Reading the code does not mean you understand the code. One lesson that experience in software gave me: I never understood the code. You think it works a certain way, until you find out that it doesn't.
What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.
If we are committed to it, if we put the resources towards it and dedicate the time to it (and we could do this just by saying: it will take half as long as it used to take!), software built by llms in healthcare, finance, automotive, defense, power plans, aviation, manufacturing can all be made MORE reliable and better with LLMs... without ever reading a single line of code. The LLMS are very good at logic, by the way.
Anyway all of this reads like someone who is not actually using LLMs to build software or hasn't tried them in a while. I felt the same way in 2025. I've written 100s of thousands of lines of difficult code. You, the person reading this, has probably interacted with software I've written. For a time you would've interacted with it every time you made a debit card transaction in the united states, for example. I understand code, and care about quality, and that's why I'm all in on LLMs for code.
> I never understood the code. You think it works a certain way, until you find out that it doesn't.
What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.
Testing isn’t the same as understanding the code, or proving (even informally) that it is correct. Having the LLM do all these things above doesn’t lead you or the LLM to understand the code, to logically reason about its behavior over all possible states and inputs.
“Finding out that it doesn't” means that you didn’t properly reason through the code beforehand, checking all your assumptions against what the code and underlying systems are actually guaranteeing. This may be a matter of formal education (proving computer science theorems and algorithmic correctness in university), I don’t know.
> Reading the code does not mean you understand the code.
Reading the code may not be enough to understand the behaviour of your program, but believing you can understand the behaviour of a program without at least reading the high level code is truly silly.
(by high level, I mean the code living in the higher layers - of course we don't often read the code of the generated assembly, or the interpreter, or the browser, but that's because they're reliable abstractions, unlike prompts!)
I agree, I think this false dichotomy between using LLMs and caring about quality/reliability needs to stop. All these mission critical industries listed in the article rely on extensive testing for quality assurance, with human code review being a layer on top of all that, but far from the most critical one.
Interpretability is the same, our abilities to do that have increased rather than decreased. I think a codebase generated by AI is actually more understandable than one generated by humans at this point, and you can ask clarifying questions whenever you get stuck.
TFA's points only make sense if the mental model the author has in mind is someone who writes a prompt then immediately puts an app into production without any thought behind it.
> I agree, I think this false dichotomy between using LLMs and caring about quality/reliability needs to stop. All these mission critical industries listed in the article rely on extensive testing for quality assurance, with human code review being a layer on top of all that, but far from the most critical one.
At least some places are abolishing formal QA because LLMs. There's a cult of speed uber alles that has a big intersection with LLM enthusiasm.
>TFA's points only make sense if the mental model the author has in mind is someone who writes a prompt then immediately puts an app into production without any thought behind it.
If coding were solved, then this would be true no?
There are far more moving pieces in deploying an application than coding. My impression is TFA is arguing that replacing humans with LLMs for coding would make other things like quality assurance more difficult, in which case I disagree.
Before 2024, I once went to an ATM to retrieve money and selected 50. Note that I selected it from a menu, not typed it. The ATM then told me that it cannot give me 50 because it is not a multiple of 5.
I mean, I think the problem isn't that the LLM doesn't know how to code, it's that companies are expecting 3-5x velocity with the bottleneck of code review and testing becoming much more severe than before
if you're an MBA-brained exec who doesn't actively use LLMs to code and you just believe whatever slop it outputs at first without checking it, you're not going to realize how recklessly it can be used, how you need to be critical and skeptical of its outputs, that you need to explore it's reasoning and logic (which is still really easy compared to understanding legacy code and barely takes any time!)
say you also believe all this marketing hype about 'how dangerous (ie capable) AI agents are.' LLMs can do anything you think so you just say 'ship it' without building out the tooling and capabilities to enable faster code review and better tests. and to keep the shareholders happy, you start cutting jobs that you can't directly connect to a KPI (ie the platform/SRE team who would be the ones who can trial, onboard, and maintain those capabilities for your teams)
and from this, suddenly a lot of debit card stops working and the only one getting the blame are individual SWEs trying to hit their sprint velocity. the fact that you fucked up the whole SDLC real bad with your incompetence gets you a golden parachute and you job hop to a better paycheck. rinse and repeat
> Your debit card transactions for example worked.
I've built payment rails. Six nines SLA, high capacity, resilient distributed systems.
I haven't written a single line of code since February, and I don't think I ever will again. These systems are incredibly good at replacing much of our work. They're only going to get better.
Rather than debating if these models are good (they are), we should be trying to figure out if most of us will still be around in three years. You don't need a two pizza team anymore.
"Look to the person to your left and to your right. Only one of you will remain by graduation" kind of energy. I'm not sure all of us is going to be in this career much longer. We'll have to see what the demand side looks like.
I don't think the discrepancy is in LLM capability improvements over the past year.
Correctness has never been a priority across an industry where rapid iteration and feature delivery drive sales. There's always some opportunity cost to doing things right, at the price of technical debt down the road. If AI is primarily used to produce fragile code, people will be wary of AI solutions. There's also ongoing public debate about AI safety and alignment. Deploying AI in safety critical applications feels riskier than ever in the current environment, even though it doesn't have to be.
> I never understood the code. You think it works a certain way, until you find out that it doesn't.
Those are two separate claims, unless by the former you mean “I never perfectly understood the code.” You can understand code imperfectly. And even with LLMs, you can’t get truly infallible guarantees about a system.
I’m using it a bunch. It saves a ton of time writing or reviewing code. It will catch things I won’t. But I’d express caution about the analysis or evaluation they do - LLMs will often confidently proclaim problems as solved or explain functionality and be wrong about it. Sometimes subtly, but sometimes just completely wrong. This is no different from humans, of course, except for the unabated confidence.
What I've found is that AI allows lazy and incompetent developers to be more lazy and more incompetent. This then has the effect that product quality suffers more, faster. As a result of the sheer amount of code now being pushed out, code reviews, a thing that previously somewhat prevented lazy and incompetent developers from pushing out horrible code, is effectively dead in the water since no human can actually review such amounts of code realistically anymore. Some companies have adopted AI to review code, which, well ... you have AI make code, AI review code ... I hope you can see the stupidity here if you expect to see any deterministic results at all.
I guess time will tell if the consumer will adapt to the lower quality of products, allowing companies to justify the existence of lazy and incompetent developers, or if the consumer will push back, forcing companies to increase the quality of their developers.
Note: I use AI every day and it is entirely possible to create high quality software with it, so long as you are not lazy and incompetent.
"""I distinguish four types. There are clever, hardworking, stupid, and lazy officers. Usually two characteristics are combined. Some are clever and hardworking; their place is the General Staff. The next ones are stupid and lazy; they make up 90 percent of every army and are suited to routine duties. Anyone who is both clever and lazy is qualified for the highest leadership duties, because he possesses the mental clarity and strength of nerve necessary for difficult decisions. One must beware of anyone who is both stupid and hardworking; he must not be entrusted with any responsibility because he will always only cause damage"""
The problem here is that AI is consistently one of the four things: hardworking. This makes it very efficient at transforming "stupid and lazy" inputs into "stupid and hardworking" outputs.
Now instead of 90% stupid and lazy (harmless, useful for grunt work) you have 90% stupid and hardworking (aggressively causing damage).
We developed languages that removed GOTO so that developers don't shoot themselves in the foot. We will surely develop harnesses that will ensure that majorly occurring problems are solved before they hit production.
Someone who knows that 1 + 1 = 2 will not decide that it's suddenly 3 unless we start accounting for health problems. Making mistakes is not the same as non-deterministic.
I think that actually reinforces the distinction being made. An LLM’s nondeterminism is in the generation process: given the same prompt and model state, sampling can produce different outputs. That doesn’t mean the underlying fact itself becomes nondeterministic.
A human who knows 1+1=2 can still say “3” because they misread the question, misspoke, were distracted, or made some other cognitive error. Likewise, an LLM can output “3” because the generation process selected an incorrect continuation. Those are both errors in producing an answer, not evidence that 1+1 somehow has multiple answers.
So yes, human mistakes and LLM sampling are mechanistically different. If your argument is that LLMs and humans can both make mistakes, then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.
> Someone who knows that 1 + 1 = 2 will not decide that it's suddenly 3 unless we start accounting for health problems.
And?
