11 ms·

Failing to Understand the Exponential, Again

• kaashif 1y ago
  And today, COVID has infected 5000 quadrillion people!
• loloquwowndueo 1y ago
  This extrapolates based on a good set of data points to predict when AI will reach significant milestones like being able to “work on tasks for a full 8 hours” (estimates by 2026). Which is ok - but it bears keeping https://xkcd.com/605/ https://xkcd.com/605/ in mind when doing extrapolation.
• gyomu 1y ago
  > Instead, even a relatively conservative extrapolation of these trends suggests that 2026 will be a pivotal year for the widespread integration of AI into the economy: > Models will be able to autonomously work for full days (8 working hours) by mid-2026. At least one model will match the performance of human experts across many industries before the end of 2026. > By the end of 2027, models will frequently outperform experts on many tasks. First commandment of tech hype: the pivotal, groundbreaking singularity is always just 1-2 years away. I mean seriously, why is that? Even when people like OP try to be principled and use seemingly objective evaluation data, they find that the BIG big thing is 1-2 years away. Self driving cars? 1-2 years away. AR glasses replacing phones? 1-2 years away. All of us living our life in the metaverse? 1-2 years away. Again, I have to commend OP on putting in the work with the serious graphs, but there’s something more at play here. Is it purely a matter of data cherry picking? Is it the unknowns unknowns leading to the data driven approaches being completely blind to their medium/long term limitations?
  • yazaddaruvala 1y ago
    Self driving cars have existed for at least a year now. It only took a decade of “1 years away” but it exists now, and will likely require another decade of scaling up the hardware. I think AGI is going to follow a similar trend. A decade of being “1 years away”. Meanwhile, unlike self driving the industry is preemptively solving the scaling up of hardware concurrently.
  • LegionMammal978 1y ago
    Many people seem to assert that "constant relative growth in capabilities/sales/whatever" is a totally reasonable (or even obvious or inevitable) prior assumption, and then point to "OMG relative growth produces an exponential curve!" as the rest of their argument. And at least the AI 2027 people tried to one-up that by asserting an increasing relative growth rate to produce a superexponential curve. I'd be a fool to say that we'll ever hit a hard plateau in AI capabilities, but I'll have a hard time believing any projected exponential-growth-to-infinity until I see it with my own eyes.
  • EliRivers 1y ago
    Because I need to specify an amount of time short enough that big investors will hand over a lot of money, long enough that I can extract a big chunk of it for myself before it all comes crashing down. A couple of years is probably a bit tight, really, but I'm competing for that cash with other people so the timeframe we make up is going to about the lowest we think we can get away with.
• noiv 1y ago
  > Again we can observe a similar trend, with the latest GPT-5 already astonishingly close to human performance: I have issues with "human performance" as single data point in times where education keeps to excel in some countries and degrades in others. How far away are we from saying, better than "X percent of humans" ?
  • marcosdumay 1y ago
    Nah, that part is ok. Human wherever you set it, human competence takes decades to really change, and those things have visible changes ever year or so. The problem with all of the article's metrics is that they are all absolutely bullshit. It just throws claims like that AI can write full programs 50% of the time by itself in there and moves on like if it had any resemblance to what happens on the real world.
  • kzrdude 1y ago
    This reminds me -- very tenuously -- of how the shorthand for very good performance in the Python community is "like C". In the C community, we know that programs have different performance depending on algorithms chosen..
    • Jtsummers 1y ago
      > In the C community, we know that programs have different performance depending on algorithms chosen.. Yes. Only the C community knows this. What a silly remark. Regarding the "Python community" remark, benchmarks against C and Fortran go back decades now. It's not just a Python thing. C people push it a lot, too.
• HexDecOctBin 1y ago
  Exponential curves don't last for long fortunately, or the universe would have turned into a quark soup. The example of COVID is especially ironic, considering it stopped being a real concern within 3 years of its advent despite the exponential growth in the early years. Those who understand exponentials should also try to understand stock and flow.
  • smohare 1y ago
    Stopped being a concern primarily due to heavy vaccination campaigns though. It is still raging, just not nearly as many people are dying. The immunity from infection these days is pretty paltry.
  • nkrisc 1y ago
    Reminds me a bit of the "ultraviolet catastrophe". > The ultraviolet catastrophe, also called the Rayleigh–Jeans catastrophe, was the prediction of late 19th century and early 20th century classical physics that an ideal black body at thermal equilibrium would emit an unbounded quantity of energy as wavelength decreased into the ultraviolet range. [...] > The phrase refers to the fact that the empirically derived Rayleigh–Jeans law, which accurately predicted experimental results at large wavelengths, failed to do so for short wavelengths. https://en.wikipedia.org/wiki/Ultraviolet_catastrophe https://en.wikipedia.org/wiki/Ultraviolet_catastrophe
    • analog31 1y ago
      Right. Nobody believed that the intensity would go to infinity. What they believed was that the theory was incomplete, but they didn't know how or why. And the solution required inventing a completely new theory.
  • analog31 1y ago
    It's possible to understand both exponential and limiting behavior at the same time. I work in an office full of scientists. Our team scrammed the workplace on March 10, 2020. To the scientists, it was intuitively obvious that the curve could not surpass 100% of the population. An exponential curve with no turning point is almost always seen as a sure sign that something is wrong with your model. But we didn't have a clue as to the actual limit, and any putative limit below 100% would need a justification, which we didn't have, or some dramatic change to the fundamental conditions, which we couldn't guess. The typical practice is to watch the curve for any sign of a departure from exponential behavior, and then say: "I told you so." ;-) The first change may have been social isolation. In fact that was pretty much the only arrow in our quivers. The second change was the vaccine, which changed both the infection rate and the mortality rate, dramatically.
• crazygringo 1y ago
  > 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.
