I think the whole AI vs non. AI debate is a bit besides the point. Engineers are stuck in the old paradigm of "perfect" algorithms.
I think the image you post at the beginning basically sums it up for me: ChatGPT o3/5 Thinking can one-shot 75% of most reasonably sized tasks I give it without breaking a sweat, but struggles with tweaks to get it to 100%. So I make those tweaks myself and I have cut my code writing task in half or one third of the time.
ChatGPT also knows more idioms and useful libraries than I do so I generally end up with cleaner code this way.
Ferrari's are still hand assembled but Ford's assembly line and machines help save up human labor even if the quality of a mass-produced item is less than a hand-crafted one. But if everything was hand-crafted, we would have no computers at all to program.
Programming and writing will become niche and humans will still be used where a quality higher than what AI can produce is needed. But most code will be done by minotaur human-ai teams, where the human has a minimal but necessary contribution to keep the AI on track... I mean, it already is.
Hard disagree. We'll be able to use more expressive languages with better LLM support for understanding how to express ourselves and to understand compiler results. LLMs are only good at stuff that better languages don't require you to do. After that they fall off the cliff quickly.
LLMs are a communication technology, with a huge trained context of conversation. They have a long way to go before becoming anything intelligent.
LLMs lack intentionality, and they lack the ability to hold a series of precepts "in mind" and stick to those precepts. That is, if I say "I want code that satisfies properties A, B, C, D..." at some point the LLM just can't keep track of all the properties, which ones are satisfied, which ones aren't, what needs to be done or can be done to make them all satisfied.
But LLMs aren't "only good at stuff that better languages don't require you to do." In fact they are very good at taking a bad function definition and turning it into an idiomatic one that does what I wanted to do. That's very intelligent, there is no language that can take a bad spec and make it specific and fit for the specified task. LLMs can. (not perfectly mind you, but faster and often better than I can.) The problem is they just can't always figure out when what they've written is off-spec. But "always" isn't "never" and I've yet to meet an intelligence that is perfect.
> LLMs ... lack the ability to hold a series of precepts "in mind" and stick to those precepts.
That is perhaps the biggest weakness I've noticed lately, too. When I let Claude Code carry out long, complex tasks in YOLO mode, it often fails because it has stopped paying attention to some key requirement or condition. And this happens long before it has reached its context limit.
It seems that it should be possible to avoid that through better agent design. I don't know how to do it, though.
Depends on what you do, and what systems you develop for I would reckon. If it's another TODO app or some kind of table + form system that's been done to death - AI can probably have a go at creating a barebones minimal viable product. Targeting code that's outside the sweet spot of the training data ("blurry" area), you'll start to stumble. I've also found agents to be useless in large code bases with distributed logic where parts are in react, web back-end, service system). Slow and unreliable for large systems. Good for small tasks and scaffolding up proof of concepts.
This comment captures it.
AI can do 80% of the work. I can review it later. And I spend much less time reviewing than I would have typing up everything manually.
I recently used it to add some logging and exception handling. It had to be done in multiple places.
A simple 2 line prompt one shotted it. Why do I need to waste time writing boring code?
Are you still going to have the skills to review it a year from now? Or 5 years from now when you’ve become accustomed to only writing <20% of the code? I’m already witnessing my coworkers skills degrading because of this, and it’s only going to get worse. Programming is a language, and when you don’t use it, it fades.
What will happen is that we as developers will move one layer up in the abstraction. In the future it would seem a bit nonsensical to focus on individual lines of code and syntax because AI can more or less deal with it.
We will be focusing more higher level design - which database, where the data flows, which service is used where and so on. So you will just need different skills. Coding as a skill won't be that important.
Anyone old enough to remember when UML promised the same thing? Never have to touch code again -- just draw boxes, charts, activity diagrams, etc. and right-click and generate code! Rational Rose is the future of programming!
It always turns out that when you have to make a computer execute a very precise set of operations, the absolute best way to communicate this is not English, not diagrams, but code.
It also turns out that the challenge is generally not writing that code, but figuring out that precise set of operations in the first place.
Yes, and in the enterprise space, many workflows are nowadays done with lowcode/no-code tooling, like PowerApps, Adobe Workfront Fusion, Outsystems, among others.
Many of these tools, have started to go one step further, and the traditional ways are supplanted with AI, reducing even further the amount of low code/no code scenarios that a human has to produce.
The only thing left are the few serverless deployments to call into some code that needs more complex integration logic, and that is about it.
I have seen enough of these projects, to realize it is going to happen, not everywhere maybe, but in specific industry sectors certainly, it already is.
