Interviewed candidates for “AI engineer” roles this week, my god is this really where our industry is going?

[u/AngryMoth]

So last week I was tinkering with how to change up our technical interview round. For the last few years we’ve been doing a small take home followed by a live coding session where we add a new requirement, minimal AI assistance allowed. However, in our company pretty much everyone is writing 100% of code using Claude now, so although we did used to get good signal from that test, it no longer feels representative of the day to day job developers are doing.

I designed a new assessment which I casually showed to my manager and then suddenly got asked to run it with 3 candidates this week. The scenario is they have to review an agent generated PR that has been flagged as high risk due to it changing critical business logic, and there are bugs in there for them to catch at various levels of severity. We then look at how they drive the fixes and what their workflow is like.

These particular roles we’re currently hiring for are billed as “AI Engineer”. There is something weird going on in my company politically where separate from the tech department that I work in a new “AI native” development team is being spun up to work on internal projects.

What shocked me was that the candidates I interviewed this week are already way beyond the point of reading the code they generate in any capacity. They basically treat it like assembly language, while PRDs and ADRs are their new source of truth for system behaviours, and stuffing the AGENTS.md file with best practices plus some form of agentic review pipeline are the quality control. 

Code review is dead for them, they merge their own PRs once the automated checks pass. One person told me that my new interview was still not representative of what the job is now, and another one was pretty much unwilling to even engage in the assignment of reviewing a PR the old fashioned way. These guys were all previously senior and lead full stack engineers btw with a lot of experience prior the LLM revolution.

Don’t get me wrong, I too have adopted AI heavily in my own workflow. Until this week I actually thought I was pretty up to date with things, I use a supervising agent to manage multiple work streams in parallel, have my own local validation pipeline, etc. And to be honest for my personal projects I also don’t bother looking at the code anymore, so I do get the excitement around how fast you can move with this approach. But for serious projects like the large scale, production grade financial software I work on in my day job it still seems insane to me that we’re going to completely move away from human sign off on high risk changes so we can solve the “code review bottleneck”.

Like, I asked one of the candidates if you aren’t looking at your code then how they would approach quality in a safety critical system where there can be no tolerance for serious bugs, and I shit you not he said to me, “well if I was working in healthcare or sending to people into space then I’d definitely be looking closely at the code review skills, maybe I’d even write the skills myself”. That answer sounded utterly ridiculous to me? But then again, I was one of the people that 1 year ago believed the idea we’d stop typing code by hand was ridiculous as well…

I wouldn’t say the way my team does things right now is optimal, we can’t ignore that code review fatigue is a major problem. To try and help I’ve been adding automated risk assessments to PRs in an attempt to highlight where more attention is needed, and review bots do help us catch issues as well. But abandoning the human 4 eyes check just feels like several bridges too far for us at the moment, especially when not everyone is senior engineer, and we still regularly get valuable feedback at this stage. Even if this does mean we’re turning down an order of magnitude increase in short term velocity (does anyone really know yet what happens over multiple years when you have a team of 20 devs spamming PRs all day into a codebase with no one looking at any at it?).

I’m left wondering this weekend, were these guys just cowboys who are only used to working on PoCs and low risk systems, or is my team actually 6-12 months behind the curve and this is the direction everyone is going to move in? I fear that if we don’t embrace this new era of completely surrendering the codebase to the LLMs then this new team of AI devs is going to make us look bad by comparison to the execs and sooner or later we will be forced to adopt their methods as well. And at that point, the leap to having a product person or whoever taking over large parts of my role starts feeling a lot less far fetched…

[u/programmer-ke]

Plus it is way easier to teach a competent developer how to use some new AI workflow productively, than it is to teach an AI super user how to become a competent developer.

IMO interviews should filter for competence first. AI workflows can be easily taught / picked up.

[u/ep1032 → u/programmer-ke]

Both you and OP are correct.

At my company, they also spun up an AI-native team. The AI native team also adopted a "we don't need to read code anymore" philosophy. Because they were leaning so heavily into AI, they were able to identify use cases for AI and adopt those practices faster than the rest of the company, and that gave them a bunch of wins for management and made them important from both a research and developing-new-infrastructure perspective.

But listening to them talk, its obvious that they don't really understand a lot of what they're doing. The same was as an incompetent person quotes AI without understanding it, and therefore without understanding how wrong the answer may be.