• deleted 1y ago
  [deleted]
• silvestrov 1y ago
  The model (of the world) is not the world. Just because the model fits so far does not mean it will continue to fit.
• IshKebab 1y ago
  > Again we can observe a similar trend, with the latest GPT-5 already astonishingly close to human performance: Yes but only if you measure "performance" as "better than the other option more than 50% of the time" which is a terrible way to measure performance, especially for bullshitting AI. Imagine comparing chocolate brands. One is tastier than the other one 60% of the time. Clear winner right? Yeah except it's also deadly poisonous 5% of the time. Still tastier on average though!
• konmok 1y ago
  As they say, every exponential is a sigmoid in disguise. I think the exponential phase of growth for LLM architectures is drawing to a close, and fundamentally new architectures will be necessary for meaningful advances. I'm also not convinced by the graphs in this article. OpenAI is notoriously deceptive with their graphs, and as Gary Marcus has already noted, that METR study comes with a lot of caveats: [https://garymarcus.substack.com/p/the-latest-ai-scaling-graph-and-why?utm_source=publication-search https://garymarcus.substack.com/p/the-latest-ai-scaling-grap...]
  • j_maffe 1y ago
    Yes that's logistic growth basically
  • tripplyons 1y ago
    What makes you believe the exponential phase will end soon?
• airstrike 1y ago
  Failing to understand the sigmoid, again
• ctrlp 1y ago
  Where in nature/reality do we actually see exponential trends continued long? It seems like they typically encounter a governing effect quite quickly.
  • NetMageSCW 1y ago
    The issue is quite quickly can be really slow for humans. Moore’s law has been brought up multiple times.
• k__ 1y ago
  I didn't plot it, but I had the impression the Aider benchmark success rates for SOTA over time were a hockey curve. Like the improvements between 60 and 70 felt much faster than those between 80 and 90.
• entee 1y ago
  A lot of this post relies on the recent open ai result they call GDPval (link below). They note some limitations (lack of iteration in the tasks and others) which are key complaints and possibly fundamental limitations of current models. But more interesting is the 50% win rate stat that represents expert human performance in the paper. That seems absurdly low, most employees don’t have a 50% success rate on self contained tasks that take ~1 day of work. That means at least one of a few things could be true: 1. The tasks aren’t defined in a way that makes real world sense 2. The tasks require iteration, which wasn’t tested, for real world success (as many tasks do) I think while interesting and a very worthy research avenue, this paper is only the first in a still early area of understanding how AI will affect with the real world, and it’s hard to project well from this one paper. https://cdn.openai.com/pdf/d5eb7428-c4e9-4a33-bd86-86dd4bcf12ce/GDPval.pdf https://cdn.openai.com/pdf/d5eb7428-c4e9-4a33-bd86-86dd4bcf1...
  • drc500free 1y ago
    That's not 50% success rate at completing the task, that's the win rate of a head-to-head comparison of an algorithm and an expert. 50% means the expert and the algorithm each "win" half the time.
    • viernullvier 1y ago
      For the METR rating (first half of the article), it is indeed 50% success rate at completing the task. The win rate only applies to the GDPval rating (second half of the article).
• podgorniy 1y ago
  > By the end of 2027, models will frequently outperform experts on many tasks. In passing the quiz-es > Models will be able to autonomously work for full days (8 working hours) by mid-2026. Who will carry responsibility for the consequences of these model's errors? What tools will be avaiable to that resposible _person_? -- Tehchno optimists will be optimistic. Techno pessimists will be pessimistic. Processes we're discussing have their own limiting factors which no one mentiones. Why to mention what exactly makes graph go up and holds it from going exponential? Why to mention or discuss inherit limitations of the LLMs architecture? Or what is legal perspective on AI agency? Thus we're discussing results of AI models passing tests and people's perception of other people opinions.
  • wyager 1y ago
    You don't actually need to have a "responsible person"; you can just have an AI do stuff. It might make a mistake; the only difference between that and an employee is that you can't punish an AI. If you're any good at management and not a psychopath, the ability to have someone to punish for mistakes isn't actually important
    • afthonos 1y ago
      The importance of having a human be responsible is about alignment. We have a fundamental belief that human beings are comprehensible and have goals that are not completely opaque. That is not true of any piece of software. In the case of deterministic software, you can’t argue with a bug. It doesn’t matter how many times you tell it that no, that’s not what either the company or the user intended, the result will be the same. With an AI, the problem is more subtle. The AI may absolutely be able to understand what you’re saying, and may not care at all, because its goals are not your goals, and you can’t tell what its goals are. Having a human be responsible bypasses that. The point is not to punish the AI, the point is to have a hope to stop it from doing things that are harmful.
• j_maffe 1y ago
  It's interesting that he brings up the example of "exponential" growth in the case of COVID infections even though it was actually logistic growth[1] that saturates once resources get exhausted. What makes AI different? [1] https://en.wikipedia.org/wiki/Logistic_function#Modeling_early_COVID-19_cases https://en.wikipedia.org/wiki/Logistic_function#Modeling_ear...
• stickfigure 1y ago
  "Models will be able to autonomously work for full days (8 working hours)" does not make them equivalent to a human employee. My employees go home and come back retaining context from the previous day; they get smarter every month. With Claude Code I have to reset the context between bite-sized tasks. To replace humans in my workplace, LLMs need some equivalent of neuroplasticity. Maybe it's possible, but it would require some sort of shift in the approach that may or may not be coming.
  • FrustratedMonky 1y ago
    Maybe when we get updating models. Right now, they are trained, and released, and we are using that static model with a context window. At some point when we have enough processing to have models that are always updating, then that would be plastic. I'm supposing.
