LLM-powered tools amplify developer capabilities rather than replacing them
249 comments
·April 21, 2025skydhash
jandrese
I have a feeling that people who got bogged down in step 3 were the kind of people who write a lot of wordy corporate boilerplate with multiple levels of abstraction for every single thing. AKA "best practices" type coding.
For me the most important part of a project is working out the data structures and how they are accessed. That's where the rubber meets the road, and is something that AI struggles with. It requires a bit too high a level of abstract thinking and whole problem conceptualization for existing LLMs. Once the data structures are set the coding is easy.
xyzzy123
> Once the data structures are set the coding is easy.
I don't always find this, because there's a lot of "inside baseball" and accidental complexity in modern frameworks and languages. AI assist has been very helpful for me.
I'm fairly polyglot and do maintenance on a lot of codebases. I'm comfortable with several languages and have been programming for 20 years but drop me in say, a Java Spring codebase and I can get the job done but I'm slow. Similarly, I'm fast with TypeScript/CDK or Terraform but slow with cfndsl because I skipped learning Ruby because I already knew Python. I know Javascript and the DOM and the principles of React but mostly I'm backend. So it hurts to dive into a React project X versions behind current and try to freshen it up because in practice you need reasonably deep knowledge of not just version X of these projects but also an understanding of how they have evolved over time.
So I'm often in a situation where I know exactly what I want to do, but I don't know the idiomatic way to do it in a particular language or framework. I find for Java in particular there is enormous surface area and lots of baggage that has accumulated over the years which experienced Java devs know but I don't, e.g. all the gotchas when you upgrade from Spring 2.x to 3.x, or what versions of ByteBuddy work with blah maven plugin, etc.
I used to often experience something like a 2x or 3x hit vs a specialised dev but with AI I am delivering close to parity for routine work. For complex stuff I would still try to pair with an expert.
remus
This matches my experience. In practice there's just a lot of stuff (libraries, function names, arguments that go in, library implementation details etc.) you need to remember for most of the programming I do day to day, and AI tools help with recalling all that stuff without having to break out of your editor to go and check docs.
For me this becomes more and more relevant as I go into languages and frameworks Im not familiar with.
Having said that you do need to be vigilant. LLMs seem to love generating code that contains injection vulnerabilities. It makes you wonder about the quality of the code it's been trained on...
Nevermark
> I don't always find this, because there's a lot of "inside baseball" and accidental complexity in modern frameworks and languages. AI assist has been very helpful for me.
My use of esoteric C++ has exploded. Good thing I will have even better models to help me read my code next week.
The much lowered bar to expanding one’s toolkit is certainly noticeable, across all forms of tool expansion.
raghavbali
Can't express it more clearly than this. Data structures are just one part of the story not the only spot where the rubber meets the road IMO too. But going back to top of the thread, for new projects it is indeed steps 2 and 3 that consume most time not step 3
Xmd5a
ByteBuddy is atrocious.
>In October 2015, Byte Buddy was distinguished with a Duke's Choice award by Oracle. The award appreciates Byte Buddy for its "tremendous amount of innovation in Java Technology". We feel very honored for having received this award and want to thank all users and everybody else who helped making Byte Buddy the success it has become. We really appreciate it!
Don't misread me. It's solid software. And an instance of a well structure objet-oriented code base.
But it's impossible to do anything without having a deep and wide understanding of the class hierarchy (which is just as deep and wide). Out of 1475 issues on the project's Github page, 1058 are labelled as questions. You can't just start with a few simple bricks and gradually learn the framework. The learning curve is super steep from the get go, all of the complexity is thrown into your face as soon as you enter the room.
This is the kind of space where LLM would shine
palmotea
> I have a feeling that people who got bogged down in step 3 were the kind of people who write a lot of wordy corporate boilerplate with multiple levels of abstraction for every single thing. AKA "best practices" type coding.
Or they're the kind of people who rushed to step 3 too fast, substantially skipping steps 1 and/or 2 (more often step 2). I've worked with a lot of people like that.
soco
You mean, move fast and break things? This was usually seen as a good thing in a certain culture. Maybe the whole current discussion (here and everywhere) is the two cultures clashing?
ehnto
It also often requires knowledge the LLM doesn't contain, which is internal historical knowledge of a long running business. Many businesses have a "person", an oracle of sorts, that without their input you would never be able to deliver a good outcome. Their head is full of years of business operations history and knowledge unique to that business.
nyrikki
While probably not useful for everyone, the best method for myself actually leverages that.
I am using a modified form of TDD's red/green refactor, specifically with an LLM interface independent of my IDE.
While I error on good code over prompt engineering, I used the need to submit it to both refine the ADT and domain tests, after creating a draft of those I submit them to the local LLM, and continue on with my own code.
If I finish first I will quickly review the output to see if it produced simpler code or if my domain tests ADT are problematic. For me this avoids rat holes and head of line blocking.
If the LLM finishes first, I approach the output as a code base needing a full refactor, keeping myself engaged with the code.
While rarely is the produced code 'production ready' it often struggles when I haven't done my job.
You get some of the benefits of pair programming without the risk of demoralizing some poor Jr.
But yes, tradeoff analysis and choosing the least worst option is the part that LLM/LRMs will never be able to do IMHO.
Courses for horses and nuance, not "best practices" as anything more than reasonable defaults that adjust for real world needs.
exe34
same, the amount of work I have to put into thinking of what to say to the llm is the same or more work than just telling the compiler or interpreter what I want (in a language I'm familiar with), and the actual coding is the trivial part. in fact I get instant feedback with the code, which helps change my thinking. with the llm, there's an awkward translating for the llm, getting the code, checking that it might do what I want, and then still having to run it and find the bugs.
the balance only shifts with a language/framework I'm not familiar with.
jimbokun
I think it’s useful to think of LLMs performing translation from natural language to a program language. If you already speak the programming language fluently, why do you need a translator?
lherron
I agree for method-level changes, but the more you’re willing to cede control for larger changes, even in a familiar language, the more an LLM accelerates you.