The p(that kind of error) is pretty small now. At what point does a probability coming out of an LLM look like "knowing", such that spitting out the wrong answer despite that probability looks like a health problem, a typo, or even just boredom? (Thinking of the Lizardman constant here: https://en.wiktionary.org/wiki/Lizardman%27s_Constant)
It's a continuum for both them and us, even if the mechanism is wildly different.
> Making mistakes is not the same as non-deterministic.
i.e. when the dismissal is "non-deterministic" when it should be "Making mistakes", is itself a mistake.
Lol, humans make such absurd mistakes (and worse) all the time through simple typos, which is effectively random. The key for 2 is right next to the key for 3, after all.
I mean these guys are not even pretending to be reviewing the code.
It just gets “reviewed” by an LLM, which will find a nitpick while ignoring the huge fire in the core of the design, force the planner to make even more sloppy code to cover for an irrelevant test case. Rinse old tokens and repeat until you hit limits.
For example, I recently got brought in to help with quality on a large-scale system that had been ported to a new platform with the help of coding agents. The project was completed and declared operational in record time, but soon after the business discovered that:
1. The promised scalability improvements did not materialize. Instead, it got worse.
2. Observability had been lost. The telemetry was no longer trustworthy.
3. Users stopped trusting it because it was producing incorrect outputs.
What I ended up discovering was that, while it scrupulously kept existing automated tests passing, any behavior that wasn't explicitly covered by a test was free to change any which way. And there were plenty of small things that weren't explicitly covered. Perhaps because the original authors thought they were so obvious and commonsense that they didn't need one, perhaps because mistakes happen. The why doesn't matter. The point is that reality is messy and imperfect, so giving someone a chance to look at things and think, "Huh, that's funny..." is an essential part of defense in depth.
But the real worst part was, this whole replatforming was a huge waste of time, anyway. The improvements they were looking for could easily have been accomplished with some controlled incremental changes to the original system. Mostly just removing a few basic and well-known performance antipatterns.
But way back at the outset, the person in charge of the project asked their agent, "What's the best way to X," and the agent gave them a trendslop answer about how Y alternative technology is more scalable and we should just port to that. It was convincing and they were under intense time pressure to just ship some code because leadership is bought into the AI hype and now has the patience of a 4 year old, so they just went with it.
Can the users of the software even keep up at that point? We may have reached diminishing returns on software production, and not enough impact on the rest of the process.
> What I've found is that AI allows lazy and incompetent developers to be more lazy and more incompetent. This then has the effect that product quality suffers more, faster.
Yeah. To me it seems very much like the "use dynamic typing for everything" fad. You had a bunch of junior and/or incompetent developers who went around insisting that type declarations are bad, static typing slows down development, you just code so much faster if everything is dynamically typed. And in the context of a new project, they were totally right. It took a few years for the debt to finally catch up, and people realized that these massive, untyped monoliths they had were unmaintainable. Now the two biggest dynamic languages (Python/JavaScript) are effectively typed languages, because nobody uses their untyped variants for serious work.
Dynamic typing still has great uses -- interactive data exploration, putting together quick scripts (though less relevant with AI...), or even just simple prototypes -- but what we tried to do with it at the start, as an industry, was clearly dumb as hell. I suspect we'll look back in 5-10 years and realize that with some of the stuff we're doing with AI, too. It's already happened with things like Gastown.
> Note: I use AI every day and it is entirely possible to create high quality software with it, so long as you are not lazy and incompetent.
What I in general try to teach the other people about AI: It can be a great tool, but check the results! Especially in the case of engineering: Check and then double check.
"optimize this code", "fix this code", "extend this code", "add this feature", "find errors and patch them", "find bugs and fix them", "rewrite this from python to rust".
This is all that's needed to actually use LLMs nowadays. How is it a "multiplier" rather than an "equalizer"?
> How is it a "multiplier" rather than an "equalizer"?
Because without the responsible human engineer in the loop, it'll all gradually decay in a cascade of edge-cases. This happens with human written code as well (every "we'll replace this prototype before we ship" you've ever worked on), but with LLMs it happens at 10-100x the rate.
If that's how you create software then you belong to the lazy and incompetent group in my book. I provide AI with valuable context such as code coverage information, architecture analysis, test requirements, important "gotcha's" that a competent engineer would know about in their architecture or system etc. I'm still very much the person who comes up with the solutions. For me AI is replacing the code editor, it's not replacing the thinking.
The skill floor has definitely been lowered, but if this were actually true then firms would be replacing senior software positions with entry level ones, not the other way around.
Do you use the word "equalizer" in this context to mean that AI has made the playing field equal for both competent developers and laypeople? Do you reckon that competence plays no role these days?
> you have AI make code, AI review code ... I hope you can see the stupidity here...
You will be surprised how many times, catches errores made by the AI coding agent. However,as you point, isn't deterministic. And you can guarantee the end results is 100% fine code
Reading this article its just clear the author hasn't used the current generation for real work.
> LLMs can wing it for tasks that are related to natural language (e.g. writing social media posts, reports, articles, etc.) but when it comes to code, the same engine that struggles to count number of R’s in “Raspberry” or suggests a walk to the carwash, also exposes other logical fallacies
Weirdly none of those things matter when writing code and actually LLMs fail at social media posts and articles to anyone who has seen enough of it can clock it's AI straight away, yet everyone who's used these models properly has solved harder problems than walk to the carwash with them, neither of the problems he's claiming are code were proposed as code problems or tested as code problems.
A lot of what's said just comes across as wishful thinking and being out of touch with the level of output current models can do, and I mean hard problems too.
I think the author underestimates how boring and simple 90% of enterprise software is. The part that isn’t powering aircraft and power plants. So much of it originates from one-nighters, badly managed subcontractors, and requirements that are of low quality to begin with (because they are written by people who have very different day jobs). And you know what? Most of that runs 24/7 without a glitch. LLMs just gives us more of that. And maybe it’s even better
Reading the article, I definitely agreed with the author, but I also found myself agreeing with the counter arguments in the comments.
What I find conflicting personally about AI coding practices, is that I completely agree that AI is incredibly impressive at completing even complicated tasks, and I can at the very least say it is much much better than I am at writing code.
My issue with it, is that it gives you a "lazy" option every time that doesn't require the same level of thinking. I understand that this is completely on me as the developer, and the simple solution is that I need to make sure I'm taking my time to learn and understand what exactly the LLM is producing. I try this and have set up separate skills to make sure I'm building my understanding as I go.
Regardless, if I sit down today and implement something without the use of LLM, it takes me a lot longer, but once I get into it, I find a state of flow that I can never get from the back and forth reading of LLM output. Then when I finish, even if my solution is not perfect, I have learned so much more and my own context of problem is so much better, where usually then I can review with an LLM. This usually leaves me with a better implementation and more importantly one I can stand over.
I think for a newer dev like me (~2 years experience), since I haven't built up years and years of problem solving experience, if I don't carve out time in my day to put down the AI tools and improve on my problem solving, I'll plateau and that's my biggest push against all this LLM use. I don't necessarily disagree that 'coding is solved', to be honest, I think it largely is, but it's still the foundation for me to be a good Software Engineer and I definitely haven't solved it.
I totally agree and think this is The New Skill of software engineering: can you steer agents well enough to get work done at the speed they will allow, while still keeping enough context/understanding to step in when it matters?
> You cannot be responsible for what you can’t control either. That understanding is key to reasoning about system behavior and fixing it when the AI inevitably fails.
This is not a good premise. All over law, you will find people made responsible for what they don't control and they kind of own. Unleash a dog that harms a child, or just have it in an environment where it can escape, and see what happens.
There is such things as unpredictable situations where one might not be held responsible, as a problem might occur well past reasonable guidelines.
So of course you can be held accountable for what an AI that uou supposedly cannot quite control does, or for the AI-written code you deliver. Treat it like the releasing a wolf pack, or selling an unsafe toy that can maim children. There's precedent everywhere.
Came here to give an answer but your last sentence kinda made the point I was gonna make. If one is legally in control, then one is accountable (the dog or unsafe toy example in reality is OpenAI's agents hacking huggingface for example).
The difference seems to be that some companies are above the law apparently.
Just responded to a different thread, but it’s the same comment:
I was just at the Explore DDD conference in Denver and a portion of Friday was sitting at the cafe tables informally discussing the impact of GenAI on software engineering with notable people.