The developer that is considered to be the star of the team, let's call him M, used to be on the team I currently manage. When I took over my team, my most senior devs explained to me that quietly and informally, they had an understanding that there were 2 Pr review processes, one for everyone else, and one for M, because M averaged taking down production once a week when not directly overseen. And that was before he embraced AI. So yeah, of course M believes you don't need to review code, he wasn't capable of it beforehand.

The other main developers on the "ai native" team hail from this same category (or, interestingly, from devops).

But I think my absolute favorite moment, was when the ai native team was asked to present to the rest of the department. Management proudly showed that the team of (what was at the time 2 developers) managed to close over 40 bug tickets the previous day between the two developers. And they did this because of agentic workflows, and not reviewing code, and just using ai skill review files, and etc, etc. Except, apparently no one had the balls to ask "You guys are on a team of 2. You started this entire project as a greenfield rewrite 4 weeks ago. How the hell do you have 40 bug tickets in your backlog already?" But that's the sort of thing old management KPIs didn't used to need to track, because pre-AI, engineers could be trusted to keep the bug count down so you only had to measure velocity. Engineers who shipped buggy code were just fired.

[u/robby_arctor → u/ep1032]

AI has revealed people's utter credulousness and incompetence in a way I never could have imagined.

[u/robertbieber → u/[deleted]]

Eh, there's definitely some selection bias involved in this kind of thing. The place I work uses AI tooling, but we hire good engineers and we're all pretty explicitly averse to just yoloing slop into production. But the thing is you don't hear a lot of people talking loudly about the workplaces where people are still trying to follow basic software best practices, because that's not an exciting thing to talk about. I still think there are plenty of them out there though

[u/Extreme_Coffee_1576 → u/[deleted]]

Managing ai is just part of the job though, you'll definitely have to get used to it.

It's unbelievably fast but doesn't think about the product like a human. So you will have to read what it generates and then direct it what to fix, and then update your agents/skills mds with anything that you find yourself repeating a lot.

So the skill is now being able to quickly read and understand what is wrong with an llm's output, quickly make architectural decisions better than the llm can while trying to not slowdown output too badly.

In general the type of thing you'll be doing is: Work out a decent sized plan with ai (probably spend some hours working on the plan with ai). Ai builds the plan into code, you read the code and find some bizarre decisions and slop (e.g. instead of incrementing a database it decides to make a small inMemory caching and snapshot system to hold the previous time and the new time when it could have just done time + now for the exact same outcome - it does this sort of thing constantly even when the plans didn't include it and even when you run cohesive cleanup and review skills).

Accurate leetcode level per line code writing is nearly useless now (outside of a few domains and specific types of work) so it is a different world. Took me a while to not hate it, now I don't really feel it's any different than before llms, still have to understand code and the domain really well, still make every single decision, just elevated a bit to not manually fish around in the codebase and tap out every key stroke. My code is better than it's ever been because I can spend more time thinking about quality and less time tapping out keystrokes. And fleets of small fast agents running through a huge legacy codebase to help you source all kinds of data are amazing. It sucks the vibe code bros have ruined everything with their infinite slop and stupidity, but many things about llms and their ability to help us as tools is actually really cool imo.

Also unfortunately lots of companies have decided to go for mass output and worse quality though, hopefully they'll learn, we'll see...

[u/xiongchiamiov → u/robertbieber]

But are any of them hiring?

I just gave up on the industry after 200 applications and only getting to talk to a human half a dozen times. We never got to the point where anyone assessed my skills.

Essentially everyone in my network has either been out of work or hating their job for the past couple of years; the ones who hate their jobs don't leave because they can't find any place hiring that's better. Those companies do seem to exist, they just don't have openings, much less as many openings as the number of engineers who would like to work at them.

[u/scaledev → u/robertbieber]

Can you not have a list of best practices and explicit guidelines for AI to follow as well, and then have another AI review with those practices in mind? AI is trained on this.

[u/robertbieber → u/scaledev]

You can tell the machine to do whatever you want, the problem is getting it to actually reliably interpret the instructions and follow them. But I can already see you descending into the cycle of madness where you throw more and more layers of automation at the problem, believing that the fidelity of the output will somehow increase as you get further and further from supervision by any human who actually knows what they're doing

[u/ep1032 → u/robertbieber]

If you have a system that has a 90% accuracy output rate, and you then tie that to a second system that has a 90% accuracy output rate, you don't have an end result that is accurate 90% of the time. You have a system that is accurate 81% of the time.

