7 ms·

Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-ge

Disagree with the overall argument. Human effort is still a moat. I've been spending the past couple of months creating a codebase that is almost entirely AI-generated. I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. There's some truth in there that judgement is as important as ever, though I'm not sure I'd call it taste. I'm finding that you have to have an extremely clear product vision, along with an extremely clear language used to describe that product, for AI to be used effectively. Know your terms, know how you want your features to be split up into modules, know what you want the interfaces of those modules to be. Without the above, you run into the same issue devs would run into before AI - the codebase becomes an incoherent mess, and even AI can't untangle it because the confusion gets embedded into its own context.

taude 6mo ago
I feel like you're pretty strongly agreeing that taste is important: " I'm finding that you have to have an extremely clear product vision..."" Clear production vision that you're building the right thing in the right way -- this involves a lot of taste to get right. Good PMs have this. Good enginers have this. Visionary leaders have this.... The execution of using AI to generate the code and other artifacts, is a matter of skill. But without the taste that you're building the right thing, with the right features, in a revolutionary way that will be delightful to use.... I've looked at three non-engineer vibe-coded businesses in the past month, and can tell that without taste, they're building a pretty mediocre product at best. The founders don't see it yet. And like the article says, they're just setting themselves up for mediocrity. I think any really good PM would be able to improve all these apps I looked at almost immediately.
- arnorhs 6mo ago
  The way I understood it, the original article is saying the _only_ remaining differentiator is taste and the comment you replied to is saying "wrong, there are also other things, such as effort". I don't necessarily interpret the comment you replied to as saying that "taste is not important", which seems like what you are replying to, just that it's not the only remaining thing. I agree that taste gets you far. And I agree with all the examples of good taste that you brought up. But even with impeccable taste, you still need to learn, try things, have ideas, change your mind etc.. putting all of that in the bucket of "taste" is stretching it.. However, having good taste when putting in the effort, gets your further than with effort alone. In fact, effort alone gets you nowhere, and taste alone gets you nowhere. Once you marry the two you get somewhere.
  - hellisothers 6mo ago
    Aren’t you just making their point stronger? Effort is what is being replaced here, with some taste and a pile of AI (formerly effort) you can go to the moon.
    - Jensson 6mo ago
      But you still need effort, its not only taste. "Only" means you can do it with no effort.
      - theowaway213456 6mo ago
        In other words, it requires a tremendous amount of effort to fully communicate your tastes to the AI. Not everybody wants to expend the time or mental effort doing this! (Once we have more direct brain/computer interfaces, this effort will go down, but I expect it will not be eliminated fully)
        Shog9 6mo ago
        This is the second time in two days I've seen a subthread here with folks seemingly debating whether or not defining and communicating requirements counts as work if the target of those requirements is an LLM system. I'm confused as to why this is even a question. We used to call this "systems analysis" and it was like... a whole-ass career. LLMs seem to be remarkably capable of using the output, but they're not even close to the first software systems sold as being able to take requirements and turn them into working code (for various definitions of "requirements" and "working"). I'm also skeptical that direct brain interfaces would make this any less work; I don't think "typing" or "english" are the major barriers here, anymore than "drafting" is the major barrier to folks designing their own cars and houses... Any fool thinks they know what they need!
        Izkata 6mo ago
        Thinking might even be more difficult: Unfiltered thoughts, intrusive thoughts, people with no inner voice to encode as text...
- germinalphrase 6mo ago