It's almost as if we write code because it is a very precise language... a feature that natural languages don't have.
I'm just going to leave this here...
You still communicate in code and fix the errors.
> You still communicate in code and fix the errors.
But you just said
> Coding as a skill won't be that important.
So coding is only important if you need to make your software actually work, I guess?
I just said its not that important. Your time won't be spent coding but rather fixing stuff here and there.
... so how are you supposed to fix things without coding, given that you admitted you need to dive into the code to fix things?
Its not a binary - we will spend less time thinking about code and occasionally fix things and more time on higher level abstractions.
Just like I sometimes need to understand and dive into assembly but not always. Its a spectrum and the value in the spectrum changing has consequences for how we work.
That is wishful thinking. Every layer we added between humans and the machines (and even the ones in the machines themselves) take hordes of dedicated humans to maintain: IDEs, compilers/interpreters, linters, CI tools, assemblers, linkers, operating systems, firmware, microcode, circuitry, circuit elements (manufacturing processes).
Just about every time somebody on this site says “we developers”, you can assume they’re ignoring the (large majority of) developers that don’t work on the same things they do, in the same way.
Yes, all those ever-growing layers of intricate abstraction that you take for granted and “don’t have to worry about” are conceived of, designed, built, and maintained by developers. Who do you think wrote the compiler for that syntax you don’t want to learn?
The point of abstraction is that it doesn’t leak. Most developers don’t need to understand compiler theory or assembly to be productive.
No one in my company writes assembly. very few developers work at that level of abstraction - this means those who made the compilers are doing a good job.
Yes, and very few people working on compilers do OS kernels, and very few people working on databases do compilers, etc. etc. My point is, they're all developers, so when you say "we developers", you'd better be speaking for all of them.
I agree with you. But not many people work with or understand the abstraction at OS or circuitry level.
That’s kind of my point: most people will work on higher abstractions but there will be some who maintain lower ones.
I write C# but I barely care about memory, gc nor microcontrollers nor assembly. Vast majority of people work on higher abstractions.
I would challenge that it is really a vast majority working at these highest levels of abstractions. There are thousands of people working on C#, Java and JavaScript runtimes and basic libraries. There are thousands of people working on compilers and thousands more (morw likely tens of thousands) working on operating systems and drivers etc... I think that the amount of effort that goes into all of this is severely underestimated because it so far removed from the perspective of a high level application developer.
if this is true we will have to trust committing prompts not code.
I don’t worry about assembly because the abstraction layer is reliable. I don’t worry about ISAs, ucode, transistors, etc. the abstraction layer is reliable.
The same is not true for LLMs today. Circumstantial evidence: people commit the lower layer. That’s not an abstraction any more than an IDEs tab complete or “new project template” is an abstraction.
When someone stops reading the output entirely and has a codebase that is only prompts, I’ll hear them out on skill obsolescence.
(Edit: changed to hypothetical)
> individual lines of code and syntax because AI can more or less deal with it
Individual lines of code can bring down an entire system.
> and syntax
I’ve never seen this be a real issue even for lower performing devs.
[dead]
If that’s a real effect, the best programmers absolutely will. You could spend 10% of your working time doing exercises, and still have double the productivity you used to have.
> Why do I need to waste time writing boring code?
Some people actually enjoy that, believe it or not.
seriously! excuse me while I cry with the artists about the robot doing the fun part. automate my goddamned dishes, not the coding! I chose this field because I liked that part ;_;
honestly I've largely stopped coding for fun since Claude Code got popular. It's too expensive to use for personal projects, and it does the fun part. I don't want to pay but if I'm not using it, all I can think about is how inefficient doing everything manually is
..
I'm going to get into gardening or something :(
I can highly recommend gardening. Even before LLM's I found it much more satisfying than coding as a hobby.
> AI can do 80% of the work. I can review it later. And I spend much less time reviewing than I would have typing up everything manually
Are you sure you're actually reviewing the code? Deeply, properly reviewing and understanding it? Because from what I've seen people that say they do, don't. That's why they 'speed up' from using LLM-generated code.
The old adage that it's far harder to review code than to write it still holds true.
> Why do I need to waste time writing boring code?
The better question is: should that boring code be written? Code should only be non-boring.
The boredom of writing the code is not the only problem. The subsequent continued indefinite existence of that code is also a problem.
Yes who needs logging :)
This reminds me of the phenomenon of competence being inversely correlated with confidence until some inflection point is reached on the journey to mastery.