• olooney 1y ago
  Good article, the METR metric is very interesting. See also Leopold Aschenbrenner's work in the same vein: https://situational-awareness.ai/from-gpt-4-to-agi/ https://situational-awareness.ai/from-gpt-4-to-agi/ IMO this approach ultimately asks the wrong question. Every exponential trend in history has eventually flattened out. Every. single. one. Two rabbits would create a population with a mass greater than the Earth in a couple of years if that trend continues indefinitely. The left hand side of a sigmoid curve looks exactly like exponential growth to the naked eye... until it nears the inflection point at t=0. The two curves can't be distinguished when you only have noisy data from t<0. A better question is, "When will the curve flatten out?" and that can only be addressed by looking outside the dataset for which constraints will eventually make growth impossible. For example, for Moore's law, we could examine as the quantum limits on how small a single transistor can be. You have to analyze the context, not just do the line fitting exercise. The only really interesting question in the long term is if it will level off at a level near, below, or above human intelligence. It doesn't matter much if that takes five years or fifty. Simply looking at lines that are currently going up and extending them off the right side of the page doesn't really get us any closer to answering that. We have to look at the fundamental constraints of our understanding and algorithms, independent of hardware. For example, hallucinations may be unsolvable with the current approach and require a genuine paradigm shift to solve, and paradigm shifts don't show up on trend lines, more or less by definition.
• _fizz_buzz_ 1y ago
  Failing to Understand Sigmoid functions, again?
• xg15 1y ago
  OP failing to understand S-curves again... I think the first comment on the article put it best: With COVID, researchers could be certain that exponential growth was taking place because they knew the underlying mechanisms of the growth. The virus was self-replicating, so the more people were already infected, the faster would new infections happen. (Even this dynamic would only go on for a certain time and eventual slow down, forming an S-curve, when the virus could not find any more vulnerable persons to continue the rate of spread. The critical question was of course if this would happen because everyone was vaccinated or isolated enough to prevent infection - or because everyone was already infected or dead) With AI, there is no such underlying mechanism. There is the dream of the "self-improving AI" where either humans can make use of the current-generation AI to develop the next-generation AI in a fraction of the time - or where the AI simply creates the next generation on its own. If this dream were reality, it could be genuine exponential growth, but from all I know, it isn't. Coding agents speed up a number of bespoke programming tasks, but they do not exponentially speed up development of new AI models. Yes, we can now quickly generate large corpora of synthetic training data and use them for distillation. We couldn't do that before - but a large part of the training data discussion is about the observation that synthetic data can not replace real data, so data collection remains a bottleneck. There is one point where a feedback loop does happen, and this is with the hype curve: Initial models produced extremely impressive results compared to everything we had before - there caused an enormous hype and unlocked investments that allowed more resources for the developed of the next model - which then delivered even better results. But it's obvious that this kind of feedback loop will eventually end when no more additional capital is available and diminishing returns set in. Then we will once again be in the upper part of the S-curve.
• hnlmorg 1y ago
  Just because something exhibits an exponential growth at one point in time, that doesn’t mean that a particular subject is capable of sustaining exponential growth. Their Covid example is a great counter argument to their point in that covid isn’t still growing exponentially. Where the AI skeptics (or even just pragmatists, like myself) chime in is saying “yeah AI will improve. But LLMs are a limited technology that cannot fully bridge the gap between what they’re producing now, and what the “hypists” claim they’ll be able to do in the future.” People like Sam Altman know ChatGPT is a million miles away from AGI. But their primary goal is to make money. So they have to convince VCs that their technology has a longer period of exponential growth than what it actually will have.
  • zwnow 1y ago
    > Just because something exhibits an exponential growth at one point in time, that doesn’t mean that a particular subject is capable of sustaining exponential growth. Which is pretty ironic given the title of the post
  • Inufu 1y ago
    Author here. The argument is not that it will keep growing exponentially forever (obviously that is physically impossible), rather that: - given a sustained history of growth along a very predictable trajectory, the highest likelihood short term scenario is continued growth along the same trajectory. Sample a random point on an s-curve and look slightly to the right, what’s the most common direction the curve continues? - exponential progress is very hard to visualize and see, it may appear to hardly make any progress while far away from human capabilities, then move from just below to far above human very quickly
    • bawolff 1y ago
      That feels like you're moving the goal posts a bit. Exponential growth over the short term is very uninteresting. Exponential growth is exciting when it can compound. E.g. if i offered you an investing opportunity 500% / per year compounded daily - that's amazing. If the fine print is that that rate will only last for the very near term (say a week), then it would be worse than a savings account.
• Isamu 1y ago
  >they somehow jump to the conclusion that AI will never be able to do these tasks at human level I don’t see that, I mostly see AI criticism that it’s not up to the hype, today. I think most people know it will approach human ability, we just don’t believe the hype that it will be here tomorrow. I’ve lived through enough AI winter in the past to know that the problem is hard, progress is real and steady, but we could see a big contraction in AI spending in a few years if the bets don’t pay off well in the near term. The money going into AI right now is huge, but it carries real risks because people want returns on that investment soon, not down the road eventually.
• ath3nd 1y ago
  Failing to acknowledge we are in a bigger and more dangerous bubble, again. If AI was so great, why all curl hackerone submissions have been rejected? Slop is not a substitute of skill.
• nextworddev 1y ago
  These takes (both bears and bulls) are all misguided. AI agents' performance depends heavily on the context / data / environment provided, and how that fits into the overall business process. Thus, "agent performance" itself will be very unevenly distributed.
