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I think that will happen. I think several things can be true at the same time: - AI Hype - AI Psychosis - AI keeps getting better and better until it can wor

I think that will happen. I think several things can be true at the same time: - AI Hype - AI Psychosis - AI keeps getting better and better until it can work around big AI slop code bases

bluefirebrand 5mo ago
> AI keeps getting better and better until it can work around big AI slop code bases The belief in this is a form of AI psychosis, I think. Maybe in the future but certainly no evidence of this anytime soon
- fooker 5mo ago
  > Maybe in the future but certainly no evidence of this anytime soon Here's some anecdotal evidence from me - I cleaned up multiple GPT 4.x era vibecoded projects recently with the latest claude model and integrated one of those into a fairly large open source codebase. This is something AI completely failed at last year. Maybe you should try something like this or listen to success stories before claiming 'certainly no evidence' in future?
- whimsicalism 5mo ago
  No evidence? Chatgpt came out 3 years ago. You basically just need to stick a ruler up on a curve
  - asveikau 5mo ago
    I'm no expert, but the skeptic's opinion I've heard would be to ask: What evidence is there that we're not at or close to a plateau of what LLMs are capable of? How do you know the growth rate from 2023 to present will continue into 2029? eg. Is it more training data? More GPUs? What if we're kind of reaching the limits of those things already?
    - literalAardvark 5mo ago
      Since we're not experts, we treat it as a black box. What are the results? Is the quality of the results improving? Is the improvement accelerating or decelerating? And the answer appears to be that the improvement is accelerating. So how could it be stopping? https://metr.org/time-horizons/ https://metr.org/time-horizons/
      - mbgerring 5mo ago
        I don’t think improvement is accelerating. We went from “computers can’t do these things at all” to “now they can” in a few years with the discovery of transformers, and now we get “it can do the same things, except incrementally better, at a drastically higher cost” every few months. I don’t think that the current AI paradigm has infinite headroom for improvement, similar to how every other AI approach before it eventually hit a limit.
        literalAardvark 5mo ago
        Incrementally, higher cost? A model I'm running on a 10 year old entry level computer is better at programming than GPT4. Those are multiple orders of magnitude of improvement in a few years. And the link I posted shows the amount of work a query can do increasing non linearly. You can explore the site for more detail and a graph that shows error rates getting halved every couple of months. No one said anything about infinite. It doesn't mean we don't have headroom to spare. Software itself took 80-120 years to get where it is today depending on how you count. Time is on AIs side here.
    - whimsicalism 5mo ago
      Ultimately, you are describing a fundamental problem with induction -- Hume's problem of induction to be specific. How can we know that anything that has been shown empirically in the past will continue to be true - we can't. Best to investigate mechanistically: I don't see why we would assume that we are at a plateau for RL. In many other settings, Go for instance, RL continues to scale until you reach compute limits. Some things are more easily RL'd than others, but ultimately this largely unlocks data. We are not yet compute/energy/physical world constrained. I think you would start observing clear changes in the world around you before that becomes a true bottleneck. Regardless, currently the vast majority of compute is used for inference not training so the compute overhang is large. Assuming that we plateau at {insert current moment} seems wishful and I've already had this conversation any number of times on this exact forum at every level of capability [3.5, 4, o1, o3, 4.6/5.5, mythos] from Nov 2022 onwards.