Most of these people were deeply concerned that if we lean into using GenAI for “everything” that our collective knowledge will dissipate.
I was the vocal contrarian. There are many historical examples of humans obfuscating knowledge to simplify progress.
Does anyone solder their own microchips at scale anymore? No. We have highly sophisticated robots and machinery to do that work with extraordinary outcomes.
In software engineering, if you remove “coding” as a discipline you’re left with all the other aspects of designing software which I contend can be retargeted in college CS curriculum.
The leap isn’t about code reviews. It’s about design reviews and that’s where better outcomes are served regardless of whether GenAI is involved or not.
I have a roughly year old codebase at https://github.com/ChicagoDave/sharpee/ that is designed by me, but generated by Claude Code with my own skills and agents as guardrails. I’m fairly certain the code I extract from Claude doesn’t require human review, but the design of the system and its changes are continually reviewed by me.
My contention is that we “collectively” are still trying to discern where the AI/human line is and most are still “holding” that line to human interactions.
Let it go. Define what part you do need human decisions on and focus on those things.
The process of writing code is the process of clarifying your own thought and being forced to answer questions that may not have been obvious before. To the extent that AI makes assumptions, it introduces bugs and incorrect code, maybe not from the perspective of the code in isolation, but from the broader context it lives in. To the extent it doesn't make assumptions and asks you, well that assumes it knows what should and shouldn't be assumed and that's not necessarily something AI can know a priori.
"AI can explain it to you but cannot understand it for you". Code is just a side-effect of reaching clarity. The reason these LLMs can emit any code at all is because they're not bound by the constraints of a compiler. That's until we create a feedback loop and force them to keep trying until syntax errors are gone. The next gate is tests. Loop till tests pass (including cheating of course, gotta keep your eyes open). Then there are the runtime errors, and then after all of that the developer gets to test the results and further refine what the specs missed or confused the model.
A couple of days building can really save us from a couple of hours of thinking.
$DAYJOB recently introduced a AI writing policy because people were sending each other mountains of slop back and forth enough that it became a huge time suck. The policy is basically: don't, with the justification being "writing is thinking". It's like they're so close to getting it.
From the authors of "coding is solved": Today, a colleague trying to run Claude Code ran into an issue where it shows the Bun help menu instead [1]. Previously, Claude Code uninstalled itself several times when I used it. [2]
Frankly, I don't really see how it isn't solved, even with the current state of LLMs. Frontier models can write, understand, correct, and optimize code in practically any language at a superhuman level. I haven't come across a single problem that LLMs can't solve. You can easily give them a research paper, ask them to implement it and in an hour or two it's done. Or even point them to a video or screenshot of something and say "implement this feature in our game engine" and... they just do it. It might not be optimally perfect, but what % of human written code is? Even if you ignore the time amortization (given how models can spit out weeks of human work in an hour) they still obliterate even an experienced developer.
> Most software that requires hiring and paying software engineers has low risk tolerance:
I think a few of the industries listed like defense and aviation have low risk tolerance. However, from my (somewhat brief) experience of working in two health techs for a couple of years, I strongly disagree that healthcare has low risk tolerance for tech. Granted, they make run-of-the-mill CRMs, but I was baffled at how tolerable it is to have egregious user experience that makes users waste multiple hours per month with clerical work that is very painful because the UIs are very slow and buggy.
"Risk" has nothing to with designing functional and elegant UIs, so I'm not sure why you would even make the comparison.
It means risk that the software stops working after an update. Which usually trades off iteration speed and best practices (i'm pretty sure the average startup has way better security practices by just delegating to google/aws than the average manufacturing software business) in exchange for a rigorous testing and rollout schedule.
So I'm also not sure that the article has a point at all, the human writing the code was never relevant to avoiding the "risk" in these industries in the first place.
GitHub Copilot is now written entirely in Rust, with AI agents doing most of the porting work.
The migration cost about $120,000 in AI token usage plus about three weeks of a developer's time.
The effort updated the runtime module-by-module until the job was completed, spanning over 135 releases across a 14.5-week time period.
430,000 lines of TypeScript were converted into 800,000 lines of Rust.
Porting a system to rust without changing the observable behavior is not that difficult with AI, and porting to a more strict language is not that remarkable. I have a tough time understanding why people equate straight shot porting where a test suite already functionally documents the behavior or where the prior application can be used as an oracle with success in all coding tasks. I would be far more impressed if someone did a clean room implementation of all of GitHub Copilot, from scratch, and got to a better point than the TypeScript or port codebase.
I have no doubt that if you provide any AI system with an oracle with expected behavior that it can match that oracle with some amount of $ and tokens. I haven't seen any demonstration of anything else. Rewriting a codebase was always a challenge for humans not because of complexity, but because of the time and effort involved in matching the old version's prior behavior. It doesn't have anything to do with the serious level of work required to build something truly new from scratch in a performant way.
Seriously? I have no idea where this cognitive dissonance comes from. Or are people just lying (outwards or to themselves)? A rewrite of this magnitude would easily take a skilled human team months if not years to finish. This is on top of Rust not being an easy language to work with. Which, btw, is the sole reason why not everything is written in C/C++/Rust.
I'm absolutely saying that AI has sped up the porting and rewrite process! It is amazing! But the reality is that rewriting has been part of programming culture since time immemorial. People want to rewrite for performance or for other reasons all the time, and the cost is now relatively low (i.e., now it's an opex line item in cash instead of time investment). But that doesn't mean that all of coding has been solved.
For example, any amount of software development involves fixing bugs, getting feedback from users on ideal workflows, an iteration loop of performance and bug tuning, etc. AI cannot simply create, from scratch, perfect software. Even using the SOTA models on max effort does not produce bug free software of any meaningful complexity or innovation out of the box. All that has changed is that the act of physically writing code and implementing existing patterns is now effectively a marginal cost.
Most line of business software is not e.g., delivering a company's income. Most software is in back-of-the-house internal products that do various internal tasks. I have no doubt that these processes are now far easier to build.
If the new Copilot is so great, why is it completely out of the current zeitgeist when compared to Codex and Claude Code?
This is true, real, and impressive. However, a comment I posted on HN a couple months ago might counterbalance this fact:
GitHub's Copilot cloud agent offering is suffering with a case of some of the worst corporate ADHD I've seen. We built a cloud agentic development pipeline on it, and it seems like almost every other week they silently change something with zero public announcement or documentation that creates real disruption for our team.
That's real, breaking changes to the platform that clearly aren't being tested/reviewed before being pushed to prod. Again with zero public announcement or documentation.
Support is useless – we're paying customers in the 4-5 figures and our tickets go unanswered.
$120k to port 430,000 loc seems quite expensive. That's dozens of cents per line of code, and equivalent to the all-in cost of a senior engineer in London for a year.
Especially expensive when you take into account the amount of that code which must have been boilerplate & meta-code in nature, meaning it should have been straightforward to move.
File by file porting can be done almost always with local reasoning. I don't think it proves much for novel projects which still seems to crumble under complexity past a small sloc limit.
sounds like 2x the code that no one understands, one more reason to never consider using copilot again
would be curious to know how many times "unsafe" appears in there, have seen rust devs comment on how the ais like to use unsafe to work around difficulties with memory management, like how they will sometimes subvert tests
- Coding in the small is solved. I have a current state, I want to change it, and I know how I want to change it. Eg, I have a blocking TCP handler for some reason, and I want to make it async. I can either fiddle with it or just let LLM make the changes for me.
- Coding in the larger sense is never solved. You need judgement to decide what you want made. No matter what you're building, there will be decisions to make (Who/what is it for?) and those decisions change over time. LLMs can take some default decisions for you, and if you're fine with those, you get the default (great for POCs). However you might not even realize what it decided to do for you. At some scale, you will be spending a lot of time going over those decisions. But what we have now is that the friction of changing the decisions is quite a lot lower. You can now test a lot of things that previously were very time consuming.
- The point that LLMs are probabilistic is not as important as it's made out to be. If I ask a junior dev to code up something, I also don't know what he'll make. Heck, you can be sure that you are able to solve something, yet you yourself don't know what the solution will look like. Maybe it turns out the library you were going to use isn't appropriate after all. You don't know what you will use in the end, but you do know that something will fix the issue. There can be more than one solution to a problem, and it doesn't always matter which one you find.
