7 ms·

> Given consistent trends of exponential performance improvements over many years and across many industries, it would be extremely surprising if these improvem

> Given consistent trends of exponential performance improvements over many years and across many industries, it would be extremely surprising if these improvements suddenly stopped. I'm sure people were saying that about commercial airline speeds in the 1970's too. But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. With LLM's at the moment, the limiting factors might turn out to be training data, cost, or inherent limits of the transformer approach and the fact that LLM's fundamentally cannot learn outside of their context window. Or a combination of all of these. The tricky thing about S curves is, you never know where you are on them until the slowdown actually happens. Are we still only in the beginning of the growth part? Or the middle where improvement is linear rather than exponential? And then the growth starts slowing...

ttoinou 1y ago
Yes exponential is only an approximation of the first part of S curves. And this author claims that he understands the exponential better than others…
- _fizz_buzz_ 1y ago
  That is even true for covid for obvious reasons, because Covid runs out of people it can infect at some point.
  - marginalia_nu 1y ago
    Infectious diseases rarely see actual exponential growth for logistical reasons. It's a pretty unrealistic model that ignores that the disease actually needs to find additional hosts to spread, the local availability of which starts to go down from the first victim.
    - heavenlyblue 1y ago
      If you assume the availability of hosts is local to the perimeter of the infected hosts, then the relative growth is limited to 2/R where R is the distance from patient 0 in 2 dimensions. It's becuase an area of the circle defines how many hosts are already ill but the interaction can only happen on the perimeter of the circle. The disease is obviously also limited by the total amount of hosts, but I assume there's also the "bottom" limit - i.e. the resource consumption of already-infected hosts.
      - CamperBob2 1y ago
        It also depends on how panicked people are. Covid was never going to spread like ebola, for instance: it was worse. Bad enough to harm and kill people, but not bad enough to scare them into self-enforced isolation and voluntary compliance with public health measures. Back on the subject of AI, I think the flat part of the curve has always been in sight. Transformers can achieve human performance in some, even many respects, but they're like children who have to spend a million years in grade school to learn their multiplication tables. We will have to figure out why that is the case and how to improve upon it drastically before this stuff really starts to pay off. I'm sure we will but we'll be on a completely different S-shaped curve at that point.
    - ttoinou 1y ago
      Yes the model where the S curves comes out is extremely simplified. Looking at covid curves we could have well said it was parabolic, but that’s much less worrisome
  - shmel 1y ago
    It's obvious, but the problem was that enough people would die in the process for people to be worried. Similarly, if the current AI will be able to replace 99% of devs in 5-10 years (or even worse, most white collar jobs) and flatten out there without becoming a godlike AGI, it will still have enormous implications for the economy.
- blibble 1y ago
  the author is an anthropic employee if the money dries up because the investors lose faith on the exponential continuing, then his future looks much dimmer
cs702 1y ago
Yes. It's true that we don't know, with any certainty, (1) whether we are hitting limits to growth intrinsic to current hardware and software, (2) whether we will need new hardware or software breakthroughs to continue improving models, and (3) what the timing of any necessary breakthroughs, because innovation doesn't happen on a predictable schedule. There are unknown unknowns.[a] However, there's no doubt that at a global scale, we're sure trying to maintain current rates of improvement in AI. I mean, the scale and breadth of global investment dedicated to improving AI, presently, is truly unprecedented. Whether all this investment is driven by FOMO or by foresight, is irrelevant. The underlying assumption in all cases is the same: We will figure out, somehow, how to overcome all known and unknown challenges along the way. I have no idea what the odds of success may be, but they're not zero. We sure live in interesting times! --- [a] https://en.wikipedia.org/wiki/There_are_unknown_unknowns https://en.wikipedia.org/wiki/There_are_unknown_unknowns
- xg15 1y ago
  I hope the crash won't be unprecedented as well...
  - cs702 1y ago
    I hope so too. Capital spending on AI appears to be holding up the entire economy: https://am.jpmorgan.com/us/en/asset-management/adv/insights/market-insights/market-updates/on-the-minds-of-investors/is-ai-already-driving-us-growth/ https://am.jpmorgan.com/us/en/asset-management/adv/insights/...
    - deleted 1y ago
      [deleted]
ajross 1y ago
> I'm sure people were saying that about commercial airline speeds in the 1970's too. Or CPU frequencies in the 1990's. Also we spent quite a few decades at the end of the 19th century thinking that physics was finished. I'm not sure that explaining it as an "S curve" is really the right metaphor either, though. You get the "exponential" growth effect when there's a specific technology invented that "just needs to be applied", and the application tricks tend to fall out quickly. For sure generative AI is on that curve right now, with everyone big enough to afford a datacenter training models like there's no tomorrow and feeding a community of a million startups trying to deploy those models. But nothing about this is modeled correctly as an "exponential", except in the somewhat trivial sense of "the community of innovators grows like a disease as everyone hops on board". Sure, the petri dish ends up saturated pretty quickly and growth levels off, but that's not really saying much about the problem.
jsnell 1y ago
Indeed you can't be sure. But on the other hand a bunch of the commentariat has been claiming (with no evidence) that we're at the midpoint of the sigmoid for the last three years. They were wrong. And then you had the AI frontier lab insiders who predicted an accelerating pace of progress for the last three years. They were right. Now, the frontier labs rarely (never?) provide evidence either, but they do have about a year of visibility into the pipeline, unlike anyone outside. So at least my heuristic is to wait until a frontier lab starts warning about diminishing returns and slowdowns before calling the midpoint or multiple labs start winding down capex. The first component might have misaligned incentives, but if we're in a realistic danger of hitting a wall in the next year, the capex spending would not be accelerating the way it is.
