4 ms·

"LLM's Aren't Going to Fundamentally Change Software Development" Says Increasingly Nervous Man For Seventh Time This Year

slopinthebag 6mo ago
I didn't get the sense that the author is nervous. What I tend to see are people who are nervous that going all-in on LLM workflows might not have the payoff they are expecting, and are becoming increasingly fanatical as a result. Just one more harness bro. Just one more agentic swarm. Please bro, just one more Claude Max subscription. Please bro.
- aspenmartin 6mo ago
  You say this as though performance has not followed a very clear and extremely rapid improvement in a startlingly short amount of time. You’re definitely right that people adopt agentic workflows and are disappointed or worse, but the point is the disappointment has already reduced substantially and will continue to do so. We know this because we know the scaling laws, and also because learning theory has been around for many decades.
  - slopinthebag 6mo ago
    Yes but we don't know the shape of the curve and where we are on it.
    - aspenmartin 6mo ago
      See chinchilla scaling laws, we have the functional form of the curve and know the constants (though they change and are domain and model specific): L(N,D) ~= 1.69 + 406 / N^0.339 + 411 / D^0.285 L is loss (pre training test loss) D is the scale of the data N is the number of model parameters
      - slopinthebag 6mo ago
        You need to touch grass dude, seriously.
        aspenmartin 6mo ago
        Why deflect from the conversation and attempt to insult someone? What I’m saying is literally canonical and extremely well known literature.
  - paganel 6mo ago
    > very clear and extremely rapid improvement in a startlingly short amount of time. We're almost 6 months into all this AI-code madness and I've yet to see that "rapid improvement" you mention. As in software products that are genuinely better compared to 6 months ago, or new software products (and good software products at that) which would have not existed had this AI craze not happened.
    - handoflixue 6mo ago
      Can you name literally any other technology that had hundreds of millions of users within the first six months of being invented? Six months after the internet was invented, you could send email between a few universities. Six months after the computer was invented, they still hadn't actually built one. The first transcontinental railroad, took about six YEARS just to build.
      - nothinkjustai 6mo ago
        GPT did not have hundreds of millions of users when it was invented almost a decade ago…
        handoflixue 6mo ago
        If you want to move the goal posts, that's fine, acknowledge it: the original claim I'm responding to was "We're almost 6 months into all this AI-code madness" If you want to set GPT as the target, that's even easier! In that decade it has passed the Turing Test, solved novel open math problems, generates audio, video, and music, and can write coherent code. Again, there is no technology that has improved more rapidly than LLMs.
        nothinkjustai 6mo ago
        I’d say the internet did, since it literally connected people across the globe in real time, which actually provided the technology that allows LLMs and other similar tech to exist in the first place. I think it’s pretty clear the internet has had 10x the impact of LLMs so far. Maybe 100x
  - cyclopeanutopia 6mo ago
    Perhaps you are confusing performance with instability?
    - aspenmartin 6mo ago
      No. Time horizon I’m talking about spans years. “We don’t know” is just wrong, we’ve had scaling laws for many years and they continue to hold up. Benchmarks, in all their ugliness, tell a consistent story.
  - strange_quark 6mo ago
    What rapid improvement has occurred, because in this six month AI coding fever dream we've been living in, I really haven't seen anything new in awhile, both in terms of new ideas for AI coding or in new consumer products or services. I'll give you the coding harnesses themselves are better because that was a new product category with a lot of low-hanging fruit, but have the models actually improved in a way that isn't just benchmaxxing? I'd argue the models seem to be regressing. Even the most AI-pilled people at my company have all complained that Opus 4.7 is a dud. Anecdotally, GPT 5.5 seems decent, but it's rumored to be a 10T parameter model, isn't noticeably better than 5.4 or 5.3, is insanely expensive to use, and seems to be experiencing model collapse since the system prompt has to beg the thing to not talk about goblins and raccoons.
    - jatora 6mo ago