- I STILL think that LLMs are at their best mostly as advanced predictive text. In the sense that it's mostly good at implementing things that you've decided are needed. This can mean a heck of a lot of code, but you have to know the tradeoffs. What was decided, what were the costs of those decisions in terms of maintainability, money, time to change it, and so on.
Most of the problems people commonly encounter is solved by someone somewhere sometime. Today I wanted to add a simple search bar in a UI over log files in a directory. LLM ("through their unique ability to make the glue code adapt to any problems") solved my problem. That's all I care for now. Let people like Terry Tao push the frontiers. I am happy in my circumstance.
The author is right to categorise AI as a good programmer, but not a complete coder. We can all agree that programming has become really fast since the release of GPT-5 series and Opus models because they're pretty good. Not only this, they've also changed the pace expectations across teams where a feature that should ideally be delivered within weeks, should now take days.
All this doesn't change the fact that software engineers are going nowhere because nobody trusts AI. If a model can escape highly secured sandboxes, then we're definitely not running these agents overnight on our systems. I am sure the next-gen of models will focus more on security and the trust factor will start developing, but that's a long way down the road.
Coding is not solved, but the author’s arguments are wrong (at least the first two: LLMs are unaccountable and LLMs are “stochastic and probabilistic”. We can hold the LLM’s promoter accountable and probabilistic doesn’t mean stupid. The author also gives examples of idiosyncratic LLM failures that have been fixed for months now).
Coding is not solved because you can’t simply prompt an LLM to make an AAA game or enterprise tool.
Articles like this keep measuring to a red herring standard that was never achievable in the first place.
As for accountability, it always laid with the employer. You think those nameless contractors whom Boeing hired suffered any consequences for that 737 Max glitch? Using AI won't change that.
AI doesn't have to solve all these coding problems to be worth handing the reins to it: it just has to substantially better on average than humans over the long haul, which it already is, especially if you have good verification of "done" and "working" in place through automated testing mechanisms. Perhaps we might say that QA is having its moment.
It doesn't mean humans aren't needed, but they aren't writing much if any code anymore.
I don’t think viewing LLMs as “stochastic” or “probabilistic” is the right viewpoint. It’s directionally correct but not the right level of abstraction. LLMs are able to isolate patterns, generalize them, and apply them to new facts. That is very similar to what humans do in performing knowledge work. To the extent that humans also use logic, LLMs are able to generate logical propositions using pattern generalization, then call out to tools that check the proposed logic. Again, that’s similar to what humans do when they formulate some idea, then analyze the idea rigorously.
It's very interesting. I'm very enthusiastic about AI and coding, But I find myself agreeing with the author. Coding is not solved.
Instead, I think what's closer to solved and what we're in the process of solving is product development.
Story: A while ago, I had a few programmers who were really, really fast almost always missed the mark on the assignment wrong. I loved having them on projects because in the time my senior precise engineers could deliver a MVP, the fast engineers would build the wrong thing, collect feedback, reiterate, build the wrong thing, collect feedback, eventually inching closer and closer to a product people would pay for, and it would almost always get delivered faster than my seniors.
Yeah I have a well established ... well designed codebase that I had before agentic coding and it does support horizental scaling (more services integrations doing more or less the same).
I got lazy around claude fable and astra, and asked them to work in loop (pick specified issue, develop it, qa it ...) have a separate CTO checking on arch.
at the end both models swore that the code is perfect and well designed and nothing is lacking.
I ran the software and it suddenly started writing large amount of data to CSV files instead of the typical DB usage.
AI decided to use csv for testing, and just drifted away. 0 regards to the actual project, 0 regards to common sense.
anecdotal but really weird, the project category is rather standard, I wouldn't accept such a mistake from a junior developer.
Similar experience with a game engine. While working on one isolated component, like the render pipeline, a portion of the backing sparse data buffers were effectively duplicated with a different ABI. It’s like it forgot how to query meshes and game state, then assumed the plumbing didn’t exist so it was all rebuilt from scratch.
It compiled and ran just fine. If you weren’t reviewing the code holistically or keeping tight book keeping of your allocations you would not have noticed. Every single commit in isolation looks perfect. Very eye-opening
>Isn't it the opposite? How to build something is rather solved, but what to build isn't?
But that's not solved in traditional product development either.
Product development an iterative process to get a product fully functional. In 2021, if you ask me what the timeline for a small product/substantial feature, I'd say a few weeks to a month to get a basic MVP, and then another 12 to 18 months to get a feature polished and in a good shape to be stable.
When people put it in the coding frame, what they do it as is saying we've gone from 18 months to minutes or days. That's just not true.
We have gone from eighteen months to depending on the complexity, a 1-4 months.
aside: To be candid though, the compressed time also means the frustrations people experience with a product in 18 months have also been compressed. They still exist, they're all there, they're now just non-stop.
The problem with LLMs is that: popularity of an answer != correctness.
That concept might work a lot of the time but you will definitely run into situations where that'll never produce a correct or working response. To actually learn something you need an environment/playground to apply what you think you know and observe the results. Without that you're not really learning, you're jus regurgitating what people want to hear.
> AI cannot be held accountable. It cannot suffer any consequences. The worst thing you can do to AI is to unplug it. And although it mimics human emotions (due to training data), it couldn’t care less. AI doesn’t die either. It cannot suffer a prison sentence or fines. You cannot punish AI, therefore it can never be held accountable.
Dear lord. Is that supposed to reflect the average thoughts and motivation of a person you want to hire? Or that of their employer?
To give the benefit of the doubt for that sentence, think of it more as “every human knows that there is implied social contract and implied downsides to badly screwing up.”
Nobody has to be in fear, but we do have an ingrained knowledge that there are consequences, good and bad, for our actions
Not a fan of the article even though I somewhat agree with the title depending on your definition of coding.
AI can write CRUD API endpoints almost perfectly now. It can also write quicksort, a heap, whatever much quicker than I can.
It really sucks at designing types and apis though and when it creates types and apis it doesn't think or plan for the future way the system will evolve (even if it's known up front how the system will evolve).
I suspect this will remain a problem for the models for a long time. All the things that the models are currently good at are the low hanging fruit of reinforcement learning for coding.
Think about the kind of reinforcement learning environment that needs to be created to train a model to become good at building and designing large scale software end to end. It would be a slog because you need to build the large scale software up front and then break it down to train the model to construct it in a systematic manner that allows for the software to evolve. And then you need enough of these training environments for it to generalize. I think they will eventually figure it out though but it may take a while.
> It really sucks at designing types and apis though and when it creates types and apis it doesn't think or plan for the future way the system will evolve (even if it's known up front how the system will evolve).
Does that really matter? Those are things so that humans can better understand and extend a code base. That mattered when writing code was expensive and took time.
Now if it can pass all the tests it’s fine. If there’s an issue just have it rewrite things immediately. New bug? Generate a new test and rewrite code.
All, or many, of the old things that mattered just sort of don’t anymore.
It does, LLMs are almost like electrical current in that they take the fastest path to completing the immediate goal and it takes you to a local optima instead of a global one. Your app will be worse and lower quality. It will introduce subtle bugs that you could have made impossible from the beginning.
I think you’re just imagining things. Our teams have exclusively used LLMs for coding for 6 months. Literally 0 lines written by hand. 100% more PRs than a year ago and code quality is as high due to thousands of tests.
Much of the code needs to be performant and the LLM knows this and grinds on it. Less and less human inspection is needed.
I think 90% of software can be written like this today.
The LLM. And you can use an alternate LLM to antagonize the coding LLM.
You’re simply testing outputs. Make a spec but ultimately ungodly amounts of tests can be built quickly to ensure the program is outputting the right things.
Sorry for starting a definitional debate but coding is definitely solved. I can generally read some code, get an idea of what edit I need to make and prompt an AI with a description of the logic and it will deliver syntactically correct, idiomatic code and test it. It has been months since I've been frustrated at junk being spit out.
However, software engineering isn't solved. Which is basically what this article is talking about. But having coding solved is still very beneficial - not long ago many software engineers would struggle very hard with turning a description of the logic required into syntactically correct code - and even for those capable it was incredibly time consuming.
So now the question becomes: Is software engineering solved? And my answer is no. People still need to read the code and understand the code and how it fits into the bigger picture. However, I feel like we are kidding ourselves if we think that we can go from producing code being a niche task for nerds to getting syntactically correct code from plain language without any deskilling of our work and careers. I feel like my personal "moat" has gone from "can speak computer" to "has ok reading, writing, comprehension and judgement skills" (I hate the word "taste" being used here ).