For me, I give Gemini the full context of my repo, tell it the sweeping changes I want to make, and let it do the zero to one planning step. Then I modify (mostly prune) the output and let Cursor get to work.
ornornor
Enterprise code with layers of abstraction isnt best practice. It’s enterprise code.
delecti
I would imagine that's why they had "best practices" in quotes. Lots of enterprisey things get pushed as a "good practice" to improve reuse (of things that will never be reused) or extensibility (of things that will never be extended) or modularity (of things that will never be separated).
DonHopkins
Mindless repetition of something you've internalized and never think about and never get any better at is "Best Reflex" not "Best Practice".
otabdeveloper4
Nah, these are the people who don't know the difference between a variable and a function call and who think FizzBuzz is a "leetcode interview problem".
DonHopkins
I hate it when variables won't and constants aren't and functions don't.
null
vlovich123
And the article is overstating it as well. My confidence in the LLM's ability to reduce the "how" part is primarily based on "am I doing something the LLM is good at?". If I'm doing HTML React where there's a million examples of existing stuff, then great. The more novel what I'm asking is, the less useful the LLM becomes and the more stuff I need to handcode is. Just as with self-driving cars, this is a challenge because switching from "the LLM is generating the code" to "I'm hand-tweaking things" is equivalent to self-driving disconnecting randomly and telling you "you drive" in the middle of the highway. Oh and I've caught the LLM randomly inserting vulnerabilities for absolutely no reason (e.g. adding permissions to the iframe sandbox attribute when I complained about UI layout issues).
It's a useful tool that can accelerate certain tasks, but it has a lot of sharp edges.
bluefirebrand
> If I'm doing HTML React where there's a million examples of existing stuff, then great
I do quite a bit of this and even here LLMs seem extremely hit and miss, leaning towards the miss side more often than not
ijk
I think React is one of those areas where consistency is more important than individual decisions. With a lot of front-end webdev there's many right answers, but they're only right if they are aligned with the other design decisions. If you've ever had to edit a web page with three different approaches to laying out the CSS you know what I mean.
LLMs _can_ do consistency, they're pretty good continuing a pattern...if they can see it. Which can be hard if it's scattered around the codebase.
snovv_crash
> Oh and I've caught the LLM randomly inserting vulnerabilities for absolutely no reason (e.g. adding permissions to the iframe sandbox attribute when I complained about UI layout issues).
Yeah, now get the LLM to write C++ for a public facing service, what could possibly go wrong?
elevaet
This is the real reason why it's an amplifier and not a replacer.
UltraSane
LLMs are MUCH better generating Python or SQL than APL.
corytheboyd
I agree, they are very much exaggerating both time spent writing code, as well as the amount of time LLMs shave off. LLM coding very much does NOT take “near zero time,” I would argue that sometimes it can take the same amount of time or longer, compared against simply knowing what you are doing, with tools you know how to use, referring to documentation you know how to interpret, understanding the systems around, the business around, what team A and C need in two quarters so we better keep it in mind… etc.
Snuggly73
I had the weirdest experience the other day. I wanted to write an Expo React Native application - something I have zero experience with (I’ve been writing code non-stop since I was a kid, starting with 6502 assembly). I’ve leaned heavily on Sonnet 3.7 and off we went.
By the end of the day (10-ish hours) all I got to show was about 3 screens with few buttons each… Something a normal React developer probably would’ve spat out in about a hour. On top of that, I can’t remember shit about the application itself - and I can practically recite most of the codebases that I’ve spent time on.
And here I read about people casually generating and erasing 20k lines of code. I dunno, I guess I am either holding it wrong, or most of the time developing software isn’t spent vomiting code.
Graphon1
Interesting. I had the exact opposite experience recently.
I've also been writing code for a long time, did the 6502 assembly thing way back when, and lots since then. For this current project I wanted to build a web app with a frontend in Angular and a backend in Java 21 relying on javalin.io for the services layer. It had a few other integrations as well - into a remote service requiring OAuth and also into subtlecrypto. After less than 10 hours I had a fully functioning MVP that was far superior to anything I could have created without an assistant. It gave me build files, even a test skeleton. Restyling the UI or reflowing the UX to include confirmations, additional steps, modals, ... was really easy. I just had to type it, and those changes would get made. It felt like I was "director of development" for a day.
I used Aider, plugged into Gemini 2.5.
baq
yesterday I've had a numpy+matplotlib prototype spat out by gemini pro, had sonnet 3.7 convert it to a react component and did some final tweaks in chatgpt. took a couple hours of back and forth iteration from idea to a working tool.
of course not everything is how i'd truly like it, but it's 80% there.
before LLMs I wouldn't have bothered with starting. I knew exactly what I want to do, but it'd take me a couple days to proof the idea in Python and then translating it to web ui in TS? forget it
gengstrand
I really appreciate the overall gist of this blog and find value in the mech suit vs human replacement analogy. The "near zero time" observation is not consistent with my own findings which I have blogged about at https://glennengstrand.info/software/llm/migration/java/type... and covers a more specific use case of service migration. Using LLMs did cut the heads down coding time by two thirds but certainly not to zero.
alooPotato
I think you're overly painting that process as a waterfall method. In reality, i think its more of a loop. You do the loop a bunch of times and the solution gets better and better. The act of coding sometimes exposes a lot more of the requirement questions you didn't even know to ask in the first few steps.
