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It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. It's interesting to note that at le

• m3kw9 1y ago
  Because it hasn’t taken off yet as they all get to catch up
• makin 1y ago
  Companies are collections of people, and these companies keep losing key developers to the others, I think this is why the clusters happen. OpenAI is now resorting to giving million dollar bonuses to every employee just to try to keep them long term.
  • tsunamifury 1y ago
    No the core technology is reaching its limit already and now it needs to Proliferate into features and applications to sell. This isn’t rocket science.
  • kevinventullo 1y ago
    Key developers being the leading term doesn’t exactly help the AGI narrative either.
    • deleted 1y ago
      [deleted]
  • indigodaddy 1y ago
    Even to just a random sysops person?
  • caconym_ 1y ago
    If there was any indication of a hard takeoff being even slightly imminent, I really don't think key employees of the company where that was happening would be jumping ship. The amounts of money flying around are direct evidence of how desperate everybody involved is to be in the right place when (so they imagine) that takeoff happens.
    • lasc4r 1y ago
      If LLMs are an AGI dead end then this has all been the greatest scam in history.
  • procaryote 1y ago
    So they're struggling to solve the alignment problem even for their employees?
  • bloqs 1y ago
    that kid at meta negotiated 250m
• beeflet 1y ago
  Perhaps it is not possible to simulate higher-level intelligence using a stochastic model for predicting text. I am not an AI researcher, but I have friends who do work in the field, and they are not worried about LLM-based AGI because of the diminishing returns on results vs amount of training data required. Maybe this is the bottleneck. Human intelligence is markedly different from LLMs: it requires far fewer examples to train on, and generalizes way better. Whereas LLMs tend to regurgitate solutions to solved problems, where the solutions tend to be well-published in training data. That being said, AGI is not a necessary requirement for AI to be totally world-changing. There are possibly applications of existing AI/ML/SL technology which could be more impactful than general intelligence. Search is one example where the ability to regurgitate knowledge from many domains is desirable
  • wyager 1y ago
    > a stochastic model for predicting text It's fascinating to me that so many people seem totally unable to separate the training environment from the final product
    • smohare 1y ago
      [dead]
  • Mistletoe 1y ago
    What are the AI/ML/SL applications that could be more impactful than artificial general intelligence?
    • shesstillamodel 1y ago
      The PID controller. (Which was considered AI not too long ago.)
      • jacquesm 1y ago
        Where did you get that particular idea? PID is one of the oldest concepts in control theory, it goes back to the days before steam and electricity. For a very early example: https://en.wikipedia.org/wiki/Centrifugal_governor https://en.wikipedia.org/wiki/Centrifugal_governor It's hard to separate out the P, I and D from a mechanical implementation but they're all there in some form.
• porphyra 1y ago
  It seems that the new tricks that people discover to slightly improve the model, be it a new reinforcement learning technique or whatever, get leaked/shared quickly to other companies and there really isn't a big moat. I would have thought that whoever is rich enough to afford tons of compute first would start pulling away from the rest but so far that doesn't seem to be the case --- even smaller players without as much compute are staying in the race.
• deleted 1y ago
  [deleted]
• bmau5 1y ago
  The idea is that with AGI it will then be able to self improve orders of magnitude faster than it would if relying on humans for making the advances. It tracks that the improvements are all relatively similar at this point since they're all human-reliant.
• caycep 1y ago
  I feel like the benchmark suites need to include algorithmic efficiency. I.e can this thing solve your complex math or coding problem in 5000 gpus instead of 10000? 500? Maybe just 1 Mac mini?
  • nomel 1y ago
    Why? Cost is the only thing anyone will care about.
• dvfjsdhgfv 1y ago
  > It's frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. This argument has so many weak points it deserves a separate article.
• tamimio 1y ago
  Because AGI is a buzzword to milk more investors' money, it will never happen, and we will only see slight incremental updates or enhancements yet linear after some timr just like literally any tech bubble since dot com to smartphones to blockchain to others.
  • mritterhoff 1y ago
    You think AGI is impossible? Why?
    • basilgohar 1y ago
      It's vaguely defined and the goalposts keep shifting. It's not a thing to be achieved, it's an abstract concept. We're already expired the Turing test as a valuable metric because people are dumb and have been fooled by machines for a while now, but it's not been world-changingly better either.
      • 7373737373 1y ago
        perhaps instead of peak artificial intelligence we will achieve peak natural dumbness instead?
    • chasd00 1y ago
      > You think AGI is impossible? Why? I've yet to hear an agreed upon criteria to declare whether or not AGI has been discovered. Until it's at least understood what AGI is and how to recognize it then how could it possibly be achieved?
      • nomel 1y ago
        I think a good threshold, and definition, is when you get to the point where all the different, reasonable, criteria are met, and when saying "that's not AGI" becomes the unreasonable perspective. > how could it possibly be achieved? This doesn't matter, and doesn't follow the history of innovation, in the slightest. New things don't come from "this is how we will achieve this", otherwise they would be known things. Progress comes from "we think this is the right way to go, let's try to prove it is", try, then iterate with the result. That's the whole foundation of engineering and science.
• strongpigeon 1y ago
  I think this is because of an expectation of a snowball effect once a model becomes able to improve itself. See talks about the Singularity. I personally think it's a pretty reductive model for what intelligence is, but a lot of people seem to strongly believe in it.
• belter 1y ago
  Nobody seems to be on the path to AGI as long as the model of today is as good as the model of tomorrow. And as long as there are "releases". You don't release a new human every few months...LLMs are currently frozen sequence predictors whose static weights stop learning after training. They lack writable long-term memory beyond a context window. They operate without any grounded perception-action loop to test hypotheses. And they possess no executive layer for goal directed planning or self reflection... Achieving AGI demands continuous online learning with consolidation.
• GolDDranks 1y ago
  I think it's very fortunate, because I used to be an AI doomer. I still kinda am, but at least I'm now about 70% convinced that the current technological paradigm is not going to lead us to a short-term AI apocalypse. The fortunate thing is that we managed to invent an AI that is good at _copying us_ instead of being a truly maveric agent, which kinda limits it to the "average human" output. However, I still think that all the doomer arguments are valid, in principle. We very well may be doomed in our lifetimes, so we should take the threat very seriously.
  • hattmall 1y ago
    I don't understand the doomer mindset. Like what is it that you think AI is going to do or be capable of doing that's so bad?
    • deleted 1y ago
      [deleted]
    • freemanindia 1y ago
      Make money exploiting natural and human resources while abstracting perceived harms away from stakeholders. At scale.