• coldtea 1y ago
  >People notice that while AI can now write programs, design websites, etc, it still often makes mistakes or goes in a wrong direction, and then they somehow jump to the conclusion that AI will never be able to do these tasks at human levels, or will only have a minor impact. When just a few years ago, having AI do these things was complete science fiction! Both things can be true, since they're orthogonal. Having AI do these things was complete fiction 10 years ago. And after 5 years of LLM AI, people do start to see serious limits and stunted growth with the current LLM approaches, while also seeing that nobody has proposed another serious contended to that approach. Similarly, going to the moon was science finction 100 years ago. And yet, we're now not only not in Mars, but 50+ years without a new moon manned landing. Same for airplanes. Science fiction in 1900. Mostly stale innovation wise for the last 30 years. A lot of curves can fit an exponential line plot, without the progress going forward being exponential. We would have 1 trillion transistor cpus following Moore's "exponential curve"
  • Tenemo 1y ago
    I agree with all your points, just wanted to say that transistor count is probably a counter example. We have been keeping with the Moore's Law more or less[1] and M3 Max, a 2023 consumer-grade CPU, has ~100B of transistors, "just" one order of magnitude away from yout 1T. I think that shows we haven't stagnated much in transistor density and the progress is just staggering! [1] https://en.m.wikipedia.org/wiki/Transistor_count https://en.m.wikipedia.org/wiki/Transistor_count
    • szatkus 1y ago
      That one order of magnitude is about 7 years behind the Moore's Law. We're still progressing but it's slower, more expensive and we hit way more walls than before.
  • senordevnyc 1y ago
    Except it’s not been five years, it’s been at most three, since approximately no one was using LLMs prior to ChatGPT’s release, which was just under three years ago. We did have Copilot a year before that, but it was quite rudimentary. And really, we’ve had even less than that. The first large scale reasoning model was o1, which was released 12 months ago. More useful coding agents are even newer than that. This narrative that we’ve been using these tools for many years and are now hitting a wall doesn’t match my experience at all. AI-assisted coding is way better than it was a year ago, let alone five.
• derbOac 1y ago
  Aside from the S-versus-exp issue, this area is one of these things where there's a kind of disconnect between my personal professional experience with LLMs and the criteria measures he's talking about. LLMs to me have this kind of superficially impressive feel where it seems impressive in its capabilities, but where, when it fails, it fails dramatically, in a way humans never would, and it never gets anywhere near what's necessary to actually be helpful on finishing tasks, beyond being some kind of gestalt template or prototype. I feel as if there needs to be a lot more scrutiny on the types of evaluation tasks being provided — whether they are actually representative of real-world demands, or if they are making them easy to look good, and also more focus on the types of failures. Looking through some of the evaluation tasks he links to I'm more familiar with, they seem kind of basic? So not achieving parity with human performance is more significant than it seems. I also wonder, in some kind of maxmin sense, whether we need to start focusing more on worst-case failure performance rather than best-case goal performance. LLMs are really amazing in some sense, and maybe this essay makes some points that are important to keep in mind as possibilities, but my general impression after reading it is it's kind of missing the core substance of AI bubble claims at the moment.
• lapcat 1y ago
  It should be noted that the article author is an AI researcher at Anthropic and therefore benefits financially from the bubble: https://www.julian.ac/about/ https://www.julian.ac/about/ > The current discourse around AI progress and a supposed “bubble” reminds me a lot of the early weeks of the Covid-19 pandemic. Long after the timing and scale of the coming global pandemic was obvious from extrapolating the exponential trends, politicians, journalists and most public commentators kept treating it as a remote possibility or a localized phenomenon. That's not what I remember. On the contrary, I remember widespread panic. (For some reason, people thought the world was going to run out of toilet paper, which became a self-fulfilling prophesy.) Of course some people were in denial, especially some politicians, though that had everything to do with politics and nothing to do with math and science. In any case, the public spread of infectious diseases is a relatively well understood phenomenon. I don't see the analogy with some new tech, although the public spread of hype is also a relatively well understood phenomenon.
• thegrim33 1y ago
  AI company employee whose livelihood depends on people continuing to pump money into AI writes a blog post trying to convince people to keep pumping more money into AI. Seems solid. The "exponential" metric/study they include is pretty atrocious. Measuring AI capability by how long humans would take to do the task. By that definition existing computers are already super AGI - how long would it take humans to sort a list of a million numbers? Computers can do it in a fraction of a second. I guess that proves they're already AGI, right? You could probably fit an exponential curve to that as well, before LLMs even existed.
• izacus 1y ago
  Ah, employee of an AI company is telling us the technology he's working on and is directly financially interested in hyping will... grow forever and be amazing and exponential and take over the world. And everyone who doesn't believe this employee of AI company hyping AI is WRONG about basics of math. I absolutely would NOT ever expect such a blog post. /s.
  • NetMageSCW 1y ago
    That is a complete strawman - you made up forever growth and then argued against it. The OP is saying the in the short term, it makes more sense to assume exponential growth continues instead of thinking it will flatten out any moment now.
• Earw0rm 1y ago
  You'd think that boosters for a technology whose very foundations rely on the sigmoid and tanh functions used as neuron activation functions would intuitively get this...
  • andy99 1y ago
    It's all relu these days
    • seanhunter 1y ago
      When people want a smooth function so they can do calculus they often use something like gelu or the swish function rather than relu. And the swish function involves a sigmoid. https://en.wikipedia.org/wiki/Swish_function https://en.wikipedia.org/wiki/Swish_function
      • tripplyons 1y ago
        The gated variants of these functions have been dominant for a few years.
    • tripplyons 1y ago
      Most LLMs use GeGLU or SwiGLU.
• Ambolia 1y ago
  I will worry when I see Startups competing on products with companies 10x, 100x, or 1000x times their size. Like a small team producing a Photoshop replacement. So far I haven't seen anything like that. Big companies don't seem to be launching new products faster either, or fixing some of their products that have been broken for a long time (MS teams...) AI obviously makes some easy things much faster, maybe helps with boilerplate, we still have to see this translate into real productivity.