So anything that can let you iterate the loop faster is good. The analogy is kind of like if you can make your compile and tests faster, its way easier to code. Because you don't just code and test at the very end, you do it as part of a thinking loop.
skydhash
You can get a lot of stuff designed before having to start the loop, just like you can get the boilerplate code written (or use a framework), before writing any business logic.
Writing code to find specs is brute-forcing the solution. Which is only useful when there's no answer or data (kinda rare in most domains). Taking some time to plan and do research can resolve a lot of inconsistency in your starting design. If you have written the code before, then you'll have to refactor even if the program is correct, because it will be a pain to maintain.
In painting, even sketching is a lot of work. Which is why artists will collect references instead, mentally select the part they will extract. Once you start sketching, the end goal is always a final painting, even if you stop and redo midway. Actual prototyping is called a study and it's a separate activity.
walleeee
> So anything that can let you iterate the loop faster is good.
I think the major objection is that you only want to automate real tedium, not valuable deliberation. Letting an llm drive too much of your development loop guarantees you don't discover the things you need to unless the model does by accident, and in that case it has still trained you to be a tiny bit lazier and stolen an insight you would have otherwise had yourself, so are you really better off?
alooPotato
This is a confusion that comes up often - 100% agree with chat-in-the-loop style interfaces. That slows me down way too much and its too hard to fix when it inevitably gets something wrong.
I'm mostly talking about Cursor Tab - the souped up autocomplete. I think its the perfect interface, it monitors what I type and guesses my intention (multiline autocomplete, and guessing which line I'm going to next).
It lets me easily parse if the LLM is heading in the right direction, in which case pressing tab speeds up the tedium. If its wrong, I just keep typing till it understands what I'm trying to do. It works really really well for me.
I went back to using a non-LLM editor for a bit and I was shocked at how much I had become dependent on it. It was like having an editor that didn't understand types and didn't autocomplete function names. I guess if you're a purist and never used any IDE functionality, then this also wouldn't be for you. But for me, its so much better of an experience.
dragonwriter
> So anything that can let you iterate the loop faster is good.
That's only true if it doesn't negatively impact the value produced each iteration by enough to offset the speed improvement.All other things being equal, going yhrough the loop faster is better, but automation doesn and always keep all other things equal.
hiAndrewQuinn
>That last part is actually the easiest
If it were, the median e.g. business analyst should be getting paid significantly more than the median software engineer. That's not what the data shows, however.
>I can genuinely say in all of my programming projects, I spent more time understanding the problem than writing code.
This is almost trivially true for anyone who understands the problem via writing code, though.
Aperocky
You're assuming only the last part is what software engineer do.
The business analyst mostly just scratch the top half of the first part.
But I do encourage them to go vibe coding! It's providing a lot of entertainment. On off chance, they would become one of us and would be most welcomed.
hiAndrewQuinn
I'm not, but any time spent on #3 is time not spent on #1 and #2. So why wouldn't a profession specialized in the harder tasks make more money?
My real point is claiming #3 is the easiest is just silly. It's obviously much easier to come up with good business ideas in the abstract than to bring them into being. The mixture works because software as a business is an O-ring problem. These 3 tasks are not cleanly separable, they're all part of a feedback loop together.
jimbokun
> If it were, the median e.g. business analyst should be getting paid significantly more than the median software engineer.
What if you replace "business analyst" with "software architect"?
skydhash
Or tech lead and senior engineer. Because they spend more time doing the first two than that last one.
otabdeveloper4
"Business analyst" is not a real job and problem domain analysis is done by software engineers in 100 percent of the workplaces I know.
r00fus
It's a real job - but more in IT and less software development. IT departments in many places prefer to do the buy rather than build since build is more expensive for them.
null
gen220
I'm somebody who used to think your way until very recently (long-time vim user, fast typer, etc.).
I'd recommend you give `aider` specifically a try. It's slowly taken over more and more of the "what" and "how" buckets outlined in the article, especially for large-surface-area code bases.
It turns out, for me at least, there is a big mental activation hurdle between "what" and "how". I need a lot of focus time to pivot between "what" and "how" efficiently, especially for work that spans large parts of the codebase, and work that I'm not super excited about doing. Using `aider` has lowered this activation threshold dramatically. It's made "writing code" about as simple as talking about a technical solution with an intelligent colleague.
I usually follow the format (1) describe the context of the app / problem you're trying to solve (2) describe what you know the solution will look like (3) ask it for clarifying questions / if it needs any examples or context to know the problem space better, and if the solution makes sense / do they see any issues with it? (4) ask it to outline the solution in greater detail, do not write code (5) add any clarifications, now do the thing.
i.e. it's kind of similar to interacting with a super fast, eager, indefatigable junior engineer. Sometimes it misses things or misunderstands, but not nearly enough to make the juice not worth the squeeze. These days, I'd say I spend more time reading/editing claude-generated code, writing commit messages, and managing deployments than I do writing code. It's a higher level of abstraction and I get way more leverage out of the deal. The code I'm writing is, on balance, better than the code I wrote before. It took maybe a few months to get here, but I'm happier for giving it a shot.
matthewsinclair
This more or less describes my experience exactly. There’s obviously going to be a range of views and experiences, but as someone who’s been writing code on and off for nearly 30 years, there’s definitely something in this, notwithstanding the obvious footguns, many of which have been faithfully called out here.
bgwalter
[Replying to the quoted part.]
So when it is convenient, the waterfall model is suddenly modern again?
The waterfall model didn't work, because many inefficiencies and wrong assumptions are found when writing code. This is also the advantage of Lisp and other languages that are malleable and not like a block of concrete.
LLMs are like a block of concrete in that they spit out the same (plagiarized) solutions over and over again. They remove you from the code, they impede flow state due to the constant interactions and outsource your thinking to some GPUs in California.
