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This is why I don't listen at all to the fearmongers that say programmers will disappear. At most, our jobs will slightly change. There will always be people t

• ACCount36 1y ago
  What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? Let alone "this wouldn't EVER be capable of that"?
  • benterix 1y ago
    > What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? I wouldn't say they're completely incapable. * They can spot (and fix) low hanging fruit instantly * They will also "fix" things that were left out there for a reason and break things completely * even if the code base fits entirely in their context window, as does the complete company knowledge base, including Slack conversations etc., the proposed solutions sometimes take a very strange turn, in spite of being correct 57.8% of the time.
    • ACCount36 1y ago
      That's about right. And this kind of performance wouldn't be concerning - if only AI performance didn't go up over time. Today's AI systems are the worst they'll ever be. If AI is already capable of doing something, you should expect it to become more capable of it in the future.
      • binary132 1y ago
        why is “the worst they’ll ever be” such a popular meme with the AI inevitabilist crowd and how do we make their brains able to work again?
        • ACCount36 1y ago
          It's popular because it's true. By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable.
          • disgruntledphd2 1y ago
            > By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable. OK, so where is the new data going to come from? Fundamentally, LLMs work by doing token prediction when some token(s) are masked. This process (which doesn't require supervision hence why it scaled) seems to be fundamental to LLM improvement. And basically all of the AI companies have slurped up all of the text (and presumably all of the videos) on the internet. Where does the next order of magnitude increase in data come from? More fundamentally, lots of the hype is about research/novel stuff which seems to me to be very, very difficult to get from a model that's trained to produce plausible text. Like, how does one expect to see improvements in biology (for example) based on text input and output. Remember, these models don't appear to reason much like humans, they seem to do well where the training data is sufficient (interpolation) and do badly where there isn't enough data (extrapolation). I'd love to understand how this is all supposed to change, but haven't really seen much useful evidence (i.e. papers and experiments) on this, just AI CEOs talking their book. Happy to be corrected if I'm wrong.
            • fragmede 1y ago
              Fundamentally the bottleneck is on data and compute. If we accept as a given that a) some LLM is bad at writing eg rust code because there's much less of it on the Internet compared to say react js code but that b) the LLM is able to generate valid rust code and c) the LLM is able to "tool use"the rust compiler and a runtime to validate the rust it generates, and iterate until the code is valid, and finally d) use that generated rust code to train on, then it seems that barring any algorithmic improvements in training, that the additional data should allow later versions of the LLM to be better at writing rust code. If you don't hold a-d to be possible then sure, maybe it's just AI CEOs talking their book. The other fundamental bottleneck is compute. Moore's law hasn't gone away, so if the LLM was GPT-3, and used 1 supercomputer's worth of compute for 3 months back in 2022, and the supercomputer used for training is, say, three times more powerful (3x faster CPU and 3x the RAM), then training on a latest generation supercomputer should lead to a more powerful LLM simply by virtue of scaling that up and no algorithmic changes. The exact nature of the improvement isn't easily back of the envelope calculatable, but even with a laymen's understanding of how these things work, that doesn't seem like an unreasonable assumption on how things will go, and not "AI CEOs talking their book". Simply running with a bigger context window should allow the LLM to be more useful. Finally though, why do you assume that, absent papers up on arvix, that there haven't and won't be any algorithmic improvements to training and inference? We've already seen how allowing the LLM to take longer to process the input (eg "ultrathink" to Claude) allows for better results. It seems unlikely that all possible algorithmic improvements have already been discovered and implemented. Just because OpenAI et Al aren't writing academic papers to share their discovery with the world and are, instead, preferring to keep that improvement private and proprietary, in order to try and gain a competitive edge in a very competitive business seems like a far more reasonable assumption. With literal billions of dollars on the line, would you spend your time writing a paper, or would you try and outcompete your competitors? If simply giving the LLM longer to process the input before user facing output is returned, what other algorithmic improvements on the inference side on a bigger supercomputer with more ram available to it are possible? Deepseek seems to say there's a ton of optimization still as of yet to be done. Happy to hear opposing points of view, but I don't think any of the things I've theorized here to be totally inconceivable. Of course there's a discussion to be had about diminishing returns, but we'd need a far deeper understanding is the state of the art on all three facets I raised in order to have an in depth and practical discussion on the subject. (Which tbc I'm open to hearing, though the comments section on HN is probably not the platform to gain said deeper understanding of the subject at hand).
      • croes 1y ago
        That’s not how it works. There are already cases where the fix of one problem made a previous existing capability worse.
        • ACCount36 1y ago
          That's exactly how it works. Every input of AI performance improves over time, and so do the outcomes. Can you damage existing capabilities by overly specializing an AI in something? Yes. Would you expect that damage to stick around forever? No. OpenAI damaged o3's truthfulness by frying it with too much careless RL. But Anthropic's Opus 4 proves that you can get similar task performance gains without sacrificing truthfulness. And then OpenAI comes back swinging with an algorithmic approach to train their AIs for better truthfulness specifically.
          • croes 1y ago
            Depends on the input. More BS training data leads to worse answers and the good sources are nearly all already used. The next round of data is partially AI generated what leads to further deterioration
          • binary132 1y ago
            that must be why gpt5 can’t count the number of B’s in “blueberry”
      • IsTom 1y ago
        We're somewhere on an S-curve and you can't really determine on which part by just looking at the past progress.
  • croes 1y ago
    Turn the question around „oh, this totally is capable of describing a problem and figuring out what's actually wrong“ Even a broken clock is right two times a day. The question is reliability. What worked today may not work tomorrow and vice versa.
  • satyrun 1y ago
    At this point it is just straight denial. Like when a relationship is obviously over. Some people enjoy the ending fleeting moments while others delude themselves that they just have to get over the hump and things will go back to normal. I suspect a lot of the denial is from the 30 something CRUD app lottery winner. One of the smart kids all through school, graduated into a ripping CRUD app job market and then if they didn't even feel the 2022 downturn, they now see themselves as irreplaceable CRUD app genius. Something understandable since the environment has never signaled anything to the contrary until now.
    • sho_hn 1y ago
      My psychological reaction to what's going on is somehow pretty different. I'm a systems/embedded/GUI dev with 25 years of C++ etc., and nearly every day I'm happy and grateful to be the last generation to get really proficient before AI tools made us all super dependant and lazy. Don't get me wrong, I'm sure people will find other ways to remain productive and stand out from each other - just a new normal -, but I'm still glad all that mental exercise and experience can't be taken away from me. I'm more compelled to figure out how I can contribute to making sure younger colleagues learn all the right stuff and treat their brains with self-respect than I feel any need to "save my own ass" or have any fears about the job changing.
      • jononor 1y ago
        You made me think of the role of mental effort/exercise. In parts of the western world, we are already experiencing a large increase in dementia/alzheimer and related. Most of it is because we are doing better with other killers like heart etc, and many cancers also. But is said that mental activity is important to stave off degenerative diseases of the brain. Could widespread AI trigger a dementia epidemic? It would be 30 years out, but still...
• croes 1y ago
  The problem isn’t the AI but the management that believes the PR. It doesn’t matter if AI can replace developers but if the management thinks it can.
  • breakpointalpha 1y ago
    That's only a problem in the short term. Watch the company fire 50% of the engineering team then hit a brick wall at 100mph.