  “ I've looked at three non-engineer vibe-coded businesses in the past month, and can tell that without taste, they're building a pretty mediocre product at best.” Are you doing this altruistically for friends - or as a consultant?
  - taude 6mo ago
    Both a) to help a friend out and b) to help non-technical founders I've meet at some Meetups/AI events to launch their product. My short-term goal is to put together a checklist/cheatsheet for all the technical things someone needs to do to launch a business because it's not just having a webapp running on Vercel with Supabase. And if they do have an app, is it a complete mess or not. I think the solo-founder hype is an overplayed unless the person has the right skills, and even worked at a tech company, and knows what they're getting into. Alerting and monitoring for example is one of like 30 things they should be aware of.
andai 6mo ago
I think you're missing the point. Effort is a moat now because centaurs (human+AI) still beat AIs, but that gap gets smaller every year (and will ostensibly be closed). The goal is to replicate human labor, and they're closing that gap. Once they do (maybe decades, but probably will happen), then only that "special something" will remain. Taste, vision... We shall all become Rick Rubins. Until 2045, when they ship RubinGPT
- monknomo 6mo ago
  do you need taste if you can massively parallel a/b test your way to something that is tasteful? say like you take your datacenter of geniuses and have a a rubin-loop supervising testing different directions. shouldn't that be close enough?
  - all2 6mo ago
    "taste" here is an intractable solution. Just take a look at how architecture has varied throughout the history of mankind, building materials, assembly, shape, flow, all of it boils down to taste. Some of it can be reduced to 'efficiency' -- like the 3 point system for designing kitchens, but even that is a matter of taste. Find three professional chefs and they will give you three distinct visions for how a kitchen should be organized. The same goes for any professional field, including software engineering.
  - skejeke 6mo ago
    That approach leads you to products like instagram.
  - timacles 6mo ago
    Can infinite monkeys produce Shakespeare?
    - theYipster 6mo ago
      It was the best of times. It was the blurst of times.
- 6mo ago
abadar 6mo ago
You make a really salient point about having a clear vision and using clear language. Patrick Zgambo says that working with AI is spellcasting; you just need to know the magic words. The more I work with AI tools, the more I agree. Now, figuring out those words? That's the hard part.
- gopher_space 6mo ago
  > Now, figuring out those words? That's the hard part. To be clear, this is the hard part for comp sci majors who can't parse other disciplines. Language isn't a black box for everyone.
crystal_revenge 6mo ago
> ... for AI to be used effectively. I'm continually fascinated by the huge differences in individual ability to produce successful results with AI. I always assumed that one of the benefits of AI was "anyone can do this". Then I realized a lot of people I interact with don't really understand the problem they're trying to solve all that well, and have some irrational belief that they can get AI to brute force their way to a solution. For me I don't even use the more powerful models (just Sonnet 4.6) and have yet to have a project not come out fairly successful in a short period of time. This includes graded live coding examples for interviews, so there is at least some objective measurement that these are functional. Strangely I find traditional software engineers, especially experienced ones, are generally the worst at achieving success. They often treat working with an agent too much like software engineering and end up building bad software rather than useful solutions to the core problem.
- alfalfasprout 6mo ago
  > Strangely I find traditional software engineers, especially experienced ones, are generally the worst at achieving success. They often treat working with an agent too much like software engineering and end up building bad software rather than useful solutions to the core problem. This feels a bit like a strawman. How do you assess it to be bad software without being an engineer yourself? What constitutes successful for you? If anything, AI tools have revealed that a lot of people have hubris about building software. With non-engineers believing they're creating successful work without realizing it's a facade of a solution that's a ticking time bomb.
  - crystal_revenge 6mo ago