This waterfall rationalization is just one of the latest absurdities in the LLM blogging industrial complex.
hedora
I watched extreme programming, and then agile morph into "waterfall, but on a one week cycle!" (For extreme programming, two weeks for agile.)
"Modern" software development is based on the idea that waterfall doesn't work, but that you can fix it by only allowing projects that take either one week (dot com boom) or two weeks (web 2.0 boom).
Personally, I avoid all this stuff as much as possible, since I've never seen any of it work.
I have had good luck with LLMs, for what it's worth. I've found for step 4, writing an informal spec at the top of the source file works well:
// $ curl https://api.com/foo/bar?baz
// { json: "response", goes: "here" }
Then I have to read what it wrote and fix the inevitable 2-3 bugs + compilation errors. It usually gets the boilerplate right, but misunderstands fundamental things. This maybe saves me 50% of coding time, since my first draft usually has bugs + doesn't compile on the first attempt either.
This really shines in step 5 (not listed above): Writing tests. I generally end up with more thorough tests than I'd write from scratch in maybe 20% of the time.
Anyway, LLM's let me get about 10-20% more done per week. Most time is still spent designing stuff and evaluating solutions. The above workflow doesn't work for non-trivial modules. It just saves time on boilerplate. It's plagiarism the same way IDE auto-complete and copy / paste (from the same codebase) are.
ebiester
So, while I've written about it before (see https://www.ebiester.com/agile/2023/04/22/what-agile-alterna... ), all methodologies are based on the constraints of the time. Consider that The Pragmatic Programmer (and Andrew Hunt and Dave Thomas are original signatories to the manifesto) suggested in Pragmatic Programmer, “Build One to Throw Away (You will anyway)” And Fred Brooks said the same thing in the Mythical Man Month.
The new constraint is that we can build a prototype much quicker for user testing. If it takes a week to build a trash version to throw away by vibe coding, you should build the trash version and get it in front of users to try out. Then, you can throw it away or do it again.
If you can hammer out a prototype in 2 days (or two or three even) then that's pretty agile.
If I can hammer out that prototype apart from the larger system, even better, because the cost of that prototype is cheap. And so I can totally choose to build it in larger chunks.
I can think of an 18 month project - with a team - back in the day that I could bang out today in a prototype in a month. And I could have gotten it into the customer's hands screen by screen rather than a slow increment every two weeks. (This was an agile project.) I could have built a mock server with mock data. Some of the project would have taken just as long, but that would have been a hell of a lot more agile.
And this project needed 20% novel solution and 80% best practices. Like most software.
valenterry
> That last part is actually the easiest
The last part is wrong, unless it's purely greenfield.
Instead, you first need to read and then modify existing code and ensure it can later be still understood and easily/safely changed by whoever works on it. That is the hard part that is totally missed here.
jongjong
That's correct. Writing code isn't like building a house. It's not like you just add one brick on top of another and every brick you added stays in its place forever. It's much more complex. As requirements change, you may have to pull out some bricks, sometimes redo entire floors.
I've worked with low-code platforms and also built my own low-code platform which allows me to assemble CRUD apps quickly and avoid/bypass a huge range of possible bugs, but even then, it's still not quite like laying bricks... What happens is that the bottleneck becomes UX decision-fatigue. Translating complex business requirements into a working product is rife with conflicts at the level of requirements engineering and UX. You can attain a certain level of software complexity much faster but the requirements also evolve faster to the point where you're constantly thinking about how to make different parts of the UX work well together in a way that's not confusing.
ttul
I started my career as a developer in the 1990s and cut my teeth in C++, moving on to Python, Perl, Java, etc. in the early-2000s. Then I did management roles for about 20 years and was no longer working at the “coal face” despite having learned some solid software engineering discipline in my early days.
As an old geezer, I appreciate very much how LLMs enable me skip the steep part of the learning curve you have to scale to get into any unfamiliar language or framework. For instance, LLMs enabled me to get up to speed on using Pandas for data analysis. Pandas is very tough to get used to unless you emerged from the primordial swamp of data science along with it.
So much of programming is just learning a new API or framework. LLMs absolutely excel at helping you understand how to apply concept X to framework Y. And this is what makes them useful.
Each new LLM release makes things substantially better, which makes me substantially more productive, unearthing software engineering talent that was long ago buried in the accumulating dust pile of language and framework changes. To new devs, I highly encourage focusing on the big picture software engineering skills. Learn how to think about problems and what a good solution looks like. And use the LLM to help you achieve that focus.
luckylion
> So much of programming is just learning a new API or framework.
Once you're good at it in general. I recently witnessed what happens when a junior developer just uses AI for everything, and I found it worse than if a non-developer used AI: at least they wouldn't confuse the model with their half-understood ideas and wouldn't think they could "just write some glue code", break things in the process, and then confidently state they solved the problem by adding some jargon they've picked up.
It feels more like an excavator: useful in the right hands, dangerous in the wrong hands. (I'd say excavators are super useful and extremely dangerous, I think AI is not as extreme in either direction)
alabastervlog
It used to (pre-'08 or so) be possible to be "good at Google".
Most people were not. Most tech people were not, even.
Using LLMs feels a ton like working with Google back then, to me. I would therefore expect most people to be pretty bad at it.
(it didn't stop being possible to be "good at Google" because Google Search improved and made everyone good at Google, incidentally—it's because they tuned it to make being "bad at Google" somewhat better, but eliminated much of the behavior that made it possible to be "good at Google" in the process)
consumer451
This is an excellent analogy. I will be borrowing it. Thank you.
daxfohl
I've been using it to learn Lean, the proof assistant language, and it's great. The code doesn't always compile, but the general structure and approach is usually correct, and it helps understand different ways of doing things and the pros, cons, and subtleties of each.
