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- Hacker News
- I am going through this burnout cycle since the COVID-19 days. I noticed that the origin of my burnout is not just due to my job, but also several other factors. Though my job constitutes 80% of it. Within that 80% I noticed that doing a monotonous job contribute to 80% and remaining due to orgn. policies, boss and coworkers.
When I first started using AI agents to automate my work tasks I felt liberated for a few months, but after that I again started feeling burnout. There were days when I tried to compete with AI Agents to complete my tasks earlier like zooming in a race car which added to my burnout.
The quest to learn or gain knowledge will alleviate your burnout and the moment you start unlearning your burnout reappears. Thus I stick to HN for most of the day to kill my burnout though it is overwhelming.
by tesnorindian - It's wild we're seeing people rediscover the same solution to the same problems we've had forever: being precious about your darlings.
Code generators reduced the friction for implementing every shitty idea someone has ever had, but because developers seem to have never experienced throwing shitty ideas away (because if it was written in code it _has_ to be important right?) they treat everything coming out of the Codegen as a precious idea that eventually builds up to clutter one's brain leading to Implementation Fatigue the author experienced. The solution? Go through that same process but focus on one idea at a time and make it good.
It does make me wonder if the people experiencing this had hobbies outside of software engineering (let's say, anything creative) they'd be able to spot the signs earlier.
by jalev - Funny. In writing, the advice is to "kill your darlings". Don't let a particular paragraph that you love a lot stop the whole story because it doesnt fit.
- > I had something like 40 “proof of concept” projects going on
Right. That's the problem. In the non-coding world it's the idea that your pile of crap expands into the available space. So, if you get a garage that is twice as large, it will eventually be just as overloaded and piled-up with crap as the first, smaller, space.
The secret to happiness is to control how much crap you put on your pile. It can be that simple. Or, live with the crap you have and maybe a little bit more, but not 40x more.
by robomartin - I will never believe the premise of 100x boost from AI, why are we still pushing this narative? Yes, AI is amazing in small, constrained, focused pieces of code but the code is nowhere fit for production and the last 10% needed to ship shows that the 90% that's already been done by AI is absolutely trash. Hacks on top of hacks.
Is no one trying to ship polished things to customers anymore, will each of us have a hacky, bugged version of the same thing with different quirks?
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My first ever software project even before I worked as a swe had less bugs and frictions than all recent projects where AI was used. Some people like mitchellh seem to know what they are doing (I have not taken a look at ghostty's codebase as it's in zig) so I sometimes get the feeling I'm holding it wrong but in the end everyone around me seems to have similar problems with shipping.
by taosx - I think the trick is to learn exactly what things AI can give a 100x speed boost with, and use it for those tasks, and only those.
The tasks where there's little-to-no speed boost at all -- do those yourself. You'll feel better and happier.
The best thing is you'll have much more time to work on those things now, because AI is doing all the stupid/boring/dumb/mundane/I-hate-this-so-much/I-don't-ever-want-to-think-about-this tasks for you, the ones that normally make your brain dribble out your earholes.
by epihelix - > So logically, if AI can help us complete tasks 2-100x faster than before, we should be UNDERWORKED and experiencing ZERO burnout, right?
Got to admit, I stopped reading here.
In my experience, burnout isn't related to the amount of work, but to your confidence in being able to do your job and if you find your job meaningful (like, does it even matter?).
I'm probably doing twice as much work now compared to the period just before I burned out, but I'm also nowhere near a burnout. Part of that is general experience, but it's also because I feel like I know what I'm doing (so, no imposter syndrome) and because I've chosen my job based on what I find meaningful to do (is it ethical? does it make a difference in society? is it interesting?).
I understand why LLM assisted development can lead to burnout. You now have a machine that "does most of the work for you". It's a gateway to questions like "how good are you really at your job?" and "anyone could push these buttons, couldn't they?" and "is this what the rest of my life should be?". It probably also reduces how interesting or fulfilling the job is for many people. You've been reduced to a cog in the machine, and you might be questioning your ability to be anything but.
by Skinney - I agree with your points here - I don't know if I feel more burnt out, but I definitely feel like my work has become less meaningful and fulfilling despite doing more work and iterating faster on a daily basis. I miss the satisfaction that comes with solving a problem on my own after wrestling with it for a bit.
It raises the uncomfortable question of, if so much of my work could be automated, was it ever really that important to begin with? It definitely has me asking a lot of questions about what I'm doing and where my life is headed.
