoption of ai, totally decoupled from the r&d effort for the model capabilities.
Investment scaling with volume looks like an important effect.
You’re right, I was switching between performance s-curves and market size s-curves in my thinking without realizing it. I do think the general point holds that there’s a pattern of hit minimum viability --> get some adoption --> adoption accelerates learning, iteration, and innovation --> performance and cost improve --> viability increases --> repeat until you hit a wall or saturate the market.
I think S-curves are not, in general, caused by increases in investment. They’re mainly the result of how the performance of a technology changes in response to changes in the design/methods/principles behind it. For example, with particle accelerators, switching from Van der Graaff generators to cyclotrons might give you a few orders of magnitude once the new method is mature. But it takes several iterations to actually squeeze out all the benefits of the improved approach, and the first few and last few iterations give less of an improvement than the ones in the middle.
This isn’t to say that the marginal return on investment doesn’t factor in. Once you’ve worked out some of the kinks with the first couple cyclotrons, it makes more sense to invest in a larger one. This probably makes S-curves more S-like (or more step like). But I think you’ll get them even with steadily increasing investment that’s independent of the marginal return.
https://en.m.wikipedia.org/wiki/Swanson’s_law
So Swanson’s law is an observation for solar panel cost, where each increase in production volume results in lower cost and it is driving an S curve.
It seems like there would be 2 separate effects running here: the S curve like technology improvement to photovoltaic cells, and as production volume increases, greater and greater automation is justified.
Note also you would expect that for silicon PV we are well into the diminishing returns area of the curve, yet costs continue to decline.
https://en.m.wikipedia.org/wiki/Solar-cell_efficiency
Eyeballing the plot..well....it actually looks kinda flat. Like increased solar cell volume is leading to scaling r&d investment and leading to almost linear efficiency improvements with time.
I would argue that AI inference hardware production would be an example of something that should benefit from the learning effect and lead to a similar S curve adoption of ai, totally decoupled from the r&d effort for the model capabilities.
Investment scaling with volume looks like an important effect.
You’re right, I was switching between performance s-curves and market size s-curves in my thinking without realizing it. I do think the general point holds that there’s a pattern of hit minimum viability --> get some adoption --> adoption accelerates learning, iteration, and innovation --> performance and cost improve --> viability increases --> repeat until you hit a wall or saturate the market.