From this it has me wondering if AI could increase the adoption of provably correct code. Dependent types have a reputation for being hard to work with, but with AI help, it seems like they could be a lot more tractable. Moreover, it'd be beneficial it the other direction too: the more constraints you can build into the type system of your domain model, the harder it will be for an AI to hallucinate something that breaks it. Anything that doesn't satisfy the constraints will fail to compile.
I doubt it, but wishful thinking.
sanderjd
Yep, I absolutely relate to this. ChatGPT happened to come out right when I needed to learn how to use kubernetes, after having used a different container orchestrator. It made this so much easier.
Ever since, this has been my favorite use case, to cut through the accidental complexity when learning a new implementation of a familiar thing. This not only speeds up my learning process and projects using the new tool, it also gives me a lot more confidence in taking on projects with unfamiliar tools. This is extremely valuable.
Freedom2
Also agree. I've been playing with Godot for some super simple game dev, and it's been surprisingly fantastic at helping me navigate Godot's systems (Nodes, how to structure a game, the Godot API) so I can get to the stuff that I find enjoyable (programming gameplay systems).
No, it's not perfect and I imagine there's some large warts as a result, but it was much, much better than following a bog-standard tutorial on YouTube to get something running, and I'm always able to go refactor my scripts later now that I'm past initial scaffolding and setup.
schainks
> primordial swamp of data science
This deeply resonates with me every time I stare at pandas code seeking to understand it.
moregrist
Yes. I am routinely aghast at its poor legibility compare to either R dataframe or the various idioms you learn in Matlab/bumpy for doing the same things.
Centigonal
Yep, same. I'm an old hand at pandas, and writing a 300 line script in pandas and asking Claude to rewrite it to polars taught me polars faster than any other approach I've used to learn a new framework.
mettamage
I guess LLMs are good at mapping one thing to another. Just like translating a real language.
consumer451
I am pretty much in the same boat, although I was never that advanced a dev to begin with.
It is truly amazing what a superpower these LLM tools are for me. This particular moment in time feels like a perfect fit for my knowledge level. I am building as many MVP ideas as quickly as I can. Hopefully, one of them sticks with users.
Aperocky
> Experience Still Matters
My personal opinion is that now experience matters a lot more.
A lot of times, the subtle mistakes that LLM makes or wrong direction that it takes can only be corrected by experience. LLM also don't tend to question its own decisions in the past, and will stick with them unless explicitly told.
This means LLM based project accumulate subtle bugs unless there is a human in the loop who can rip them out, and once a project accumulated enough subtle bugs it generally becomes unrecoverable spaghetti.
diggan
> LLM also don't tend to question its own decisions in the past, and will stick with them unless explicitly told.
Dangerous as well, is that LLMs won't (unless aggressively prompted to) question your own decisions either, in contrast to something like a mentor which would help you discover a better way, if there is one.
warkdarrior
I've never seen anyone claim that coding LLMs are mentors, but rather junior devs there to help you. Taking them as mentors changes the task completely. LLM-as-junior-dev definitely requires you to know what you want the code to do and what you expect as quality output.
Aperocky
That part didn't change with or without LLMs though. At least LLM is one more set of eye on my own decisions.
MetaWhirledPeas
> LLM also don't tend to question its own decisions in the past
An attribute I would like to see is the ability for an LLM to express justified self-doubt. Likewise (and perhaps directly related) would be the ability to self-critique prior to providing an answer. It's possible they are already steered to do this; if so I would like to see more of that dialogue surfaced to the user.
onefreecomputer
[dead]
scrlk
> The developers who thrive in this new environment won’t be those who fear or resist AI tools, but those who master them—who understand both their extraordinary potential and their very real limitations. They’ll recognise that the goal isn’t to remove humans from the equation but to enhance what humans can accomplish.
I feel like LLMs are just the next step on the Jobs analogy of "computers are bicycles for the mind" [0]. And if these tools are powerful bicycles available to everyone, what happens competitively? It reminds me of a Substack post I read recently:
> If everyone has AI, then competitively no one has AI, because that means you are what drives the differences. What happens if you and LeBron start juicing? Do you both get as strong? Can you inject your way to Steph’s jumpshot? What’s the differentiator? This answer is inescapable in any contested domain. The unconventionally gifted will always be ascendant, and any device that’s available to everyone manifests in pronounced power laws in their favor. The strong get stronger. The fast get faster. Disproportionately so. [1]
[0] https://youtu.be/ob_GX50Za6c?t=25
[1] https://thedosagemakesitso.substack.com/p/trashbags-of-facts...
jrk
Simon Willison nailed exactly this 2 years ago:
> I've been thinking about generative AI tools as "bicycles for the mind" (to borrow an old Steve Jobs line), but I think "electric bicycles for the mind" might be more appropriate.
> They can accelerate your natural abilities, you have to learn how to use them, they can give you a significant boost that some people might feel is a bit of a cheat, and they're also quite dangerous if you're not careful with them!
onefreecomputer
https://packet.boutique/technohumanism/codex/index.html
Technology is part of humanity. Just as a hammer extends the hand, so too does the LLM extend the mind.
croes
Or it is the car for the mind. It extends your range but you become fat and lazy by driving everywhere.
matthewsinclair
Gosh. That’s bang on. And very prescient for 2 years ago.
cellis
If you and Lebron both had mech-suits you'd be equal in strength. Actually you might find that maybe you communicate better with the machine as you've had to communicate a lot more for work and have further refined those neural patterns. So actually, I would expect those with exceptional communication skills and creativity to be the best able to take advantage of AI. In a world where functional code in any language can be spat out at 1000 tokens/s, it matters a lot more that you can communicate your vision than understanding the inscrutable byte-level architecture of ARM-64 or how to write a CUDA kernal, or how to use a static class properly.
coeneedell
LeBron James still knows more about how to play basketball than whoever is in the mech suit opposite him.
chii
> LeBron James still knows more about how to play basketball
i would bet that there are currently lots of people who would beat LeBron in theoretical basketball, but don't have the body nor the endurance to compete.
