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> because their improvements are stagnating as they hit financial and technical challenges. I don't mean to be rude, but how is it even possible for someone to

• Zenul_Abidin 13d ago
  Basically, continuously building out compute is costing these labs enormous amounts of money, which VCs and other companies have been bankrolling in order for them to develop powerful models. So if the new models they can develop right now are less frontier, it could be a net loss on their balance sheets.
• tuvix 13d ago
  Why would someone hold the opposite opinion? It’s much more likely that our current approach to large language models for general use will eventually show diminishing improvements (even if you think it hasn’t yet), than the opposite situation where valuable improvements can be made forever. The threat of distillation and efficiency gains from competitors mean that providing value at the top end of the market is an existential necessity for these labs. I don’t think they can do that forever and, in my opinion, for the vast majority of use cases we’ve already reached very little improvement for new models when compared with available offerings.
  • simianwords 13d ago
    Sorry, this is ridiculous. OpenAI said that they have a step change in model performance. They proved it by solving a Millenium problem. HF incidents are public and vetted. If you still think there's a stall despite all evidence pointing to opposite, I don't know what to say..
    • vrganj 13d ago
      What reason could OpenAI possibly have to lie about their own performance? The fact they were desperate enough for a PR win they stole the research leading to the Millenium problem further hurts their case imo.
      • simianwords 13d ago
        they lied about their performance by.... solving a famously hard problem to solve? Edit: The "reputable academic" who accused OpenAI had to say this about LLMs and the recent result. source: https://cims.nyu.edu/~tristanb/statement.pdf https://cims.nyu.edu/~tristanb/statement.pdf > “the results are not the important thing.” > “the important thing is instead the significance that a mathematician and an LLM model can now do all this work in a month.” > “This is a Deep Blue-Kasparov moment.” > “incredibly important developments.” > “If indeed an OpenAI model did close the gap to Navier-Stokes, that is a remarkable thing and it should be said loudly, by them, with the history intact.” Clearly Buckmaster (who is probably one of the most accomplished academics in the field) himself doesn't believe that AI has stalled. What makes you think you are right?
        • vrganj 13d ago
          Solving is a very interesting way of describing what happened. Unless you believe them over reputable academics I suppose? I will note the remarkable goal post shift in your edit - giving direct counter evidence to your own earlier claim of an OpenAI proof - and leave it at that.
          • rieie 12d ago
            [dead]
        • mac-attack 12d ago
          We are approaching what is likely the biggest existential crisis in human history. Unfortunately, there will be people that refuse to tackle the issue head on and will instead narrow their focus on data that suggests that tomorrow will be like today.
      • snaking0776 13d ago
        Two things can be true. Open AI is a huge company. 1. It has people in it who are career obsessed and who are willing to ruthlessly go after any opportunity to improve their standing/stock valuation. Using a 2 week old model to snipe a millenium prize for PR is in line with that. 2. There are many researchers and even executives at the company who genuinely think we are speedrunning the end of the world. I don’t know anyone in this field who honestly argues that if we build ASI soon it doesn’t lead to extinction. This group of people can output warnings about the state of research and fears for the future while pushing for regulation out of genuine fear of what they’re building. I tend to agree with them. You can’t view these companies as a monolith. They’re actions won’t be consistent because it’s built of many people with conflicting beliefs. Please look at arguments regarding AI risk and the current pace of progress and value them as it relates to the argument itself, not who said it. We are in a dangerous place and no one is sure how quickly we’ll get to a bad spot.
    • deleted 13d ago
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    • tuvix 13d ago
      Solving millennium problems doesn’t pay the bills. Their best models might be quite good when directed at extremely difficult and very focused problems, but most business use cases are nothing like that. Edit: Not trying to imply these new frontier models aren’t also better at other things, just that there’s really no reason for most people to use them when cheaper alternatives exist that get the job done just as well.