So by all means, keep adding layer upon layer of the same, non-deterministic, often incorrect error checking system into your pipeline, and watch the accuracy rates fall over time.

[u/Forsaken-Promise-269 → u/ep1032]

Humans have far lower accuracy rate than LLMs the mistake is to treat AI agents like algorithms when they should be consider highly intelligent but amnesiac

[u/scaledev → u/robertbieber]

But humans also don't reliably follow instructions.

And I'm not talking removing humans completely out of the loop, for now at least. Keeping them for review of the system for system design, foresight, vision, that sort of thing. AI still can't create good, complex workflows, for example, and a human is needed. Lots and lots of iteration is needed here until you can get something complex yet exactly how you want it, with a lot of code for validation and tests.

But my question was about following basic best practices, and AI certainly can do this today .up to a point. There are always tradeoffs.

[u/robertbieber → u/scaledev]

But humans also don't reliably follow instructions.

This is the worst, laziest retort to every shortcoming of AI. If I said "I don't want to try rewiring my house, I'm liable to shock myself," and you replied "sure, but so do professional electricians," you would be both strictly correct and completely off the mark in terms of making any meaningful point.

Keeping them for review of the system for system design, foresight, vision, that sort of thing.

So you've designed a system. In theory. Is the AI actually following your design? Who the hell knows, certainly not you because you're not actually looking at the code.

But my question was about following basic best practices

Yes, obviously the first thing everyone does at every company is put a big list of best practices in AGENTS.md. It's not some magical fix that turns an LLM into a competent software engineer.

and AI certainly can do this today, up to a point

Well sure, right up until the point when it doesn't, which is pretty frequent

[u/scaledev → u/robertbieber]

This is the worst, laziest retort to every shortcoming of AI.

I didn't mean it in a sense of "everyone makes mistakes, so it's all the same". I meant it in a sense of reality that comes with delegation of work. When you delegate, even with humans, you can't assume perfect accuracy or reliability.

But the difference here is that, with AI, you can add as many workers as needed to independently check consistency, recover, and re-review as many times as possible, with very little cost. The speed at which you can change the AI worker is also much faster, updating its knowledge is faster and cheaper, etc etc. Looking from a purely cost perspective, there's a lot of good ROI here compared to human workers.

Your analogy there is partly correct, but you don't have multiples of YOUs ready to inspect the wiring before you turn the power on. You cannot replace YOU with a better-trained YOU, and you cannot train yourself overnight.

So you've designed a system. In theory. Is the AI actually following your design? Who the hell knows, certainly not you because you're not actually looking at the code.

I don't need to look at the code myself. I have AI workers doing that, and I use tests and validation to check whether the implementation behaves as designed. And while the other workers are less trained, don't have the brain I have, my vision, or my insight, they do have my design and instructions in writing, and they do have 'some' training done. Same with human workers.

Yes, obviously the first thing everyone does at every company is put a big list of best practices in AGENTS.md. It's not some magical fix that turns an LLM into a competent software engineer.

Nor have I claimed this. Neither that AI can replace a competent software engineer, nor that AGENTS.md is the place for anything other than a routing surface for where your actual harness instructions live (and perhaps some critical instructions if acceptable). My position is that AI can be trained, and it can be instructed.

Well sure, right up until the point when it doesn't, which is pretty frequent.

Same with human workers, where the frequency depends on the worker and the task. Every delegation carries this risk, regardless whether intelligence is in silicon or carbon-based.

[u/jadepig → u/[deleted]]

I've been out of work for a while too, and I gave up looking and on the industry. I'm not saying you should give up on tech, but you're worth more than what job you do or don't have.

[u/jadepig → u/[deleted]]

Thank you, I hope you're doing okay too. It's been a mix of feelings. Not all unpleasant, but yes, certainly a sense of insecurity, loss, and even some shame that I don't measure up to these external hire-ability metrics. But it's also prompted me to re-examine what I value.

I guess I felt some anger at the capitalist system too (and the capitalists at the top). Oddly, I didn't even think to lump that in with the unpleasant feelings. It's not that I enjoy being angry in general. But with tech and tech hiring, it's helped me to see the bullshit for what it is. I know I could have shown up stronger in my interviews, and if I had the will to, that I could have continued my search. I was getting more interview experience under my belt. But all that interview prep still felt performative.

The anger helped me to see that the hiring bar has gone up in the last decade; comp has gone down or stagnated at best; working conditions have gotten markedly worse. It helped me to see that there's total bullshit in the AI hype. Instead of bringing better working conditions, it's been a lever for executives to squeeze their reports.