- yojo 1y ago
  Capex requirements might be on a different curve than model improvements. E.g. you might need to accelerate spending to get sub-linear growth in model output. If valuations depend on hitting the curves described in the article, you might see accelerating capex at precisely the time improvements are dropping off. I don’t think frontier labs are going to be a trustworthy canary. If Anthropic says they’re reaching the limit and OpenAI holds the line that AGI is imminent, talent and funding will flee Anthropic for OpenAI. There’s a strong incentive to keep your mouth shut if things aren’t going well.
  - queenkjuul 1y ago
    I think you nailed it. The capex is desperation in the hopes of maintaining the curve. I have heard actual AI researchers say progress is slowing, just not from the big companies directly.
- deleted 1y ago
  [deleted]
- airstrike 1y ago
  > And then you had the AI frontier lab insiders who predicted an accelerating pace of progress for the last three years. Progress has most definitely not been happening at an _accelerating_ pace.
fhe 1y ago
I am getting the sense that the 2nd deriative of the curve is already hitting negative teritory. models get updated, and I don't feel I'm getting better answers from the LLMs. On the application front though, it feels that the advancements from a couple of years ago are just beginning to trickle down to product space. I used to do some video editing as a hobby. Recently I picked it up again, and was blown away by how much AI has chipped away the repetitive stuff, and even made attempts at the more creative aspects of production, with mixed but promising results.
- tehjoker 1y ago
  What are some examples of tasks you no longer have to do?
  - fhe 1y ago
    one example is auto generating subtitles -- elements of this tasks, e.g. speech to text with time coding, have been around for a while (openai whisper and others), but they have only recently been integrated into video editors and become easy to use for non-coders. other examples: depth map (estimating object distance from the camera; this is useful when you want to blur the background), auto-generating masks with object tracking.
breuleux 1y ago
> But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. I'd argue all of them. Any true exponential eventually gets to a point where no computer can even store its numerical value. It's a physically absurd curve.
- saretup 1y ago
  You clearly haven’t played my idle game.
- marginalia_nu 1y ago
  The narrative quietly assumes that this exponential curve can in fact continue since it will be the harbinger of the technological singularity. Seems more than a bit eschatological, but who knows. If we suppose this tech rapture does happen, all bets are off; in that sense it's probably better to assume the curve is sigmoidal, since the alternative is literally beyond human comprehension.
  - ndriscoll 1y ago
    Barring fully reversible processes as the basis for technology, you still quickly run into energy and cooling constraints. Even with that, you'd have time or energy density constraints. Unlimited exponentials are clearly unphysical.
    - TeMPOraL 1y ago
      Yes, this is an accurate description, and also completely irrelevant to the issue at hand. At the stage of development we are today, no one cares how fast it takes for the exponent to go from eating our galaxy to eating the whole universe, or whether it'll break some energy density constraint before it and leave a gaping zero-point energy hole where our local cluster used to be. It'll stop eventually. What we care about is whether it stops before it breaks everything for us, here on Earth. And that's not at all a given. Fundamental limits are irrelevant to us - it's like worrying that putting too many socks in a drawer will eventually make them collapse into a black hole. The limits that are relevant to us are much lower, set by technological, social and economic factors. It's much harder to say where those limits lay.
FrustratedMonky 1y ago
"I'm sure people were saying that about commercial airline speeds in the 1970's too." But there are others that keep going also. Moore's law is still going (mostly, slowing), and made it past a few pinch points where people thought it was the end. The point is, that over 30 decades, many people said Moore's law was at an end, and then it wasn't, there was some breakthrough that kept it going. Maybe a new one will happen. The thing with AI is, maybe the S curve flattens out , after all the jobs are gone. Everyone is hoping the S curve flattens out somewhere just below human level, but what if it flattens out just beyond human level? We're still screwed.
Nevermark 1y ago
Progress in information systems cannot be compared to progress in physical systems. For starters, physical systems compete for limited resources and labor. For another, progress in software vastly reduces the cost of improved designs. Whereas progress in physical systems can enable but still increase the cost of improved designs. Finally, the underlying substrate of software is digital hardware, which has been improving in both capabilities and economics exponentially for almost 100 years. Looking at information systems as far back as the first coordination of differentiating cells to human civilization is one of exponential improvement. Very slow, slow, fast, very fast. (Can even take this further, to first metabolic cycles, cells, multi-purpose genes, modular development genes, etc. Life is the reproduction of physical systems via information systems.) Same with human technological information systems, from cave painting, writing, printing, telegraph, phone, internet, etc. It would be VERY surprising if AI somehow managed to fall off the exponential information system growth path. Not industry level surprising, but "everything we know about how useful information compounds" level surprising.
- travisjungroth 1y ago
  > Progress in information systems cannot be compared to progress in physical systems. > For starters, physical systems compete for limited resources and labor. > Finally, the underlying substrate of software is digital hardware… See how these are related?
  - Nevermark 1y ago
    By physical systems, I meant systems whose purpose is to do physical work. Mechanical things. Gears. Struts. Computer hardware is an information system. You are correct that it is has a physical component. But its power comes from its organization (information) not its mass, weight, etc. Transistors get more powerful, not less, when made from less matter. Information systems move from substrate to more efficient substrate. They are not their substrate.