At the same time - I don't yet feel like there's a sudden abundance of competent software engineers - it's just that the folks who used to submit untested spaghetti code now submit big bowls of barely working slop. So maybe it the moat was never "can speak computer" - that was just the expression of more general skills.
The existential question for me is how far the deskilling will go. Because right now - you still need a solid grasp on computer science and software engineering concepts to do this job, as well as sufficient levels of grit and problem solving ability - but I'm not too confident that will last, and when it goes I don't think many of us will find this career enjoyable.
> People who claim “LLMs can write decent code” don’t understand how code works.
It's not clear to me if the claim is:
(1) "If you used an LLM to generate code, and the code works, you're wrong if you think the code is okay"
or
(2) "If you used an LLM to generate code, you reviewed the code and found it to be of decent quality, then you're wrong".
> If you’re toying around, LLMs do a great job. That’s why some of the most aggressive proponents of the “coding is solved” narrative have nothing to show for it.
I also don't get the "LLM proponents have nothing to show for it" statement.
It's really quite common now to see on HN all sorts of LLM-assisted programming projects. The quality varies from slop where little thought was put into it, to high quality results where LLM coding assistance was able to let talented developers produce things they otherwise wouldn't have time to do.
I'd say it's obvious that LLM coding agents can be very useful for a lot of programming related tasks.
EDIT: That is to say, LLMs are obviously useful for use cases above/beyond toying around. It's not a dichotomy between "I'm never touching an AI" and "thoughtlessly accepting everything the LLM outputs".
"Most software that requires hiring and paying software engineers has low risk tolerance" The problem is that this statement simply isn't true. Most software engineers do not work on low risk tolerance code.
I don’t use gastown specifically but the level of complexity agents can code for is very high now.
Over this weekend in chat with the games discord watching as it iterated a harness built an entire implementation of the board game terraforming mars https://tfmbot.com using agents and harnesses for them.
I think if you can implement a board game end to end by feeding in the rulebooks and having a harness spawn agents to validate it’s reasonably solved.
Different words for the same thing. The idea that "coding" is just turning a spec into source code without any engineering decisions to be made was always laughable, for that to happen the spec would need to be as detailed as the source code (of course the whole idea that spec and code are separate things doesn't make a lot of sense).
LLMs even a version number or two ago can write all the code I've ever been paid to write in the last 20 years; but they are not, I think, yet competent enough to be able to handle the project planning and self-QA I was doing even in my first 6 months of my first job after graduating.
That's not a boast, I don't think I was particularly good at that back then, e.g. I didn't really get how to think about automated tests until much later.
It's just to say that no, coding and software engineering are not the same thing. "Code Monkey" is a dead (or perhaps "undead") role now, but it wasn't always so.
If anyone claims that coding is solved or not solved with such conviction, I expect some hard data, like comparing the density of bugs in human written vs. AI code, and how it trends over time. This article is just vibes.
"Don't confuse coding with software engineering" is a valid point, the rest seems like ranting.
I think the entire framing is wrong, I don't see coding as a "problem" which can be "solved," sounds the same as "we solved writing," like what does that even mean or look like?
I mean, was typing out code by hand really that bad?
If you were a professional software developer, you a) learned to touch type, b) started using vim/emacs keybindings to navigate around the project, and c) used a framework which already abstracted away a large part of the menial work.
And going all-in on the loop and no-code-review nonsense in a project someone is actually paying you for, I can only assume means you're hoping not to be around when the slop tower collapses.
Code is not solved seems to reflect the idea that using AI badly is a bad idea, and that to use it well you have to be good at making software.
The real tension is in the human AI interface and there are many unknowns. Can a software engineer with weak design skill use AI to produce good code. Will the future make those requisites less important. Will productivity increase with AI stagnate even for the best. Can a new way to interface humans and AI break that wall. Will software be created in a new way using dynamic libraries that AI agents prepare to cover a large scope of problems. Nobody knows yet. The article is strong on what is closed, accountability, ownership, NFRs, slop, and silent on what is open, which is where the argument actually is.
Could you elaborate? I don't use Anthropic's products for ethical reasons. Honest question. And when you say it's "solved" do you think an engineer should have the same income as a non-engineer if their outcome is the same?
PS: Has anyone watched DHH with Matz lately on Rails Conf where DHH is pretty much badmouthing ruby in front of Matz and Matz ends up saying "its my life's work"?
I do think that was a dumb gotcha. That list could've functioned with bullets instead of numbers as the content was unordered. I suspect very few people would pay attention to the numbers there.
Very little of the article is actually focused on stochasticity. I'd also say drawing an equivalent between the non-determinism of a person and an LLM is not that accurate either.
It's for example impossible to have a discussion with an LLM where you both learn something which you can apply tomorrow. The LLM doesn't learn until the next model is released and by then your discussion is just a tiny fraction of the training data (if present at all). AGENTS.md, skills and so on are just a proxy for what we actually want, an agent that listens and understands. A proxy mind you, that requires constant tweaking with no sign of generalisation in sight.
Does humans being non-deterministic make coding solved? I'm not sure how this relates to the main point.
I'm also not sure what humans being non-deterministic even means here. The point is if you're comparing results with NFR, pure agentic coding falls short.
Author here: thanks whoever shared this here. I love the brutal criticism and critical thinking of this community. I'm also fully aware of the emotions this stirs. If it makes you feel better, I'm not here to change anyone's workflow but I'm fed up with paying full price for degrading service. Just last week Github went down due to a stupid retrial error. We also had AI agents going rogue and hacking companies and governments. I use AI (specifically LLMs) every day since they came out 4 years ago. I also build AI-powered products. This is not about being anti-AI. I'm just fed up with slop being pushed as progress. Get your sh*t together. That's all.
If anyone has counter-arguments or cares to make me smarter, I'm all ears.
I mean, as far as counter arguments go, github had plenty of downtime before LLMs, and they didn't deal with exponential growth then. If you don't count the "it gets harder" side but only counts the "they had problems" side, then yea, that might look bad, but that's not very honest imo.
Not looking up the outage stats for Github (not sure how accurate they are historically). But going off by what I notice on HN the past months / year, it definitely seems like GH is experiencing more downtown than pre-2022.
But maybe I'm misremembering how fragile GH was in the 2010s.
Sure. They also have 10x the load or something crazy like that. It's rather incredible that it's still up at all. Microsoft must be pouring crazy amounts of money down that drain and gnashing their teeth at their decision to buy GitHub.
Coding might not be solved out of the box with these providers, but there are increasingly setups and harnesses that do have a great deal of it solved.
The audacity of publishing self-promotional AI slop clickbait claiming that AI can't code and everyone who doesn't agree with your asinine assertions is incompetent is bold. Respect the hustle I guess.
But to anyone even vaguely thinking of taking this seriously, go look at what antirez, dhh, jared sumner, mark brooker, and many other real engineers who have ship real things are doing and saying.
Most of these people have spent their entire lives contributing to open source, and they have proved their skill shipping working software and scale for decades. They are really trying to help people by showing and telling them exactly how AI works and how to use it to make better software.
I'm sure Opus 5.5 is smart and probably the next version gets even smarter. The main point of the article is accountability and that's not something we can delegate to AI.
What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.
If we are committed to it, if we put the resources towards it and dedicate the time to it (and we could do this just by saying: it will take half as long as it used to take!), software built by llms in healthcare, finance, automotive, defense, power plans, aviation, manufacturing can all be made MORE reliable and better with LLMs... without ever reading a single line of code. The LLMS are very good at logic, by the way.
Anyway all of this reads like someone who is not actually using LLMs to build software or hasn't tried them in a while. I felt the same way in 2025. I've written 100s of thousands of lines of difficult code. You, the person reading this, has probably interacted with software I've written. For a time you would've interacted with it every time you made a debit card transaction in the united states, for example. I understand code, and care about quality, and that's why I'm all in on LLMs for code.
Testing isn’t the same as understanding the code, or proving (even informally) that it is correct. Having the LLM do all these things above doesn’t lead you or the LLM to understand the code, to logically reason about its behavior over all possible states and inputs.
“Finding out that it doesn't” means that you didn’t properly reason through the code beforehand, checking all your assumptions against what the code and underlying systems are actually guaranteeing. This may be a matter of formal education (proving computer science theorems and algorithmic correctness in university), I don’t know.