- ashdksnndck 5mo ago
  I have personally had success telling Claude that some AI-written system is too complicated and ask it to rewrite it in a more logical way. This sometimes results in thousands of lines of code being deleted. I give an instruction like that if I see certain red flags, eg: 1) same business logic implemented in two different places, with extra code to sync between them 2) fixing apparently simple bugs results in lots of new code being written It’s a sign I need to at least temporarily dedicate more effort to overseeing work in that area. I somewhat agree with the AI psychosis framing of the OP. It takes some taste and discipline to avoid letting things dissolve into complete slop.
- asveikau 5mo ago
  It's amusing to me that: * A belief that AI will keep getting better, presented without evidence, does not yield a lot of skepticism around these parts. * Your comment saying it is wrong to believe AI will keep getting better, also presented without evidence, is downvoted.
  - jacobjjacob 5mo ago
    AI is currently, actively getting better. That might stop in a year, and you can argue whether it’s linear or exponential, but it’s very difficult to argue that it won’t get better in the near term. On the other hand, arguing that we hit a plateau at this time is just ignoring reality.
    - asveikau 5mo ago
      This doesn't address anything I said in the above comment.
- gwerbin 5mo ago
  There are untold billions of dollars to be had if you can make this future come to pass. You don't need AGI to make it happen either. You just need to keep making the context windows bigger and keep coming up with updated training data. It's not the outcome I want, but it really does feel within reach. The only limiting factor is going to be token count and cost to process/generate those tokens. But if you don't particularly care about quality, costs are going to have to go up by several orders of magnitude before you start to regret firing your software engineers. I don't know what happens in a decade when there are no junior engineers, skilled senior engineers are becoming rare, and the only data left the train LLMs on is 200th-generation slop. But AI slop being qualitatively slop is not enough of a obstacle to prevent that future from coming to pass. And billions of dollars will be "saved" along the way.
  - johnnyanmac 5mo ago
    Companies are already putting billions out there just to secure and produce training data. And that's the isseue; spending X billions to make X-Y) billions isn't a profit, it's a gamble hoping Y becomes negative (or at least close to zero with a commodity that is profitable) . Real profits have not been made directly from the work on AI as of now. It's made from marketing a narrative of AI working. That's what makes this whole house of cards dangerous. The prescription to psychosis is profitable. Aka, selling a grift.
user34283 5mo ago
With GPT 5.4 or 5.5 I did not notice degradation in performance when it was working on a large 5k line file containing a WebView, JS scripts, as well as native UI. I instructed it to split it up anyway, yet I wonder how often the concerns around the mess are imaginative rather than practical.
- ansgri 5mo ago
  [dead]