  • seanhunter 1y ago
    I think the real turning point is when there isn’t the need for something like photoshop. Creatives that I speak to yearn for the day when they can stop paying the adobe tax.
    • NetMageSCW 1y ago
      If they don’t like it, they can stop now. It may have consequences, however.
    • NathanaelRea 1y ago
      There will always be an adobe tax so to speak. Creatives want high quality and reliable tools to be able to produce high quality things. I could imagine a world where a small team + AI creates an open source tool that is better than current day Photoshop. However if that small team has that power, so does adobe, and what we perceive as "good" or "high quality" will shift.
• andy99 1y ago
  This doesn't feel at all credible because we're already well into the sigmoid part of the curve. I thought the gpt5 thing made it pretty obvious to everyone. I'm bullish on AI, I don't think we've even begun to understand the product implications, but the "large language models are in context learners" phase has for now basically played out.
• credit_guy 1y ago
  I think the author of this blog is not a heavy user of AI in real life. If you are, you know there things AI is very good at, and thing AI is bad at. AI may see exponential improvements in some aspects, but not in other aspects. In the end, those "laggard" aspects of AI will put a ceiling on its real-world performance. I use AI in my coding for many hours each day. AI is great. But AI will not replace me in 2026 or in 2027. I have to admit I can't make projections many years in the future, because the pace of progress in AI is indeed breathtaking. But, while I am really bullish on AI, I am skeptical of claims that AI will be able to fully replace a human any time soon.
  • mrshu 1y ago
    The author is an AI researcher at Anthropic: https://www.julian.ac/about/ https://www.julian.ac/about/ He likely has his substantial experience using AI in real life (particularly when it comes to coding).
  • senordevnyc 1y ago
    How much better is AI-assisted coding than it was in September 2023?
    • xenobeb 1y ago
      This all depends on how you define "better". I am an amateur programmer and tried to port a python 2.7 library to python 3 with GPT5 a few weeks ago. After a few tries, I realized both myself and the model missed that a large part of the library is based on another library that was never ported to 3 either. That doesn't stop GPT5 from trying to write the code as best it can with a library that doesn't exist for python 3. That is the part we have made absolutely no progress on. Of course, it can do a much better react crud app than in Sept 2023. In one sense, LLMs are so amazing and impressive and quite fugazi in another sense.
• deleted 1y ago
  [deleted]
• bwfan123 1y ago
  > Instead, even a relatively conservative extrapolation of these trends suggests that 2026 will be a pivotal year for the widespread integration of AI into the economy Integration into the economy takes time and investment. Unfortunately, ai applications dont have an easy adoption curve - except for the chatbot. Every other use case requires an expensive and risky integration into an existing workflow. > By the end of 2027, models will frequently outperform experts on many tasks fixed tasks like tests - maybe. But, the real world is not a fixed model. It requires constant learning through feedback.
• atoav 1y ago
  Many of the "people don't understand Exponential functions" posts are ultimately about people not understanding logistic functions. Because most things in reality that seemingly grow exponentially will eventually, unevitably taper off at some point when the cost for continued growth gets so high, accelerated growth can't be supported anymore. Viruses can only infect so many people for example. If the growth was truly exponential you would need infinite people for it to be truly exponential.
• slaucon 1y ago
  I feel like there should be some take away from the fact that we have to come up with new and interesting metrics like “Length of a Task That Can Be Automated” in order to point out that exponential growth is still happening. Fwiw, it does seem like a good metric, but it also feels like you can often find some metric that’s improving exponentially even when the base function is leveling out.
  • jsnell 1y ago
    It's the only benchmark I know of with a well-behaved scale. Benchmarks with for example a score from 0-100% get saturated quite quickly, and further improvements on the metric are literally impossible. And even excluding saturation, they just behave very oddly at the extremes. To use them to show long term exponential growth you need to chain together benchmarks, which is hard to make look credible.
  • xenobeb 1y ago
    From Nassim Taleb "Unless you have confidence in the ruler’s reliability, if you use a ruler to measure a table you may also be using the table to measure the ruler." Seems like that is exactly what we are doing.
• BenFranklin100 1y ago
  This guy isn’t even wrong. Sure these models are getting faster, but they are barely getter better at actual reasoning, if at all. Who cares if a model can give me a bullshit answer in five minutes instead of ten? It’s still bullshit.
• Rexxar 1y ago
  Even if the computational power evolve exponentially, we need to evaluate the utility of additional computations. And if the utility happens to increase logarithmically with computation spend, it's possible that in the end, we will observe just a linear increase in utility.
• pyrale 1y ago
  > Given consistent trends of exponential performance improvements over many years and across many industries, it would be extremely surprising if these improvements suddenly stopped. The difference between exponential and sigmoid is often a surprise to the believers, indeed.
• chad1n 1y ago
  So the author is in a clear conflict of interest with the contents of the blog because he's an employee of Anthropic. But regarding this "blog", showing the graph where OpenAI compares "frontier" models and shows gpt-4o vs o3-high is just disingenuous, o1 vs o3 would have been a closer fight between "frontier" models. Also today I learned that there are people paid to benchmark AI models in terms of how close they are to "human" level, apparently even "expert" level whatever that means. I'm not a LLM hater by any means, but I can confidently say that they aren't experts in any fields.