I think the argument being put forward here is that writing that boring code is part of the journey to mastery. If you haven't crossed the inflection point, a backwards slide in skills will result in less competence and more confidence, which is a scary thought given how software runs the world.
What you shouldn't forget also is that, while AI may be good at coming up with a "first shot" solution, it may be much worse if you want to change/correct parts of it.
In my experience, AI very often gets into a sort of sunk-cost fallacy (sunk prompt?) and then it is very hard to get it to make significant changes, especially architecturally.
I recently wrote an extension for a popular software product and gave AI the same task. It created a perfectly working version however it was 5x the lines of code of my version because it didn't know the extension API as well, even though I gave it the full documentation. It also hard coded some stuff/solutions to challenges that we totally don't want to be hard coded. A big reason why I arrived at a much better solution was that I used a debugger to step through the code and noted down just the API interactions I needed.
The AI also was convinced that some things were entirely impossible. By stepping through the code I saw that they would be possible by using parts of the internal API. I suggested a change to make the public API better for my use case in a GitHub issue and now it is totally not impossible.
At the end of the day I have to conclude that, the amount of time invested guiding and massaging the AI was too much and not really worth it. I would've been better off debugging the code right away and then creating my own version. The potential for AI to do the 80% is there. At this time though I personally can't accept its results yet but that may also be due to my personal flavour of perfectionism.
The fear and pain of writing boring code gave us the processes and systems we have today. i.e. microservices, (I know they have their own problems) were born out of pain from maintaining monoliths. Agile (again, own problems) was born out of pain from waterfall.
What happens when all pain and boredom is taken away? Why do we need to invent new frameworks, systems, design patterns in that scenario?
This comment captures it: https://news.ycombinator.com/item?id=44906987
However, from my experience, the quality of code produced by developers in project we are working for the last 3+ years gone south. Amount of bugs now literally tripled year on year. I bet the reason is extensive use of AI tools, as the developers are the same.
Its because most people don't have the discipline and obsession of attention to detail to know when one should use an LLM and when one shouldn't.
I highly doubt the best of the best folks are even touching LLMs (barely) because they can see the flaws and the tradeoffs that are not-so-visible to others until they are 50 levels deep, with sunk investments, unwilling to go back to how they used to do things.
Engineering isn't stuck on perfect algorithms. Management is. There's lip service for AI code gen, but if my name is on a module, I still have to vouch for its correctness. If it's wrong, that might become my problem. I don't always write perfect code, but I aspire to. If I see evidence that these tools are writing more correct and reliable code than I do, then I will start to take it more seriously. For some code, it matters whether it's robust basically.
Yep, I don't think you'll ever see AI code near the payment system. Or the code managing the store and the cart systems.
But now, I'm not sure a lot of programmers even know what a profiler is. When was the last time you saw someone profile their code?
Problem is we've taken the idea of "minimum viable product" too far. People are saying "Doesn't have to be perfect, just has to work." I think most people agree. But with the current state of things? I disagree that things even work. We're so far away from the question of optimization. It's bad enough that there are apps that require several gigs to just edit a 30kb document but FFS we're living in a world where Windows Hello crashes Microsoft Outlook. It's not the programs are ugly babies that could be better, they are monstrosities begging to be put to death.
I WISH we could talk about optimization. I WISH our problem was perfectionism. But right now our problem is that everything is a steaming pile of garbage and most people are just shrugging their arms like "it is the way it is". Just because you don't clean up that steaming pile of garbage doesn't mean someone else doesn't. So stop passing the buck.
> When was the last time you saw someone profile their code?
A year ago. I heavily relied on one to optimize a complex data import that took an hour for a million line Excel file. The algorithm translated it to a graph according to a user-specified definition and would update an existing graph in neo4j, keeping the whole thing consistent.
The only other guy who understood the algorithm (a math PhD) thought it was as optimal as it could get. I used the profiler to find all the bottlenecks, which were all DB checks for the existence of nodes, and implemented custom indices to reduce import time from an hour to 3 minutes.
It did introduce a bunch of bugs that I had to fix, but I also discovered some bugs in the original algorithm.
It was one of my best programming experiences ever. Especially the payoff at the end when it went down from an hour to 3 minutes is a dopamine rush like never before. Now I want to optimize more code.
I don't think users cared, though; originally this work would take days by hand, so an hour was already pretty good. Now I made something fiendishly complex look trivial.