But with a mecha-suit, the advantages of any natural born talent, and any issue with endurance or strength, etc, are diminished, leaving only mental capability as a differentiator.
That's not to say that LeBron's mental capability (in regards to basketball) is low - surely it's high. But the combination of high athletisism and mental capability is a rarity right now. Removing one of these conditions (via the mecha-suit) will then increase the pool of "high" performers imho.
cellis
Mech-basketball is a different game than basketball. There would be an entirely new metagame. I just watched Magnus Carlsen get checkmated in 7 moves in magic chess, a move even a 10 year old could find ( vs GM Hansen if you want so see it ).
Another analogy:
"A good archer is going to be an amazing sharpshooter and therefore I only want to field archers (with guns) as soldiers", might be a horrible way to run a modern military.
This "the best at the old thing will be the best at the new thing too!" needs to die in a fire.
iugtmkbdfil834
I don't think bicycle analogy is adequate. It seems that they are more like cars. And if we follow that analogy, it suggests that the direction of the evolution will depend on whether we make our society dependent on being able to drive cars and grow fat from lack of activity or use them in a more mindful 'for purpose intended' way.
croes
Analogies are traps for the mind.
What if LLMs are cars for the mind not bicycles?
Both I and Usain Bolt get a Prius. Who is faster at the shopping mall? What will happen to our fitness? Who will be the next Usain Bolt and would we even care?
bigfishrunning
> If everyone has AI, then competitively no one has AI, because that means you are what drives the differences.
Except everyone doesn't have AI. Only huge corporations with billion dollar data centers do. What happens when Sam Altman decides to de-prioritize you? The little toy model you downloaded from github won't cut it.
These AI tools shift a lot of power into a few Silicon Valley companies. Keep those skills sharp, you'll need them. Best case scenario, the VC money bonfire runs out of fuel before 3 companies own the entire software industry.
strict9
A lot of good points here which I agree with.
Another way to think about it is SWE agents. About a year ago Devin was billed as a dev replacement, with the now common reaction that it's over for SWEs and it's no longer a useful to learn software engineering.
A year later there have been large amounts of layoffs that impacted sw devs. There have also been a lot of fluff statements attributing layoffs to increased efficiency as a result of AI adoption. But is there a link? I have my doubts and think it's more related to interest rates and the business cycle.
I've also yet to see any AI solutions that negate the need for developers. Only promises from CEOs and investors. However, I have seen how powerful it can be in the hands of people that know how to leverage it.
I guess time will tell. In my experience the current trajectory is LLMs making tasks easier and more efficient for people.
And hypefluencers, investors, CEOs, and others will continue promising that just around the corner is a future in which human software developers are obsolete.
apwell23
ceos found AI as escape hatch for their over hiring during pandemic boom year.
they were just playing to this market reaction
layoffs = bad
layoffs because of AI = good
namaria
Yup. Also I am excited for the bump in my rates when the cycle inverts and there's a dearth of people who can code without the then defunct LLMs.
throw1235435
On my side we've refrained from hiring people/training people due to AI. Its mostly been a good decision especially at the frontend layer where those teams are starting to do more with less. Don't get me wrong - I don't like the path the SWE profession is going and I'm not an AI fan for a variety of reasons. But at the same time I don't want to over hire and have no work for the people to do (i.e. the bottleneck being business ideas, regulation, ability to iterate in our domain, etc rather than tech). I can't just "work faster" and "ship more" - you start hitting other bottlenecks. Over hiring is not a great problem to manage either; morale at the very least decreases as people have no work to do.
The people in those bottlenecks anecdotally are seeing pay increases btw which goes to show - the inefficient get the spoils.
antirez
I agree that AI powered programming can give you a boost, and the points made in the post I would agree with if they were not made about Claude Code or other "agentic" coding tools. The human-LLM boosting interaction exists particularly when you use the LLM in its chat form, where you inspect and reshape with both editing the code and explaining with words what the LLM produced, and where (this is my golden rule) you can only move code from the LLM environment to your environment after inspecting and manually cut & pasting stuff. Claude Code and other similar systems have a different goal: to allow somebody to create a project without much coding at all, and the direction is to mostly observe more the result itself of the code, that how it is written and the design decisions. This is fine with me, I don't tell people what to do, and many people can't code, and with systems like that they can build a certain degree of systems. But: I believe that tody, 21 April 2025 (tomorrow it may change) the human+LLM strict collaboration on the code, where the LLM is mostly a tool, is what produces the best results possible, assuming the human is a good coder.
So I would say there are three categories of programmers:
1. Programmers that just want to prompt, using AI agents to write the code.
2. Programmers, like me, that use LLM as tools, writing code by hand, letting the LLM write some code too, inspecting it, incorporating what makes sense, using the LLM to explore the frontier of programming and math topics that are relevant to the task at hand, to write better code.
3. Programmers that refuse to use AI.
I believe that today category "2" is what has a real advantage over the other two.
If you are interested in this perspective, a longer form of this comment is contained in this video in my YouTube channel. Enable the English subtitles if you can't understand Italian.
Hyperlisk
This is my experience as well. I've been skeptical for a long time, but recent releases have changed my mind (it's important to try new things even if skeptical). Large context windows are game-changers. I can't copy/paste fast enough.
The future is coming, but you still need fundamentals to make sure the generated code has been properly setup for growth. That means you need to know what you expect your codebase to look like before or during your prompting so you can promote the right design patterns and direct the generation towards the proper architecture.
