The New AI Superpowers: Focus and Followthrough
I discovered that using AI to speed up tasks often leads to burnout by encouraging endless side projects instead of deep work. Rather than expanding horizontally, the real power of AI lies in going vertical: focusing ruthlessly on what matters and following through to deliver exceptional results. This shift from quantity to quality allows us to reclaim our time and avoid the trap of make-work.
You think the difference between a 99% and a 100% eclipse is a mere 1%, a rounding error. But visually, it's a 100x difference.
- cgearhart
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.
- LogicFailsMe
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.
- crucialfelix
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.
- bigyax
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!
- jalev
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.