I’m a professional developer and have tested AI tools extensively over the last few years as they develop. The economic implications of the advancements made over the last few months are simply impossible to ignore. The tools aren’t perfect, and you certainly need to structure their use around their strengths and weaknesses, but assigned to the right tasks they can be 10% or less of the cost with better results. I’ve yet to have a project where I’ve used them and they didn’t need an experienced engineer to jump in and research an obscure or complex bug, have a dumb architectural choice rejected, or verify if stuff actually works (they like reporting success when they shouldn’t), but again the economics; the dev can be doing other stuff 90% of the time.
Don’t get me wrong, on the current trajectory this tech would probably lead to deeply terrible socioeconomic outcomes, probably techno neofeudalism, but for an individual developer putting food on the table I don’t see it as much of a choice. It’s like the industrial revolution again, but for cognitive work.
I keep hearing stuff like this, but I haven’t found a good use or workflow for AI (other than occasional chatbot sessions). Regular autocomplete is more accurate (no hallucinations) and faster than AI suggestions (especially accounting for needing to constantly review the suggestions for correctness). I guess stuff like Cursor is OK at making one-off tools on very small code-bases, but hits a brick-wall when the code base gets too big. Then you’re left with a bunch of unmaintainable code you’re not very familiar with and you would to spend a lot of time trying to fix yourself. Dunno if I’m doing something wrong or what.
I guess what I’m saying is that using AI can speed you up to a point while the project accumulates massive amounts of technical debt, and when you take into account all the refactoring and debugging time, it results in taking longer to produce a buggier project. At least, in my experience.
I’ve used it most extensively doing Ruby on Rails greenfield apps, and also some JS front ends, some Python mid sized apps, and some Rust and Nix utilities. You’re absolutely right about it struggling with code base scale, I had to rework the design process around this. Essentially, design documentation telling the story, workflow documentation describing in detail every possible functionality, and an iteration schedule. So the why, what, and how formalized and in detail, in that order. It can generate the bulk of those documents given high level explanations, but require humans to edit them before making them the ‘golden’ references. Test driven development is beyond critical, telling it everywhere to use it extensively with writing failing tests first seems to work best.
So to actually have it do a thing I load those documents into context, give it a set unit of work from the iteration schedule, and work on something else.
It does go down some seriously wrong paths sometimes, like writing hacky work arounds if it incorrectly diagnosing some obscure problem. I’ve had a few near misses where it tried to sneak in stuff that would bury future work in technical debt. Most problematic is it’s just subtle enough that a junior dev might miss it; they’d probably get sent down a rabbit hole with several layers of spaghetti obscuring the problem.
That sounds like you’re still doing a lot of work. Is that net new work you wouldn’t have done before (like would you have needed to write those docs before as well)? Writing code never feels like the complicated or time expensive part to me. Figuring out what I want to do is, and I need to do that with either approach, and then thinking through how I’d like to organize things is another time sink, and perhaps that can be replaced/augmented by ai, but organizing things well requires long term thinking and is very hard to explain
That’s perfect for higher ups. They don’t care if what you release has bugs as long as you work on them when they pop up, they consider that part of your job. They want a result quickly and will accept 85% if it moves the needle forward.
These people don’t care about technical debt, they don’t care about exploits until it happens to them, then it’s how bad and how long to fix. No one cares about doxxes anymore, it’s just the cost of doing business. Like recalls.
This is perfect for CEOs and billionaires because they don’t care how something is done at a 35,000 foot view, they just want it now. AI is a nightmare of exploits that haven’t even begun to be discovered yet. Things that will be easily exploitable, especially by other algorithms.
Coders are just as effected by supply and demand, and the demand is for AI products.
Hmm, a lot of my career was done doing embedded programming, where mistakes in production are very costly, and software/hardware has to be released with basically zero bugs, so that may be where the disconnect is. I still think bugs and technical debt are costly elsewhere too if the product is going to have a long lifecycle, but executives are just dumb.
I’m finding AI effectively automates entry level jobs and interns. The long term implications is very few will be able to enter the field. What do we do when all the experienced engineers retire? How will we shift our economy to work for everyone under this model?
Can you give an example of what those entry level jobs may be? Because I have yet to encounter a position where an AI would be as smart as an entry level person.