I think that will happen. I think several things can be true at the same time: - AI Hype - AI Psychosis - AI keeps getting better and better until it can work around big AI slop code bases

bluefirebrand 5mo ago

> AI keeps getting better and better until it can work around big AI slop code bases The belief in this is a form of AI psychosis, I think. Maybe in the future but certainly no evidence of this anytime soon

fooker 5mo ago
> Maybe in the future but certainly no evidence of this anytime soon Here's some anecdotal evidence from me - I cleaned up multiple GPT 4.x era vibecoded projects recently with the latest claude model and integrated one of those into a fairly large open source codebase. This is something AI completely failed at last year. Maybe you should try something like this or listen to success stories before claiming 'certainly no evidence' in future?
whimsicalism 5mo ago
No evidence? Chatgpt came out 3 years ago. You basically just need to stick a ruler up on a curve
- asveikau 5mo ago
  I'm no expert, but the skeptic's opinion I've heard would be to ask: What evidence is there that we're not at or close to a plateau of what LLMs are capable of? How do you know the growth rate from 2023 to present will continue into 2029? eg. Is it more training data? More GPUs? What if we're kind of reaching the limits of those things already?
  - literalAardvark 5mo ago
    Since we're not experts, we treat it as a black box. What are the results? Is the quality of the results improving? Is the improvement accelerating or decelerating? And the answer appears to be that the improvement is accelerating. So how could it be stopping? https://metr.org/time-horizons/ https://metr.org/time-horizons/
    - mbgerring 5mo ago
      I don’t think improvement is accelerating. We went from “computers can’t do these things at all” to “now they can” in a few years with the discovery of transformers, and now we get “it can do the same things, except incrementally better, at a drastically higher cost” every few months. I don’t think that the current AI paradigm has infinite headroom for improvement, similar to how every other AI approach before it eventually hit a limit.
      - literalAardvark 5mo ago
        Incrementally, higher cost? A model I'm running on a 10 year old entry level computer is better at programming than GPT4. Those are multiple orders of magnitude of improvement in a few years. And the link I posted shows the amount of work a query can do increasing non linearly. You can explore the site for more detail and a graph that shows error rates getting halved every couple of months. No one said anything about infinite. It doesn't mean we don't have headroom to spare. Software itself took 80-120 years to get where it is today depending on how you count. Time is on AIs side here.
  - whimsicalism 5mo ago
    Ultimately, you are describing a fundamental problem with induction -- Hume's problem of induction to be specific. How can we know that anything that has been shown empirically in the past will continue to be true - we can't. Best to investigate mechanistically: I don't see why we would assume that we are at a plateau for RL. In many other settings, Go for instance, RL continues to scale until you reach compute limits. Some things are more easily RL'd than others, but ultimately this largely unlocks data. We are not yet compute/energy/physical world constrained. I think you would start observing clear changes in the world around you before that becomes a true bottleneck. Regardless, currently the vast majority of compute is used for inference not training so the compute overhang is large. Assuming that we plateau at {insert current moment} seems wishful and I've already had this conversation any number of times on this exact forum at every level of capability [3.5, 4, o1, o3, 4.6/5.5, mythos] from Nov 2022 onwards.
ashdksnndck 5mo ago
I have personally had success telling Claude that some AI-written system is too complicated and ask it to rewrite it in a more logical way. This sometimes results in thousands of lines of code being deleted. I give an instruction like that if I see certain red flags, eg: 1) same business logic implemented in two different places, with extra code to sync between them 2) fixing apparently simple bugs results in lots of new code being written It’s a sign I need to at least temporarily dedicate more effort to overseeing work in that area. I somewhat agree with the AI psychosis framing of the OP. It takes some taste and discipline to avoid letting things dissolve into complete slop.
asveikau 5mo ago
It's amusing to me that: * A belief that AI will keep getting better, presented without evidence, does not yield a lot of skepticism around these parts. * Your comment saying it is wrong to believe AI will keep getting better, also presented without evidence, is downvoted.
- jacobjjacob 5mo ago
  AI is currently, actively getting better. That might stop in a year, and you can argue whether it’s linear or exponential, but it’s very difficult to argue that it won’t get better in the near term. On the other hand, arguing that we hit a plateau at this time is just ignoring reality.
  - asveikau 5mo ago
    This doesn't address anything I said in the above comment.
gwerbin 5mo ago
There are untold billions of dollars to be had if you can make this future come to pass. You don't need AGI to make it happen either. You just need to keep making the context windows bigger and keep coming up with updated training data. It's not the outcome I want, but it really does feel within reach. The only limiting factor is going to be token count and cost to process/generate those tokens. But if you don't particularly care about quality, costs are going to have to go up by several orders of magnitude before you start to regret firing your software engineers. I don't know what happens in a decade when there are no junior engineers, skilled senior engineers are becoming rare, and the only data left the train LLMs on is 200th-generation slop. But AI slop being qualitatively slop is not enough of a obstacle to prevent that future from coming to pass. And billions of dollars will be "saved" along the way.
- johnnyanmac 5mo ago
  Companies are already putting billions out there just to secure and produce training data. And that's the isseue; spending X billions to make X-Y) billions isn't a profit, it's a gamble hoping Y becomes negative (or at least close to zero with a commodity that is profitable) . Real profits have not been made directly from the work on AI as of now. It's made from marketing a narrative of AI working. That's what makes this whole house of cards dangerous. The prescription to psychosis is profitable. Aka, selling a grift.

user34283 5mo ago

With GPT 5.4 or 5.5 I did not notice degradation in performance when it was working on a large 5k line file containing a WebView, JS scripts, as well as native UI. I instructed it to split it up anyway, yet I wonder how often the concerns around the mess are imaginative rather than practical.

ansgri 5mo ago
[dead]