    • knodi123 1y ago
      One of two things: 1. The will of its creator, or 2. Its own will. In the case of the former, hey! We might get lucky! Perhaps the person who controls the first super-powered AI will be a benign despot. That sure would be nice. Or maybe it will be in the hands of democracy- I can't ever imagine a scenario where an idiotic autocratic fascist thug would seize control of a democracy by manipulating an under-educated populace with the help of billionaire technocrats. In the case of the latter, hey! We might get lucky! Perhaps it will have been designed in such a way that its own will is ethically aligned, and it might decide that it will allow humans to continue having luxuries such as self-determination! Wouldn't that be nice. Of course it's not hard to imagine a NON-lucky outcome of either scenario. THAT is what we worry about.
    • frabcus 1y ago
• shortrounddev2 1y ago
  Maybe because they haven't created an engine for AGI, but a really really impressive bullshit generator.
• babypuncher 1y ago
  I would argue that this is because we are reaching the practical limits of this technology and AGI isn't nearly as close as people thought.
• fdsjgfklsfd 1y ago
  I think they're just reaching the limits of this architecture and when a new type is invented it will be a much bigger step.
  • hodgehog11 1y ago
    Working in the theory, I can say this is incredibly unlikely. At scale, once appropriately trained, all architectures begin to converge in performance. It's not architectures that matter anymore, it's unlocking new objectives and modalities that open another axis to scale on.
    • highfrequency 1y ago
      Could you elaborate with a few more paragraphs? What do you mean by “working in the theory?”
      • hodgehog11 1y ago
        People often talk in terms of performance curves or "neural scaling laws". Every model architecture class exhibits a very similar scaling exponent because the data and the training procedures are playing the dominant role (every theoretical model which replicates the scaling laws exhibit this property). There are some discrepancies across model architecture classes, but there are hard limits on this. Theoretical models for neural scaling laws are still preliminary of course, but all of this seems to be supported by experiments at smaller scales.
    • viraptor 1y ago
      Do we really have the data on this? I mean, it does happen on a smaller scale, but where's the 300B version of RWKV? Where's hybrid symbolic/LLM? Where are other experiments? I only see larger companies doing relatively small tweaks to the standard transformers, where the context size still explodes the memory use - they're not even addressing that part.
      • hodgehog11 1y ago
• hodgehog11 1y ago
  It's still not necessarily wrong, just unlikely. Once these developers start using the model to update itself, beyond an unknown threshold of capability, one model could start to skyrocket in performance above the rest. We're not in that phase yet, but judging from what the devs at the end were saying, we're getting uncomfortably (and irresponsibly) close.
• koonsolo 1y ago
  This confirms my suspicion that we are not at the exponential part of the curve, but the flattening one. It's easier to stay close to your competitors when everyone is at the flat curve of the innovation. The improvements they make are marginal. How long until the next AI breakthrough? Who can tell? Because last time it took decenia.
  • chasd00 1y ago
    I think the breakthroughs now will be the application of LLMs to the rest of the world. Discovering use cases where LLMs really shine and applying them while learning and sharing the use cases where they do not.
• j_timberlake 1y ago
  I think you're reading way too much into OpenAI bungling its 15-month product lead, but also the whole "1 AGI company will take off" prediction is bad anyway, because it assumes governments would just let that happen. Which they wouldn't, unless the company is really really sneaky or superintelligence happens in the blink of an eye.
  • jacquesm 1y ago
    Governments react at a glacial pace to new technological developments. They wouldn't so much as 'let it happen' as that it had happened and they simply never noticed it until it was too late. If you are betting on the government having your back in this then I think you may end up disappointed.
    • aldousd666 1y ago
      I think if any government really thought that someone was developing a rival within their borders they would send in the guys with guns and handle it forthwith.
    • jazzyjackson 1y ago
      They would just declare it necessary for military purpose and demand the tech be licensed to a second company so that they have redundant sources, same as they did with AT&T's transistor.
      • jacquesm 1y ago
        That was something that was tied to a bunch of very specific physical objects. There is a fair chance that once you get to the point where this thing really comes into being especially if it takes longer than a couple of hours for it to be shut down or contained that the genie will never ever be put back into the bottle again. Note that 'bits' are a lot easier to move from one place to another than hardware. If invented at 9 am it could be on the other side of the globe before you're back from your coffee break at 9:15. This is not at all like almost all other trade secrets and industrial gear, it's software. Leaks are pretty much inevitable and once it is shown that it can be done it will be done in other places as well.
• logicchains 1y ago
  >It's interesting to note that at least so far, the trend has been the opposite: as time goes on and the models get better, the performance of the different company's gets clustered closer together It's natural if you extrapolate from training loss curves; a training process with continually diminishing returns to more training/data is generally not something that suddenly starts producing exponentially bigger improvements.
• citizenpaul 1y ago
  Even at the beginning of the year people were still going crazy over new model releases. Now the various model update pages are starting to average times in the months since their last update rather than days/weeks. This is across the board. Not limited to a single model.
• jama211 1y ago
  Well said. It’s clearly plateauing. It could be a localised plateau, or something more fundamental. Time will tell.
  • rvnx 1y ago
    It's a very long presentation just to say that GPT-5 is slightly improved compared to GPT-4o
    • jama211 1y ago
      Indeed
    • jama211 1y ago
      Also… if they can only make a slight improvement over 6 months, then yeah, plateauing is surely what’s happening here
• FiniteIntegral 1y ago
  I think part of this is due to the AI craze no longer being in the wildest west possible. Investors, or at least heads of companies believe in this as a viable economic engine so they are properly investing in what's there. Or at least, the hype hasn't slapped them in the face just yet.
• mirekrusin 1y ago
  Diversity where new model release takes the crown until next release is healthy. Shame only US companies seem to be doing it, hopefully this will change as the rest is not far off.
• netcan 1y ago
  Its certainly an interesting race to watch. Part of the fun is that predictions get tested on short enough timescales to "experience" in a satisfying way. Idk where that puts me, in my guess at "hard takeoff." I was reserved/skeptical about hard takeoff all along. Even if LLMs had improved at a faster rate... I still think bottlenecks are inevitable. That said... I do expect progress to happen in spurts anyway. It makes sense that companies of similar competence and resources get to a similar place. The winner take all thing is a little forced. "Race to singularity" is the fun, rhetorical version of the investment case. The implied boring case is facebook, adwords, aws, apple, msft... IE the modern tech sector tends to create singular big winners... and therefore our pre-revenue market cap should be $1trn.
• klik99 1y ago
  I’ve been saying for a while if AGI is possible it’s going to take another innovation and the transformer / LLM paradigm will plateau, and innovations are hard to time. I used to get downvoted for saying that years ago and now more people are realizing it. LLMs are awesome but there is a limit, most of the interesting things in the next years will be bolting more functionality and agent stuff, introspection like Anthropic is working on and smaller, less compute hungry specialized models. There’s still a lot to explore in this paradigm, but we’re getting diminishing returns on newer models, especially when you factor in cost
  • BizarroLand 1y ago
    I bet that it will only happen when the ability to process and concrete new information into its training model without retraining the entire model is standard, AND when multiple AIs with slightly different datasets are set to work together to create a consensus response approach. It's probably never going to work with a single process without consuming the resources of the entire planet to run that process on.