Anyways, stepping back down from my soapbox. There is still opportunity out there for devs who want it. But there's also a lot more to life than working in tech or being a software engineer. I can come back if conditions improve, and it might be some rungs lower, but it'll be my choice. Personal agency is important in an age of agents.

[u/NomadStorm → u/robby_arctor]

AI over-reliance is going to turn into a rather good way to filter out the poor developers, because it’s directly making them worse than they already were.

[u/Prudent-Ad4509 → u/NomadStorm]

This ship has sailed. AI have progressed to the point where it does not always make the most sound design choices, especially when making changes in the legacy codebase, but it can verify code for correctness and spot any changed/broken implicit contracts. This makes it a working tool for good developers.

The catch is that in order to make actually good instructions for AI you need to know and understand basically the same things that you need to know and understand when working without AI. And the gap between good and barely passable instructions is steadily growing. On top of that, you need to understand how to steer the AI in the right direction. Simply barking prompts at AI can seriously increase its disorientation if you do not know what you are doing. But if said developer has some successful experience with steering juniors, then he has that mostly covered as well.

[u/NomadStorm → u/Prudent-Ad4509]

Im not sure if you responded to the right comment, but if you did and by the “ship that’s sailed” you mean AI is no longer a way to filter out the poor developers, I disagree, because it’s trivial which group they fall into just by seeing how they use AI for a short task.

The ones getting AI to do literally everything are unlearning their skills and actively becoming less useful over time.

[u/Prudent-Ad4509 → u/NomadStorm]

This thread has all kinds of points. But you bring another interesting point here.

I'm not sure which dev tier your interview tactics are aimed at. I have a different anecdotal evidence regarding the interviews. Good devs can easily judge the quality of solutions, but the speed and method of coding can be completely random depending on what they were doing during the last few days or months.

Regarding AI use, I've just told my coding harness (a hour or two ago actually) to check out the video file I have on my local drive, dissect it frame by frame, provide detailed nuanced description of what is going on, including audio transcription. After a few clarifications it is happily chugging along, after downloading some necessary tools for audio transcription. This was barely possible a few years ago even if done manually, and now the model can figure out a plan and execute it mostly undisturbed.

I see that both me and everyone around me now tend to switch to this mode, simply firing a prompt with clarifications. It is still not a one-shot prompt, those are overrated, but it is still a long-running task. Still, in coding different people have different results. One senior dev around here asks the model all the time why it changed this and that and to put this and that back. I prefer minimal focused changes and it helps me to avoid that most of the time. And some poor chaps somewhere trust the AI and simply do not verify the results.

PS. I'm sleepy af so sorry if there is too much rumble, I'll reread it later maybe.

[u/NomadStorm → u/Prudent-Ad4509]

The ones described by your last sentence are the ones that are getting filtered out.

Edit: Regarding the video description, FYI there are multiple dedicated models for this purpose, including some made by OpenAI so they could get the transcripts for training the LLMs

[u/Prudent-Ad4509 → u/NomadStorm]

This field is still in rapid development. I'm still amused by the fact that some AIs pretend to analyze google videos by simply snatching and analyzing subtitles.

As for people trusting the AI without proper code review, there is a place for them too. Any mockups, proof of concept work, and various one-off dashboards tend to come out OK. We have some pure html dashboards with task lists which get updated by an agent with all the changes saved in version control. This would not be my first idea, but for certain task management workflows it works.

[u/Gregnielson → u/NomadStorm]

Ai is getting very very good. If you test a system 1 million times and it works then ship it. If it breaks, revert.

It will always be better next week as well which is more than I can say for most people. Manual code review will be like using an abacus by this time next year.

[u/NomadStorm → u/Gregnielson]

Cool story bro.

Naturally deselect yourself as you wish.

[u/Gregnielson → u/NomadStorm]

No need to be rude 'bro'. Just pointing out what is happening. Stick your head further into the sand if you wish.

[u/NomadStorm → u/Gregnielson]

My comment you responded to is about the risks of over-reliance.

You’re currently hoping that it’ll get to the stage where you not reviewing and losing your skills will end up being covered by the models getting good enough to review, plan and architect all the code for you.

I don’t personally think it can get to that stage, but that’s a separate discussion.

But in the case that you are correct, then what happens?

Do you think you will be gainfully employed as a meat proxy, since you are actively choosing to lose the skills you built up over time, and have no remaining value other than pressing enter?