    > without being an engineer yourself? When did I say I'm not a software engineer? I have a software engineering background (I've written reasonably successful books on software), I've just done a lot of other stuff as well that people tend to find more valuable. > What constitutes successful for you? The problem I need to solve is solved? I'm not sure what other measure you could have. Honestly, people really misunderstand how to use agents. If you're aim is to "build software" you're going to get in trouble, if your aim is to "solve problems" then you're more aligned with where these tools work most effectively.
rvz 6mo ago
> Without the above, you run into the same issue devs would run into before AI - the codebase becomes an incoherent mess, and even AI can't untangle it because the confusion gets embedded into its own context. We have a term for this and it is called "Comprehension Debt" [0] [1]. [0] https://arxiv.org/abs/2512.08942 https://arxiv.org/abs/2512.08942 [1] https://medium.com/@addyosmani/comprehension-debt-the-hidden-cost-of-ai-generated-code-285a25dac57e https://medium.com/@addyosmani/comprehension-debt-the-hidden...
- danielvaughn 6mo ago
  I'm not sure I agree the term applies. Comprehension debt, as I understand it, is just the dependency trap mentioned in that arxiv paper you linked. It means that the AI might have written something coherent or not, but you as a human evaluator have little means to judge it. Because you've relied on it too much and the scope of the code has exceeded the feasibility of reading it manually. When I talk about an incoherent mess, I'm talking about something different. I mean that as the codebase grows and matures, subtle details and assumptions naturally shift. But the AI isn't always cleaning up the code that expressed those prior assumptions. These issues compound to the point that the AI itself gets very confused. This is especially dangerous for teams of developers touching the same codebase. I can't share too much detail here, but some personal experience I ran into recently: we had feature ABC in our platform. Eventually another developer came in, disagreed with the implementation, and combined some aspects of it into a new feature XYZ. Both were AI generated. What _should_ have happened is that feature ABC was deleted from the code or refactored into XYZ. But it wasn't, so now the codebase has two nearly identical modules ABC and XYZ. If you ask Claude to edit the feature, you've got a 50/50 shot on which one it chooses to target, even though feature ABC is now dead, unreachable code. You might say that resolving the above issue is easy, but these inconsistencies become quite numerous and unsustainable in a codebase if you lean on AI too much, or aren't careful. This is why I say that having a super clear vision up front is important, because it reduces this kind of directional churn.
  - all2 6mo ago
    > This is why I say that having a super clear vision up front is important, because it reduces this kind of directional churn. I'm on my 6th or 7th draft of a project. I've been picking away at this thing since the end of January; I keep restarting because the core abstractions get clearer and clearer as I go. AI has been great in this discovery process because it speeds iteration much more quickly. I know its starting to drift into a mess when I no longer have a clear grasp of the work its doing. To me, this indicates that some mental model I had and communicated was not sufficiently precise.
stronglikedan 6mo ago
> I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. By the time I'm done learning about the structure of the code that AI wrote, and reviewing it for correctness and completeness, it seems to be as much effort as if I had just written it myself. And I fear that will continue to be the reality until AIs can be trusted.
- edgyquant 6mo ago
  Well that is not how anyone is doing agentic coding though. That sounds like just a worse version of traditional coding. Most people are building test suites to verify correctness and not caring about the code
  - skydhash 6mo ago
    Test suites don't verify correctness. They just ensure that you haven't broke something so bad the specific instances that the tests assert have turned into a failure. You can have a factorial function and more likely the test cases will only be a few numbers. Which does not guarantee correctness as someone who know about the test cases can just put a switch and return the correct response for those specific cases. The compromise is worth it in traditional coding, because someone will care about the implementation. The test cases are more like the canary in the coal mine. A failure warrants investigations but an all green is not a guarantee of success.