So software design is not going away. Or it shouldn't for software that expects to grow.
noisy_boy
> The human-LLM boosting interaction exists particularly when you use the LLM in its chat form
I feel reassured to see that I'm not the only one who feels this way. With all the talk about in-IDE direct code editing, I was thinking that I was being somewhat of a luddite who feels like the chat form is the best balance between getting help from the AI and understanding/deciding how things are actually structured/working.
matthewsinclair
What you describe as #2 is more or less what I do, and I use CC to do that.
I also have both GPT and the Claude UI open and I will often flick out to one or the other (Claude seems to be a lot better at Elixir code for me than GPT) and go into “discussion” mode if I want to open the aperture on a topic.
I’m certainly never letting (not at anymore, at least) it go and wrote swathes of raw code on its own. I learned that lesson the hard way. It generates absolute nonsense if left to its own devices.
Snuggly73
2. is mostly what works for me.
Usually when I am in the flow of writing code, I can think, write, tab away and review without breaking it. If I need a smallish (up to 100-ish lines) piece of code that I know the shape of - I would use the chat to generate it and merge it back after review.
Letting the agent rip always has led to more pain and suffering down the line :(
aerhardt
I've also settled on a copy-paste LLM workflow.
I can see the usefulness of agents however for (a) some tedious refactorings where the IDE features might not reach and (b) occasionally writing a first pass of a low-value module when I am low on energy.
For the rest of stuff I feel very happy with copy-paste.
jillesvangurp
Like all tool improvements in software engineering do, LLMs simply increase the demand for software as fast as software engineers can step up to use the new capabilities provided by tools. This is not a closed world and it's nothing new. It's not like we're all going to sit on our hands now. Improvements in tools (like LLMs) enable individuals to do more and more complicated things. So the complexity of what is acceptable as a minimum simply goes up along wit that. And this will allow a wider group of individuals to start messing around with software.
When the cost for something goes down, demand for that thing goes up. That fancy app that you never had time to build is now something that you are expected to ship. And that niche feature that wasn't really worth your time before, completely different story now that you can get that done in 30 minutes instead of 1 week.
Individual software engineers will simply be expected to be able to do a lot more than they can do currently without LLMs. And somebody that understands what they are doing will have a better chance of delivering good results than somebody that just asks "build me a thingy conforming to my vague and naive musings/expectations that I just articulated in a brief sentence". You can waste a lot of time if you don't know your tools. That too is nothing new.
In short everything changes and that will generate more work, not less.
scellus
Yes, price elasticity increases the demand for software work in total, including LLMs and humans. But to me at least, it is not clear that humans will increase their total amount of work as LLMs obviously do. Is it possible that LLM coding grows faster than the total, so that the human piece of cake actually shrinks?
bcrosby95
This may depend upon every individual, but for me "How am I going to do it" is not actually writing code. It's about knowing how I'm going to do it before I write the code. After that point, its an exercise in typing speed.
If I'm not 100% sure something will work, then I'll still just code it. If it doesn't work, I can throw it away and update my mental model and set out on a new typing adventure.
codr7
And the only way to be 100% sure is to have written exactly the same thing before, which makes zero sense.
Hyperlisk
That's not true. I just wrote a similar comment about design coming first. If you've written software for awhile you just know what it looks like and which design patterns will be useful. Then when you see what your LLM says is the right code you can glance at it and see if it is even on the right track.
If you're trying to LLM your way to a new social site you're going to need to know what entities make up that site and the relationships they have ahead of time. If you have no concept of an idea then of course the LLM will be "correct" because there were no requirements!
Software design is important today and will be even more important in the future. Many companies do not require design docs for changes and I think it is a misstep. Software design is a skill that needs to be maintained.
pjmlp
Keep believing it is augmentation.
The end game is outsourcing, instead of team mates doing the actual programing from the other side of the planet, it will be from inside the computer.
Sure the LLMs and Agents are rather limited today, just like optimizating compilers were still a far dream in the 1960's.
sly010
And just like optimizing compilers LLMs also emit code that is difficult to verify and no-one really understands, so when the shit hits the fan you have no idea what's going on.
Aperocky
Is it though? Most code that LLM emits are easier to understand than equivalent code by humans in my experience, helped by overt amount of comment added at every single step.
That's not to say the output is correct, there are usually bugs and unnecessary stuff if the logic generated isn't trivial, but reading it isn't the biggest hurdle.
I think you are referring to the situation where people just don't read the code generated at all.. in that case it's not really LLM's fault.
bluefirebrand
> Most code that LLM emits are easier to understand than equivalent code by humans in my experience
Even if this were true, which I strongly disagree with, it actually doesn't matter if the code is easier to understand
> I think you are referring to the situation where people just don't read the code generated at all.. in that case it's not really LLM's fault
It may not be the LLM's "fault", but the LLM has enabled this behavior and therefore the LLM is the root cause of the problem
null
gregMN
[dead]
akra
That's kinda obvious that's their goal especially with the current focus on coding of most of the AI labs in most announcements - it may be augmentation now but that isn't the end game. Everything else these AI labs do, while fun seems like at most a "meme" to most people in relative terms.
Most Copilot style setup's (not just in this domain) are designed to gather data and train/gather feedback before full automation or downsizing. If they outright said it they may not have got the initial usage needed to do so from developers. Even if it is augmentation it feels like at least to me the other IT roles (e.g. BA's, Solution Engineers maybe?) are safer than SWE's going forward. Maybe its because dev's have a skin in the game and without AI its not that easy of a job over time makes it harder for them to see. Respect for SWE as a job in general has fallen in at least my anecdotal conversations mainly due to AI - after all long term career prospects are a major factor in career value, social status and personal goals for most people.