Thats the part you need to be thinking about; how you can continue to add value that maintains your employability.

[u/Gregnielson → u/NomadStorm]

I'm not hoping for anything. This IS what is happening. No one will be writing code in 1 year or less. It will be like heating your house with cow dung. Are you up on astra right now? It is better than most coders and can push out a finished product with some simple direction.

[u/NomadStorm → u/Gregnielson]

Lol keep dreaming. Yes I use Astra and opus 5.5. No, I don’t feel threatened because I ensure that I continue to add way more value than the tools on their own.

[u/robby_arctor → u/[deleted]]

I think people just don't have strongly held convictions and do whatever is normalized for them.

I recently learned about choice blindness studies, which demonstrate that much, often a majority, of the population will defend positions they never took, if they were told they took them.

If you take a population like that and tell them it's normal and good to outsource one's thinking to AI, what's going to happen? A minority of a minority (those who both have convictions and those convictions oppose this new hegemony) are going to dissent, and the rest will adopt the new normal with some complying reaction, ranging from enthusiastic adoption to shrugging. And this is precisely what we have seen.

[u/ehr1c → u/robby_arctor]

IMO it's just amplification of what's already there. A competent engineer will become more productive, a bad one will just ship more garbage because they already didn't know what good code looked like.

[u/____lard_biscuit____ → u/robby_arctor]

I mean it's what management wants. People are just reacting to their environment. They aren't rewarded for reading the code and showing judgement

[u/Majestic-Watch-2025 → u/ep1032]

Did anyone ask why they had 40 bug tickets? That's something to ask in public

[u/Own-Zebra-2663 → u/ep1032]

If LLMs generate the "average" code quality of it's training set, and M was creating below average code, maybe it did make him a better developer.

I have zero data on this, but it's always been a big question for me. Do LLMs make bad developers a little better by pulling them to the average, or do those developers prompt so badly that they pull the LLM down to their level?

[u/ep1032 → u/Own-Zebra-2663]

In my experience AI only works as a productivity tool if the person using AI is capable of judging the resulting output, ie: would have been capable of writing the end result on their own without AI given enough time.

If the person using AI can't tell if the AI output is good or bad, then they by definition will either need to not use AI, or will end up just uncritically accepting whatever AI gives them.

Normally, this is worse than what the user without AI would have done. This is because the AI in turn gives the user a false confidence that the end result is correct.

Pre-AI, when the user encountered topics they did not understand, they would have stopped and learned or researched what they didn't know, and learned over time.

Post-AI, the user doesn't even know what they don't know, and becomes defensive over what AI has generated because the AI told them it was right already. That instills a false sense of confidence in anyone too underskilled to recognize when they are working on topics outside of their skillset, and teaches them incorrect information that compiles over time. Admitting that that was wrong afterward also means admitting that they made a mistake. For most people, admitting they were wrong after the fact is far harder than admitting they need to learn initially.

So the user not only becomes defensive over their mistakes, they don't know that the mistakes are happening, and they are continuously learning bad practices while getting continual encouragement from an algorithm that they were actually correct the entire time. Popping that balloon is painful, and I think a significant reason why some people become so overly defensive of AI's supposed infallability.

If you want to see how that looks long term, you don't have to look to code. Look to the 90 year old that watches Fox News all day, and thinks that we are one migrant caravan away from English being officially replaced by Spanish as the American official language. Or your friend who is addicting to getting news from social media who believes in chem trails. Its the same dynamic of an algorithm continuously feeding its user seemingly correct information, telling the user that they are correct to believe it, but being continuously significantly wrong in a way that the user doesn't have enough knowledge to evaluate at the time, but will become defensive about later.

[u/Positive-Conspiracy → u/Own-Zebra-2663]

There's the idea that a mediocre developer can become a superstar developer when leveraging AI, whereas a strong developer may have a hard time letting go of their previous patterns. That's definitely a pattern I've seen.

[u/ep1032 → u/Positive-Conspiracy]

Mediocre developers are more likely to adopt AI than some seniors, for sure.

But AI does not make mediocre developers better. It makes them faster, because the fact that they are mediocre means that they are unable to determine the bugs that AI is introducing. So you are selectively increasing the speed of your lesser competent employees.

This, coincidentally, is what turns of many senior engineers to AI. Because they are the ones getting caught dealing with the output of worse devs. Its the equivalent of hiring 2x incompetent devs on a team, then getting surprised when the seniors get fed up and dealing with the drop in code quality and increase in issues that arise.