• morsecodist 1y ago
  > When just a few years ago, having AI do these things was complete science fiction! This is only because these projects only became consumer facing fairly recently. There was a lot of incremental progress in the academic language model space leading up to this. It wasn't as sudden as this makes it sound. The deeper issue is that this future-looking analysis goes no deeper than drawing a line connecting a few points. COVID is a really interesting comparison, because in epidemiology the exponential model comes from us understanding disease transmission. It is also not actually exponential, as the population becomes saturated the transmission rate slows (it is worth noting that unbounded exponential growth doesn't really seem to exist in nature). Drawing an exponential line like this doesn't really add anything interesting. When you do a regression you need to pick the model that best represents your system. This is made even worse because this uses benchmarks and coming up with good benchmarks is actually an important part of the AI problem. AI is really good at improving things we can measure so it makes total sense that it will crush any benchmark we throw at it eventually, but there will always be some difference between benchmarks and reality. I would argue that as you are trying to benchmark more subtle things it becomes much harder to make a benchmark. This is just a conjecture on my end but if something like this is possible it means you need to rule it out when modeling AI progress. There are also economic incentives to always declare percent increases in progress at a regular schedule. Will AI ever get this advanced? Maybe, maybe even as fast as the author says, but this just isn't a compelling case for it.
• IanCal 1y ago
  Somewhat missed by many comments proclaiming that it’s sigmoidal is that sigmoid curves exhibit significant growth after it stops looking exponential. Unless you think things have already hit a dramatic wall you should probably assume further growth. We should probably expect compute to get cheaper at the same time, so that’s performance increases with lowering costs. Even after things flatline for performance you would expect lowering costs of inference. Without specific evidence it’s also unlikely you randomly pick the point on a sigmoid where things change.
• poopiokaka 1y ago
  “Guy who personally benefits from AI hype says we aren’t in a bubble” - don’t we have enough of these already ??
  • lairv 1y ago
    (I'm the one who posted the url, not author post) Julian Schrittwieser (author of this post) has been in AI for a long time, he was in the core team who worked on AlphaGo, AlphaZero and MuZero at DeepMind, you can see him in the AlphaGo movie. While it doesn't make his opinion automatically true, I think it makes it worth considering, especially since he's a technical person, not a CEO trying to raise money "extrapolating an exponential" seems dubious, but I think the point is more that there is no clear sign of slowing down in models capabilities from the benchmarks, so we can still expect improvements
    • poopiokaka 1y ago
      Benchmarks are notoriously easy to fake. Also he doesn’t need to be a CEO trying to raise money in order to have an incentive here to push this agenda / narrative. He has a huge stock grant from Anthropic that will go to $0 when the bubble pops
• phyzome 1y ago
  "Failing to Understand the Sigmoid, Again"
• whatever1 1y ago
  There are no exponentials in nature. Everything is finite.
• doug_durham 1y ago
  The 50% success rate is the problem. It means you can’t reliably automate tasks unattended. That seems to be where it becomes non-exponential. It’s like having cars that go twice as far as the last year but will only get you to your destination 50% of the time.
  • bwfan123 1y ago
    > It’s like having cars that go twice as far as the last year but will only get you to your destination 50% of the time Nice analogy. All human progress is based on tight-abstractions describing a well-defined machine model. Leaky abstractions with an undefined machine are useful too but only as recommendations or for communication. It is harder to build on top of them. Precisely why programming in english is a non-starter - or - just using english in math/science instead of formalism.
• 1970-01-01 1y ago
  Another 'numbrr go up' analyst. Yes, models are objectively better at tasks. Please include the fact that hundreds of billions of dollars are being poured into making them better. You could even call it a technology race. Once the money avalanche runs it's course, I and many others expect 'the exponential' to be followed by an implosion or correction in growth. Data and training is not what LLMs crave. Piles of cash is what LLMs crave.
• vjerancrnjak 1y ago
  There’s no exponential improvement in go or chess agents, or car driving agents. Even tiny mouse racing. If there is, it would be such nice low hanging fruit. Maybe all of that happens all at once. I’d just be honest and say most of it is completely fuzzy tinkering disguised as intellectual activity (yes, some of it is actual intellectual activity and yes we should continue tinkering) There are rare individuals that spent decades building up good intuition and even that does not help much.
• mwkaufma 1y ago
  Pure Cope from a partner at Anthropic. However, I _do_ agree AI is comparable to COVID, but not in the way our author intends.
• dsr_ 1y ago
  117 comments so far, and the word economics does not appear. Any technology which produces more results for more inputs but does not get more efficient at larger scale runs into a money problem if it does not get hit by a physics problem first. It is quite possible that we have already hit the money problem.
• bawolff 1y ago
  Measuring "how long" an AI can work for seems bizarre to me. Its a computer program. What does it even mean that soon it "will be able to work 8 hour days"?
• zdw 1y ago
  I'm less concerned about "parity with industry expert" and more concerned about "Error/hallucination rate compared to industry expert". Without some guarantee of correctness, just posting the # of wins seems vacuous.
• danlitt 1y ago
  I am constantly astonished that articles like this even pass the smell test. It is not rational to predict exponential growth just because you've seen exponential growth before! Incidentally, that is not what people did during COVID, they predicted exponential growth for reasons. Specific, articulable reasons, that consisted of more than just "look, like go up. line go up more?". Incidentally, the benchmarks quoted are extremely dubious. They do not even really make sense. "The length of tasks AI can do is doubling every 7 months". Seriously, what does that mean? If the AI suddenly took double the time to answer the same question, that would not be progress. Indeed, that isn't what they did, they just... picked some times at random? You might counter that these are actually human completion times, but then why are we comparing such distinct and unrelated tasks as "count words in a passage" (trivial, any child can do) and "train adversarially robust image model" (expert-level task, could take anywhere between an hour and never-complete). Honestly, the most hilarious line in the article is probably this one: > You might object that this plot looks like it might be levelling off, but this is probably mostly an artefact of GPT-5 being very consumer-focused. This is a plot with three points in it! You might as well be looking at tea leaves!
  • jsnell 1y ago
    > but then why are we comparing such distinct and unrelated tasks as ... Because a few years ago the LLMs could only do trivial tasks that a child could do, and now they're able to do complex research and software development tasks. If you just have the trivial tasks, the benchmark is saturated within a year. If you just have the very complex tasks, the benchmark is has no sensitivity at all for years (just everything scoring a 0) and then abruptly becomes useful for a brief moment. This seems pretty obvious, and I can't figure out what your actual concern is. You're just implying it is a flawed design without pointing out anything concrete.