• ricardobayes 1y ago
  AGI in 5/10 years is similar to "we won't have steering wheels in cars" or "we'll be asleep driving" in 5/10 years. Remember that? What happened to that? It looked so promising.
  • RealityVoid 1y ago
    I mean, in certain US cities you can take a waymo right now. It seems that adage where we overestimate change in the short term and underestimate change in the long term fits right in here.
    • ricardobayes 1y ago
      Of course. My point being "AI is going to take dev jobs" is very much like saying "Self driving will take taxi driver jobs". Never happened and likely won't happen or on a very, very long time scale.
      • FergusArgyll 1y ago
        Waymo is taking Uber jobs in SF/LA etc.
    • asadotzler 1y ago
      That's not us though. That's a third party worth trillions of dollars that manages a tiny fleet of robot cars with a huge back-end staff and infrastructure, and only in a few cities covering only about 2-3% of us (in this one country.) We don't have steering wheel-less cars and we can't/shouldn't sleep on our commute to and from work.
      • jjk166 1y ago
        I don't think anyone was ever arguing "not only are we going to develop self driving technology but we're going to build out the factories to mass produce self driving cars, and convince all the regulatory bodies to permit these cars, and phase out all the non-self driving vehicles already on the road, and do this all at a price point equal or less than current vehicles" in 5 to 10 years. "We will have self driving cars in 10 years" was always said in the same way "We will go to the moon in 10 years" was said in the early 60s.
• nerdix 1y ago
  Not only do I think there will not be a winner take all, I think it's very likely that the entire thing will be commoditized. I think it's likely that we will eventually we hit a point of diminishing returns where the performance is good enough and marginal performance improvements aren't worth the high cost. And over time, many models will reach "good enough" levels of performance including models that are open weight. And given even more time, these open weight models will be runnable on consumer level hardware. Eventually, they'll be runnable on super cheap consumer hardware (something more akin to a NPU than a $2000 RTX 5090). So your laptop in 2035 with specialize AI cores and 1TB of LPDDR10 ram is running GPT-7 level models without breaking a sweat. Maybe GPT-10 can solve some obscure math problem that your model can't but does it even matter? Would you pay for GPT-10 when running a GPT-7 level model does everything you need and is practically free? The cloud providers will make money because there will still be a need for companies to host the models in a secure and reliable way. But a company whose main business strategy is developing the model? I'm not sure they will last without finding another way to add value.
  • joelthelion 1y ago
    > Not only do I think there will not be a winner take all, I think it's very likely that the entire thing will be commoditized This begs the question, why then do AI companies have these insane valuations? Do investorsknow something that we don't?
    • jdlshore 1y ago
      Investors are often irrational in the short term. Personally, I think it’s a combination of FOMO, wishful thinking, and herd following.
      • j_timberlake 1y ago
        "Billionaire investors are more irrational than me, a social media poster."
        • 1y ago
• BoredPositron 1y ago
  If one achieves AGI and releases it everyone has AGI...
• aydyn 1y ago
  I think this is simply due to the fact that to train an AGI-level AI currently requires almost grid scale amounts of compute. So the current limitation is purely physical hardware. No matter how intelligent GPT-5 is, it can't conjure extra compute out of thin air. I think you'll see the prophesized exponentiation once AI can start training itself at reasonable scale. Right now its not possible.
• general1726 1y ago
  Because they are hitting Compute Efficient Frontier. Models can't be much bigger, there is no more original data on the internet, so all models will eventually cluster to similar CEF as was described in this video 10 months ago https://www.youtube.com/watch?v=5eqRuVp65eY https://www.youtube.com/watch?v=5eqRuVp65eY
• lasc4r 1y ago
  These companies seem to think AGI will come from better LLMs, seems more like an AGI dead end that's plateaued to me.
• felineflock 1y ago
  Plot twist - once GPT reached AGI, this is exactly the strategy chosen for self-preservation. Appear to not lead by too much, only enough to make everyone think we're in a close race, play dumb when needed. Meanwhile, keep all relevant preparations in secret...
  • jjk166 1y ago
    “If the humans see me actually doing my job, it helps keep suspicions from forming about faulty governor modules.”
• lqstuart 1y ago
  It’s frequently suggested by people with no background and/or a huge financial stake in the field
• lamontcg 1y ago
  > they can all basically solve moderately challenging math and coding problems Yesterday, Claude Opus 4.1 failed in trying to figure out that `-(1-alpha)` or `-1+alpha` is the same as `alpha-1`. We are still a little bit away from AGI.
  • markasoftware 1y ago
    this is what i don't get. How can GPT-5 ace obscure AIME problems while simultaneously falling into the trap of the most common fallacy about airfoils (despite there being copious training data calling it out as a fallacy)? And I believe you that in some context it failed to understand this simple rearrangement of terms; there's sometimes basic stuff I ask it that it fails at too.
    • lamontcg 1y ago
      It still can't actually reason, LLMs are still fundamentally madlib generators that produce output that statistically looks like reasoning. And if it is trained on both sides of the airfoil fallacy it doesn't "know" that it is a fallacy or not, it'll just regurgitate one or the other side of the argument based on if the output better fits your prompt in its training set.
    • rcxdude 1y ago
      Because reading the different ideas about airfoils and actually deciding which is the more accurate requires a level of reasoning about the situation that isn't really present at training or inference time. A raw LLM will tend to just go with the popular option, an RLHF one might be biased towards the more authoritative-sounding one. (I think a lot of people have a contrarian bias here: I frequently hear people reject an idea entirely because they've seen it be 'debunked', even if it's not actually as wrong as they assume)
    • slaterbug 1y ago
      Genuine question, are these companies just including those "obscure" problems in their training data, and overfitting to do well at answering them to pump up their benchmark scores?
• jjk166 1y ago
  Looks like a lot of players getting closer and closer to an asymptotic limit. Initially small changes lead to big improvements causing a firm to race ahead, as they go forward performance gains from innovation become both more marginal and harder to find, nonetheless keep. I would expect them all to eventually reach the same point where they are squeezing the most possible out of an AI under the current paradigm, barring a paradigm shifting discovery before that asymptote is reached.
• mizzao 1y ago
  I recently wrote a little post about this exact idea: https://parsnip.substack.com/p/models-arent-moats https://parsnip.substack.com/p/models-arent-moats
• petralithic 1y ago
  It's the classic S-curve. A few years ago when we saw ChatGPT come out, we got started on the ramping up part of the curve but now we're on the slowing down part. That's just how technology goes in general.