One optimistic view is that AI could free up time for people who see their job as just a job, allowing them to invest more in other parts of their lives... but who knows how far down the line that is, and whether it'll even reach that point.
by bzhang33 - I mostly agree with your observations, but in my experience, if you're spread very thin, you may be nearing the sudden burnout onset.
You feel motivated, full of energy, your juggle millions things at once, and then stumble once, then another time. Then your mistakes become more frequent. And you don't even realize you make them, others do... Until eventually you no longer operate efficiently.
Can take a few years to develop.
Take some rest. Especially if you feel productive like never before. It's gonna be hard to shut down for the first few days, so it should be at least a week of rest. Better two.
Chances are: after coming back you'll immediately notice a dozen of stupid decisions you've made.
by koiueo - Felt like the article could have benefitted from more focus and follow through tbh. Relatable problem, but it didn't say anything beyond exactly what you would have gotten from reading the title.by TheWrongGuy
- I'm actually not feeling the 'burnout', though I too have a bunch of projects sitting at various levels of vibe-completeness. Mostly one just kind of yanks on my attention a bit more than the others and so they end up getting a little neglected. I'm fine with it though, I love having all these little forever projects of mine finally coming to life. My worry is kindof the opposite, what happens when I've finished all the things. Will I run out of ideas? I hope not, but already some of my pet projects have reached a level of completeness where I just cannot see anymore to add, for me they have reached a level of done. That frees me up for the next project but what if I run out of projects!by bigyax
- Get another hobby like gardening?by pixl97
- Then just set aside some time to be bored. People generate some of their best ideas during this time.by gritspants
- I was getting frothy and feeling stress due to juggling sessions. I switched to managing my backlog (Obsidian base), writing specs, launching agents in the background. I review and make changes,merge and release. It's a fixed cycle and I optimize to maintain relaxed engagement. Rate of features and fixes went up a lot.by crucialfelix
- I came close to burning out around two years ago. It wasn't even the workload so much as the uncertainty of being able to solve hard problems and feature requests on time. I was in a constant state of stress and fear, thinking about those problems in the evening, at night, and in the morning.
With AI—or coding agents, to be more specific—I'm perhaps working even more than before, yet I feel considerably more relaxed. It feels like the variance in task difficulty and uncertainty has flattened out. If I had a difficult network-related issue before, it might have taken me hours of research and debugging before even being able to tell what was even going on and how long it would take to fix. Now I can just spawn an agent with the right keywords, in the right environment, and with the right permissions, and get feedback within minutes while focusing on other stuff in the meantime.
While I was pessimistic about where software development was heading just a year ago, I now feel more optimistic and happy about how things are going. My only concern, however, is whether it will reduce the demand for software engineers in the long run.
by kklemon - At least in Enterprise IT, where I work, the challenge is not how you scale technology, but how you scale trust. Big IT problems are always trust problems. That messy CRM systems everyone complains about? The messiness is likely due to low organizational trust, departments working in silos, and disagreements on definitions (what is a customer?).
LLMs can scale technology, but they are terrible with scaling trust. You know what scales trust in Enterprise? Good leaders, good culture, good conversations, good meetings, good narratives, good insights, good decisions. Exactly the things that scaled trust 50 years ago, and exactly the things LLMs cannot directly impact.
But now see what is happening. Rather than doing the hard work of gaining trust and consent, employees are using LLMs to build their localized agents. Of course they are happy! But the Enterprise, as a whole, is not better off, the real problems have not been addressed, the consensus has not been reached.
by kfk - In my side hustle time, I use coding agents to explore what if projects I would otherwise never get to build.
At my day job, I use AI to fix config, container, and installation issues so I can spend my time coding instead of struggling to fix mostly other people's mistakes. And really, in this age of python wheels, containers, virtual environments, and fragmented APIs, the cognitive load of doing this by hand would shut me down for the day.
1.5 years in and I haven't burnt out. If anything, I'm ramping up. I'm no longer afraid of adding a web UI, or insane numbers of unit tests, or deploying other people's software.