    - travisjungroth 1y ago
      They still depend on physical resources and labor. They’re made by people and machines. There’s never been more resources going into information systems than right now, and AI accelerated that greatly. Think of all the server farms being built next to power plants.
naasking 1y ago
> But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. S curves are exponential before they start tapering off though. It's hard to predict how long that could continue, so there's an argument to be made that we should milk that while we can.
naasking 1y ago
> But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. S curves are exponential before they start tapering off though. It's hard to predict how long that could continue, so there's an argument to be made that we should remain optimistic and milk that while we can lest pessimism cut off investment top early.
naasking 1y ago
> But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. S curves are exponential before they start tapering off though. It's hard to predict how long that could continue, so there's an argument to be made that we should remain optimistic and milk that while we can lest pessimism cut off investment too early.
baxtr 1y ago
It never ceases to amaze me how people consistently mistake the initial phase of a sigmoid curve for an exponential function.
deleted 1y ago
[deleted]
TeMPOraL 1y ago
> I'm sure people were saying that about commercial airline speeds in the 1970's too. They'd be wrong, of course - for not realizing demand is a limiting factor here. Airline speeds plateaued not because we couldn't make planes go faster anymore, but because no one wanted them to go faster. This is partially economical and partially social factor - transit times are bucketed by what they enable people to do. It makes little difference if going from London to New York takes 8 hours instead of 12 - it's still in the "multi-day business trip" bucket (even 6 hours goes into that bucket, once you add airport overhead). Now, if you could drop that to 3 hours, like Concorde did[0], that finally moves it into "hop over for a meet, fly back the same day" bucket, and then business customers start paying attention[1]. For various technical, legal and social reasons, we didn't manage to cross that chasm before money for R&D dried out. Still, the trend continued anyway - in military aviation and, later, in supersonic missiles. With AI, the demand is extreme and only growing, and it shows no sign of being structured into classes with large thresholds between them - in fact, models are improving faster than we're able to put them to any use; even if we suddenly hit a limit now and couldn't train even better models anymore, we have decades of improvements to extract just from learning how to properly apply the models we have. But there's no sign we're about to hit a wall with training any time soon. Airline speeds are inherently a bad example for the argument you're making, but in general, I don't think pointing out S-curves is all that useful. As you correctly observe: > But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. But, what happens when one technology - or rather, one metric of that technology - stops improving? Something else starts - another metric of that technology, or something built on top of it, or something that was enabled by it. The exponent is S-curves on top of S-curves, all the way down, but how long that exponent is depends on what you consider in scope. So, a matter of accounting. So yeah, AI progress can flatten tomorrow or continue exponentially for the next couple years - depending on how narrowly you define "AI progress". Ergo, not all that useful. -- [0] - https://simpleflying.com/concorde-fastest-transatlantic-crossing/ https://simpleflying.com/concorde-fastest-transatlantic-cros... [1] - This is why Elon Musk wasn't immediately laughed out of the room after proposing using Starship for moving people and cargo across the Earth, back in 2017. Hopping between cities on an ICBM sounds borderline absurd for many reasons, but it also promised cutting flight time to less than one hour between any two points on Earth, which put it a completely new bucket, even more interesting for businesses.
oofbey 1y ago
There’s a key way to think about a process that looks exponential and might or might not flatten out into an S curve: reasoning about fundamental limits. For COVID it would obviously flatten out because there are finite humans, and it did when the disease had in fact infected most humans on the planet. For commercial airlines you could reason about the speed of sound or escape velocity and see there is again a natural upper limit- although which of those two would dominate would have very different real world implications. For computational intelligence, we have one clear example of an upper limit in a biological human brain. It only consumes about 25W and has much more intelligence than today’s LLMs in important ways. Maybe that’s the wrong limit? But Moore’s law has been holding for a very long time. And smart physicists like Feynman in his seminal lecture predicting nanotechnology in 1959 called “there’s plenty of room at the bottom” have been arguing that we are extremely far from running into any fundamental physical limits on the complexity of manufactured objects. The ability to manufacture them we presume is limited by ingenuity, which jokes aside shows no signs of running out. Training data is a fine argument to consider. Especially since there are training on “the whole internet” sorta. The key breakthrough of transformers wasn’t in fact autoregressive token processing or attention or anything like that. It was that they can learn from (memorize / interpolate between / generalize) arbitrary quantities of training data. Before that every kind of ML model hit scaling limits pretty fast. Resnets got CNNs to millions of parameters but they still became quite difficult to train. Transformers train reliably on every size data set we have ever tried with no end in sight. The attention mechanism shortens the gradient path for extremely large numbers of parameters, completely changing the rules of what’s possible with large networks. But what about the data to feed them? There are two possible counter arguments there. One is that humans don’t need exabytes of examples to learn the world. You might reasonably conclude from this that NNs have some fundamental difference vs people and that some hard barrier of ML science innovation lies in the way. Smart scientists like Yann LeCun would agree with you there. I can see the other side of that argument too - that once a system is capable of reasoning