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chis 1 days ago [-]
AI is automating all the easier tasks in people’s jobs, leaving them to spend 8 hours a day on the hardest problems which AIs cannot yet solve.
Software engineers are probably already familiar with the feeling of burnout from thinking too hard. The reality is very few people can work on the hardest problems they’re capable of for 8 hours a day.
Writing routine Python code for some system you know well is not that mentally taxing. Managing an agent that rapidly finishes tasks but needs careful review and big-picture planning is much more exhausting, and has higher returns on intelligence and deep careful thought.
I think this points towards the opposite conclusion of the OP. It’s not realistic to expect 8 hours of hard work out of a knowledge worker. Remote work naturally allows this transition, as employees can work a bit less but still overachieve with AI.
(I hate AI. Just observing the world we live in)
tw04 1 days ago [-]
> It’s not realistic to expect 8 hours of hard work out of a knowledge worker.
It wasn’t really realistic to expect hard physical labor for 70 hours a week, and yet in the 1800s before unions were established to negotiate workers rights, that’s exactly what we had.
What on earth makes you think non unionized IT workers aren’t going to be pushed to their breaking point and then pushed further? If AI truly starts eating all the knowledge work, there will be an endless supply of people lining up to work themselves to death.
zombot<!-- --> 11 hours ago [-]
> It wasn’t really realistic to expect hard physical labor for 70 hours a week
That makes no sense at all. If it was common, it was obviously realistic.
port11 3 hours ago [-]
People were, in a very literal sense, working themselves to death. The birth of the industrial age is documented as one of the worst periods in Humanity’s record for quality of life.
disgruntledphd2 3 hours ago [-]
To be fair, the industrial revolution also produced more books and other records, so there's a bias there in that without the changes to technology, there might not be as many books written about that period remaining.
hnthrow10282910 1 days ago [-]
Is this your reality? I’ve noticed that while the team is way more burned out people are also way less engaged and no longer critically think about edge cases or design reviews, etc anymore.
icedchai 24 hours ago [-]
Yep. Someone will copy-and-paste a Jira ticket into AI and blindly accept the output without thinking about the actual intent and context behind the request. It's frustrating. I use AI sparingly, mostly on personal projects where I am prototyping and the quality is not of the greatest concern.
Yiin 22 hours ago [-]
your comment with "I use Ai" is as useful as saying "I eat food and it's not the best". Details matter
icedchai 22 hours ago [-]
I use Claude Code. I bet that doesn’t help you much. Also, that was not the main point of my comment, which is why it didn't have much detail.
leoqa 22 hours ago [-]
Yeah this is true on my team for two archetypes: the new junior engineer and the old staff engineer that was too “busy” to ever actually write code. Now he’s just busy in a different superficial way.
jmalicki 1 days ago [-]
Then they get fired for poor performance and you hire new fresh people.
Rinse, repeat.
kykat 23 hours ago [-]
Fresh people meaning burned out from the previous job?
zombot 11 hours ago [-]
> overachieve with AI
But only for bullshit criteria. If your work needs to be actually good to reach the goal posts, AI can have a supporting role in the background at best.
konovalov-nk 24 hours ago [-]
I had a few aha moments recently while thinking about this problem (automating easy parts and leaving "hard parts" unsolved).
1. Understanding the world is the bottleneck to thinking.
2. The world is an unbounded system with unpredictable behavior. Chaos.
3. An outcome is a possible state of a system.
4. Formulating an outcome means choosing a future state of the system.
5. That future state may be desired or undesired.
6. Therefore an outcome is not purely objective. It is a consensus problem.
7. Consensus is concentration around a shared outcome.
8. Consensus around an undesirable outcome is maladaptive consensus.
9. Intent is a decision that constrains future system states.
10. One outcome may relate to another. It may also relate to intents.
11. Related outcomes and intents form a problem domain. Or simply: a semantic graph.
12. A graph gives structure to otherwise ambiguous future states.
13. Structure reduces the number of possible interpretations. Or simply: reduces uncertainty.
14. High uncertainty prevents action. Low uncertainty makes action obvious.
15. A problem without structure requires thinking.
16. A structured problem can be acted upon.
17. A sufficiently structured problem becomes the solution to itself.
18. An insufficiently structured problem keeps producing chaos.
19. Problem solving is creating structure from chaos.
20. Therefore thinking is problem solving before the structure exists.
The work is boring and unsatisfying now so you're not engaged and easily bored to sleep. I can relate.