    • threecheese 13d ago
      If you believe Yann LeCun and David Silver (and others), there's an architecture wall; maybe labs are starting to see this on the horizon. Maybe the DRAM supply constraints are forcing it. Are these real step changes - big picture wise, or refinements in RL/agentic orchestration/"taste" and advancements due to bigger models and hardware technology/capacity scaling? If they not, does this tactic - and hardware improvements - continue to scale non-linearly like they need to? It is clear that whatever does change in each model increment has resulted in meaningfully better end user capabilities (as well as regressions in some areas, honestly), but that doesn't prove anything. I'm not sure what I personally believe, but stating with your full chest that a stall is ridiculous ignores a lot of potential evidence to the contrary. Stupid example: Astra. Its main improvements are: much much better computer use and 3d modeling capabilities; better subagent orchestration; better and more reliable tool use; slightly worse coding. This looks to me, from a feature perspective, to be an incremental improvement across several functional areas, plus new features which are unquestionably excellent but are probably the result of RL focus, not magic. Step change? Ehhhh depends on how you squint. But how many more iterations of this do we have? Are we going to squeeze quintillion parameter transformers into GPUs?
• johnnienaked 13d ago
  How can you not believe it? Neither of them are even close to making a profit and the market is saturated with cheaper alternatives. This is a ploy for regulatory capture.
  • Footnote7341 13d ago
    You're wrong about profits.
    • quikoa 13d ago
      Are you sure you don't mean revenue? Given the enormous investments profits seem very unlikely.
    • johnnienaked 13d ago
      Anthropic's profit estimate they released a while ago was a masterclass in deceptive reporting. They didn't include training costs, stock based compensation, or liabilities. Token gross margin is not profit.
    • Insanity 12d ago
      They haven’t posted a profit in any quarter, and even IPO dates are slipping. They have revenue, not profit.
• simianwords 13d ago
  [flagged]
  • ofjcihen 13d ago
    I mean, more simply (and with less vitriol) - AI has yet to be profitable for these large labs (also zero moat etc.) - There is considerable hype and people are tired of it. - HF incident (and apparently many others that these labs are not reporting on or don’t even know about) proves they have terrible security practices and that’s about it. - NS is incredibly suspicious timing wise. You are sprinting around these threads to misrepresent the average persons issues with AI. I don’t know why people having those issues seems to be taken as a personal attack for you.
• dumberquestions 13d ago
  I don't think the technical aspect is true, performance has been growing fairly consistently, but the financial aspect, or more accurately the logistical aspect, is plausible; with competition as fierce as it currently is, for how long can they keep growing at the same pace? People can't afford RAM anymore and datacenter build outs aren't particularly popular, OAI has been doubling compute capacity every 7 months for the past 3 years, it's just physically impossible for this to go on for long, something has to give. And this is not to say that the tech isn't revolutionary or the demand isn't there, it's simply just currently too competitive, and to avoid getting into anti trust trouble they need to involve the government. Note this being a plausible doesn't necessarily make it true, for all I know they could have witnessed some safety incident and decided they need a pause, or maybe it's both reasons.
  • simianwords 13d ago
    I just find it strange how multiple people throw completely different arguments just to land on same conclusion. Your argument seems to mix a few arguments like: OpenAI has so much demand and it has made so much compute that it is a bad thing (?!). It can't go on (why?) hence the capabilities are stalling (how?). Then you also say that it is too competitive which is a totally different argument to compute. So in this thread we have multiple vectors of arguments like - high competition - high demand for compute (not sure how this hurts OpenAI but whatever) - capability has stalled
    • dumberquestions 13d ago
      I never mentioned capability stalling, my point is that if you extrapolate the same rate of growth just a few years into the future, you quickly reach logistical impracticality, I don't think It's hard to deduce that AI compute can't keep doubling every 6 months for long. The other part is that this is happening because the foundational model business is both extremely successful and competitive, if it wasn't as successful the market demand would've been saturated by now, and if it wasn't as competitive a single monopoly could've paced things more reasonably from the beginning.
    • webern777 12d ago
• freejazz 12d ago
  >Can you explain why you believe this? Well, as a starter, I've been told that AI will take my job for at least 3 years now. That's not happening. In fact, what's happening is that I now have an AI vendor BEGGING me to use their product, which of course, cannot do my job.