    - edgyquant 6mo ago
      Regardless that is why most people are moving faster than writing by hand
      - skydhash 6mo ago
        Moving faster towards what? The biggest diff? The most tickets closed? Or a working software?
zer00eyz 6mo ago
> Disagree with the overall argument. It's leaning in a good direction, but the author clearly lacks the language and understanding to articulate the actual problem, or a solution. They simply dont know what they dont know. > Human effort is still a moat. Also slightly off the mark. If I sat one down with all the equipment and supplies to make a pair of pants, the majority of you (by a massive margin) are going to produce a terrible pair of pants. Thats not due to lack of effort, rather lack of skill. > judgement is as important as ever, Not important, critical. And it is a product of skill and experience. Usability (a word often unused), cost, utility, are all the things that people want in a product. Reliability is a requirement: to quote the social network "we dont crash". And if you want to keep pace, maintainability. > issue devs would run into before AI - the codebase becomes an incoherent mess The big ball of mud (https://www.laputan.org/mud/ https://www.laputan.org/mud/ ) is 27 years old, and still applies. But all code bases have a tendency to acquire cruft (from edge cases) that don't have good in line explanations, that lack durable artifacts. Find me an old code base and I bet you that we can find a comment referencing a bug number in a system that no longer exists. We might as an industry need to be honest that we need to be better librarians and archivists as well. That having been said, the article should get credit, it is at least trying to start to have the conversations that we should be having and are not.
socalgal2 6mo ago
Isn't this a temporary situation though. Today: Ask AI to "do the thing", manual review because don't trust the AI Tomorrow: Ask AI to "do the thing" I'm just getting started on my AI journey. It didn't take long before I upgraded from the $17 a month claude plan to the $100 a month plan and I can see myself picking the $200 a month plan soon. This is for hobby projects. At the moment I'm reviewing most of the code for what I'm working on, and I have tests and review those too. But, seeing how good it is (sometimes), I can imagine a future where the AI itself has both the tech chops and the taste and I can just say "Maybe me an app to edit photos" and it will spit out a user friendly clone of photoshop with good UX. We already kind of see this with music - it's able to spit out "Bangers". How long until it can spit out hit rom-coms, crime shows, recipes, apps? I don't think the answer is "never". I think more likely the answer is in N years where N is probably a single digit.
- guzfip 6mo ago
  > We already kind of see this with music - it's able to spit out "Bangers" “Bangers” being roughly equivalent to garbage mass marketed radio pop? Or “We are Charlie Kirk” lol
- danielvaughn 6mo ago
  No, I don't think it is temporary. As AI becomes more powerful, we'll simply ask it to do more difficult things. There's a level of complexity where "do the thing" is insufficient. We'll never be at a place where AI can infer vast amounts of nuance from simple human requests, which means that humans will always need to be able to describe precisely what they want. This has always been the core skill for software developers, and I just don't see that changing.
  - drivebyhooting 6mo ago
    Do you believe a junior developer now will never surpass you? Why couldn’t AI do the same?
    - danielvaughn 6mo ago
risyachka 6mo ago
It doesn’t really matter how good your taste is if you are drowning in the ocean of crap. Customers can’t find you
- mrdonbrown 6mo ago
  This is an underrated comment. You could have the best product out there, but AI has not only lowered the effort for competitors but has flooded traditional ways to get your product known, from outbound sales to content marketing. Sometimes make you question whether there are customers anymore.
nopinsight 6mo ago
Jensen Huang said he commands thousands of AGIs but still feels pretty useful. Founders and CEOs are still needed to set direction, bring unique vision to life, and build relationships for long-term partnerships—-as long as humans still control the economy, that is.