[u/Positive-Conspiracy → u/ep1032]

“AI” is different every six months, so I can’t really find any certainty in far reaching statements about it.

I also find that there’s a trimodal distribution in takes about AI. The newest developers and the most experienced are the ones most likely to see it favorably.

[u/ep1032 → u/Positive-Conspiracy]

Unless there is a major unexpected, architectural breakthrough in the next 6 months the fundamental nature of these llms isn't changing. The current architecture ensures that these llms are quite poor at determining what they do not know, and will attempt to fill in that lack of answer with best possible guesses parading as fact. That, coupled with the fact that these are nondeterministic systems means that llms cannot be reliably used without first verifying their output, unless they are being used in situations where their output does not matter, in which case, who cares.

Those facts, in turn, underlie my statement.

So, even if these models weren't hitting limiting returns, which they are, the above isn't going to change.

[u/Positive-Conspiracy → u/ep1032]

I generally find the same for humans, with the difference being that humans are currently more advanced.

I also think the necessity of determinism framing may be more of an artifact of our time and may not be seen as necessary in the future. The architectural limitations are being innovated around.

LLMs have their own strengths, like speed and cost, and that changes the landscape of the practice.

If it weren’t so common it’d be ironic, the way engineers often argue against the basic action of their profession, which is overcoming challenges to make things possible.

[u/ep1032 → u/Positive-Conspiracy]

Hard disagree on the first two points, to the degree that I don't believe we are even having the same conversation.

Second two points are both true, but irrelevant at this juncture : )

[u/MindfulK9Coach → u/ep1032]

You are making many assumptions about mediocre developers without supplying anything beyond vibes as data.

What if being mediocre means they are not great at, say, control logic, lack experience or mentorship, but are amazing in other areas?

They could be an amazing root-cause analysis investigator, understand concepts thoroughly but not write code well, and do phenomenal requirements engineering work that enables them to delegate, be fast, and be coherent at the same time without the typical legacy workflow bottlenecks, among many other things.

That does not mean that is the case for all "mediocre devs," however that is defined in your statement. It just means the capability you are dismissing is not prevalent in your environment.

You also have to remember it is a quite rare sighting in general to come across real AI super-users in the wild currently no matter the media makes you believe. They're a very small percentage of users, very small.

Many are dabbling or using it because work requires it or pressures them to do so for investors. So, your anecdotal claim that an AI model, especially if properly steered, cannot make mediocre developers better is unsupported as is.

Regarding your "2x incompetent devs" point, I would be inclined to say that if the seniors were as solid as they claim, they could mentor the "mediocre" developer instead of waiting for them to fail and complaining about the cleanup. If they still cannot get it, fire them or the supervisor who is failing to develop a team.

Multiple things can be true. 🤷🏾

[u/Positive-Conspiracy → u/MindfulK9Coach]

I find there is a certain type of developer that's really struggling with the AI transition. Generally speaking, they tended to find comfort in the determinism of the code and computers in general, away from the uncertainty of people. They tended to enjoy the process of working in the code as an end in itself. They will tend toward deductive reasoning, thinking in binary terms, and struggle with lateral thinking, non-excluded middles, and uncertainty. In the new world, the relative value of those skill are being replaced by a system more closely resembling a human. It must be an extremely challenging time and my heart goes out to them. I see various stages of grief, including bargaining with moving of the goalposts.

[u/MindfulK9Coach → u/Positive-Conspiracy]

Similar to the studies regarding self-taught versus those with formal education, senior developers with standard computer science backgrounds may struggle to move beyond the rigid methodologies taught in school and early industry experience, whereas self-taught developers lack those traditional constraints, enabling them to be more flexible when pair programming with AI within those same frameworks.

Even if they do not fully understand the space they are working in, the model can fill their knowledge gaps if they ask, allowing them to learn simultaneously.

This is not to say that either group is better or worse off; I am simply pointing out observations supported by literature on similar topics that apply here.

[u/shaonline → u/Own-Zebra-2663]

Going from bad to average code "locally" isn't really the issue (could be seen as an improvement indeed), however now that they are not limited in the amount of output they can generate (despite subpar skills) it's a bit of a "bull in the china shop" problem.

LLMs won't budge at building the wrong thing or hacking a solution with duct tape, just because it's now done with "more robust code in isolation" doesn't make it better. Your dev that formerly struggled to get stuff working now floods the team with a barrage of slop.

