Comments
Hacker News
> 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
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
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
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
Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- > 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 - 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
- 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 - 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