taude 6mo ago

I feel like you're pretty strongly agreeing that taste is important: " I'm finding that you have to have an extremely clear product vision..."" Clear production vision that you're building the right thing in the right way -- this involves a lot of taste to get right. Good PMs have this. Good enginers have this. Visionary leaders have this.... The execution of using AI to generate the code and other artifacts, is a matter of skill. But without the taste that you're building the right thing, with the right features, in a revolutionary way that will be delightful to use.... I've looked at three non-engineer vibe-coded businesses in the past month, and can tell that without taste, they're building a pretty mediocre product at best. The founders don't see it yet. And like the article says, they're just setting themselves up for mediocrity. I think any really good PM would be able to improve all these apps I looked at almost immediately.

arnorhs 6mo ago
The way I understood it, the original article is saying the _only_ remaining differentiator is taste and the comment you replied to is saying "wrong, there are also other things, such as effort". I don't necessarily interpret the comment you replied to as saying that "taste is not important", which seems like what you are replying to, just that it's not the only remaining thing. I agree that taste gets you far. And I agree with all the examples of good taste that you brought up. But even with impeccable taste, you still need to learn, try things, have ideas, change your mind etc.. putting all of that in the bucket of "taste" is stretching it.. However, having good taste when putting in the effort, gets your further than with effort alone. In fact, effort alone gets you nowhere, and taste alone gets you nowhere. Once you marry the two you get somewhere.
- hellisothers 6mo ago
  Aren’t you just making their point stronger? Effort is what is being replaced here, with some taste and a pile of AI (formerly effort) you can go to the moon.
  - Jensson 6mo ago
    But you still need effort, its not only taste. "Only" means you can do it with no effort.
    - theowaway213456 6mo ago
      In other words, it requires a tremendous amount of effort to fully communicate your tastes to the AI. Not everybody wants to expend the time or mental effort doing this! (Once we have more direct brain/computer interfaces, this effort will go down, but I expect it will not be eliminated fully)
      - Shog9 6mo ago
        This is the second time in two days I've seen a subthread here with folks seemingly debating whether or not defining and communicating requirements counts as work if the target of those requirements is an LLM system. I'm confused as to why this is even a question. We used to call this "systems analysis" and it was like... a whole-ass career. LLMs seem to be remarkably capable of using the output, but they're not even close to the first software systems sold as being able to take requirements and turn them into working code (for various definitions of "requirements" and "working"). I'm also skeptical that direct brain interfaces would make this any less work; I don't think "typing" or "english" are the major barriers here, anymore than "drafting" is the major barrier to folks designing their own cars and houses... Any fool thinks they know what they need!
        Izkata 6mo ago
        Thinking might even be more difficult: Unfiltered thoughts, intrusive thoughts, people with no inner voice to encode as text...
germinalphrase 6mo ago
“ I've looked at three non-engineer vibe-coded businesses in the past month, and can tell that without taste, they're building a pretty mediocre product at best.” Are you doing this altruistically for friends - or as a consultant?
- taude 6mo ago
  Both a) to help a friend out and b) to help non-technical founders I've meet at some Meetups/AI events to launch their product. My short-term goal is to put together a checklist/cheatsheet for all the technical things someone needs to do to launch a business because it's not just having a webapp running on Vercel with Supabase. And if they do have an app, is it a complete mess or not. I think the solo-founder hype is an overplayed unless the person has the right skills, and even worked at a tech company, and knows what they're getting into. Alerting and monitoring for example is one of like 30 things they should be aware of.

andai 6mo ago

I think you're missing the point. Effort is a moat now because centaurs (human+AI) still beat AIs, but that gap gets smaller every year (and will ostensibly be closed). The goal is to replicate human labor, and they're closing that gap. Once they do (maybe decades, but probably will happen), then only that "special something" will remain. Taste, vision... We shall all become Rick Rubins. Until 2045, when they ship RubinGPT

monknomo 6mo ago
do you need taste if you can massively parallel a/b test your way to something that is tasteful? say like you take your datacenter of geniuses and have a a rubin-loop supervising testing different directions. shouldn't that be close enough?
- all2 6mo ago
  "taste" here is an intractable solution. Just take a look at how architecture has varied throughout the history of mankind, building materials, assembly, shape, flow, all of it boils down to taste. Some of it can be reduced to 'efficiency' -- like the 3 point system for designing kitchens, but even that is a matter of taste. Find three professional chefs and they will give you three distinct visions for how a kitchen should be organized. The same goes for any professional field, including software engineering.
- skejeke 6mo ago
  That approach leads you to products like instagram.
- timacles 6mo ago
  Can infinite monkeys produce Shakespeare?
  - theYipster 6mo ago
    It was the best of times. It was the blurst of times.
6mo ago

abadar 6mo ago

You make a really salient point about having a clear vision and using clear language. Patrick Zgambo says that working with AI is spellcasting; you just need to know the magic words. The more I work with AI tools, the more I agree. Now, figuring out those words? That's the hard part.

gopher_space 6mo ago
> Now, figuring out those words? That's the hard part. To be clear, this is the hard part for comp sci majors who can't parse other disciplines. Language isn't a black box for everyone.

crystal_revenge 6mo ago

> ... for AI to be used effectively. I'm continually fascinated by the huge differences in individual ability to produce successful results with AI. I always assumed that one of the benefits of AI was "anyone can do this". Then I realized a lot of people I interact with don't really understand the problem they're trying to solve all that well, and have some irrational belief that they can get AI to brute force their way to a solution. For me I don't even use the more powerful models (just Sonnet 4.6) and have yet to have a project not come out fairly successful in a short period of time. This includes graded live coding examples for interviews, so there is at least some objective measurement that these are functional. Strangely I find traditional software engineers, especially experienced ones, are generally the worst at achieving success. They often treat working with an agent too much like software engineering and end up building bad software rather than useful solutions to the core problem.