What I'm not doing is using it to build products, but instead just doing so to augment the supporting stack. All of this is subject to change as the AI landscape evolves.
by LogicFailsMe - Ya this is exactly how I use it, the LLM does everything i don't want to do. Which is basically everything except the exact code im writing. Its very freeing to not have to worry or touch a jira ticket ever again. The LLM just does it for me.by acedTrex
- AI as an expensive bandaidby danshipt
- My biggest struggle is UI. I can make functional, but my design skills couldn't sell water to someone in death valleyby voicedYoda
- > At my day job, I use AI to fix config, container, and installation issues so I can spend my time coding instead of struggling to fix mostly other people's mistakes. And really, in this age of python wheels, containers, virtual environments, and fragmented APIs, the cognitive load of doing this by hand would shut me down for the day.
I think it was a mistake to add this much complexity to the world of software in the first place, so I'd view AI as a bandaid to a problem rather than addressing the underyling issue. But on the whole, the cost of all this extra complexity, data centers to run AI to fix layers of crud and buildup don't seem to provide much of a net benefit to society (except to those who are at the top and need to see growth to pad their retirement portfolios).
by vouaobrasil - That's exactly what the article highlight might actually put your "competitors" (if you see others like that) in front of you, as focus and "what you actually did" becomes more important. Might be that while you shipped and experimented with 50 different APIs, your "competitor"/peer just did 3, but they were more carefully considered so the payoff ended up bigger/better, in whatever way that could be.
That's how I understood the article at least.
- This seems very related to a trend I’m seeing as my company goes all in on AI: everyone thinks that every problem is “a couple hours” with AI now, and they all want zero external dependencies because they can move faster alone. As a result, we’re now in an even worse “yet-another-…” age where everyone has built approximately the same (but somehow incompatible) versions of all the same beginner-level software, and (ironically) while they want no external dependencies they’re also pushing for org-level mandates to require everyone else to use their solution. Meanwhile, no one wants to do the slow/bottleneck part that cant easily be automated or scaled; they just throw an “agent” at it and call it done—but there’s nothing _there_. You can trust the agent on easy tasks and you can’t trust it on hard ones, but you can’t tell which ones are easy or hard. Improvements in foundation model tech move thresholds of the problem but can’t eliminate it.
Long story short, I think we’re in a phase where the organizational value function is lagging behind the tech. A “proof of concept” used to be correlated with “proof of work” and some amount of domain understanding, but I think now what we need is a focus on “proof of understanding” or else you’re probably just wasting tokens on a baby version of the problem. A decent proxy right now is that if you have zero external dependencies then your solution is probably a toy.
by cgearhart - problem is that no one knows what to build and there are way too many ppl employedby dominotw
- Given the limits of LLM's regarding context, why is zero dependencies the goal ?
Shouldn't something like maximum reuse/abstraction, minimal written code, should be a better goal ?
Possible leading to more llm-scalable systems ?
by petra - > they’re also pushing for org-level mandates to require everyone else to use their solution.
That's how you get promotions when you don't do anything valuable lol ok done with the cynicism
by tayo42 - AI completely broke some of the signals management used as proxies for team competence. In the past, teams that moved quickly and had a proof of concept to show management were valued for the speed of their execution. Usually a team that could execute quickly had some competent developers who could also quickly execute on the challenges of the implementation when it scaled up to real world problems.
Now anyone with enough LLM tokens can move quickly and have a pretty proof of concept in front of management in days or maybe hours. There's a land grab to get things in front of management so you can stake your claim on something. The implementation details are assumed to be solvable by enough Claude sessions and ChatGPT prompts.
Where this falls apart is that the real difficult problems don't scale well with LLM brute force techniques. You need people who understand the problem and know how to drive the LLM agents around properly, as well as when to set them aside and go look at the code and data themselves.
The teams who try to brute force everything with LLM tokens can get a little farther every few months, but for complex problems I've seen enough variations of teams hitting a wall of complexity and tech debt with a team that doesn't know much about how their system works because they've just been asking agents to solve every problem as it comes up. With each iteration it takes a little longer for the agents to come to a solution and the system becomes a little more brittle. Even with our current mega-context-window LLMs you can reach a point where every other change to your big ball of code breaks something else, which requires another agent session to diagnose and fix, which might then break another thing down the line. The refactors to try to clean things up end up breaking even more. Having a zillion tests starts to backfire when the team can't tell which tests are testing valid results and which are baking in incorrect results.
Maybe there's an end game where agents get so good that none of this matters, but I think it's more likely that we're going to have to build up better intuitions about what good execution looks like. The old signals of moving fast are meaningly and potentially even negative now.
by Aurornis