Reading the code may not be enough to understand the behaviour of your program, but believing you can understand the behaviour of a program without at least reading the high level code is truly silly.
(by high level, I mean the code living in the higher layers - of course we don't often read the code of the generated assembly, or the interpreter, or the browser, but that's because they're reliable abstractions, unlike prompts!)
Interpretability is the same, our abilities to do that have increased rather than decreased. I think a codebase generated by AI is actually more understandable than one generated by humans at this point, and you can ask clarifying questions whenever you get stuck.
TFA's points only make sense if the mental model the author has in mind is someone who writes a prompt then immediately puts an app into production without any thought behind it.
At least some places are abolishing formal QA because LLMs. There's a cult of speed uber alles that has a big intersection with LLM enthusiasm.
If coding were solved, then this would be true no?
This sounds like a typical testimonial whose mind has become captive to Claude. It is like Scientology.
How can you not see the progress?!
if you're an MBA-brained exec who doesn't actively use LLMs to code and you just believe whatever slop it outputs at first without checking it, you're not going to realize how recklessly it can be used, how you need to be critical and skeptical of its outputs, that you need to explore it's reasoning and logic (which is still really easy compared to understanding legacy code and barely takes any time!)
say you also believe all this marketing hype about 'how dangerous (ie capable) AI agents are.' LLMs can do anything you think so you just say 'ship it' without building out the tooling and capabilities to enable faster code review and better tests. and to keep the shareholders happy, you start cutting jobs that you can't directly connect to a KPI (ie the platform/SRE team who would be the ones who can trial, onboard, and maintain those capabilities for your teams)
and from this, suddenly a lot of debit card stops working and the only one getting the blame are individual SWEs trying to hit their sprint velocity. the fact that you fucked up the whole SDLC real bad with your incompetence gets you a golden parachute and you job hop to a better paycheck. rinse and repeat
It must have been a huge shock when you were suddenly transported from a working parallel universe into ours back in 2024.
I've built payment rails. Six nines SLA, high capacity, resilient distributed systems.
I haven't written a single line of code since February, and I don't think I ever will again. These systems are incredibly good at replacing much of our work. They're only going to get better.
Rather than debating if these models are good (they are), we should be trying to figure out if most of us will still be around in three years. You don't need a two pizza team anymore.
"Look to the person to your left and to your right. Only one of you will remain by graduation" kind of energy. I'm not sure all of us is going to be in this career much longer. We'll have to see what the demand side looks like.
Correctness has never been a priority across an industry where rapid iteration and feature delivery drive sales. There's always some opportunity cost to doing things right, at the price of technical debt down the road. If AI is primarily used to produce fragile code, people will be wary of AI solutions. There's also ongoing public debate about AI safety and alignment. Deploying AI in safety critical applications feels riskier than ever in the current environment, even though it doesn't have to be.
> I understand code
Are you sure?
Those are two separate claims, unless by the former you mean “I never perfectly understood the code.” You can understand code imperfectly. And even with LLMs, you can’t get truly infallible guarantees about a system.
> I understand code
Er, ok.
I guess time will tell if the consumer will adapt to the lower quality of products, allowing companies to justify the existence of lazy and incompetent developers, or if the consumer will push back, forcing companies to increase the quality of their developers.
Note: I use AI every day and it is entirely possible to create high quality software with it, so long as you are not lazy and incompetent.
Brings to mind this classification https://en.wikipedia.org/wiki/Kurt_von_Hammerstein-Equord#Cl...
"""I distinguish four types. There are clever, hardworking, stupid, and lazy officers. Usually two characteristics are combined. Some are clever and hardworking; their place is the General Staff. The next ones are stupid and lazy; they make up 90 percent of every army and are suited to routine duties. Anyone who is both clever and lazy is qualified for the highest leadership duties, because he possesses the mental clarity and strength of nerve necessary for difficult decisions. One must beware of anyone who is both stupid and hardworking; he must not be entrusted with any responsibility because he will always only cause damage"""
Now instead of 90% stupid and lazy (harmless, useful for grunt work) you have 90% stupid and hardworking (aggressively causing damage).
> I hope you can see the stupidity here if you expect to see any deterministic results at all.
Are you expecting humans to be deterministic in the code they produce?
A human who knows 1+1=2 can still say “3” because they misread the question, misspoke, were distracted, or made some other cognitive error. Likewise, an LLM can output “3” because the generation process selected an incorrect continuation. Those are both errors in producing an answer, not evidence that 1+1 somehow has multiple answers.
So yes, human mistakes and LLM sampling are mechanistically different. If your argument is that LLMs and humans can both make mistakes, then major question here is why are we building out huge amounts of infrastructure at unsustainable spending levels to enable LLMs to make the same mistakes as humans.
And?
The p(that kind of error) is pretty small now. At what point does a probability coming out of an LLM look like "knowing", such that spitting out the wrong answer despite that probability looks like a health problem, a typo, or even just boredom? (Thinking of the Lizardman constant here: https://en.wiktionary.org/wiki/Lizardman%27s_Constant)
It's a continuum for both them and us, even if the mechanism is wildly different.
> Making mistakes is not the same as non-deterministic.
i.e. when the dismissal is "non-deterministic" when it should be "Making mistakes", is itself a mistake.
It just gets “reviewed” by an LLM, which will find a nitpick while ignoring the huge fire in the core of the design, force the planner to make even more sloppy code to cover for an irrelevant test case. Rinse old tokens and repeat until you hit limits.
For example, I recently got brought in to help with quality on a large-scale system that had been ported to a new platform with the help of coding agents. The project was completed and declared operational in record time, but soon after the business discovered that:
1. The promised scalability improvements did not materialize. Instead, it got worse.
2. Observability had been lost. The telemetry was no longer trustworthy.
3. Users stopped trusting it because it was producing incorrect outputs.
What I ended up discovering was that, while it scrupulously kept existing automated tests passing, any behavior that wasn't explicitly covered by a test was free to change any which way. And there were plenty of small things that weren't explicitly covered. Perhaps because the original authors thought they were so obvious and commonsense that they didn't need one, perhaps because mistakes happen. The why doesn't matter. The point is that reality is messy and imperfect, so giving someone a chance to look at things and think, "Huh, that's funny..." is an essential part of defense in depth.
But the real worst part was, this whole replatforming was a huge waste of time, anyway. The improvements they were looking for could easily have been accomplished with some controlled incremental changes to the original system. Mostly just removing a few basic and well-known performance antipatterns.
But way back at the outset, the person in charge of the project asked their agent, "What's the best way to X," and the agent gave them a trendslop answer about how Y alternative technology is more scalable and we should just port to that. It was convincing and they were under intense time pressure to just ship some code because leadership is bought into the AI hype and now has the patience of a 4 year old, so they just went with it.
Yeah. To me it seems very much like the "use dynamic typing for everything" fad. You had a bunch of junior and/or incompetent developers who went around insisting that type declarations are bad, static typing slows down development, you just code so much faster if everything is dynamically typed. And in the context of a new project, they were totally right. It took a few years for the debt to finally catch up, and people realized that these massive, untyped monoliths they had were unmaintainable. Now the two biggest dynamic languages (Python/JavaScript) are effectively typed languages, because nobody uses their untyped variants for serious work.
Dynamic typing still has great uses -- interactive data exploration, putting together quick scripts (though less relevant with AI...), or even just simple prototypes -- but what we tried to do with it at the start, as an industry, was clearly dumb as hell. I suspect we'll look back in 5-10 years and realize that with some of the stuff we're doing with AI, too. It's already happened with things like Gastown.
I like to put this as "LLMS give lazy and incompetent developers more runway."
What I in general try to teach the other people about AI: It can be a great tool, but check the results! Especially in the case of engineering: Check and then double check.
This is all that's needed to actually use LLMs nowadays. How is it a "multiplier" rather than an "equalizer"?
Because without the responsible human engineer in the loop, it'll all gradually decay in a cascade of edge-cases. This happens with human written code as well (every "we'll replace this prototype before we ship" you've ever worked on), but with LLMs it happens at 10-100x the rate.