      Uninformed opinion of someone who clearly doesnt consistently use AI coding tools, clearly. And why are you limiting it to 6 months? Whats wrong with you?
      - zapataband1 6mo ago
        How many years of real-life, in-production problem solving/coding have you done? That's what I base how informed you are not how much you use your favorite new $100/month token-prediction subscription
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        jatora 6mo ago
        15 years. But that's irrelevant to this point. The person im replying to clearly doesnt use the tools if they think there hasnt been constant improvement. "token-prediction subscription" is funny, coming from a glorified biological token predictor
        zapataband1 6mo ago
  - leptons 6mo ago
    You say this as though AI company debt has not followed a very clear and extremely rapid ballooning in a startlingly short amount of time. It's the "YOLO" of business strategies.
    - aspenmartin 6mo ago
      Always amazes me how we’re on a platform with “ycombinator” in the url and people don’t understand how private companies scale to capture market share. You’re right Uber was that company that ran at a loss for so long and collapsed, another YOLO business strategy. Or maybe it was Amazon or…hmm I forget
      - leptons 6mo ago
        I can't afford to take Ubers anymore. A trip that used to cost $7 now costs $40. AI is going to be the same to cover all the massive amounts of money already spent. You like your $200/mo plan now? How about when it's $2000/mo?
        aspenmartin 6mo ago
        You are not at all wrong. Also get ready for the insidious advertising!
- atleastoptimal 6mo ago
  Complaining about every one off issue with LLM's ignores the bigger picture: they are getting better every month and there is no fundamental reason why they wouldn't surpass humans in coding. Everything else is secondary. All I would need from an LLM doubter is evidence that at tractable software engineering task LLM's are not improving. The strongest argument against the increasing general capabilities of LLM's are the ARC-AGI tasks, however the creators admit that each generation of LLM's exceed their expectations, and that AGI will be achieved within the decade.
  - wavemode 6mo ago
    Your logic is flawed because, a thing can improve for an infinite amount of time while never surpassing a certain limit. It's called an asymptote. That being said, I don't even think that arguing about this from a mathematical perspective is a worthwhile use of time. Calling something an asymptote in the first place requires defining a quantifiable "X" and "Y", which we don't even have. What we have are a bunch of synthetic benchmarks. Even ignoring the fact that the answers to the questions are known to regularly leak into the training data (in other words, it's possible for scores to increase while capabilities remain the same), there's also the fundamental fact that performance on benchmarks is not the same thing as performance in the real world. And being able to answer some arbitrary set of arbitrary questions on a benchmark which the previous model couldn't, does not have a quantifiable correlation to some specific amount of real-world improvement. The OP article focuses on research papers which assess real-world impact of LLMs within software organizations, which I think are more representative. I wouldn't call myself an "AI doubter" - I use LLMs every day. When you say "doubter" you're not referring to "AI" in general, or the fact that AI is helpful or boosts productivity (which I believe it does). You're rather referring to the very specific, very extraordinary claim, that LLMs will surpass humans in coding. If that's the case then yeah I'm a doubter, at least on any foreseeable timescale.
    - handoflixue 6mo ago
      > Your logic is flawed because, a thing can improve for an infinite amount of time while never surpassing a certain limit. It's called an asymptote. Have you ever once looked at a METR chart? https://files.civai.org/assets/METR_Chart.jpg https://files.civai.org/assets/METR_Chart.jpg That's not an asymptote. > there's also the fundamental fact that performance on benchmarks is not the same thing as performance in the real world Again, yes, you're correct in the general case but it has very little to do with the specific case. Would you find it convincing if I simply said "some internet arguments are wrong"? It's certainly a true statement, and you've made an internet argument here, so clearly you should accept that you're wrong, right?
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slopinthebag 6mo ago