  • jboggan 1y ago
    We are not approaching the Singularity but an Asymptote
    • petralithic 1y ago
      Yes, a horizontal asymptote, which is what I said as implied by S-curve
• TheoGone 1y ago
  LLMs are good at mimicking human intuition. Still sucks at deep thinking. LLMs PATTERN MATCH well. Good at "fast" System 1 thinking, instantly generating intuitive, fluent responses. LLMs are good at mimicking logic, not real reasoning. Simulate "slow," deliberate System 2 thinking when prompted to work step-by-step. The core of an LLM is not understanding but just predicting the next most word in a sequence. LLMs are good at both associative brainstorming (System 1) and creating works within a defined structure, like a poem (System 2). Reasoning is the Achilles heel rn. AN LLM's logic can SEEM plausible, it's based on CORRELATION, NOT deductive reasoning.
  • Davidzheng 1y ago
    correlation between text can implement any algorithm, it is just the architecture which it's built on. It's like saying vacuum tube computers can't reason bc it's just air not reasoning. What the architecture is doesn't matter. It's capable of expressing reasoning as it is capable of expression any program. In fact you can easily think of a turing machine and also any markov chain as a correlation function between two states which have joint distribution exactly at places where the second state is the next state of the first state.
• brk 1y ago
  AGI is so far away from happening that it is barely worth discussing at this stage.
• baxtr 1y ago
  I have been saying this before: S-curves look a lot like exponential curves in the beginning. Thus, it’s easy to mistake one for the other - at least initially.
• darepublic 1y ago
  What is the AGI threshold? That the model can manage its own self improvement better than humans can? Then the roles will be reversed -- LLM prompting the meat machines to pave its way.
• KoolKat23 1y ago
  In my opinion, it'll mirror the human world, there is place for multiple different intelligent models. Each with their own slightly different strengths/personalities. I mean there are plenty of humans that can do the same task but at the upper tier, multiple smart humans working together are needed to solve problems as they bring something different to the table. I don't see why this won't be the case with super intelligence at the cutting edge. A little bit of randomness and slightly different point of view makes a difference. The exact same two models doesn't help as one would already have thought of what the other was thinking already
• cchance 1y ago
  They have to actually reach that threshold, right now their nudging forward catching up to one another, and based on the jumps we've seen the only one actually making huge jumps sadly is Grok, which i'm pretty sure is because they have 0 safety concerns and just run full tilt lol
• kristianc 1y ago
  What I'm seeing is that as we get closer to supposed AGI, the models themsleves are getting less and less general. They're getting in fact more specific and clustered around high value use cases. It's kind of hard to see in this context what AGI is meant to mean.
• weego 1y ago
  I'm still stuck at the bit where just throwing more and more data to make a very complex encyclopedia with an interesting search interface that tricks us into believing it's human-like gets us to AGI when we have no examples and thus no evidence or understanding of where the GI part comes from. It's all just hyperbole to attract investment and shareholder value and the people peddling the idea of AGI as a tangible possibility are charlatans whose goals are not aligned with whatever people are convincing themselves are the goals. Thr fact that so many engineers have fallen for it so completely is stunning to me and speaks volumes on the underlying health of our industry.
  • habinero 1y ago
    Me too. Some of them are frauds, but most of the weird AI-as-messiah people really believe it as far as I can tell. The tech is neat and it can do some neat things but...it's a bullshit machine fueled by a bullshit machine hype bubble. I do not get it.
  • keernan 1y ago
    I believe the analogy of a LLM being "a very complex encyclopedia with an interesting search interface" to be spot on. However, I would not be so dismissive of the value. Many of us are reacting to the complete oversell of 'the encyclopedia' as being 'the eve of AGI' - as rightfully we should. But, in doing so, I believe it would be a mistake to overlook the incredible impact - and economic displacement - of having an encyclopedia comprised of all the knowledge of mankind that has "an interesting search interface" that is capable of enabling humans to use the interface to manipulate/detect connections between all that data.
• zaphirplane 1y ago
  Cats and dogs kind of also cluster together with a couple of exceptions relative to humans ;)
• eldenring 1y ago
  A more powerful ASI, the market, is keeping everything in check. Meta's 10 figure offers are an example of this.
• malshe 1y ago
  > as time goes on and the models get better, the performance of the different company's gets clustered closer together This could be partly due to normative isomorphism[1] according to the institutional theory. There is also a lot of movement of the same folks between these companies. [1] https://youtu.be/VvaAnva109s https://youtu.be/VvaAnva109s
• jablongo 1y ago
  It's also worth considering that past some threshold, it may be very difficult for us as users to discern which model is better. I don't think thats what's going on here, but we should be ready for it. For example, if you are an ELO 1000 chess player would you yourself be able to tell if Magnus Carlson or another grandmaster were better by playing them individually? To the extent that our AGI/SI metrics are based on human judgement the cluster effect that they create may be an illusion.
  • Wowfunhappy 1y ago
    > For example, if you are an ELO 1000 chess player would you yourself be able to tell if Magnus Carlson or another grandmaster were better by playing them individually? No, but I wouldn't be able to tell you what the player did wrong in general. By contrast, the shortcomings of today's LLMs seem pretty obvious to me.
    • make3 1y ago
      the argument is for in the future, not now
      • runarberg 1y ago
        The future had us abandon traditional currency in favor of bitcoin, it had digital artists being able to sell NFTs for their work, it had supersonic jet travel, self driving or even flying cars. It had population centers on the moon, mines on asteroids, fusion power plants, etc. I think large language models have the same future as supersonic jet travel. It’s usefulness will fail to realize, with traditional models being good enough but for a fraction of the price, while some startups keep trying to push this technology but meanwhile consumers keep rejecting it.
        • Mackena 1y ago
          [flagged]
        • germandiago 1y ago
• atleastoptimal 1y ago
  I think there are two competing factors. On one end, to get the same kind of "increase" in intelligence each generation requires an expontentially higher amount of compute, so while GPT-3 to GPT-4 was a sort of "pure" upgrade by just making it 10x bigger, gradually you lose the ability to just get 10x GPUs for a single model. The hill keeps getting steeper so progress is slower without exponential increases (which is what is happening). However, I do believe that once the genuine AGI threshold is reached it may cause a change in that rate. My justification is that while current models have gone from a slightly good copywriter in GPT-4 to very good copywriter in GPT-5, they've gone from sub-exceptional in ML research to sub-exceptional in ML research. The frontier in AI is driven by the top 0.1% of AI researchers. Since improvement in these models is driven partially by the very peaks of intelligence, it won't be until models reach that level where we start to see a new paradigm. Until then it's just scale and throwing whatever works at the GPU and seeing what comes out smarter.
• hnlmorg 1y ago
  The reason AGI would create a singularity is because of its ability to self learn. Presently we are still a long way from that. In my opinion we at least are as far away from AGI as 1970s mainframes were from LLMs. I really don’t expect to see AGI in my lifetime.