    • danlitt 1y ago
      The key word is "unrelated"! Being able to count the number of words in a paragraph and being able to train an image classifier are so different as to be unrelated for all practical purposes. The assumption underlying this kind of a "benchmark" is that all tasks have a certain attribute called complexity which is a numerical value we can use to discriminate tasks, presumably so that if you can complete tasks up to a certain "complexity" then you can complete all other tasks of lower complexity. No such attribute exists! I am sure there are "4 hour" tasks an LLM can do and "5 second" tasks that no LLM can do. The underlying frustration here is that there is so much latitude possible in choosing which tasks to test, which ones to present, and how to quantify "success" that the metrics given are completely meaningless, and do not help anyone to make a prediction. I would bet my entire life savings that by the time the hype bubble bursts, we will still have 10 brainless articles per day coming out saying AGI is round the corner.
• joshdavham 1y ago
  > - Models will be able to autonomously work for full days (8 working hours) by mid-2026. > - At least one model will match the performance of human experts across many industries before the end of 2026. > - By the end of 2027, models will frequently outperform experts on many tasks. I’ve seen a lot of people make predictions like this and it will be interesting to see how this turns out. But my question is, what should happen to a person’s credibility if their prediction turns out to be wrong? Should the person lose credibility for future predictions and we no longer take them seriously? Or is that way too harsh? Should there be reputational consequences for making bad predictions? I guess this more of a general question, not strictly AI-related.
  • bwfan123 1y ago
    > Should the person lose credibility for future predictions and we no longer take them seriously If this were the case, almost every sell-side analyst should have been blacklisted by now. Its more about entertainment than facts - sort of like astrology.
• alyxya 1y ago
  The sentiment of the comments here seems rather pessimistic. A perspective that balances both sides might be that the rate of mass adoption of some technology often lags behind the frontier capabilities, so I wouldn’t expect AI to take over a majority of those jobs in GPDval in a couple of years, but it’ll probably happen eventually. There are still fundamental limitations in both the model and products using the model that restrict what AI is capable of, so it’s simultaneously true that AI can do cutting edge work in certain domains for hours while vastly underperforming in other domains for very small tasks. The trajectory of improvement of AI capabilities is also an unknown, where it’s easy to overestimate exponential trends due to unexpected issues arising but also easy to underestimate future innovations. I don’t see the trajectory slowing down just yet with more compute and larger models being used, and I can imagine AI agents will increasingly give their data to further improve larger models.
• Nevermark 1y ago
  I don't think I have ever seen a page on HN where so many people missed the main point. The phenomenon of people having trouble understanding the implications of exponential progress is really well known. Well known, I think, by many people here. And yet an alarming number of comments here interpret small pauses as serious trend breakers. False assumptions that we are anywhere near the limits of computing power relative to fundamental physics limits. Etc. Recent progress, which is unprecedented in speed looking backward, is dismissed because people have acclimatized to change so quickly. The title of the article "Failing to Understand the Exponential, Again" is far more apt than I could have imagined, on HN. See my other comments here for specific arguments. See lots of comments here for examples of those who are skeptical of a strong inevitability here. The "information revolution" started the first time design information was separated from the thing it could construct. I.e. the first DNA or perhaps RNA life. And it has unrelentingly accelerated from there for over 4.5 billion years. The known physics limits of computation per gram are astronomical. We are nowhere near any hard limit. And that is before any speculation of what could be done with the components of spacetime fragments we don't understand yet. Or physics beyond that. The information revolution has hardly begun. With all humor, this was the last place I expected people to not understand how different information technology progresses vs. any other kind. Or to revert to linear based arguments, in an exponentially relevant situation. If there is any S-curve for information technology in general, it won't be apparent until long after humans are a distant memory.
  • tim333 1y ago
    I'm a little surprised too. A lot of the arguments are along the lines of but LLMs aren't very good. But really LLMs are a brief phase in the information revolution you mention that will be superseded. To me saying we won't get AGI because LLMs aren't suitable is like saying we were not going to get powered flight because steam engines weren't suitable. Fair enough they weren't but they got modified into internal combustion engines and then were. Something like that will happen.
• insane_dreamer 1y ago
  > The evaluation tasks are sourced from experienced industry professionals (avg. 14 years' experience), 30 tasks per occupation for a total of 1320 tasks. Grading is performed by blinded comparison of human and model-generated solutions, allowing for both clear preferences and ties. It's important to carefully scrutinize the tasks to understand they actually reflect tasks that are unique to industry professionals. I just looked quickly at the nursing ones (my wife is a nurse) and half of them were creating presentations, drafting reports, and the like, which is the primary strength of LLMs but a very small portion of nursing duties. The computer programming tests are more straightforward. I'd take the other ones with a grain of salt for now.
• vonnik 1y ago
  To the people who claim that we’re running out of data, I would just say: the world is largely undigitized. The Internet digitized a bunch of words but not even a tiny fraction of all that humans express every day. Same goes for sound in general. CCTV captures a lot of images, far more than social media, but it is poorly processed and also just a fraction of the photons bouncing off objects on earth. The data part of this equation has room to grow.
  • suddenlybananas 1y ago
    "data" in the abstract is not useful, it has to contain useful stuff in it.
• Kye 1y ago
  Seems like the right place to ask with ML enthusiasts gathered in one place discussing curves and the things that bend them: what's the thing with potential to obsolete transformers and diffusion models? Is it something old people noticed once LLMs blew up? Something new? Something in-between?
• monkeyelite 1y ago
  All of those never-ending exponential graphs about Covid were wrong though.