I didn't get the sense that the author is nervous. What I tend to see are people who are nervous that going all-in on LLM workflows might not have the payoff they are expecting, and are becoming increasingly fanatical as a result. Just one more harness bro. Just one more agentic swarm. Please bro, just one more Claude Max subscription. Please bro.

aspenmartin 6mo ago
You say this as though performance has not followed a very clear and extremely rapid improvement in a startlingly short amount of time. You’re definitely right that people adopt agentic workflows and are disappointed or worse, but the point is the disappointment has already reduced substantially and will continue to do so. We know this because we know the scaling laws, and also because learning theory has been around for many decades.
- slopinthebag 6mo ago
  Yes but we don't know the shape of the curve and where we are on it.
  - aspenmartin 6mo ago
    See chinchilla scaling laws, we have the functional form of the curve and know the constants (though they change and are domain and model specific): L(N,D) ~= 1.69 + 406 / N^0.339 + 411 / D^0.285 L is loss (pre training test loss) D is the scale of the data N is the number of model parameters
    - slopinthebag 6mo ago
      You need to touch grass dude, seriously.
      - aspenmartin 6mo ago
        Why deflect from the conversation and attempt to insult someone? What I’m saying is literally canonical and extremely well known literature.
- paganel 6mo ago
  > very clear and extremely rapid improvement in a startlingly short amount of time. We're almost 6 months into all this AI-code madness and I've yet to see that "rapid improvement" you mention. As in software products that are genuinely better compared to 6 months ago, or new software products (and good software products at that) which would have not existed had this AI craze not happened.
  - handoflixue 6mo ago
    Can you name literally any other technology that had hundreds of millions of users within the first six months of being invented? Six months after the internet was invented, you could send email between a few universities. Six months after the computer was invented, they still hadn't actually built one. The first transcontinental railroad, took about six YEARS just to build.
    - nothinkjustai 6mo ago
      GPT did not have hundreds of millions of users when it was invented almost a decade ago…
      - handoflixue 6mo ago
        If you want to move the goal posts, that's fine, acknowledge it: the original claim I'm responding to was "We're almost 6 months into all this AI-code madness" If you want to set GPT as the target, that's even easier! In that decade it has passed the Turing Test, solved novel open math problems, generates audio, video, and music, and can write coherent code. Again, there is no technology that has improved more rapidly than LLMs.
        nothinkjustai 6mo ago
        I’d say the internet did, since it literally connected people across the globe in real time, which actually provided the technology that allows LLMs and other similar tech to exist in the first place. I think it’s pretty clear the internet has had 10x the impact of LLMs so far. Maybe 100x
- cyclopeanutopia 6mo ago
  Perhaps you are confusing performance with instability?
  - aspenmartin 6mo ago
    No. Time horizon I’m talking about spans years. “We don’t know” is just wrong, we’ve had scaling laws for many years and they continue to hold up. Benchmarks, in all their ugliness, tell a consistent story.
- strange_quark 6mo ago
  What rapid improvement has occurred, because in this six month AI coding fever dream we've been living in, I really haven't seen anything new in awhile, both in terms of new ideas for AI coding or in new consumer products or services. I'll give you the coding harnesses themselves are better because that was a new product category with a lot of low-hanging fruit, but have the models actually improved in a way that isn't just benchmaxxing? I'd argue the models seem to be regressing. Even the most AI-pilled people at my company have all complained that Opus 4.7 is a dud. Anecdotally, GPT 5.5 seems decent, but it's rumored to be a 10T parameter model, isn't noticeably better than 5.4 or 5.3, is insanely expensive to use, and seems to be experiencing model collapse since the system prompt has to beg the thing to not talk about goblins and raccoons.
  - jatora 6mo ago
    Uninformed opinion of someone who clearly doesnt consistently use AI coding tools, clearly. And why are you limiting it to 6 months? Whats wrong with you?
    - zapataband1 6mo ago
      How many years of real-life, in-production problem solving/coding have you done? That's what I base how informed you are not how much you use your favorite new $100/month token-prediction subscription
      - deleted 6mo ago
        [deleted]
      - jatora 6mo ago
        15 years. But that's irrelevant to this point. The person im replying to clearly doesnt use the tools if they think there hasnt been constant improvement. "token-prediction subscription" is funny, coming from a glorified biological token predictor
        zapataband1 6mo ago
- leptons 6mo ago
  You say this as though AI company debt has not followed a very clear and extremely rapid ballooning in a startlingly short amount of time. It's the "YOLO" of business strategies.
  - aspenmartin 6mo ago
    Always amazes me how we’re on a platform with “ycombinator” in the url and people don’t understand how private companies scale to capture market share. You’re right Uber was that company that ran at a loss for so long and collapsed, another YOLO business strategy. Or maybe it was Amazon or…hmm I forget
    - leptons 6mo ago
      I can't afford to take Ubers anymore. A trip that used to cost $7 now costs $40. AI is going to be the same to cover all the massive amounts of money already spent. You like your $200/mo plan now? How about when it's $2000/mo?
      - aspenmartin 6mo ago
        You are not at all wrong. Also get ready for the insidious advertising!
atleastoptimal 6mo ago
Complaining about every one off issue with LLM's ignores the bigger picture: they are getting better every month and there is no fundamental reason why they wouldn't surpass humans in coding. Everything else is secondary. All I would need from an LLM doubter is evidence that at tractable software engineering task LLM's are not improving. The strongest argument against the increasing general capabilities of LLM's are the ARC-AGI tasks, however the creators admit that each generation of LLM's exceed their expectations, and that AGI will be achieved within the decade.
- wavemode 6mo ago
  Your logic is flawed because, a thing can improve for an infinite amount of time while never surpassing a certain limit. It's called an asymptote. That being said, I don't even think that arguing about this from a mathematical perspective is a worthwhile use of time. Calling something an asymptote in the first place requires defining a quantifiable "X" and "Y", which we don't even have. What we have are a bunch of synthetic benchmarks. Even ignoring the fact that the answers to the questions are known to regularly leak into the training data (in other words, it's possible for scores to increase while capabilities remain the same), there's also the fundamental fact that performance on benchmarks is not the same thing as performance in the real world. And being able to answer some arbitrary set of arbitrary questions on a benchmark which the previous model couldn't, does not have a quantifiable correlation to some specific amount of real-world improvement. The OP article focuses on research papers which assess real-world impact of LLMs within software organizations, which I think are more representative. I wouldn't call myself an "AI doubter" - I use LLMs every day. When you say "doubter" you're not referring to "AI" in general, or the fact that AI is helpful or boosts productivity (which I believe it does). You're rather referring to the very specific, very extraordinary claim, that LLMs will surpass humans in coding. If that's the case then yeah I'm a doubter, at least on any foreseeable timescale.
  - handoflixue 6mo ago
    > Your logic is flawed because, a thing can improve for an infinite amount of time while never surpassing a certain limit. It's called an asymptote. Have you ever once looked at a METR chart? https://files.civai.org/assets/METR_Chart.jpg https://files.civai.org/assets/METR_Chart.jpg That's not an asymptote. > there's also the fundamental fact that performance on benchmarks is not the same thing as performance in the real world Again, yes, you're correct in the general case but it has very little to do with the specific case. Would you find it convincing if I simply said "some internet arguments are wrong"? It's certainly a true statement, and you've made an internet argument here, so clearly you should accept that you're wrong, right?

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