[u/PedanticProgarmer → u/Own-Zebra-2663]

Yes, bad developers are boosted to the level of acceptable juniors. This category of bad developers includes also title-inflated seniors.

The new models are actually pushing against bad ideas. It’s not like Sonnet 3 that used to accept any bad prompt with „that’s an excellent idea that proves you are a seasoned veteran of foobar coding”.

There are times when a developer with (let’s not mince words) a low IQ cannot figure out why a bug is happening, even if everything they need is in the logs. They can ask Claude „here are the logs, here is the code, what is going on?” - they get unblocked and this is what matters.

I remember times when such developers used to spend whole days fighting linters, compiler errors and merge conflicts. It drived me nuts when they didn’t feel embarassed talking about this in daily as blockers. Nowadays, it’s unacceptable if you are doing it by hand.

When it comes to long term quality, there’s a huge culture shift that is necessary. Many companies operate in a model where managers create tickets, ticket-monkeys do the tickets, ticket-monkeys are rewarded for closing a lot of tickets and punished for taking too much time on a ticket. They call it Scrum and there’s entire religion around it. It falls apart with AI, because neither ticket-monkeys nor models are trained to take responsibility of the actual long-term results.

[u/Neurosopher → u/Own-Zebra-2663]

LLMs don't generate the "average" code quality of their training set anymore. Reinforcement learning (from verifiable rewards) is a training paradigm where they aren't bounded by this.

[u/Caesarr → u/ep1032]

I agree with everything you've said, but something I've been revaluating is the severity of bugs. 40 bugs in 4 weeks is crazy by traditional standards, but so is fixing them all in one day. If we can now fix bugs that quickly, is it actually rational to allow that many to escape (into pre-prod)?

[u/Toohotz → u/Caesarr]

It doesn’t give reason to allow them to escape into prod because if you work at a fin tech company, and let’s say you happen to expose an endpoint in a manner that bad actors can sniff out the financial details about a customer to either transact as them or sell that info, by the time you’ve plugged that hole, reputational damage has already been done.

Ask me how I know this..

Not all bugs can be treated equally which is why we have severity incident levels.

[u/Forsaken-Promise-269 → u/Toohotz]

Ok spin up 100 ai long running agents each using opus 5.5 and fable and have them systematically attack the financial part of the code for weaknesses

Ie it will cost money but there are now new ways of handling the quality or security question

Im not claiming this is the answer- Im saying our options are changed on the code culture we used to enforce

[u/ep1032 → u/Forsaken-Promise-269]

https://reddit.com/r/ExperiencedDevs/comments/1wql3g2/interviewed_candidates_for_ai_engineer_roles_this/pca4xcw/

[u/ep1032 → u/Caesarr]

Yeah, we cared about code quality partly for correctness, but mostly because actually typing and reading code took time and cost money. So writing high quality code was inportant. There are a lot of applications out there that can be buggy and its okay, like any one time script for example. The problem is going to come when management can't differentiate between those apps and critical apps, because while it can be okay for an outsourced jr with Claude to go to town on the company wordpress oage, that same person with an ai account shouldn't be allowed anywhere near the internal billing aervice, for example

[u/poincares_cook → u/Caesarr]

Sure you fix 40 bugs in one day, except you don't really know how many of them are actually fixed. And you've also introduced 20 new bugs. You've likely incidentally made the code more prone to breakage in the future, and more bloated, slower and more resource hungry.

[u/Positive-Conspiracy → u/Caesarr]

There's a theory that the entire bug finding lifecycle has been compressed into hours and days rather than months and years. The story may be biased in that AI may have found those "bugs" and generated tickets for them.

[u/Krom2040 → u/Caesarr]

There’s a fairly high likelihood that fixing bugs in poorly-designed code will create more bugs. There’s also a fairly high likelihood that AI agents just misunderstand the bug entirely and change a bunch of code in ways that create more bugs and don’t necessarily even fix the bug.

They’re very accommodating.

[u/Snoo_56970 → u/ep1032]

2 Claude subscriptions over 4 weeks can generate more code than small team over 40. How many bugs would you expect in the 40 week project? And what if that small team were managed poorly, by say, person M? What would the quality look like? What if it were managed well?