and learning it doesn’t need exhaustive examples to learn to generalize. I would argue that RL reasoning systems like GRPO or GSPO do exactly this - they let the system try lots of ways to approach a difficult problem until they figure out something that works. And then they cleverly find a gradient towards whatever technique had relative advantage. They don’t need infinite examples of the right answer. They just need a well chosen curriculum of difficult problems to think about for a long time. (Sounds a lot like school.) Sometimes it takes a very long time. But if you can set it up correctly it’s fairly automatic and isn’t limited by training data. The other argument is what the Silicon Valley types call “self play” - the goal of having an LLM learn from itself or its peers through repeated games or thought experiments. This is how Alpha Go was trained, and big tech has been aggressively pursuing analogs for LLMs. This has not been a runaway success yet. But in the area of coding agents, arguably where AI is having the biggest economic impact right now, self play techniques are an important part of building both the training and evaluation sets. Important public benchmarks here start from human curated examples and algorithmically enhance them to much larger sizes and levels of complexity. I think I might have read about similar tricks in math problems but I’m not sure. Regardless it seems very likely that this has a way to overcome any fundamental limit on availability of training data as well, based on human ingenuity instead. Also, if the top of the S curve is high enough, it doesn’t matter that it’s not truly exponential. The interesting stuff will happen before it flattens out. E.g. COVID. Consider the y axis “human jobs replaced by AI” instead of “smartness” and yes it’s obviously an S curve.
theptip 1y ago
> 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.
  - 1y ago
pickledish 1y ago
Agreed! And, maybe I'm missing something, but to me it seems obvious that flat top part of the S curve is going to be somewhere below human ability... because, as you say, of the training data. How on earth could we train an LLM to be smarter than us, when 100% of the material we use to teach it how to think, is human-style thinking? Maybe if we do a good job, only a little bit below human ability -- and what an accomplishment that would still be! But still -- that's a far cry from the ideas espoused in articles like this, where AI is just one or two years away from overtaking us.
- Inufu 1y ago
  Author here. The standard way to do this is Reinforcement Learning: we do not teach the model how to do the task, we let it discover the _how_ for itself and only grade it based on how well it did, then reinforce the attempts where it did well. This way the model can learn wildly superhuman performance, e.g. it's what we used to train AlphaGo and AlphaZero.
karmakaze 1y ago
Each specific technology can be S-shaped, but advancements in achieving goals can still maintain an exponential curve. e.g. Moore's law is dead with the end of Dennard scaling, but computation improvements still happen with parallelism. Meta's Behemoth shows that scaling number of parameters has diminished returns, but we still have many different ways to continue advancements. Those who point at one thing and say "see", isn't really seeing. Of course there are limits, like energy but with nuclear energy or photon-based computing were nowhere near the limits.
insane_dreamer 1y ago
> I'm sure people were saying that about commercial airline speeds in the 1970's too. They were also saying that about CPU clock speeds.
peab 1y ago
There's a Mulla Nasrudin joke that's sort of relevant here: Nasrudin is on a flight, when suddenly the pilot comes on the intercom, saying, "Passengers, we apologize, but we have experienced an engine burn-out. The plane can still fly on the remaining three engines, but we'll be delayed in our arrival by two hours." Nasrudin speaks up "let's not worry, what's 2 hours really" A few minutes later, the airplane shakes, and passengers see smoke coming out of another engine. Again, the intercom crackles to life. "This is your captain speaking. Apologies, but due to a second engine burn-out, we'll be delayed by another two hours." The passengers are agitated, but the Mulla once again tries to remains calm. Suddenly, the third engine catches fire. Again, the pilot comes on the intercom and says, "I know you're all scared, but this is a very advanced aircraft, and it can safely fly on only a single engine. But we will be delayed by yet another two hours." At this, Nasrudin shouts, "This is ridiculous! If one more engine goes, we'll be stuck up here all day"
Balgair 1y ago
The cost of the next number in a GPT (3>4>5) seems to be in 2 ways: 1) $$$ 2) data The second (data) also isn't cheap. As it seems we've already gotten through all the 'cheap' data out there. So much so that synthetic data (fart huffing) is a big thing now. People tell it's real and useful and passes the glenn-horf theore... blah blah blah. So it really more so comes down to just: 1) $$$^2 (but really pick any exponent) In that, I'm not sure this thing is a true sigmoid curve (see: biology all the time). I think it's more a logarithmic cost here. In that, it never really goes away, but it gets really expensive to carry out for large N. [To be clear, lots of great shit happens out there in large N. An AI god still may lurk in the long slow slope of $N, the cure for boredom too, or knowing why we yawn, etc.]
maurits 1y ago
>I'm sure people were saying that about commercial airline speeds in the 1970's too. Also elegantly formulated by: https://idlewords.com/talks/web_design_first_100_years.htm https://idlewords.com/talks/web_design_first_100_years.htm
ben_w 1y ago
>> it would be extremely surprising if these improvements suddenly stopped. > But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. An S-curve is exactly the opposite of "suddenly" stopping. It is possible for us to get a sudden stop, due to limiting factors. For a hypothetical: if Moore's Law had continued until we hit atomic resolution instead of the slowdown as we got close to it, that would have been an example of a sudden stop: can't get transistors smaller than atoms, but yet it would have been possible (with arbitrarily large investments that we didn't have) to halve transistor sizes every 18 months until suddenly we can't. Now I think about it, the speed of commercial airlines is also an example of a sudden stop: we had to solve sonic booms first before even considering a Concorde replacement.
bccdee 1y ago
Ironically, given that it probably mistakes a sigmoid curve for an exponential curve, "Failing to understand the exponential, again" is an extremely apt name for this blog post.