alfalfasprout 6mo ago
> Strangely I find traditional software engineers, especially experienced ones, are generally the worst at achieving success. They often treat working with an agent too much like software engineering and end up building bad software rather than useful solutions to the core problem. This feels a bit like a strawman. How do you assess it to be bad software without being an engineer yourself? What constitutes successful for you? If anything, AI tools have revealed that a lot of people have hubris about building software. With non-engineers believing they're creating successful work without realizing it's a facade of a solution that's a ticking time bomb.
- crystal_revenge 6mo ago
  > without being an engineer yourself? When did I say I'm not a software engineer? I have a software engineering background (I've written reasonably successful books on software), I've just done a lot of other stuff as well that people tend to find more valuable. > What constitutes successful for you? The problem I need to solve is solved? I'm not sure what other measure you could have. Honestly, people really misunderstand how to use agents. If you're aim is to "build software" you're going to get in trouble, if your aim is to "solve problems" then you're more aligned with where these tools work most effectively.

rvz 6mo ago

> Without the above, you run into the same issue devs would run into before AI - the codebase becomes an incoherent mess, and even AI can't untangle it because the confusion gets embedded into its own context. We have a term for this and it is called "Comprehension Debt" [0] [1]. [0] https://arxiv.org/abs/2512.08942 https://arxiv.org/abs/2512.08942 [1] https://medium.com/@addyosmani/comprehension-debt-the-hidden-cost-of-ai-generated-code-285a25dac57e https://medium.com/@addyosmani/comprehension-debt-the-hidden...

danielvaughn 6mo ago
I'm not sure I agree the term applies. Comprehension debt, as I understand it, is just the dependency trap mentioned in that arxiv paper you linked. It means that the AI might have written something coherent or not, but you as a human evaluator have little means to judge it. Because you've relied on it too much and the scope of the code has exceeded the feasibility of reading it manually. When I talk about an incoherent mess, I'm talking about something different. I mean that as the codebase grows and matures, subtle details and assumptions naturally shift. But the AI isn't always cleaning up the code that expressed those prior assumptions. These issues compound to the point that the AI itself gets very confused. This is especially dangerous for teams of developers touching the same codebase. I can't share too much detail here, but some personal experience I ran into recently: we had feature ABC in our platform. Eventually another developer came in, disagreed with the implementation, and combined some aspects of it into a new feature XYZ. Both were AI generated. What _should_ have happened is that feature ABC was deleted from the code or refactored into XYZ. But it wasn't, so now the codebase has two nearly identical modules ABC and XYZ. If you ask Claude to edit the feature, you've got a 50/50 shot on which one it chooses to target, even though feature ABC is now dead, unreachable code. You might say that resolving the above issue is easy, but these inconsistencies become quite numerous and unsustainable in a codebase if you lean on AI too much, or aren't careful. This is why I say that having a super clear vision up front is important, because it reduces this kind of directional churn.
- all2 6mo ago
  > This is why I say that having a super clear vision up front is important, because it reduces this kind of directional churn. I'm on my 6th or 7th draft of a project. I've been picking away at this thing since the end of January; I keep restarting because the core abstractions get clearer and clearer as I go. AI has been great in this discovery process because it speeds iteration much more quickly. I know its starting to drift into a mess when I no longer have a clear grasp of the work its doing. To me, this indicates that some mental model I had and communicated was not sufficiently precise.

stronglikedan 6mo ago

> I've gotten way further than I would have otherwise at this pace, but it was still a lot of effort, and I still wasted time going down rabbit holes on features that didn't work out. By the time I'm done learning about the structure of the code that AI wrote, and reviewing it for correctness and completeness, it seems to be as much effort as if I had just written it myself. And I fear that will continue to be the reality until AIs can be trusted.