Seeing those big improvements and knowing you did more than make it faster is always really rewarding. I hope you do do more optimization :) Just remember Knuth's advice. Because IO is a common problem and Big O isn't going to tell you about that one haha
Yeah, I first want to know there's an actual performance issue to fix. That's basically what Knuth said, and that's what I live by.
> People get lulled into a false sense of security because tests pass but test just aren't enough.
Users weren't using a particular feature because they said they didn't understand it. So we explained it, again and again. Turns out that feature was incredibly buggy and basically worked the way we claimed it did, only when it was used in the specific configuration we tested for. Add another node somewhere and weird stuff starts happening.
The tests looked good, and code coverage was great, but the fact that the tests run through all the branches of the code doesn't mean you're really testing for all behaviour. So I added tests for all configurations I could think of. I think that revealed another bug.
So look at the actual behaviour you need to test, not merely the code and branch coverage.
I invite your attention to the StatsD telemetry protocol, where:
1. Every single measurement in a timeseries is encoded as a utf-8 string having (roughly) the following format:
where name is like "my.long.namespace.and.metric.name", value is a string formatted number, god only knows what type is, and tags is some gigantic comma separated key:value monstrosity.2. Each and every one of these things is fired off into the ether in the form of a UDP datagram.
3. Whenever the server receives these presumably it gets around sometime to assigning them timestamps and inserts them into a database, not necessarily in that or any other particular order.
"it is the way it is[1]."
[1] https://github.com/statsd/statsd?tab=readme-ov-file#usage
I think NodeJS goes against the idea of writing good and efficient software... JS just creates unnecessary complexity
I don't really know anything about js but this metrics protocol is how most telemetry data is transmitted on the wire. Petabytes per day of bandwidth are wasted on this.
Ah, I'm bookmarking this. Thanks for writing this :)
I love how you put it: "grab a fucking profiler before you optimize". I get complaints sometimes about using FP because of performance, and I think a variant of "grab a fucking profiler before you optimize" is much better response than "avoid premature optimization". Introducing them to a magical thing called as "profiler" is a nice bonus too.
> Problem is we've taken the idea of "minimum viable product" too far. People are saying "Doesn't have to be perfect, just has to work." I think most people agree. But with the current state of things? I disagree that things even work. We're so far away from the question of optimization. It's bad enough that there are apps that require several gigs to just edit a 30kb document but FFS we're living in a world where Windows Hello crashes Microsoft Outlook. It's not the programs are ugly babies that could be better, they are monstrosities begging to be put to death.
LOL. OMG that was beautiful. It almost feels like we are de-evolving software to a state where shit is going to stop working bad. I know this is not full of facts, but this take reminds me of Jonathan Blow's video "Preventing the Collapse of Civilization"[0] Where he talks about how code runs worse than it ever has and I think he was arguing that civilization is collapsing before our eyes in slow time.
[0]: https://youtu.be/pW-SOdj4Kkk?si=LToItJb1Cv-GgB4q&t=1089
Good talk. I did something similar to him and all that happened is everyone was just saying I'm making a lot out of nothing. They're right that each thing was "nothing" but the problem is that this is a non-trivial number of "nothings" happening every day...
Honestly, I think the problem is that it's a Lemon Market[0]. Lemon markets thrive when there is asymmetric information. When a customer cannot tell the difference between a good product (peach) and a bad product (lemon). All it takes is a bunch of tech illiterate people... not sure where we'll find those...
On your video, funny thing. When I was in my PhD I had a very hard time publishing because I was building models that were much smaller, required less data, but got similar performance. Reviewers just looked at the benchmark like "not SOTA? lol". I've seen tons of great papers solving similar problems constantly get rejected. As a reviewer I frequently defended works like that as well as works that had good ideas but just didn't have enough GPU power. It was really telling...
[0] https://en.wikipedia.org/wiki/The_Market_for_Lemons
[P.S.] A nice solution I found for the pasting problem he mentioned (and in various forms) is that I first paste the text into the url bad or search bar then copy that and then paste. {<c-k>,<c-l>}<c-v><c-a><c-c>. Works 98% of the time every time.
Another hard disagree. The crux here is that if u are not an expert in the given domain you do not know where that missing 25% is wrong. You think you do but you dont.
I have seen people bring in thousands of lines of opencv lut code in ai slop form because they didnt understand how to interpolate between two colors and didnt have the experience to know that is what they needed to do. This is the catch 20/20 of the ai expert narrative.
The other part is that improvement has massively stagnated in the space. It is painfully obvious too.