You will be surprised how many times, catches errores made by the AI coding agent. However,as you point, isn't deterministic. And you can guarantee the end results is 100% fine code
> LLMs can wing it for tasks that are related to natural language (e.g. writing social media posts, reports, articles, etc.) but when it comes to code, the same engine that struggles to count number of R’s in “Raspberry” or suggests a walk to the carwash, also exposes other logical fallacies
Weirdly none of those things matter when writing code and actually LLMs fail at social media posts and articles to anyone who has seen enough of it can clock it's AI straight away, yet everyone who's used these models properly has solved harder problems than walk to the carwash with them, neither of the problems he's claiming are code were proposed as code problems or tested as code problems.
A lot of what's said just comes across as wishful thinking and being out of touch with the level of output current models can do, and I mean hard problems too.
My issue with it, is that it gives you a "lazy" option every time that doesn't require the same level of thinking. I understand that this is completely on me as the developer, and the simple solution is that I need to make sure I'm taking my time to learn and understand what exactly the LLM is producing. I try this and have set up separate skills to make sure I'm building my understanding as I go.
Regardless, if I sit down today and implement something without the use of LLM, it takes me a lot longer, but once I get into it, I find a state of flow that I can never get from the back and forth reading of LLM output. Then when I finish, even if my solution is not perfect, I have learned so much more and my own context of problem is so much better, where usually then I can review with an LLM. This usually leaves me with a better implementation and more importantly one I can stand over. I think for a newer dev like me (~2 years experience), since I haven't built up years and years of problem solving experience, if I don't carve out time in my day to put down the AI tools and improve on my problem solving, I'll plateau and that's my biggest push against all this LLM use. I don't necessarily disagree that 'coding is solved', to be honest, I think it largely is, but it's still the foundation for me to be a good Software Engineer and I definitely haven't solved it.
This is not a good premise. All over law, you will find people made responsible for what they don't control and they kind of own. Unleash a dog that harms a child, or just have it in an environment where it can escape, and see what happens.
There is such things as unpredictable situations where one might not be held responsible, as a problem might occur well past reasonable guidelines.
So of course you can be held accountable for what an AI that uou supposedly cannot quite control does, or for the AI-written code you deliver. Treat it like the releasing a wolf pack, or selling an unsafe toy that can maim children. There's precedent everywhere.
The difference seems to be that some companies are above the law apparently.
I was just at the Explore DDD conference in Denver and a portion of Friday was sitting at the cafe tables informally discussing the impact of GenAI on software engineering with notable people. Most of these people were deeply concerned that if we lean into using GenAI for “everything” that our collective knowledge will dissipate. I was the vocal contrarian. There are many historical examples of humans obfuscating knowledge to simplify progress. Does anyone solder their own microchips at scale anymore? No. We have highly sophisticated robots and machinery to do that work with extraordinary outcomes. In software engineering, if you remove “coding” as a discipline you’re left with all the other aspects of designing software which I contend can be retargeted in college CS curriculum. The leap isn’t about code reviews. It’s about design reviews and that’s where better outcomes are served regardless of whether GenAI is involved or not. I have a roughly year old codebase at https://github.com/ChicagoDave/sharpee/ that is designed by me, but generated by Claude Code with my own skills and agents as guardrails. I’m fairly certain the code I extract from Claude doesn’t require human review, but the design of the system and its changes are continually reviewed by me. My contention is that we “collectively” are still trying to discern where the AI/human line is and most are still “holding” that line to human interactions. Let it go. Define what part you do need human decisions on and focus on those things.
[1] https://github.com/anthropics/claude-code/issues/88715
[2] https://github.com/anthropics/claude-code/issues/7547
Just a simple reactor, my laptop's only little. But still.
I think a few of the industries listed like defense and aviation have low risk tolerance. However, from my (somewhat brief) experience of working in two health techs for a couple of years, I strongly disagree that healthcare has low risk tolerance for tech. Granted, they make run-of-the-mill CRMs, but I was baffled at how tolerable it is to have egregious user experience that makes users waste multiple hours per month with clerical work that is very painful because the UIs are very slow and buggy.
It means risk that the software stops working after an update. Which usually trades off iteration speed and best practices (i'm pretty sure the average startup has way better security practices by just delegating to google/aws than the average manufacturing software business) in exchange for a rigorous testing and rollout schedule.
So I'm also not sure that the article has a point at all, the human writing the code was never relevant to avoiding the "risk" in these industries in the first place.
I have no doubt that if you provide any AI system with an oracle with expected behavior that it can match that oracle with some amount of $ and tokens. I haven't seen any demonstration of anything else. Rewriting a codebase was always a challenge for humans not because of complexity, but because of the time and effort involved in matching the old version's prior behavior. It doesn't have anything to do with the serious level of work required to build something truly new from scratch in a performant way.
For example, any amount of software development involves fixing bugs, getting feedback from users on ideal workflows, an iteration loop of performance and bug tuning, etc. AI cannot simply create, from scratch, perfect software. Even using the SOTA models on max effort does not produce bug free software of any meaningful complexity or innovation out of the box. All that has changed is that the act of physically writing code and implementing existing patterns is now effectively a marginal cost.
Most line of business software is not e.g., delivering a company's income. Most software is in back-of-the-house internal products that do various internal tasks. I have no doubt that these processes are now far easier to build.
If the new Copilot is so great, why is it completely out of the current zeitgeist when compared to Codex and Claude Code?
GitHub's Copilot cloud agent offering is suffering with a case of some of the worst corporate ADHD I've seen. We built a cloud agentic development pipeline on it, and it seems like almost every other week they silently change something with zero public announcement or documentation that creates real disruption for our team.
That's real, breaking changes to the platform that clearly aren't being tested/reviewed before being pushed to prod. Again with zero public announcement or documentation.
Support is useless – we're paying customers in the 4-5 figures and our tickets go unanswered.
Especially expensive when you take into account the amount of that code which must have been boilerplate & meta-code in nature, meaning it should have been straightforward to move.
When the Go team ported the original compiler from C to Go, they wrote a program that did ~99% of the work
https://www.youtube.com/watch?v=QIE5nV5fDwA
https://go.dev/talks/2014/c2go.slide#18
would be curious to know how many times "unsafe" appears in there, have seen rust devs comment on how the ais like to use unsafe to work around difficulties with memory management, like how they will sometimes subvert tests
- Coding in the small is solved. I have a current state, I want to change it, and I know how I want to change it. Eg, I have a blocking TCP handler for some reason, and I want to make it async. I can either fiddle with it or just let LLM make the changes for me.
- Coding in the larger sense is never solved. You need judgement to decide what you want made. No matter what you're building, there will be decisions to make (Who/what is it for?) and those decisions change over time. LLMs can take some default decisions for you, and if you're fine with those, you get the default (great for POCs). However you might not even realize what it decided to do for you. At some scale, you will be spending a lot of time going over those decisions. But what we have now is that the friction of changing the decisions is quite a lot lower. You can now test a lot of things that previously were very time consuming.
- The point that LLMs are probabilistic is not as important as it's made out to be. If I ask a junior dev to code up something, I also don't know what he'll make. Heck, you can be sure that you are able to solve something, yet you yourself don't know what the solution will look like. Maybe it turns out the library you were going to use isn't appropriate after all. You don't know what you will use in the end, but you do know that something will fix the issue. There can be more than one solution to a problem, and it doesn't always matter which one you find.
- I STILL think that LLMs are at their best mostly as advanced predictive text. In the sense that it's mostly good at implementing things that you've decided are needed. This can mean a heck of a lot of code, but you have to know the tradeoffs. What was decided, what were the costs of those decisions in terms of maintainability, money, time to change it, and so on.
All this doesn't change the fact that software engineers are going nowhere because nobody trusts AI. If a model can escape highly secured sandboxes, then we're definitely not running these agents overnight on our systems. I am sure the next-gen of models will focus more on security and the trust factor will start developing, but that's a long way down the road.
People trust people, not systems.
Coding is not solved because you can’t simply prompt an LLM to make an AAA game or enterprise tool.
As for accountability, it always laid with the employer. You think those nameless contractors whom Boeing hired suffered any consequences for that 737 Max glitch? Using AI won't change that.
AI doesn't have to solve all these coding problems to be worth handing the reins to it: it just has to substantially better on average than humans over the long haul, which it already is, especially if you have good verification of "done" and "working" in place through automated testing mechanisms. Perhaps we might say that QA is having its moment.
It doesn't mean humans aren't needed, but they aren't writing much if any code anymore.
Instead, I think what's closer to solved and what we're in the process of solving is product development.