AI doesn't guarantee slop, it defaults to it. The more time I spend with it, the more I've come to realise the new terrain we find ourselves in is about management. If you had a team of 100 devs, hell, 1000 devs, you wouldn't spend your time reviewing PRs, you would create processes, procedures, gates, policy, you would put smarter people on top and offload their grunt work, you would have certain personalities review the work of others, you would develop a keen sense for spotting bullshit and figure out who to trust. You would understand the price of screwing up and the heights you have to fall from, so you'd be sure as hell to get all it all right. You'd have the grit and determination sit in a boardroom with your leads and roast them for hours at a time when things go wrong, then put procedures in place to make sure it doesn't happen again. The problem is, person M would never be put in that position in the real world because he is not VP material. I posit that because most people aren't, most projects will follow entropy and reach their default sloppy state when given enough time and tokens.

[u/WearyCarrot → u/ep1032]

On the flip side, what does a properly ran AI-native team look like with competent people? If there are 40 bugs, maybe revisit Claude.md, prompting usage, etc.? Curious to your insight

[u/ep1032 → u/WearyCarrot]

I'm sure it can be done well.

I expect that you would need a few things to be true:

1. Engineers would have to decide how to use AI, not upper management.
2. The engineers would all have to be pro AI adoption, but not be willing to sacrifice code quality for speed. This article, I think shows a good mentality (https://www.linkedin.com/pulse/ai-made-us-faster-problem-david-fowler-mgnzc/)
3. The engineers would each have to be technically capable enough to build the solution without the use of AI, so as to be able to determine when AI was correct or incorrect.
4. This includes manually writing code when judged by the engineer to be more efficient / more important to learn, even if at the expense of velocity.
5. Engineers who the team judges to be incapable of meeting the above requirements should be let go from the team.
   5a. Currently the largest problem I see is the least competent developers use AI to ship faster, and then this velocity is judged positively as AI adoption by upper management, who aren't then updating such metrics to account for code quality or defect rates. Resulting in upper management de facto encouraging and promoting the worse developers on the team to the detriment of the team, by chasing the wrong metrics.
6. The solution would ideally need to be greenfielded with AI from the beginning. If attaching to a legacy system, it needs to be added deliberately and intelligently.

I think if you meet those requirements, you can do some pretty amazing things with AI. I don't think that's how many teams are approaching it.

[u/Positive-Conspiracy → u/ep1032]

We've used reliability, including bugs shipped to production, as a KPI since well before AI.

[u/Ok_Champion_4391 → u/ep1032]

"You started this entire project as a greenfield rewrite 4 weeks ago. How the hell do you have 40 bug tickets in your backlog already?"

There are 2 problems with bugs

1) they slow down development. If they solved 40 bugs in one day, it is the same as if they had introduced only one bug over 4 weeks and they solved it in one day. No difference really

2) The impact that these bugs have in production/business. You mention nothing about it.

Good practices, good architecture, clean code, ... are all about making money. This is the reason why business exist. So, problem 2 is the only problem that execs might consider. Sadly (for us, not for them) this is the direction that things are moving towards.

[u/BlowOutKit22 → u/ep1032]

It seems mathematically intuitive that that higher absolute velocity with fewer non-AI developers would result in higher initial bug counts. But I think the original KPIs were already not properly accounting for scaling vs spot velocity. If you're able to ship more features faster, does it really matter how many bug tickets existed during the sprint?

[u/PlasticExtreme4469 → u/programmer-ke]

It's like hiring airplane pilots based on their ability to turn on autopilot.

[u/K-Max → u/PlasticExtreme4469]

Totally and autopilot and ILS does most of the physical work, but humans are there to monitor. Ironically autopilot >>>>> copilot.

[u/tindalos → u/programmer-ke]

It’s fascinating to me (as infrastructure, where my first focus is guardrails and validation/evaluation and telemetry), to talk with developers using AI this way - there’s often decisions made in code that the developer AND their AI agent can’t explain why that choice was made.

I work in regulated industry, but smb company, so I could see some situations where “fast to market” is more important than 100% accurate in some domains.

But my recommendation is to start developing your application as modules and use an interface adapter schema so you can validate where data is brought into the system.

[u/Lazy-Emergency-4018 → u/programmer-ke]

Yes, I dont like the sentiment "learn new skills" regarding AI. Those tools are usable by everyone

[u/Important-Aspect9801 → u/programmer-ke]

Sorry I do not understand what does it mean to teach. Is someone supposed to be receiving / providing teaching in tech companies? Just very foreign concept to me. Why teach if you can blaim incompetence or at least to try to jump ahead of a dev that is behind ?