Their end goal is to democratize/commoditize programming with AI as low hanging fruit which by definition reduces its value per unit of output. The fact that there is so much discussion on this IMO shows that many even if they don't want to admit it there is a decent chance that they will succeed at this goal.
wolvesechoes
Their end goal is to democratize
Stop repeating their bullshit. It is never about democratizing. If it was, they would start teaching everyone how to program, the same way we started to teach everyone how to read and write not that long ago.
akra
Many tech companies and/or training places did try to though didn't they? I know they do boot camps, coding classes in schools and a whole bunch of other initiatives to get people into the industry. Teaching kids and adults coding skills has been attempted; the issue is more IMO that not everyone has the interest and/or aptitude to continue with it. The problem is that there's parts of the industry/job that aren't actually easy to teach (note not all of it); can be quite stressful and require constant keeping up - IMO if you don't love it you won't stick with it. As software demand grows, despite the high salaries (particularly in the US) and training, supply didn't keep up with demand till recently.
In any case I'm not saying I think they will achieve it, or achieve it soon - I don't have that foresight. I'm just elaborating on their implied stated goals; they don't state them directly but reading their announcements on their models, code tools, etc that's IMO their implied end game. Anthrophic recently announced statistics that most of their model usage is for coding. Thinking it is just augmentation doesn't justify the money IMO put into these companies by VC's, funds, etc - they are looking for bigger payoffs than that remembering that many of these AI companies aren't breaking even yet.
I was replying the the parent comment - augmentation and/or copilots don't seem to be their end game/goal. Whether they are actually successful is another story.
ivape
If we go with this analogy, we don't have advanced mech suits yet for this. To think an IDE is going to be the "visor", and to think copy-and-pasting is going to be jury-rigged weapons on the Mech is probably not it. The future really needs to be Jarvis and that Iron Man suit, whatever the programming equivalent is.
"Hey I need a quick UI for a storefront", can be done with voice. I got pretty far with just doing this, but given my experience I don't feel fully comfortable in building the mech-suit yet because I still want to do things by hand. Think about how wonky you would feel inside of a Mech, trying to acclimate your mind to the reality that your hand movements are in unity with the mech's arm movements. Going to need a leap of faith here to trust the Mech. We've already started attacking the future by mocking it as "vibe coding". Calling it a "Mech" is so much more inspiring, and probably the truth. If I say it, I should see it. Complete instant feedback, like pen to paper.
Tijdreiziger
> We've already started attacking the future by mocking it as "vibe coding".
The term ‘vibe coding’ was coined by OpenAI’s co-founder.
throwaway314155
It's pretty clearly outgrown its original definition, as often happens with this sort of "urban dictionary"/term-of-art type of phrase.
codr7
Copy/paste coding isn't exactly a new idea, it just got a lot easier and more popular.
gigel82
When working in mature codebases and coordinating across teams, I'd say the time I spend "coding" is less than 5%. I do use GitHub Copilot to make coding faster, and sometimes to shoot ideas around for debugging, but overall its impact on my productivity has been in the lower single digits.
I'm wondering if I'm "holding it wrong", or all of these anecdotes of 10x productivity are coming from folks building prototypes or simple tools for a living.
throwawayb299
Have you considered, you may be working in a dysfunctional organization, spending only 5% on the activity that translates into actual value to the end user?
That's why these AI companies are racing to build a replacement for you and me, something that will spend 100% of its time actually building out functionality the customer is looking forward to.
I know, I know, spending 100% of our day coding is ridiculous because that all-hands conference call to get everyone onboard with which microservice is responsible for storing button colors absolutely has to happen first.
bluefirebrand
> spending only 5% on the activity that translates into actual value to the end user
You must be a junior coder if you think that typing the code into the computer is the activity that should take up most of your time
Writing code is the last step, the shortest step, and the easiest step of building software
throwawayb299
20 years and counting. Maybe you're the person at the middle point of that bell curve meme, while I'm the one to the right?
I used to drink the kool aid too: writing code is the last step, the shortest and the easiest one...
Over time I came to believe, this is what people in dysfunctional organizations say to justify endless political back and forth over painfully trivial matters and constant turf wars.
Anyone speaking up about it is of course getting shamed as inexperienced or incompetent. It's no surprise, people who are holding these bullshit jobs have their livelihood on the line if the bullshit gets called out.
By the way, I'm not saying there's no need to plan things out at least just a little bit or that communication does not come with a certain overhead. Not 95% though, not even anything close to that. Especially if you aren't breaking any new grounds, which the overwhelming majority of devs aren't. No, a LOB reporting app on microservices is not it. No, another AI-enabled social network on blockchain is not it either.
Coding isn't the shortest step either, go ahead have a look into a serious codebase such as Chromium then come back and tell me with a straight face developing that codebase was the shortest step.
DangitBobby
I've seen this said a couple of times here. FWIW I spend at least 90% of my time actually writing code, and yes I do the other things too. I'm very glad I don't work for the other type of organization.
alganet
Expectation: mech suit with developer inside.
Reality: a saddle on the developer's back.
They really want a faster horse.
> Traditionally, coding involves three distinct “time buckets”:
> Why am I doing this? Understanding the business problem and value
> What do I need to do? Designing the solution conceptually
> How am I going to do it? Actually writing the code
> For decades, that last bucket consumed enormous amounts of our time. We’d spend hours, days or weeks writing, debugging, and refining. With Claude, that time cost has plummeted to nearly zero.
That last part is actually the easiest, and if you're spending inordinate amount of time there, that usually means the first two were not done well or you're not familiar with the tooling (language, library, IDE, test runner,...).
There's some drudgery involved in manual code editing (renaming variable, extracting functions,...) but those are already solved in many languages with IDEs and indexers that automate them. And so many editors have programmable snippets support. I can genuinely say in all of my programming projects, I spent more time understanding the problem than writing code. I even spent more time reading libraries code than writing my own.
The few roadblocks I have when writing code was solved by configuring my editor.