Story: A while ago, I had a few programmers who were really, really fast almost always missed the mark on the assignment wrong. I loved having them on projects because in the time my senior precise engineers could deliver a MVP, the fast engineers would build the wrong thing, collect feedback, reiterate, build the wrong thing, collect feedback, eventually inching closer and closer to a product people would pay for, and it would almost always get delivered faster than my seniors.
I feel AI does the same thing.
I got lazy around claude fable and astra, and asked them to work in loop (pick specified issue, develop it, qa it ...) have a separate CTO checking on arch.
at the end both models swore that the code is perfect and well designed and nothing is lacking.
I ran the software and it suddenly started writing large amount of data to CSV files instead of the typical DB usage.
AI decided to use csv for testing, and just drifted away. 0 regards to the actual project, 0 regards to common sense.
anecdotal but really weird, the project category is rather standard, I wouldn't accept such a mistake from a junior developer.
It compiled and ran just fine. If you weren’t reviewing the code holistically or keeping tight book keeping of your allocations you would not have noticed. Every single commit in isolation looks perfect. Very eye-opening
Isn't it the opposite? How to build something is rather solved, but what to build isn't?
But that's not solved in traditional product development either.
Product development an iterative process to get a product fully functional. In 2021, if you ask me what the timeline for a small product/substantial feature, I'd say a few weeks to a month to get a basic MVP, and then another 12 to 18 months to get a feature polished and in a good shape to be stable.
When people put it in the coding frame, what they do it as is saying we've gone from 18 months to minutes or days. That's just not true.
We have gone from eighteen months to depending on the complexity, a 1-4 months.
aside: To be candid though, the compressed time also means the frustrations people experience with a product in 18 months have also been compressed. They still exist, they're all there, they're now just non-stop.
That concept might work a lot of the time but you will definitely run into situations where that'll never produce a correct or working response. To actually learn something you need an environment/playground to apply what you think you know and observe the results. Without that you're not really learning, you're jus regurgitating what people want to hear.
Dear lord. Is that supposed to reflect the average thoughts and motivation of a person you want to hire? Or that of their employer?
Nobody has to be in fear, but we do have an ingrained knowledge that there are consequences, good and bad, for our actions
AI can write CRUD API endpoints almost perfectly now. It can also write quicksort, a heap, whatever much quicker than I can.
It really sucks at designing types and apis though and when it creates types and apis it doesn't think or plan for the future way the system will evolve (even if it's known up front how the system will evolve).
I suspect this will remain a problem for the models for a long time. All the things that the models are currently good at are the low hanging fruit of reinforcement learning for coding.
Think about the kind of reinforcement learning environment that needs to be created to train a model to become good at building and designing large scale software end to end. It would be a slog because you need to build the large scale software up front and then break it down to train the model to construct it in a systematic manner that allows for the software to evolve. And then you need enough of these training environments for it to generalize. I think they will eventually figure it out though but it may take a while.
Does that really matter? Those are things so that humans can better understand and extend a code base. That mattered when writing code was expensive and took time.
Now if it can pass all the tests it’s fine. If there’s an issue just have it rewrite things immediately. New bug? Generate a new test and rewrite code.
All, or many, of the old things that mattered just sort of don’t anymore.
Much of the code needs to be performant and the LLM knows this and grinds on it. Less and less human inspection is needed.
I think 90% of software can be written like this today.
You’re simply testing outputs. Make a spec but ultimately ungodly amounts of tests can be built quickly to ensure the program is outputting the right things.
However, software engineering isn't solved. Which is basically what this article is talking about. But having coding solved is still very beneficial - not long ago many software engineers would struggle very hard with turning a description of the logic required into syntactically correct code - and even for those capable it was incredibly time consuming.
So now the question becomes: Is software engineering solved? And my answer is no. People still need to read the code and understand the code and how it fits into the bigger picture. However, I feel like we are kidding ourselves if we think that we can go from producing code being a niche task for nerds to getting syntactically correct code from plain language without any deskilling of our work and careers. I feel like my personal "moat" has gone from "can speak computer" to "has ok reading, writing, comprehension and judgement skills" (I hate the word "taste" being used here ).
At the same time - I don't yet feel like there's a sudden abundance of competent software engineers - it's just that the folks who used to submit untested spaghetti code now submit big bowls of barely working slop. So maybe it the moat was never "can speak computer" - that was just the expression of more general skills.
The existential question for me is how far the deskilling will go. Because right now - you still need a solid grasp on computer science and software engineering concepts to do this job, as well as sufficient levels of grit and problem solving ability - but I'm not too confident that will last, and when it goes I don't think many of us will find this career enjoyable.
They are thinking: Please input everything you know, or just use it and it will collect everything in your PC or server automatically.
Stop lazy, stupid and dangerous behaviors.
It's not clear to me if the claim is:
(1) "If you used an LLM to generate code, and the code works, you're wrong if you think the code is okay"
or
(2) "If you used an LLM to generate code, you reviewed the code and found it to be of decent quality, then you're wrong".
> If you’re toying around, LLMs do a great job. That’s why some of the most aggressive proponents of the “coding is solved” narrative have nothing to show for it.
I also don't get the "LLM proponents have nothing to show for it" statement.
It's really quite common now to see on HN all sorts of LLM-assisted programming projects. The quality varies from slop where little thought was put into it, to high quality results where LLM coding assistance was able to let talented developers produce things they otherwise wouldn't have time to do.
I'd say it's obvious that LLM coding agents can be very useful for a lot of programming related tasks.
EDIT: That is to say, LLMs are obviously useful for use cases above/beyond toying around. It's not a dichotomy between "I'm never touching an AI" and "thoughtlessly accepting everything the LLM outputs".
"coding is solved" == "gastown-like systems give a brand-new and useful software"
I don't recall whether GasTown succeeded...
Over this weekend in chat with the games discord watching as it iterated a harness built an entire implementation of the board game terraforming mars https://tfmbot.com using agents and harnesses for them.
I think if you can implement a board game end to end by feeding in the rulebooks and having a harness spawn agents to validate it’s reasonably solved.
That's not a boast, I don't think I was particularly good at that back then, e.g. I didn't really get how to think about automated tests until much later.
It's just to say that no, coding and software engineering are not the same thing. "Code Monkey" is a dead (or perhaps "undead") role now, but it wasn't always so.
What’s your number?
"Don't confuse coding with software engineering" is a valid point, the rest seems like ranting.
If you were a professional software developer, you a) learned to touch type, b) started using vim/emacs keybindings to navigate around the project, and c) used a framework which already abstracted away a large part of the menial work.
And going all-in on the loop and no-code-review nonsense in a project someone is actually paying you for, I can only assume means you're hoping not to be around when the slop tower collapses.
The real tension is in the human AI interface and there are many unknowns. Can a software engineer with weak design skill use AI to produce good code. Will the future make those requisites less important. Will productivity increase with AI stagnate even for the best. Can a new way to interface humans and AI break that wall. Will software be created in a new way using dynamic libraries that AI agents prepare to cover a large scope of problems. Nobody knows yet. The article is strong on what is closed, accountability, ownership, NFRs, slop, and silent on what is open, which is where the argument actually is.
So - prose, code, or image, it appears that some work has been done, but in fact the [actually needed] work has likely not been done.
I don't like the feeling being judged and tested by the author (missing number 5 point in the list).
Doesn't matter what you think about AI, "it isn't perfect" is clearly a nonsense reason not to object to it.
It's for example impossible to have a discussion with an LLM where you both learn something which you can apply tomorrow. The LLM doesn't learn until the next model is released and by then your discussion is just a tiny fraction of the training data (if present at all). AGENTS.md, skills and so on are just a proxy for what we actually want, an agent that listens and understands. A proxy mind you, that requires constant tweaking with no sign of generalisation in sight.
I'm also not sure what humans being non-deterministic even means here. The point is if you're comparing results with NFR, pure agentic coding falls short.
If anyone has counter-arguments or cares to make me smarter, I'm all ears.
But maybe I'm misremembering how fragile GH was in the 2010s.
But to anyone even vaguely thinking of taking this seriously, go look at what antirez, dhh, jared sumner, mark brooker, and many other real engineers who have ship real things are doing and saying.
Most of these people have spent their entire lives contributing to open source, and they have proved their skill shipping working software and scale for decades. They are really trying to help people by showing and telling them exactly how AI works and how to use it to make better software.
Long term planning in LLMs has not been solved.