edgyquant 6mo ago
Well that is not how anyone is doing agentic coding though. That sounds like just a worse version of traditional coding. Most people are building test suites to verify correctness and not caring about the code
- skydhash 6mo ago
  Test suites don't verify correctness. They just ensure that you haven't broke something so bad the specific instances that the tests assert have turned into a failure. You can have a factorial function and more likely the test cases will only be a few numbers. Which does not guarantee correctness as someone who know about the test cases can just put a switch and return the correct response for those specific cases. The compromise is worth it in traditional coding, because someone will care about the implementation. The test cases are more like the canary in the coal mine. A failure warrants investigations but an all green is not a guarantee of success.
  - edgyquant 6mo ago
    Regardless that is why most people are moving faster than writing by hand
    - skydhash 6mo ago
      Moving faster towards what? The biggest diff? The most tickets closed? Or a working software?

zer00eyz 6mo ago

> Disagree with the overall argument. It's leaning in a good direction, but the author clearly lacks the language and understanding to articulate the actual problem, or a solution. They simply dont know what they dont know. > Human effort is still a moat. Also slightly off the mark. If I sat one down with all the equipment and supplies to make a pair of pants, the majority of you (by a massive margin) are going to produce a terrible pair of pants. Thats not due to lack of effort, rather lack of skill. > judgement is as important as ever, Not important, critical. And it is a product of skill and experience. Usability (a word often unused), cost, utility, are all the things that people want in a product. Reliability is a requirement: to quote the social network "we dont crash". And if you want to keep pace, maintainability. > issue devs would run into before AI - the codebase becomes an incoherent mess The big ball of mud (https://www.laputan.org/mud/ https://www.laputan.org/mud/ ) is 27 years old, and still applies. But all code bases have a tendency to acquire cruft (from edge cases) that don't have good in line explanations, that lack durable artifacts. Find me an old code base and I bet you that we can find a comment referencing a bug number in a system that no longer exists. We might as an industry need to be honest that we need to be better librarians and archivists as well. That having been said, the article should get credit, it is at least trying to start to have the conversations that we should be having and are not.

socalgal2 6mo ago

Isn't this a temporary situation though. Today: Ask AI to "do the thing", manual review because don't trust the AI Tomorrow: Ask AI to "do the thing" I'm just getting started on my AI journey. It didn't take long before I upgraded from the $17 a month claude plan to the $100 a month plan and I can see myself picking the $200 a month plan soon. This is for hobby projects. At the moment I'm reviewing most of the code for what I'm working on, and I have tests and review those too. But, seeing how good it is (sometimes), I can imagine a future where the AI itself has both the tech chops and the taste and I can just say "Maybe me an app to edit photos" and it will spit out a user friendly clone of photoshop with good UX. We already kind of see this with music - it's able to spit out "Bangers". How long until it can spit out hit rom-coms, crime shows, recipes, apps? I don't think the answer is "never". I think more likely the answer is in N years where N is probably a single digit.

guzfip 6mo ago
> We already kind of see this with music - it's able to spit out "Bangers" “Bangers” being roughly equivalent to garbage mass marketed radio pop? Or “We are Charlie Kirk” lol
danielvaughn 6mo ago
No, I don't think it is temporary. As AI becomes more powerful, we'll simply ask it to do more difficult things. There's a level of complexity where "do the thing" is insufficient. We'll never be at a place where AI can infer vast amounts of nuance from simple human requests, which means that humans will always need to be able to describe precisely what they want. This has always been the core skill for software developers, and I just don't see that changing.
- drivebyhooting 6mo ago
  Do you believe a junior developer now will never surpass you? Why couldn’t AI do the same?
  - danielvaughn 6mo ago

risyachka 6mo ago

It doesn’t really matter how good your taste is if you are drowning in the ocean of crap. Customers can’t find you

mrdonbrown 6mo ago
This is an underrated comment. You could have the best product out there, but AI has not only lowered the effort for competitors but has flooded traditional ways to get your product known, from outbound sales to content marketing. Sometimes make you question whether there are customers anymore.

nopinsight 6mo ago

Jensen Huang said he commands thousands of AGIs but still feels pretty useful. Founders and CEOs are still needed to set direction, bring unique vision to life, and build relationships for long-term partnerships—-as long as humans still control the economy, that is.