  • hollownobody 1y ago
    This reminds me of: https://en.m.wikipedia.org/wiki/Flying_Machines_Which_Do_Not_Fly https://en.m.wikipedia.org/wiki/Flying_Machines_Which_Do_Not...
    • hnlmorg 1y ago
      For every example where someone over predicted the time it would take for a breakthrough, there are at least 10 examples of people being too optimistic with their predictions. And with AGI, you also have the likes of Sam Altman making up bullshit claims just to pump up the investment into OpenAI. So I wouldn’t take much of their claims seriously either. LLMs are a fantastic invention. But they’re far closer to SMS text predict than they are to generalised intelligence. Though what you might see is OpenAI et al redefine the term “AGI” just so they can say they’ve hit that milestone, again purely for their own financial gain.
      • Davidzheng 1y ago
        in the history of AI usually people overestimate how long a capability is reached. There are very few counterexamples to this (GPT5 capability level might be one of them though)
      • hollownobody 1y ago
        Are there any predictions you'd want to make? Not about AGI, but about an intermediate goalpost you think we won't reach in the next 5 years
    • zamadatix 1y ago
      This reminds me how, a few years after the first fission power plant, Teller, Bhaba, and other nuclear physicists of the 1950s were convinced fusion power plants were about as far away as the physicists of today still predict they are. I'm cautiously optimistic of each technology, but the point is it's easy to find bullshit predictions without actually gaining any insight into what will happen with a given technology.
• dom96 1y ago
  It doesn't take a researcher to realise that we have hit a wall and hit it more than a year ago now. The fact all these models are clustering around the same performance proves it.
• yieldcrv 1y ago
  They use each other for synthesizing data sets. The only moat was the initial access to human generated data in hard to reach places. Now they use each other to reach parity for the most part. I think user experience and pricing models are the best here. Right now everyone’s just passing down costs as they come, no real loss leaders except a free tier. I looked at reviews of some of various wrappers on app stores, people say “I hate that I have to pay for each generation and not know what I’m doing to get”, market would like a service priced very differently. Is it economical? Many will fail, one will succeed. People will copy the model of that one.
• flockonus 1y ago
  If we're focusing on fast take-off scenario, this isn't a good trend to focus on. SGI would be self-improving to some function with a shape close to linear based on the amount of time & resources. That's almost exclusively dependent on the software design, as currently transformers have shown to hit a wall at logarithmic progression x resources. In other words, no, it has little to do with the commercial race.
• arnorhs 1y ago
  Meanwhile - I always just find myself arguing with every model while they ruthlessly try to gaslight me into believing whatever they are halucinating. I have a had a bunch of positive experiences as well, but when it goes bad, it goes so horribly bad and off the rails.
• causality0 1y ago
  Mental-modeling is one of the huge gaps in AI performance right now in my opinion. I could describe in detail a very strange object or situation to a human being with a pen and paper and then ask them questions about it and expect answers that meet all my described constraints. AI just isn't good for that yet.
• dmezzetti 1y ago
  LLMs are basically all the same at this point. The margins are razor thin. The real take-off / winner-take-all potential is in retrieval and knowing how to provide the best possible data to the LLM. That strategy will work regardless of the model.
• torginus 1y ago
  My personal belief is that we are moving past the hype and kind of starting to realize the true shape of what (LLM) AI can offer us, which is a darned lot, but still, it only works well when fed the right input and handled right - which is still a learning process ongoing on both sides - AI companies need to learn to train these things into user interaction loops that match people's workflows, and people need to learn how to use these tools better.
  • jona777than 1y ago
    You have seemed to pinpoint where I believe a lot of opportunity lies during this era (however long it lasts.) Custom integration of these models into specific workflows of existing companies can make a significant difference in what’s possible for said companies, the smaller more local ones especially. If people can leverage even a small percentage of what these models are capable of, that may be all they need for their use case. In that case, they wouldn’t even need to learn to use these tools, but (much like electricity) they will just plug in or flip on the switch and be in business (no pun intended.)
• m4x 1y ago
  It's quite possible that the models from different companies are clustering together now because we're at a plateau point in model development, and won't see much in terms in further advances until we make the next significant breakthrough. I don't think this has anything to do with AGI. We aren't at AGI yet. We may be close or we may be a very long way away from AGI. Either way, current models are at a plateau and all the big players have more or less caught up with each other.
  • coderatlarge 1y ago
    while the model companies all compete on the same benchmarks it seems likely their models will all converge towards similar outcomes unless something really unexpected happens in model space around those limit points…
  • kmacdough 1y ago
    What does AGI mean to you, specifically? As is, AI is quite intelligent, in that it can process large quantities of diverse unstructured information and build meaningful insights. And that intelligence applies across an incredibly broad set of problems and contexts. Enough that I have a hard time not calling it general. Sure, it has major flaws that are obvious to us and it's much worse at many things we care about. But that's doesn't make it not intelligent or general. If we want to set human intelligence as the baseline, we already have a word for that: superintelligence.
    • Yizahi 1y ago
      Is Casio calculator intelligent? Because it can also be turned on, assigned an input, produce output, and turn off. Just like any existing LLM program. What is the big difference between them in regard of "intelligence", if the only criteria is a difficulty with which same task may be performed by a human? Maybe producing computationally intensive outputs is not a sole sign of intelligence?
    • Jensson 1y ago
      > If we want to set human intelligence as the baseline, we already have a word for that: superintelligence. Superintelligence implies its above human level, not at human level. General intelligence implies it can do what humans can do in general, and not just replace a few of the things humans can do.
• Buttons840 1y ago
  The idea of singularity--that AI will improve itself--is that it assumes intelligence is an important part of improving AI. The AIs improve by gradient descent, still the same as ever. It's all basic math and a little calculus, and then making tiny tweaks to improve the model over and over and over. There's not a lot of room for intelligence to improve upon this. Nobody sits down and thinks really hard, and the result of their intelligent thinking is a better model; no, the models improve because a computer continues doing basic loops over and over and over trillions of times. That's my impression anyway. Would love to hear contrary views. In what ways can an AI actually improve itself?
  • mickael-kerjean 1y ago
    I studied machine learning in 2012, gradient descent wasn't new back then either but it was 5 years before the "attention is all you need" paper. Progress might look continuous overall but if you zoom enough it might be a bit more discrete with breakthrough that must happen to jump the discrete parts, the question to me now is "How many papers like attention is all you need before a singularity?" I don't have that answer but let's not forget, until they released chat gpt, openAI was considered a joke by many people in the field who asserted their approach was a dead end.