ttoinou 1y ago

Yes exponential is only an approximation of the first part of S curves. And this author claims that he understands the exponential better than others…

_fizz_buzz_ 1y ago
That is even true for covid for obvious reasons, because Covid runs out of people it can infect at some point.
- marginalia_nu 1y ago
  Infectious diseases rarely see actual exponential growth for logistical reasons. It's a pretty unrealistic model that ignores that the disease actually needs to find additional hosts to spread, the local availability of which starts to go down from the first victim.
  - heavenlyblue 1y ago
    If you assume the availability of hosts is local to the perimeter of the infected hosts, then the relative growth is limited to 2/R where R is the distance from patient 0 in 2 dimensions. It's becuase an area of the circle defines how many hosts are already ill but the interaction can only happen on the perimeter of the circle. The disease is obviously also limited by the total amount of hosts, but I assume there's also the "bottom" limit - i.e. the resource consumption of already-infected hosts.
    - CamperBob2 1y ago
      It also depends on how panicked people are. Covid was never going to spread like ebola, for instance: it was worse. Bad enough to harm and kill people, but not bad enough to scare them into self-enforced isolation and voluntary compliance with public health measures. Back on the subject of AI, I think the flat part of the curve has always been in sight. Transformers can achieve human performance in some, even many respects, but they're like children who have to spend a million years in grade school to learn their multiplication tables. We will have to figure out why that is the case and how to improve upon it drastically before this stuff really starts to pay off. I'm sure we will but we'll be on a completely different S-shaped curve at that point.
  - ttoinou 1y ago
    Yes the model where the S curves comes out is extremely simplified. Looking at covid curves we could have well said it was parabolic, but that’s much less worrisome
- shmel 1y ago
  It's obvious, but the problem was that enough people would die in the process for people to be worried. Similarly, if the current AI will be able to replace 99% of devs in 5-10 years (or even worse, most white collar jobs) and flatten out there without becoming a godlike AGI, it will still have enormous implications for the economy.
blibble 1y ago
the author is an anthropic employee if the money dries up because the investors lose faith on the exponential continuing, then his future looks much dimmer

cs702 1y ago

Yes. It's true that we don't know, with any certainty, (1) whether we are hitting limits to growth intrinsic to current hardware and software, (2) whether we will need new hardware or software breakthroughs to continue improving models, and (3) what the timing of any necessary breakthroughs, because innovation doesn't happen on a predictable schedule. There are unknown unknowns.[a] However, there's no doubt that at a global scale, we're sure trying to maintain current rates of improvement in AI. I mean, the scale and breadth of global investment dedicated to improving AI, presently, is truly unprecedented. Whether all this investment is driven by FOMO or by foresight, is irrelevant. The underlying assumption in all cases is the same: We will figure out, somehow, how to overcome all known and unknown challenges along the way. I have no idea what the odds of success may be, but they're not zero. We sure live in interesting times! --- [a] https://en.wikipedia.org/wiki/There_are_unknown_unknowns https://en.wikipedia.org/wiki/There_are_unknown_unknowns

xg15 1y ago
I hope the crash won't be unprecedented as well...
- cs702 1y ago
  I hope so too. Capital spending on AI appears to be holding up the entire economy: https://am.jpmorgan.com/us/en/asset-management/adv/insights/market-insights/market-updates/on-the-minds-of-investors/is-ai-already-driving-us-growth/ https://am.jpmorgan.com/us/en/asset-management/adv/insights/...
  - deleted 1y ago
    [deleted]

ajross 1y ago

> I'm sure people were saying that about commercial airline speeds in the 1970's too. Or CPU frequencies in the 1990's. Also we spent quite a few decades at the end of the 19th century thinking that physics was finished. I'm not sure that explaining it as an "S curve" is really the right metaphor either, though. You get the "exponential" growth effect when there's a specific technology invented that "just needs to be applied", and the application tricks tend to fall out quickly. For sure generative AI is on that curve right now, with everyone big enough to afford a datacenter training models like there's no tomorrow and feeding a community of a million startups trying to deploy those models. But nothing about this is modeled correctly as an "exponential", except in the somewhat trivial sense of "the community of innovators grows like a disease as everyone hops on board". Sure, the petri dish ends up saturated pretty quickly and growth levels off, but that's not really saying much about the problem.

jsnell 1y ago

Indeed you can't be sure. But on the other hand a bunch of the commentariat has been claiming (with no evidence) that we're at the midpoint of the sigmoid for the last three years. They were wrong. And then you had the AI frontier lab insiders who predicted an accelerating pace of progress for the last three years. They were right. Now, the frontier labs rarely (never?) provide evidence either, but they do have about a year of visibility into the pipeline, unlike anyone outside. So at least my heuristic is to wait until a frontier lab starts warning about diminishing returns and slowdowns before calling the midpoint or multiple labs start winding down capex. The first component might have misaligned incentives, but if we're in a realistic danger of hitting a wall in the next year, the capex spending would not be accelerating the way it is.