• littlestymaar 1y ago
  It's funny because the author doesn't realize that this sentence at the beginning undermines his entire argument: > Or they see two consecutive model releases and don’t notice much difference in their conversations, and they conclude that AI is plateauing and scaling is over. The reason why we now fail to notice the difference between consecutive models now is because the progress isn't in fact exponential. Humans tend to have a logarithmic perception, which means we only appreciate progress when it is exponential (for instance you'd be very happy to get a $500 raise if you are living the minimum wage, but you wouldn't even call that “a raise” when on a SV engineers salary). AI models have been improving a ton for the past three years, in many direction, but the rate of progress is definitely not exponential. It's not emergent either, as the focus is now being specifically directed at solving specific problems (both riddles and real world problems) thanks to trillions of token of high quality synthetic data. On topics that aren't explicitly being worked on, progress have been minimal or even negative (for instance many people still use the 1 year old Mistral Nemo for creative writing because the more recent ones all have been STEMmaxxed)
• justlikereddit 1y ago
  The exponential progress argument is frequently also misconstrued as a >"we will get there by monotonously doing more of what we did previously" Take the independent time being an SWE metric of the article. This is a rather new( metric for measuring AI capabilitie, it's also a good metric, it is directly measurable in a quantified way, unlike nebulous goal points such as "AGI/ASI". It also doesn't necessarily predict any upheaval, which I also think is a good trait of a metric, we know it will be better when it hits 8, or 16 hours, but we can skip the hype and prophecies of civilizational transformation that are attached to terminology like "AGI/ASI". Now the caveat is that a SWE-time metric is useful at the moment because it's an intra day timescale, but if we push this number to the point of comparing 48 hour vs 54 hour SWE-time models we can easily end up chasing abstractions that have little to no explanatory power as to how good this AI really is and what consists as a proper and good incremental improvement and what comes out as a numerical benchmark number that may or may not be artificial. The same can be said of math-olympiad scores and many of the existing AI benchmarks. In the past there existed a concept of narrow AI. We could take task A, make a narrow AI become good at it. But we would expect a different application to be needed for task B. Now we have generalist AI, and we take the generalist AI and make it become good at task A because that is the flavor of the month metric, but maybe that doesn't translate for improving task B, which someone will come around to improving when that becomes flavor of the month. The conclusion? There's probably no good singular metric to get stuck on and say "this is it, this graph is the one, watch it go exponential and bring forth God" We will instead skip, hop and jump between task-or-category specific metrics that are deemed significant at the moment and arms-race style pump them up until their relevance fades.
• i420army 1y ago
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• arthurofbabylon 1y ago
  I am flabbergasted by the naivety around predicting the future. While we have hints and suggestions, our predictions are best expressed as ranges of possibilities with varying weights. The hyperbolic among us like to pretend that predictions come in the form of precise lines of predetermined direction and curve; how foolish! Predicting exponential growth is exceptionally difficult. Asymptotes are ordinary, and they often are not obvious until circumstances make them appear (in other words, they are commonly unpredictable). (I do agree with the author regarding the potential of LLM's remaining underestimated by much of the public, however I cannot hang around such abysmal reasoning.)
  • guy2345 1y ago
    [flagged]
    • arthurofbabylon 1y ago
      It’s a matter of correctness and utility. You can improve your odds of correctness (and thus usefulness) by adjusting the scope of your projection. This applies not only to predicting the future. Consider measuring something: you carefully choose your level of precision for practical reasons. Consider goal setting: you leave abundant room for variation because your goal is not expressed in hyper narrow terms, but you don’t leave it so loose that you don’t know what steps to take. When expressed in sufficiently narrow terms, no one will ever predict anything. When expressed in sufficiently broad terms, everyone can predict everything. So the point is to modulate the scope until attaining utility.
• Ravus 1y ago
  Exponential curves happen when a quantity's growth rate is a linear function of its own value. In practice they're all going to be logistic, but you can ignore that as long as you're far away from the cap of whatever factor limits growth. So what are the things that could cause "AI growth" (for some suitable definition of it) to be correlated with AI? The plausible ones I see are: - growing AI capabilities spur additional AI capex - AI could be used to develop better AIs The first one rings true, but is most definitely hitting the limit since US capex into the sector definitely cannot grow 100-fold (and probably cannot grow 4-fold either). The second one is, to my knowledge, not really a thing. So unless AI can start improving itself or there is a self-feeding mechanism that I have missed, we're near the logistic fun phase.
• conartist6 1y ago
  Wow, an exponential trendline, I guess billions of years of evolution can just give up and go home cause we have rigged the game my friends. At this rate we will create an AI which can do a task 10 years long! And then soon after that 100 years long! And that's that. Humans will be kept as pets because that's all we will be good for QED
• 11101010001100 1y ago
  Well, the article is not off to a good start. COVID-19 is modeled by an SIR dynamical system, which is at times, is approximately exponential. At times.
• YeGoblynQueenne 1y ago
  >> "Train adversarially robust image model". Should be easy to check a couple years down the line.
• gettingoverit 1y ago
  On top of other criticism here, I'd like to add that the article optimistically assumes that actors are completely honest with their benchmarks when billions of dollars and national security are at stake. I'm only an "expert" in computer science and software engineering, and can say that - neither of widely available LLMs can produce answers at the level of first year CS student; - students using LLMs can easily be distingished by being wrong in all the ways a human would otherwise never be. So to me it's not really the question of whether CS-related benchmarks are false, it's a question of how exactly did this BS even fly. Obviously in other disciplines LLMs show similar lack of performance, but I can't call myself an "expert" there, and someone might argue I tend to use wrong prompts. Until we see a website where we can put an intermediate problem and get a working solution, "benchmarks show that our AI solves problems on gold medalist level" will still be an obvious BS.