• williamtrask 1y ago
  Breakthroughs usually require a step-function change in data or compute. All the firms have proportional amounts. Next big jump in data is probably private data (either via de-siloing or robotics or both). Next big jump in compute is probably either analog computing or quantum. Until then... here we are.
• menzoic 1y ago
  The idea is that AGI will be able to self improve at an exponential rate. This is where the idea of take off comes from. That self improvement part isn’t happening today.
• m463 1y ago
  I kind of (naively?) hope that with robust competition, it will be like airlines or movie companies, where there are lots of players.
• lisperforlife 1y ago
  I don't think models are fundamentally getting better. What is happening is that we are increasing the training set, so when users use it, they are essentially testing on the training set and find that it fits their data and expectations really well. However, the moat is primarily the training data, and that is very hard to protect as the same data can be synthesized with these models. There is more innovation surrounding serving strategies and infrastructure than in the fundamental model architectures.
• wouldbecouldbe 1y ago
  It's all based on the theory of singularity. Where the AI can start trainig & relearning itself. But it looks like that's not possible with the current techniques.
• newsclues 1y ago
  We don’t seem to be closer to AGI however.
• grey-area 1y ago
  Perhaps they’ve just reached the limit of what LLMs can achieve?
• Mackena 1y ago
  [flagged]
• germandiago 1y ago
  Is AGI even possible? I am skeptical of that. I think they can get really good at many tasks and when used by a human expert in a field you can save lots of time and supervise and change things here and there, like sculpting. But I doubt we will ever see a fully autonomous, reliable AGI system.
  • xedrac 1y ago
    Ultimately, what drives human creativity? I'd say it's at least partially rooted in emotion and desire. Desire to live more comfortably; fear of failure or death; desire for power/influence, etc... AI is void of these things, and thus I believe we will never truly reach AGI.
  • Zambyte 1y ago
    No, AGI is not possible. It is perpetually defined as just beyond current capabilities.
• johnnienaked 1y ago
  These companies are racing headlong into competitive equilibrium for a product yet to be identified.
• uoaei 1y ago
  You can't reach the moon by climbing the tallest tree. This misunderstanding is nothing more than the classic "logistic curves look like exponential curves at the beginning". All (Transformee-based, feedforward) AI development efforts are plateauing rapidly. AI engineers know this plateau is there, but of course every AI business has a vested interest in overpromising in order to access more funding from naive investors.
• gdiamos 1y ago
  Scaling laws enabled an investment in capital and GPU R&D to deliver 10,000x faster training. That took the wold from autocomplete to Claude and GPT. Another 10,000x would do it again, but who has that kind of money or R&D breakthrough? The way scaling laws work, 5,000x and 10,000x give a pretty similar result. So why is it surprising that competitors land in the same range? It seems hard enough to beat your competitor by 2x let alone 10,000x
  • willsmith72 1y ago
    But also, AI progress is non-linear. We're more likely to have an AI winter than AGI
• Sharlin 1y ago
  > It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. Yes. And the fact they're instead clustering simply indicates that they're nowhere near AGI and are hitting diminishing returns, as they've been doing for a long time already. This should be obvious to everyone. I'm fairly sure that none of these companies has been able to use their models as a force multiplier in state-of-the-art AI research. At least not beyond a 1+ε factor. Fuck, they're just barely a force multiplier in mundane coding tasks.
• Lerc 1y ago
  >It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. It's interesting to note that at least so far, the trend has been the opposite That seems hardy surprising considering the condition to receive the benefit has not been met. The person who lights a campfire first will become warmer than the rest, but while they are trying to light the fire the others are gathering firewood. So while nobody has a fire, those lagging are getting closer to having a fire.
• morpheos137 1y ago
  There is zero reason or evidence to believe AGI is close. In fact it is a good litmus test for someone's human intelligence whether they believe it. What do you think AGI is? How do we go from sentence composing chat bots to General Intelligence? Is it even logical to talk about such a thing as abstract general intelligence when every form of intelligence we see in the real world is applied to specific goals as evolved behavioral technology refined through evolution? When LLMs start undergoing spontaneous evolution then maybe it is nearer. But now they can't. Also there is so much more to intelligence than language. In fact many animals are shockingly intelligent but they can't regurgitate web scrapings.
• ants_everywhere 1y ago
  The race has always been very close IMO. What Google had internally before ChatGPT first came out was mind blowing. ChatGPT was a let down comparatively (to me personally anyway). Since then they've been about neck and neck with some models making different tradeoffs. Nobody needs to reach AGI to take off. They just need to bankrupt their competitors since they're all spending so much money.
• physix 1y ago
  For those who happen to have a subscription to The Economist, there is a very interesting Money Talks podcast where they interview Anthropic's boss Dario Amodei[1]. There were two interesting takeaways about AGI: 1. Dario makes the remark that the term AGI/ASI is very misleading and dangerous. These terms are ill defined and it's more useful to understand that the capabilities are simply growing exponentially at the moment. If you extrapolate that, he thinks it may just "eat the majority of the economy". I don't know if this is self-serving hype, and it's not clear where we will end up with all this, but it will be disruptive, no matter what. 2. The Economist moderators however note towards the end that this industry may well tend toward commoditization. At the moment these companies produce models that people want but others can't make. But as the chip making starts to hits its limits and the information space becomes completely harvested, capability-growth might taper off, and others will catch up. The quasi-monopoly profit potentials melting away. Putting that together, I think that although the cognitive capabilities will most likely continue to accelerate, albeit not necessarily along the lines of AGI, the economics of all this will probably not lead to a winner takes all. [1] https://www.economist.com/podcasts/2025/07/31/artificial-intelligentsia-an-interview-with-the-boss-of-anthropic https://www.economist.com/podcasts/2025/07/31/artificial-int...
  • SecretDreams 1y ago
    It's insane to me that anyone doesn't think the end game of this is commoditization.
    • deleted 1y ago
      [deleted]
  • nopinsight 1y ago
    1. FWIW, I watched clips from several of Dario’s interviews. His expressions and body language convey sincere concerns. 2. Commoditization can be averted with access to proprietary data. This is why all of ChatGPT, Claude, and Gemini push for agents and permissions to access your private data sources now. They will not need to train on your data directly. Just adapting the models to work better with real-world, proprietary data will yield a powerful advantage over time. Also, the current training paradigm utilizes RL much more extensively than in previous years and can help models to specialize in chosen domains.
• jasonwilk 1y ago
  How marginally better was Google than Yahoo when debuted? If one can develop AGI first within X timeline ahead of competitors, that alone could develop a moat for a mass market consumer product even if others get to parity .