GuestFAUniverse 1 days ago [-]
Yay, we all become managers! Do we get manager salaries now? /S
jmalicki 1 days ago [-]
The ratio of managers:staff engineers has been decreasing, so sort of, yes.
notesinthefield 20 hours ago [-]
Ironically I have recently transitioned to a TL role because my org has slowed senior IC salaries but keeps increasing management salaries.
lovich 1 days ago [-]
No, and half the existing managers have been fired as well and their positions closed. A 1:7 ratio of managers to direct reports? What is this, the 80s? We’re doing 1:15 now. Also you have to be building software at the same time[1]
My opinion of Brian Armstrong has gone from very positive to ZERO in the past 12-24 months due to his AI zealotry.
lovich 22 hours ago [-]
Mine hasn’t changed but because he is in crypto so it was already zero.
It’s just entirely grift, speculation, and rug pulls. My friends who used to use it to buy drugs don’t even do that anymore as it’s completely lost any sense of being a currency like the original, somewhat defensible arguments of it suggested it could act as.
Software engineers are probably already familiar with the feeling of burnout from thinking too hard. The reality is very few people can work on the hardest problems they’re capable of for 8 hours a day.
Writing routine Python code for some system you know well is not that mentally taxing. Managing an agent that rapidly finishes tasks but needs careful review and big-picture planning is much more exhausting, and has higher returns on intelligence and deep careful thought.
I think this points towards the opposite conclusion of the OP. It’s not realistic to expect 8 hours of hard work out of a knowledge worker. Remote work naturally allows this transition, as employees can work a bit less but still overachieve with AI.
(I hate AI. Just observing the world we live in)
It wasn’t really realistic to expect hard physical labor for 70 hours a week, and yet in the 1800s before unions were established to negotiate workers rights, that’s exactly what we had.
What on earth makes you think non unionized IT workers aren’t going to be pushed to their breaking point and then pushed further? If AI truly starts eating all the knowledge work, there will be an endless supply of people lining up to work themselves to death.
That makes no sense at all. If it was common, it was obviously realistic.
Rinse, repeat.
But only for bullshit criteria. If your work needs to be actually good to reach the goal posts, AI can have a supporting role in the background at best.
1. Understanding the world is the bottleneck to thinking.
2. The world is an unbounded system with unpredictable behavior. Chaos.
3. An outcome is a possible state of a system.
4. Formulating an outcome means choosing a future state of the system.
5. That future state may be desired or undesired.
6. Therefore an outcome is not purely objective. It is a consensus problem.
7. Consensus is concentration around a shared outcome.
8. Consensus around an undesirable outcome is maladaptive consensus.
9. Intent is a decision that constrains future system states.
10. One outcome may relate to another. It may also relate to intents.
11. Related outcomes and intents form a problem domain. Or simply: a semantic graph.
12. A graph gives structure to otherwise ambiguous future states.
13. Structure reduces the number of possible interpretations. Or simply: reduces uncertainty.
14. High uncertainty prevents action. Low uncertainty makes action obvious.
15. A problem without structure requires thinking.
16. A structured problem can be acted upon.
17. A sufficiently structured problem becomes the solution to itself.
18. An insufficiently structured problem keeps producing chaos.
19. Problem solving is creating structure from chaos.
20. Therefore thinking is problem solving before the structure exists.
We can automate action. Not understanding.
Therefore we should optimize for it.
[1] https://www.refolk.ai/blog/coinbase-ai-native-engineering-ma...
It’s just entirely grift, speculation, and rug pulls. My friends who used to use it to buy drugs don’t even do that anymore as it’s completely lost any sense of being a currency like the original, somewhat defensible arguments of it suggested it could act as.