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As a software engineer, 6 months ago. I was better than AI in essentially every way except raw typing speed. Now AI has surpassed me in fine-grained problem-so

As a software engineer, 6 months ago. I was better than AI in essentially every way except raw typing speed. Now AI has surpassed me in fine-grained problem-solving ability. It can write more reliable code than I can, faster than I can, provided it is given the right guidance. But one thing that I'm still much better at is identifying technical opportunities and choosing the right tradeoffs. My feeling is that frontier models are incredibly smart in some ways, but incredibly dumb in other ways When I chat with Claude using deep technical language about distributed systems issues, the arguments it presents are mind-blowingly good. I worked with many skilled engineers on complex projects but the kinds of arguments Claude makes are on another level. It feels like it can read my mind because it already identified all of the relevant aspects to the current topic and it's incredibly persuasive. I would say it's even better at deep, nuanced technical discussions than I am. When I ask it to implement a feature using technical language, it does a really good job and the solution usually works out of the box. No bugs at all 95% of the time even after adding 1000 lines! But the problem it still has is that when it implements solutions, it misses so many low-hanging fruits/opportunities. Especially in terms of performance, maintainability, scalability and UX. It's like it sees and recalls all the relevant parts perfectly but yet somehow misses opportunities which seem extremely obvious to me. It feels like AI has 0 creativity. It only sees the opportunity once I mention it... And once it sees the opportunity, it demonstrates deep understanding of the technical implications. I think what's surprising is that it understands the suggested solution so well, with such nuance, that I can't understand how it didn't see the opportunity and why it never seems to see it until I mention it. This is very unhuman-like. There is no way that a human being with that degree of understanding of a topic would be presented with such highly relevant context and not make the connection.

black_knight 20d ago
This mirrors my understanding when I use Claude code for mathematics. I can have deep discussions with it and it can solve my hairy problems. But whenever we go off the beaten track into design new mathematics, it struggles to make conceptual leaps and find the right definitions. Once I give it my ideas, it is back to its super-human pace and top notch intelligence. This situation suits me fine, since I am anyways more of an ideas person, than a crunching open problems person. But I understand the desperation of my colleagues who mad solving hard problems their identity.
- int_19h 20d ago
  > Once I give it my ideas, it is back to its super-human pace and top notch intelligence. That's the real benefit of AI today: you can test your ideas almost as fast as you can generate them, in parallel even. If you have a crazy idea that is unlikely to work but has massive advantages if it does, you just set an agent to explore and prototype it while you focus your main attention elsewhere. And so on. It's still GIGO, though! An AI can make a bad idea work (and will do so if you don't carefully prompt it to not be so sycophantic!) but it won't work well. Knowing when the idea is bad and should be abandoned is also the part that currently requires human judgment. That said, given the pace at which models have been improving so far, I can't help but think that this is a transient state of affairs.
- socketcluster 20d ago
  Yes very much the same situation in software development. The people who prided themselves on raw puzzle-solving ability got hit hard. Those who are idea-driven and architecture-oriented feel like they got handed a superpower. It's quite a big shock at the industry level because the entire software engineering job interview process at essentially all large companies was heavily biased towards well-defined, raw problem-solving under time constraints which is precisely the skill which AI has replaced.
  - bharatsuthar 19d ago
    Yeah and companies continue to use leetcode style interviews even today and software engineers keep grinding for them. It's nauseating to contemplate on.
gregdeon 20d ago
This is exactly how I feel about the recent ChatGPT models. It feels like I'm working with an incredibly enthusiastic junior student who listens to everything I say, checks it against my entire codebase, and thinks through all of the possible connections 1000x faster than I can. But it doesn't spot the ideas first. It actually makes work quite fun, since I can spend so much more time in "idea space" instead of "implementation space".
bharatsuthar 20d ago
Thanks for putting it in words. I feel the same about SOTA coding models. I get visibly frustrated by this and often find myself using curse words at Astra. You're right it's very unhuman to miss obvious opportunities you mentioned. And what's why I think these models would continue sucking as long as they don't have human level general intelligence. They may get marginally better at these tasks but I don't think we can expect them to connect dots like humans before AGI.

black_knight 20d ago

This mirrors my understanding when I use Claude code for mathematics. I can have deep discussions with it and it can solve my hairy problems. But whenever we go off the beaten track into design new mathematics, it struggles to make conceptual leaps and find the right definitions. Once I give it my ideas, it is back to its super-human pace and top notch intelligence. This situation suits me fine, since I am anyways more of an ideas person, than a crunching open problems person. But I understand the desperation of my colleagues who mad solving hard problems their identity.

int_19h 20d ago
> Once I give it my ideas, it is back to its super-human pace and top notch intelligence. That's the real benefit of AI today: you can test your ideas almost as fast as you can generate them, in parallel even. If you have a crazy idea that is unlikely to work but has massive advantages if it does, you just set an agent to explore and prototype it while you focus your main attention elsewhere. And so on. It's still GIGO, though! An AI can make a bad idea work (and will do so if you don't carefully prompt it to not be so sycophantic!) but it won't work well. Knowing when the idea is bad and should be abandoned is also the part that currently requires human judgment. That said, given the pace at which models have been improving so far, I can't help but think that this is a transient state of affairs.
socketcluster 20d ago
Yes very much the same situation in software development. The people who prided themselves on raw puzzle-solving ability got hit hard. Those who are idea-driven and architecture-oriented feel like they got handed a superpower. It's quite a big shock at the industry level because the entire software engineering job interview process at essentially all large companies was heavily biased towards well-defined, raw problem-solving under time constraints which is precisely the skill which AI has replaced.
- bharatsuthar 19d ago
  Yeah and companies continue to use leetcode style interviews even today and software engineers keep grinding for them. It's nauseating to contemplate on.

gregdeon 20d ago

This is exactly how I feel about the recent ChatGPT models. It feels like I'm working with an incredibly enthusiastic junior student who listens to everything I say, checks it against my entire codebase, and thinks through all of the possible connections 1000x faster than I can. But it doesn't spot the ideas first. It actually makes work quite fun, since I can spend so much more time in "idea space" instead of "implementation space".

bharatsuthar 20d ago

Thanks for putting it in words. I feel the same about SOTA coding models. I get visibly frustrated by this and often find myself using curse words at Astra. You're right it's very unhuman to miss obvious opportunities you mentioned. And what's why I think these models would continue sucking as long as they don't have human level general intelligence. They may get marginally better at these tasks but I don't think we can expect them to connect dots like humans before AGI.