yojo 1y ago
Capex requirements might be on a different curve than model improvements. E.g. you might need to accelerate spending to get sub-linear growth in model output. If valuations depend on hitting the curves described in the article, you might see accelerating capex at precisely the time improvements are dropping off. I don’t think frontier labs are going to be a trustworthy canary. If Anthropic says they’re reaching the limit and OpenAI holds the line that AGI is imminent, talent and funding will flee Anthropic for OpenAI. There’s a strong incentive to keep your mouth shut if things aren’t going well.
- queenkjuul 1y ago
  I think you nailed it. The capex is desperation in the hopes of maintaining the curve. I have heard actual AI researchers say progress is slowing, just not from the big companies directly.
deleted 1y ago
[deleted]
airstrike 1y ago
> And then you had the AI frontier lab insiders who predicted an accelerating pace of progress for the last three years. Progress has most definitely not been happening at an _accelerating_ pace.

fhe 1y ago

I am getting the sense that the 2nd deriative of the curve is already hitting negative teritory. models get updated, and I don't feel I'm getting better answers from the LLMs. On the application front though, it feels that the advancements from a couple of years ago are just beginning to trickle down to product space. I used to do some video editing as a hobby. Recently I picked it up again, and was blown away by how much AI has chipped away the repetitive stuff, and even made attempts at the more creative aspects of production, with mixed but promising results.

tehjoker 1y ago
What are some examples of tasks you no longer have to do?
- fhe 1y ago
  one example is auto generating subtitles -- elements of this tasks, e.g. speech to text with time coding, have been around for a while (openai whisper and others), but they have only recently been integrated into video editors and become easy to use for non-coders. other examples: depth map (estimating object distance from the camera; this is useful when you want to blur the background), auto-generating masks with object tracking.

breuleux 1y ago

> But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. I'd argue all of them. Any true exponential eventually gets to a point where no computer can even store its numerical value. It's a physically absurd curve.

saretup 1y ago
You clearly haven’t played my idle game.
marginalia_nu 1y ago
The narrative quietly assumes that this exponential curve can in fact continue since it will be the harbinger of the technological singularity. Seems more than a bit eschatological, but who knows. If we suppose this tech rapture does happen, all bets are off; in that sense it's probably better to assume the curve is sigmoidal, since the alternative is literally beyond human comprehension.
- ndriscoll 1y ago
  Barring fully reversible processes as the basis for technology, you still quickly run into energy and cooling constraints. Even with that, you'd have time or energy density constraints. Unlimited exponentials are clearly unphysical.
  - TeMPOraL 1y ago
    Yes, this is an accurate description, and also completely irrelevant to the issue at hand. At the stage of development we are today, no one cares how fast it takes for the exponent to go from eating our galaxy to eating the whole universe, or whether it'll break some energy density constraint before it and leave a gaping zero-point energy hole where our local cluster used to be. It'll stop eventually. What we care about is whether it stops before it breaks everything for us, here on Earth. And that's not at all a given. Fundamental limits are irrelevant to us - it's like worrying that putting too many socks in a drawer will eventually make them collapse into a black hole. The limits that are relevant to us are much lower, set by technological, social and economic factors. It's much harder to say where those limits lay.

FrustratedMonky 1y ago

"I'm sure people were saying that about commercial airline speeds in the 1970's too." But there are others that keep going also. Moore's law is still going (mostly, slowing), and made it past a few pinch points where people thought it was the end. The point is, that over 30 decades, many people said Moore's law was at an end, and then it wasn't, there was some breakthrough that kept it going. Maybe a new one will happen. The thing with AI is, maybe the S curve flattens out , after all the jobs are gone. Everyone is hoping the S curve flattens out somewhere just below human level, but what if it flattens out just beyond human level? We're still screwed.

Nevermark 1y ago

Progress in information systems cannot be compared to progress in physical systems. For starters, physical systems compete for limited resources and labor. For another, progress in software vastly reduces the cost of improved designs. Whereas progress in physical systems can enable but still increase the cost of improved designs. Finally, the underlying substrate of software is digital hardware, which has been improving in both capabilities and economics exponentially for almost 100 years. Looking at information systems as far back as the first coordination of differentiating cells to human civilization is one of exponential improvement. Very slow, slow, fast, very fast. (Can even take this further, to first metabolic cycles, cells, multi-purpose genes, modular development genes, etc. Life is the reproduction of physical systems via information systems.) Same with human technological information systems, from cave painting, writing, printing, telegraph, phone, internet, etc. It would be VERY surprising if AI somehow managed to fall off the exponential information system growth path. Not industry level surprising, but "everything we know about how useful information compounds" level surprising.