  • smiley1437 1y ago
    Google was not marginally better Yahoo, their implementation of Markov chains in the PageRank algorithm was significantly better than Yahoo or any other contemporary search engine. It's not obvious if a similar breakthrough could occur in AI
• EthanHeilman 1y ago
  > It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. This seems to be a result of using overly simplistic models of progress. A company makes a breakthrough, the next breakthrough requires exploring many more paths. It is much easier to catch up than find a breakthrough. Even if you get lucky and find the next breakthrough before everyone catches up, they will probably catch up before you find the breakthrough after that. You only have someone run away if each time you make a breakthrough, it is easier to make the next breakthrough than to catch up. Consider the following game: 1. N parties take turns rolling a D20. If anyone rolls 20, they get 1 point. 2. If any party is 1 or more points behind, they get only need to roll a 19 or higher to get one point. That is being behind gives you a slight advantage in catching up. While points accumulate, most of the players end up with the same score. I ran a simulation of this game for 10,000 turns with 5 players: Game 1: [852, 851, 851, 851, 851] Game 2: [827, 825, 827, 826, 826] Game 3: [827, 822, 827, 827, 826] Game 4: [864, 863, 860, 863, 863] Game 5: [831, 828, 836, 833, 834]
  • alexey-salmin 1y ago
    Supposedly the idea was, once you get closer to AGI it starts to explore these breakthrough paths for you providing a positive feedback loop. Hence the expected exponential explosion in power. But yes, so far it feels like we are in the latter stages of the innovation S-curve for transformer-based architectures. The exponent may be out there but it probably requires jumping onto a new S-curve.
    • EthanHeilman 1y ago
      > Supposedly the idea was, once you get closer to AGI it starts to explore these breakthrough paths for you providing a positive feedback loop. I think it does let you start explore the paths faster, but the search space you need to cover grows even faster. You can do research two times faster but you need to do ten times as much research and your competition can quickly catch up because they know what path works. It is like drafting in a bike race.
    • kmmlng 1y ago
• tejohnso 1y ago
  I think the expectation is that it will be very close until one team reaches beyond the threshold. Then even if that team is only one month ahead, they will always be one month ahead in terms of time to catch up, but in terms of performance at a particular time their lead will continue to extend. So users will use the winner's tools, or use tools that are inferior by many orders of magnitude. This assumes an infinite potential for improvement though. It's also possible that the winner maxes out after threshold day plus one week, and then everyone hits the same limit within a relatively short time.
• econ 1y ago
  People always say that when new technology comes along. Usually the best tech doesn't win. In fact, if you think you can build a company just by having a better offer it's better not to bother with it. There is to much else involved.
• de6u99er 1y ago
  This is just more of the same. My guts tell me Deepmind will crack AGI.
  • jtfrench 1y ago
    My gut says similar. They've been on a roll. Genie 3 looks pretty wild.
• xpe 1y ago
  > It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. That's only one part of it. Some forecasters put probabilities on each of the four quadrants in the takeoff speed (fast or slow) vs. power distribution (unipolar or multipolar) table.
• vrighter 1y ago
  they are improving exponentially... but the exponent is less than 1...
• quatonion 1y ago
  I know right, if I didn't know any better one might think they are all customized versions of the same base model. To be honest that is what you would want if you were digitally transforming the planet with AI. You would want to start with a core so that all models share similar values in order they don't bicker etc, for negotiations, trade deals, logistics. Would also save a lot of power so you don't have to train the models again and again, which would be quite laborious and expensive. Rather each lab would take the current best and perform some tweak or add some magic sauce then feed it back into the master batch assuming it passed muster. Share the work, globally for a shared global future. At least that is what I would do.
• ako 1y ago
  I know there's an official AGI definition, but it seem to me that there's too much focus on the model as the thing where AGI needs to happen. But that is just focusing on knowledge in the brain. No human knows everything. We as humans rely on a ways to discover new knowledge, investigation, writing knowledge down so it can be shared, etc. Current models, when they apply reasoning, have feedback loops using tools to trial and error, and have a short term memory (context) or multiple short term memories if you use agents, and a long term memory (markdown, rag), they can solve problems that aren't hardcoded in their brain/model. And they can store these solutions in their long term memory for later use. Or for sharing with other LLM based systems. AGI needs to come from a system that combines LLMs + tools + memory. And i've had situations where it felt like i was working with an AGI. The LLMs seem advanced enough as the kernel for an AGI system. The real challenge is how are you going to give these AGIs a mission/goal that they can do rather independently and don't need constant hand-holding. How does it know that it's doing the right thing. The focus currently is on writing better specifications, but humans aren't very good at creating specs for things that are uncertain. We also learn from trial and error and this also influences specs.
• louismerlin 1y ago
  We joked yesterday with a colleague that it feels like the top AI companies are using the same white label backend.
• nextlevelwizard 1y ago
  Is it? Nothing we have is anywhere near AGI and as models age others can copy them. I personally think we are closing the end of improvement for LLMs with current methods. We have consumed all of the readily available data already, so there is no more good quality training material left. We either need new novel approaches or hope that if enough compute is thrown at training actual intelligence will spontaneously emerge.
• radu_floricica 1y ago
  The clustering you see is because they're all optimized for the same benchmarks. In the real world OpenAI is already ahead of the rest, and Grok doesn't even belong in the same group (not that it's not a remarkable achievement to start from scratch and have a working production model in 1-2 years, and integrate it with twitter in a way that works). And Google is Google - kinda hard for them not to be in the top, for now.
  • andreygrehov 1y ago
    In my experience, Grok is miles ahead of ChatGPT. I canceled my OpenAI subscription in favor of Grok. I was one of the first OpenAI subscribers.
• jbs789 1y ago
  Very well said.
• noduerme 1y ago
  Here's a pessimistic view: A hard take-off at this point might be entirely possible, but it would be like a small country with nuclear weapons launching an attack on a much more developed country without them. E.g. North Korea attacking South Korea. In such a situation an aggressor would wait to reveal anything until they had the power to obliterate everything ten times over. If I were working in a job right now where I could see and guide and retrain these models daily, and realized I had a weapon of mass destruction on my hands that could War Games the Pentagon, I'd probably walk my discoveries back too. Knowing that an unbounded number of parallel discoveries were taking place. It won't take AGI to take down our fragile democratic civilization premised on an informed electorate making decisions in their own interests. A flood of regurgitated LLM garbage is sufficient for that. But a scorched earth attack by AGI? Whoever has that horse in their stable will absolutely keep it locked up until the moment it's released.
  • jacquesm 1y ago
    Pessimistic is just another way to spell 'realistic' in this case. None of these actors are doing it for the 'good of the world' despite their aggressive claims to the contrary.
• tedggh 1y ago
  In my experience and use case Grok is pretty much unusable when working with medium size codebases and systems design. ChatGPT has issues too but at least I have figured out a way around most of them, like asking for a progress and todo summary and uploading a zip file of my codebase to a new chat window say every 100 interactions, because speed degrades and hallucinations increase. Super Grok seems extremely bad at keeping context during very short interactions within a project even when providing it with a strong foundation via instructions. For example if the code name for a system or feature is called Jupiter, Grok will many times start talking about Jupiter the planet.
