This post is extremely reasonable, and I expect that if we look back on it 20-30 years from now, we’ll see two patterns:
1) Almost all the predictions will have been basically right.
2) Because of the few that were wrong, the list will have mostly failed to capture whatever happened that actually mattered.
New materials, new manufacturing methods, and new energy sources historically require whole communities and ecosystems to fail for generations, just to move the first few rungs along the tech development curve, before someone finds a niche application that makes real-world sense, which would move the world a few rungs further, and so on. Many never do. The ones that do, pay for all the rest and more, and get retconned into normality.
As an illustration, apply your method to the past instead of the future. At what point, before it actually happened, would it have successfully predicted the historical equivalents of these things? The transition of steam engines from curiosity to industrial revolution. The transition from wood and animal muscle to oil and gas. The transition of computers from rare commercial infrastructure to cheap and omnipresent consumer goods. The transition from oil and gas to renewables. All of these were both predicted in advance, and also dismissed as impossible. In many cases, these kinds of things get dismissed as impossible even after they’ve already started happening.
My two picks for the category are reversible computers and space industries (in the long run).
For space industries like data centers, the logic of the post mostly checks out assuming you don’t have AIs that can fully automate the industries needed for space industries to be big, and human employment is optional, but if they do exist, then space becomes profitable because it’s way, way cheaper to send robotics/AIs up into space when you don’t need to support anything like livable conditions for standard humans, and the rewards of creating megastructures are much bigger (the main effect of megastructures in my mind is that they force a jump of 10-20 OOMs or more of compute power, meaning far, far more fields are amenable to full simulation without requiring empirical evidence, dramatically improving data efficiency of AI, for one example).
Harsimony’s post on the end of semiconductors and what comes next does sort of imply that he’d be convinced that reversible computers have a use case if space compute was more useful (to oversimplify things), so the space case on it’s own is probably enough, but I do have two points to make on computing tech, one general and one specialized to reversible computing specifically.
On reversible computers, I agree that you can’t make energy dissipation as heat go to 0, and that there’s a minimum energy calculated in this paper for all computing methods, which is likely much lower than classical, but not enough to make reversible computers have arbitrarily low energy at constant speed of computation.
That said, even just providing multiple OOMs worth of energy efficiency is enough, which I think is still likely inside the realm of possibility.
More general point is that I interpret the evidence for unconventional paradigms not mattering is happening in large part because we don’t need to do it, since chips already can get multiple OOMs of energy efficiency and we don’t need to invest in new approaches that soon, rather than fundamental limitations on unconventional computing.
Put another way, the demand for unconventional computing is low largely because there is no need to pick new paradigms to increase efficiency, not because they wouldn’t be valuable if successful.
Absolutely. For example, my estimate on fusion has a large enough error bar that (with some luck) it could beat solar. Just being wrong there implies a totally different energy system.
This is why I’d prefer to see people taking lots of risky shots on goal. The upside is very high.
Agreed! I just think it’s worth calling out that ‘trying things’ and ‘taking risky shots on goal’ looks, for solar and again for lithium ion batteries, like something on the order of ~$1-2 trillion and ~5 million person-years over the course of five decades spent developing the tech to the point that it’s finally becoming clear enough that this is practical at scale to pass the test this post uses. Maybe PV would have passed in 2015 and Li-ion/EVs in 2020? Maybe the trajectory made each seem more likely than not by at most a decade before that, a time when in practice most people still dismissed straight-line-on-graph projections as doomed to being over-optimistic? And that all of that only happened because enough people were using much less stringent tests throughout that timespan as sufficient reason to make steadily larger bets on them anyway.
This post is extremely reasonable, and I expect that if we look back on it 20-30 years from now, we’ll see two patterns:
1) Almost all the predictions will have been basically right.
2) Because of the few that were wrong, the list will have mostly failed to capture whatever happened that actually mattered.
New materials, new manufacturing methods, and new energy sources historically require whole communities and ecosystems to fail for generations, just to move the first few rungs along the tech development curve, before someone finds a niche application that makes real-world sense, which would move the world a few rungs further, and so on. Many never do. The ones that do, pay for all the rest and more, and get retconned into normality.
As an illustration, apply your method to the past instead of the future. At what point, before it actually happened, would it have successfully predicted the historical equivalents of these things? The transition of steam engines from curiosity to industrial revolution. The transition from wood and animal muscle to oil and gas. The transition of computers from rare commercial infrastructure to cheap and omnipresent consumer goods. The transition from oil and gas to renewables. All of these were both predicted in advance, and also dismissed as impossible. In many cases, these kinds of things get dismissed as impossible even after they’ve already started happening.
My two picks for the category are reversible computers and space industries (in the long run).
For space industries like data centers, the logic of the post mostly checks out assuming you don’t have AIs that can fully automate the industries needed for space industries to be big, and human employment is optional, but if they do exist, then space becomes profitable because it’s way, way cheaper to send robotics/AIs up into space when you don’t need to support anything like livable conditions for standard humans, and the rewards of creating megastructures are much bigger (the main effect of megastructures in my mind is that they force a jump of 10-20 OOMs or more of compute power, meaning far, far more fields are amenable to full simulation without requiring empirical evidence, dramatically improving data efficiency of AI, for one example).
Harsimony’s post on the end of semiconductors and what comes next does sort of imply that he’d be convinced that reversible computers have a use case if space compute was more useful (to oversimplify things), so the space case on it’s own is probably enough, but I do have two points to make on computing tech, one general and one specialized to reversible computing specifically.
On reversible computers, I agree that you can’t make energy dissipation as heat go to 0, and that there’s a minimum energy calculated in this paper for all computing methods, which is likely much lower than classical, but not enough to make reversible computers have arbitrarily low energy at constant speed of computation.
That said, even just providing multiple OOMs worth of energy efficiency is enough, which I think is still likely inside the realm of possibility.
More general point is that I interpret the evidence for unconventional paradigms not mattering is happening in large part because we don’t need to do it, since chips already can get multiple OOMs of energy efficiency and we don’t need to invest in new approaches that soon, rather than fundamental limitations on unconventional computing.
Put another way, the demand for unconventional computing is low largely because there is no need to pick new paradigms to increase efficiency, not because they wouldn’t be valuable if successful.
Absolutely. For example, my estimate on fusion has a large enough error bar that (with some luck) it could beat solar. Just being wrong there implies a totally different energy system.
This is why I’d prefer to see people taking lots of risky shots on goal. The upside is very high.
Agreed! I just think it’s worth calling out that ‘trying things’ and ‘taking risky shots on goal’ looks, for solar and again for lithium ion batteries, like something on the order of ~$1-2 trillion and ~5 million person-years over the course of five decades spent developing the tech to the point that it’s finally becoming clear enough that this is practical at scale to pass the test this post uses. Maybe PV would have passed in 2015 and Li-ion/EVs in 2020? Maybe the trajectory made each seem more likely than not by at most a decade before that, a time when in practice most people still dismissed straight-line-on-graph projections as doomed to being over-optimistic? And that all of that only happened because enough people were using much less stringent tests throughout that timespan as sufficient reason to make steadily larger bets on them anyway.