travisjungroth 1y ago
> Progress in information systems cannot be compared to progress in physical systems. > For starters, physical systems compete for limited resources and labor. > Finally, the underlying substrate of software is digital hardware… See how these are related?
- Nevermark 1y ago
  By physical systems, I meant systems whose purpose is to do physical work. Mechanical things. Gears. Struts. Computer hardware is an information system. You are correct that it is has a physical component. But its power comes from its organization (information) not its mass, weight, etc. Transistors get more powerful, not less, when made from less matter. Information systems move from substrate to more efficient substrate. They are not their substrate.
  - travisjungroth 1y ago
    They still depend on physical resources and labor. They’re made by people and machines. There’s never been more resources going into information systems than right now, and AI accelerated that greatly. Think of all the server farms being built next to power plants.

naasking 1y ago

> But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. S curves are exponential before they start tapering off though. It's hard to predict how long that could continue, so there's an argument to be made that we should milk that while we can.

naasking 1y ago

baxtr 1y ago

It never ceases to amaze me how people consistently mistake the initial phase of a sigmoid curve for an exponential function.

deleted 1y ago

[deleted]

TeMPOraL 1y ago

> I'm sure people were saying that about commercial airline speeds in the 1970's too. They'd be wrong, of course - for not realizing demand is a limiting factor here. Airline speeds plateaued not because we couldn't make planes go faster anymore, but because no one wanted them to go faster. This is partially economical and partially social factor - transit times are bucketed by what they enable people to do. It makes little difference if going from London to New York takes 8 hours instead of 12 - it's still in the "multi-day business trip" bucket (even 6 hours goes into that bucket, once you add airport overhead). Now, if you could drop that to 3 hours, like Concorde did[0], that finally moves it into "hop over for a meet, fly back the same day" bucket, and then business customers start paying attention[1]. For various technical, legal and social reasons, we didn't manage to cross that chasm before money for R&D dried out. Still, the trend continued anyway - in military aviation and, later, in supersonic missiles. With AI, the demand is extreme and only growing, and it shows no sign of being structured into classes with large thresholds between them - in fact, models are improving faster than we're able to put them to any use; even if we suddenly hit a limit now and couldn't train even better models anymore, we have decades of improvements to extract just from learning how to properly apply the models we have. But there's no sign we're about to hit a wall with training any time soon. Airline speeds are inherently a bad example for the argument you're making, but in general, I don't think pointing out S-curves is all that useful. As you correctly observe: > But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. But, what happens when one technology - or rather, one metric of that technology - stops improving? Something else starts - another metric of that technology, or something built on top of it, or something that was enabled by it. The exponent is S-curves on top of S-curves, all the way down, but how long that exponent is depends on what you consider in scope. So, a matter of accounting. So yeah, AI progress can flatten tomorrow or continue exponentially for the next couple years - depending on how narrowly you define "AI progress". Ergo, not all that useful. -- [0] - https://simpleflying.com/concorde-fastest-transatlantic-crossing/ https://simpleflying.com/concorde-fastest-transatlantic-cros... [1] - This is why Elon Musk wasn't immediately laughed out of the room after proposing using Starship for moving people and cargo across the Earth, back in 2017. Hopping between cities on an ICBM sounds borderline absurd for many reasons, but it also promised cutting flight time to less than one hour between any two points on Earth, which put it a completely new bucket, even more interesting for businesses.

oofbey 1y ago

There’s a key way to think about a process that looks exponential and might or might not flatten out into an S curve: reasoning about fundamental limits. For COVID it would obviously flatten out because there are finite humans, and it did when the disease had in fact infected most humans on the planet. For commercial airlines you could reason about the speed of sound or escape velocity and see there is again a natural upper limit- although which of those two would dominate would have very different real world implications. For computational intelligence, we have one clear example of an upper limit in a biological human brain. It only consumes about 25W and has much more intelligence than today’s LLMs in important ways. Maybe that’s the wrong limit? But Moore’s law has been holding for a very long time. And smart physicists like Feynman in his seminal lecture predicting nanotechnology in 1959 called “there’s plenty of room at the bottom” have been arguing that we are extremely far from running into any fundamental physical limits on the complexity of manufactured objects. The ability to manufacture them we presume is limited by ingenuity, which jokes aside shows no signs of running out. Training data is a fine argument to consider. Especially since there are training on “the whole internet” sorta. The key breakthrough of transformers wasn’t in fact autoregressive token processing or attention or anything like that. It was that they can learn from (memorize / interpolate between / generalize) arbitrary quantities of training data. Before that every kind of ML model hit scaling limits pretty fast. Resnets got CNNs to millions of parameters but they still became quite difficult to train. Transformers train reliably on every size data set we have ever tried with no end in sight. The attention mechanism shortens the gradient path for extremely large numbers of parameters, completely changing the rules of what’s possible with large networks. But what about the data to feed them? There are two possible counter arguments there. One is that humans don’t need exabytes of examples to learn the world. You might reasonably conclude from this that NNs have some fundamental difference vs people and that some hard barrier of ML science innovation lies in the way. Smart scientists like Yann LeCun would agree with you there. I can see the other side of that argument too - that once a system is capable of reasoning and learning it doesn’t need exhaustive examples to learn to generalize. I would argue that RL reasoning systems like GRPO or GSPO do exactly this - they let the system try lots of ways to approach a difficult problem until they figure out something that works. And then they cleverly find a gradient towards whatever technique had relative advantage. They don’t need infinite examples of the right answer. They just need a well chosen curriculum of difficult problems to think about for a long time. (Sounds a lot like school.) Sometimes it takes a very long time. But if you can set it up correctly it’s fairly automatic and isn’t limited by training data. The other argument is what the Silicon Valley types call “self play” - the goal of having an LLM learn from itself or its peers through repeated games or thought experiments. This is how Alpha Go was trained, and big tech has been aggressively pursuing analogs for LLMs. This has not been a runaway success yet. But in the area of coding agents, arguably where AI is having the biggest economic impact right now, self play techniques are an important part of building both the training and evaluation sets. Important public benchmarks here start from human curated examples and algorithmically enhance them to much larger sizes and levels of complexity. I think I might have read about similar tricks in math problems but I’m not sure. Regardless it seems very likely that this has a way to overcome any fundamental limit on availability of training data as well, based on human ingenuity instead. Also, if the top of the S curve is high enough, it doesn’t matter that it’s not truly exponential. The interesting stuff will happen before it flattens out. E.g. COVID. Consider the y axis “human jobs replaced by AI” instead of “smartness” and yes it’s obviously an S curve.