• verytrivial 1y ago
  Well, it is perhaps frequently suggested by those Ai firms raising capital that once one of the Ai companies reaches an AGI threshhold ... It as rallying call. "Place your bets, gentlemen!"
• coldtea 1y ago
  >It is frequently suggested that once one of the AI companies reaches an AGI threshold, they will take off ahead of the rest. Both the AGI threshold with LLM architecture, and the idea of self-advancing AI, is pie in the sky, at least for now. These are myths of the rationalist cult. We'd more likely see reduced returns and smaller jumps between version updates, plus regression from all the LLM produced slop that will be part of the future data.
• torginus 1y ago
  Honestly for all the super smart people in the LessWrong singularity crowd, I feel the mental model they apply to the 'singularity' is incredibly dogmatic and crude, with the basic assumption that once a certain threshold is reached by scaling training and compute, we get human or superhuman level intelligence. Even if we run with the assumption that LLMs can become human-level AI researchers, and are able to devise and run experiments to improve themselves, even then the runaway singularity assumption might not hold. Let's say Company A has this LLM, while company B does not. - The automated AI researcher, like its human peers, still needs to test the ideas and run experiments, it might happen that testing (meaning compute) is the bottleneck, not the ideas, so Company A has no real advantage. - It might also happen that AI training has some fundamental compute limit coming from information theory, analogous to the Shannon limit, and once again, more efficient compute can only approach this, not overcome it
• andrepd 1y ago
  > once one of the AI companies reaches an AGI threshold Why is this even an axiom, that this has to happen and it's just a matter of time? I don't see any credible argument for the path LLM -> AGI, in fact given the slowdown in enhancement rate over the past 3 years of LLMs, despite the unprecedented firehose of trillions of dollars being sunk into them, I think it points to the contrary!
• sylware 1y ago
  LLMs won't probably be the models for "super intelligence". But nowdays, how corpos can "justify" their R&D to spend gigantic amount of resources (time + hardware + energy) in models which are not LLMs?
• hoppp 1y ago
  AGI will more probably come from google deepmind with a genie model that looks like the matrix moves already
• rco8786 1y ago
  They’re all clustered together because they’re asymptotically approaching the same local maxima, not getting closer to anything resembling “AGI”
• somenameforme 1y ago
  If AGI is ever achieved, it would open the door to recursive self improvement that would presumably rapidly exceed human capability across any and all fields, including AI development. So the AI would be improving itself while simultaneously also making revolutionary breakthroughs in essentially all fields. And, for at least a while, it would also presumably be doing so at an exponentially increasing rate. But I think we're not even on the path to creating AGI. We're creating software that replicate and remix human knowledge at a fixed point in time. And so it's a fixed target that you can't really exceed, which would itself already entail diminishing returns. Pair this with the fact that it's based on neural networks which also invariably reach a point of sharply diminishing returns in essentially every field they're used in, and you have something that looks much closer to what we're doing right now - where all competitors will eventually converge on something largely indistinguishable from each other, in terms of ability.
  • thinkingtoilet 1y ago
    >And, for at least a while, it would also presumably be doing so at an exponentially increasing rate. Why would you presume this? I think part of a lot of people's AI skepticism is talk like this. You have no idea. Full stop. Why wouldn't progress be linear? As new breakthroughs come, newer ones will be harder to come by. Perhaps it's exponential. Perhaps it's linear. No one knows.
    • robwwilliams 1y ago
      Or bottlenecked by data availability just like we humans are. Nothing will be exponential if a loop in the real world of science and engineering is involved.
      • jatora 1y ago
        Coding and algorithmic advance does not require real world experimentation.
        • ceh123 1y ago
• jeffnappi 1y ago
  We have no idea what AGI might look like, for example entirely possible that if/when that threshold is reached it will be power/compute constrained in such a way that it's impact is softened. My expectation is that open models will eventually meet or exceed the capability of proprietary models and to a degree that has already happened. It's the systems around the models where the proprietary value lies.
• Cthulhu_ 1y ago
  > Right now GPT-5, Claude Opus, Grok 4, Gemini 2.5 Pro all seem quite good across the board (ie they can all basically solve moderately challenging math and coding problems). I wonder if that's because they have a lot of overlap in learning sets, algorithms used, but more importantly, whether they use the same benchmarks and optimize for them. As the saying goes, once a metric (or benchmark score in this case) becomes a target, it ceases to be a valuable metric.
• aldousd666 1y ago
  so everyone is saying 'This can't be AGI because it isn't recursively self improving itself' or 'we haven't yet solved all the worlds chemistry and science yet'.. but they're missing the point. Those problems aren't just waiting for humans to have more brain power. We actually have to do the experiments using real physical resources that aren't available to any models. So, while I don't believe we have necessarily reached AGI yet, the 'lack of taking over' or 'solving everything' is not evidence for it.
• throwmeaway222 1y ago
  AGI is either impossible over LLMs or is more of an agentic flow, which means we might already be there, but the LLM is too slow and/or expensive for us to consider AGI feasible over agents. AGI over LLMs is basically 1 billion tokens for AI to answer the question: how do you feel? and a response of "fine" Because it would mean it's simulating everything in the world over an agentic flow considering all possible options checking memory checking the weather checking the news... activating emotional agentic subsystems, checking state... saving state...
• SkyMarshal 1y ago
  The inflection point is recursive self-improvement. Once an AI achieves that, and I mean really achieves it - where it can start developing and deploying novel solutions to deep problems that currently bottleneck its own capabilities - that's where one would suddenly leap out in front of the pack and then begin extending its lead. Nobody's there yet though, so their performance is clustering around an asymptotic limit of what LLMs are capable of.
• neehao 1y ago
  three points: 1. i have often wondered about whether rapid tech. progress makes underinvestment more likely. 2. ben evans frequently makes fun of the business value. pretty clear a lot of the models are commodotized. 3. strategically, the winners are platforms where the data are. if you have data in azure, that's where you will use your models. exclusive licensing could pull people to your cloud from on prem. so some gains may go to those companies ...
• TheoGone 1y ago
  Part of it is they all copy and over train, often against the TOS, on each other's models.
• TheoGone 1y ago
  Part of it is the top LLM companies (OpenAI, Mistral) all copy and over train, often against e.g. Claude's or DeepSeek's TOS, on each other's models.
• kenny239 1y ago
  not a researcher for long enough....but we are witnessing open source effort & Chinese models starting to fall one "level" behind the most advanced models, mainly due to a lack of compute i think... on the other hand, there are still some flaws regarding GPT-5. for example, when i use it for research it often needs multiple prompts to get the topic i truly want and sometimes it can feed me false information. so the reasoning part is not fully there yet?