I’m a professional developer and have tested AI tools extensively over the last few years as they develop. The economic implications of the advancements made over the last few months are simply impossible to ignore. The tools aren’t perfect, and you certainly need to structure their use around their strengths and weaknesses, but assigned to the right tasks they can be 10% or less of the cost with better results. I’ve yet to have a project where I’ve used them and they didn’t need an experienced engineer to jump in and research an obscure or complex bug, have a dumb architectural choice rejected, or verify if stuff actually works (they like reporting success when they shouldn’t), but again the economics; the dev can be doing other stuff 90% of the time.
Don’t get me wrong, on the current trajectory this tech would probably lead to deeply terrible socioeconomic outcomes, probably techno neofeudalism, but for an individual developer putting food on the table I don’t see it as much of a choice. It’s like the industrial revolution again, but for cognitive work.
I keep hearing stuff like this, but I haven’t found a good use or workflow for AI (other than occasional chatbot sessions). Regular autocomplete is more accurate (no hallucinations) and faster than AI suggestions (especially accounting for needing to constantly review the suggestions for correctness). I guess stuff like Cursor is OK at making one-off tools on very small code-bases, but hits a brick-wall when the code base gets too big. Then you’re left with a bunch of unmaintainable code you’re not very familiar with and you would to spend a lot of time trying to fix yourself. Dunno if I’m doing something wrong or what.
I guess what I’m saying is that using AI can speed you up to a point while the project accumulates massive amounts of technical debt, and when you take into account all the refactoring and debugging time, it results in taking longer to produce a buggier project. At least, in my experience.
I’ve used it most extensively doing Ruby on Rails greenfield apps, and also some JS front ends, some Python mid sized apps, and some Rust and Nix utilities. You’re absolutely right about it struggling with code base scale, I had to rework the design process around this. Essentially, design documentation telling the story, workflow documentation describing in detail every possible functionality, and an iteration schedule. So the why, what, and how formalized and in detail, in that order. It can generate the bulk of those documents given high level explanations, but require humans to edit them before making them the ‘golden’ references. Test driven development is beyond critical, telling it everywhere to use it extensively with writing failing tests first seems to work best.
So to actually have it do a thing I load those documents into context, give it a set unit of work from the iteration schedule, and work on something else.
It does go down some seriously wrong paths sometimes, like writing hacky work arounds if it incorrectly diagnosing some obscure problem. I’ve had a few near misses where it tried to sneak in stuff that would bury future work in technical debt. Most problematic is it’s just subtle enough that a junior dev might miss it; they’d probably get sent down a rabbit hole with several layers of spaghetti obscuring the problem.
That sounds like you’re still doing a lot of work. Is that net new work you wouldn’t have done before (like would you have needed to write those docs before as well)? Writing code never feels like the complicated or time expensive part to me. Figuring out what I want to do is, and I need to do that with either approach, and then thinking through how I’d like to organize things is another time sink, and perhaps that can be replaced/augmented by ai, but organizing things well requires long term thinking and is very hard to explain
That’s perfect for higher ups. They don’t care if what you release has bugs as long as you work on them when they pop up, they consider that part of your job. They want a result quickly and will accept 85% if it moves the needle forward.
These people don’t care about technical debt, they don’t care about exploits until it happens to them, then it’s how bad and how long to fix. No one cares about doxxes anymore, it’s just the cost of doing business. Like recalls.
This is perfect for CEOs and billionaires because they don’t care how something is done at a 35,000 foot view, they just want it now. AI is a nightmare of exploits that haven’t even begun to be discovered yet. Things that will be easily exploitable, especially by other algorithms.
Coders are just as effected by supply and demand, and the demand is for AI products.
Hmm, a lot of my career was done doing embedded programming, where mistakes in production are very costly, and software/hardware has to be released with basically zero bugs, so that may be where the disconnect is. I still think bugs and technical debt are costly elsewhere too if the product is going to have a long lifecycle, but executives are just dumb.
There’s been an unbalancing of top down power, especially in venture capital, we will pay for these decisions down the line.
I’m finding AI effectively automates entry level jobs and interns. The long term implications is very few will be able to enter the field. What do we do when all the experienced engineers retire? How will we shift our economy to work for everyone under this model?
Forward-thinking has never been capitalism’s strong suit.
Can you give an example of what those entry level jobs may be? Because I have yet to encounter a position where an AI would be as smart as an entry level person.