theptip 1y ago

> 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.
- 1y ago

pickledish 1y ago

Agreed! And, maybe I'm missing something, but to me it seems obvious that flat top part of the S curve is going to be somewhere below human ability... because, as you say, of the training data. How on earth could we train an LLM to be smarter than us, when 100% of the material we use to teach it how to think, is human-style thinking? Maybe if we do a good job, only a little bit below human ability -- and what an accomplishment that would still be! But still -- that's a far cry from the ideas espoused in articles like this, where AI is just one or two years away from overtaking us.

Inufu 1y ago
Author here. The standard way to do this is Reinforcement Learning: we do not teach the model how to do the task, we let it discover the _how_ for itself and only grade it based on how well it did, then reinforce the attempts where it did well. This way the model can learn wildly superhuman performance, e.g. it's what we used to train AlphaGo and AlphaZero.

karmakaze 1y ago

Each specific technology can be S-shaped, but advancements in achieving goals can still maintain an exponential curve. e.g. Moore's law is dead with the end of Dennard scaling, but computation improvements still happen with parallelism. Meta's Behemoth shows that scaling number of parameters has diminished returns, but we still have many different ways to continue advancements. Those who point at one thing and say "see", isn't really seeing. Of course there are limits, like energy but with nuclear energy or photon-based computing were nowhere near the limits.

insane_dreamer 1y ago

> I'm sure people were saying that about commercial airline speeds in the 1970's too. They were also saying that about CPU clock speeds.

peab 1y ago

There's a Mulla Nasrudin joke that's sort of relevant here: Nasrudin is on a flight, when suddenly the pilot comes on the intercom, saying, "Passengers, we apologize, but we have experienced an engine burn-out. The plane can still fly on the remaining three engines, but we'll be delayed in our arrival by two hours." Nasrudin speaks up "let's not worry, what's 2 hours really" A few minutes later, the airplane shakes, and passengers see smoke coming out of another engine. Again, the intercom crackles to life. "This is your captain speaking. Apologies, but due to a second engine burn-out, we'll be delayed by another two hours." The passengers are agitated, but the Mulla once again tries to remains calm. Suddenly, the third engine catches fire. Again, the pilot comes on the intercom and says, "I know you're all scared, but this is a very advanced aircraft, and it can safely fly on only a single engine. But we will be delayed by yet another two hours." At this, Nasrudin shouts, "This is ridiculous! If one more engine goes, we'll be stuck up here all day"

Balgair 1y ago

The cost of the next number in a GPT (3>4>5) seems to be in 2 ways: 1) $$$ 2) data The second (data) also isn't cheap. As it seems we've already gotten through all the 'cheap' data out there. So much so that synthetic data (fart huffing) is a big thing now. People tell it's real and useful and passes the glenn-horf theore... blah blah blah. So it really more so comes down to just: 1) $$$^2 (but really pick any exponent) In that, I'm not sure this thing is a true sigmoid curve (see: biology all the time). I think it's more a logarithmic cost here. In that, it never really goes away, but it gets really expensive to carry out for large N. [To be clear, lots of great shit happens out there in large N. An AI god still may lurk in the long slow slope of $N, the cure for boredom too, or knowing why we yawn, etc.]

maurits 1y ago

>I'm sure people were saying that about commercial airline speeds in the 1970's too. Also elegantly formulated by: https://idlewords.com/talks/web_design_first_100_years.htm https://idlewords.com/talks/web_design_first_100_years.htm

ben_w 1y ago

>> it would be extremely surprising if these improvements suddenly stopped. > But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. An S-curve is exactly the opposite of "suddenly" stopping. It is possible for us to get a sudden stop, due to limiting factors. For a hypothetical: if Moore's Law had continued until we hit atomic resolution instead of the slowdown as we got close to it, that would have been an example of a sudden stop: can't get transistors smaller than atoms, but yet it would have been possible (with arbitrarily large investments that we didn't have) to halve transistor sizes every 18 months until suddenly we can't. Now I think about it, the speed of commercial airlines is also an example of a sudden stop: we had to solve sonic booms first before even considering a Concorde replacement.

bccdee 1y ago

Ironically, given that it probably mistakes a sigmoid curve for an exponential curve, "Failing to understand the exponential, again" is an extremely apt name for this blog post.