<< you do not know where that missing 25% is wrong
I think there is something to this line of thinking. I just finished a bigger project and without going into details, one person from team supposedly dedicated to providing viable data about data was producing odd results. Since the data was not making much sense, I asked for info on how the data was produced. I was given SQL script and 'and then we applied some regex' explanation.
Long story short, I dig in and find that applied regex apparently messed with dates in an unexpected way and I knew because I knew the 'shape' that data was expected to have. I corrected it, because we were right around the deadline, but.. I noted it.
Anyway, I still see llm as a tool, but I think there is some reckoning on the horizon as:
1. managers push for more use and speed given that new tool 2. getting there faster wronger, because people go with 1 and do not check the output ( or don't know how to check it or don't know when its wrong )
It won't end well, because the culture does not reward careful consideration.
Exactly. I use ai tools daily and they bite me. Not enough to stop but enough to know. Recently was building a ws merger of sorts based on another libs sub protocol. I wasnt familiar with the language or protocol but ai sure was. However the ai used a wrong id when repacking messages. Unless i knew the spec (which i didnt) i never would have known. Eventually, i did read the spec and figured it out.
To be clear here i give the spec to ai many times asking what was off and it never found the issue.
Once i did get it working, ai one shotted converting it from python to go with the exception of the above mistake being added back in again.
You dont know what you dont know. That final 25% or 5% or whatever is where the money is at, not the 80%. Almost doesnt count.
Yeah, I'm trying to branch out to build things outside my comfort zone. AI already is helpful in my normal day-to-day (backend engineer, mostly Python w/ PyCharm/Datagrip working in monorepo style), but Claude Code helped me graft on a Typescript/Javascript UI ecosystem with an independent Webstorm IDE setup that will integrate well, along with all the presumably latest build tools and package management. The research and self-education I would have needed to do this without Claude would have been extensive, and would have been nowhere as complete. I don't see any point in going back to pre-AI. And I don't generally use AI results blindly, I go back and review and set some breakpoints in an IDE and watch it do its thing.
The analogy seems to fall apart because the quality of an assembly line produced car is higher than the hand crafted one. Fords lose out because they engineer to a price point, a Ferrari doesn't have that "problem" - arguably, the more expensive the better.
>The analogy falls apart because the quality of an assembly line produced car is higher than the hand crafted one.
What? So Your Jeep Compass is higher quality than a 458?
try driving a ferrari 80,000 miles without taking it into the shop...
Try driving a Jeep Compass 5000 miles without taking it into the shop lmao. Terrible example
:^)
> Programming and writing will become niche and humans will still be used where a quality higher than what AI can produce is needed. But most code will be done by minotaur human-ai teams, where the human has a minimal but necessary contribution to keep the AI on track... I mean, it already is.
Or alternatively, we will build bigger and better things with the power of AI. Everyone talks about it replacing us, but we aren't running out of things to build. It's not like we're gonna run out of ways to improve the world, and compared to other things the digital world is unconstrained.
Why are you assuming the cases where humans can code better than AI still exists after three years say -- I think in some industries today artisanal products are also not higher quality than machine made ones.
Because we're not even close to that happening. However I've observed a sort of cognitive decline in a few formerly experienced and extremely knowledgeable developers whom I used to respect. They happen to be the most vocal supporters of LLMs now and sometimes our discussions get so ridiculous I have to practically beg them to put the LLM down and just think about what they're saying for just 5 seconds. I think they're about a year away from believing that the sky is green if the AI says so.
So maybe LLMs will win on a technicality by making us more stupid as a species.
Progress under current paradigms has gotten much slower
Extraordinary claims require extraordinary evidence and if there's one thing we've learned is that progress in AI is not linear nor predictable. We've been a few years away from fully self-driving cars for a really long time now.
Yeah, it's definitely shifting how we work. Feels like an upgrade from high-level languages, but we're still guiding the process. The performance boost is real, but once everyone adopts it, we'll probably just be building more complex things at a similar pace.
IMO LLMs have demonstrated how uncomplicated the man behind the curtain really is. If we do happen to achieve AGI it will likely have many of the problems associated with the real thing which often fails in spectacular fashion.
> ChatGPT also knows more idioms and useful libraries than I do so I generally end up with cleaner code this way ...
... and can now measure my productivity in dependencies added per day! :)
You are describing accurately where AI is now. But it wasn't there two years ago and it won't be there in two years. You still have 25% of your job today, but this will most likely gonna halve evey x-number of months.
I mean I'm opposed to all work, but the transition to jobless society might be traumatic. I hope you didn't neglect to buy crypto because that's the only remaining way to spontaneously and easily tap into the wealth of the rich when the work is gone.