AI coding is addictive. Engineers are paying the price
leaddev.com
leaddev.com
In the end, this is going to create unmaintainable code that no one understands. It also discourages reviewing the code because no dev can meaningfully review 1000s of lines of code in a day while also accomplishing their tasks.
NOTE: I am still pro AI, just like I am pro heavy machinery. I just don't want people to cut off their legs...
On the flight, it's not exactly like you directly feel the wind going through your hair as you travel 1000km/hr, but your body still knows that you did. You feel the lag immediately, not really due to a time zone difference but due to how unnatural it is to move so far in so short a time.
I feel the same way after a highly productive AI coding session. I used to anecdotally mention to others that I liked to maintain and use older machines because it felt nice to get little breaks here and there while the machine took longer to open a browser/app, return search results, render a file, etc. This is the opposite of that. Everything is happening so fast, your mind is taxed differently than if you are responsible for typing everything yourself... no matter how fast you could type code.
That said, I don't think it's entirely my increased cognitive load that makes me feel drained after a session, it's as though you can somehow feel the token burn, the water/electricity use, just as you somehow felt the wind shear on the airplane you were just in for many hours.
Also, at what point does AI take over for all the thinking and white board planning?
AI should be a rubber duckie one can use to posit and assist when hit a brick wall. But if using it for all code generation, planning and troubleshooting then one is not in control; they’re just prompt drones…
This of course needs to be coupled with actually staying accountable for what the entirety of the codebase looks like. I imagine many people are unwilling to slow down enough to actually do that accounting/review, and the architecture might gradually shift towards entropy.
Your body literally can't tell whether it's traveling at 500mph or still. After all, the earth is rotating around the sun at 70,000 mph. Of course, there might be plenty of other reasons why crossing continents in a metal tube might be exhausting (eg. jet lag, uncomfortable seats, noise/vibration), but pesudoscientific reasons like "unnatural it is to move so far" is certainly not one of them.
I'd say this is a bit akin to whether people can feel the weather in their bones - biometeorology. The only difference is that the airplane creates a temporary, highly artificial "weather" environment. But I think it's important to include the physical interactions between that environment and the one outside of it, and not only account for interior conditions like air pressure, etc.
We'll probably learn a lot more about this if we ever make it far enough as a civilization to have a shot at long distance space travel, i.e. to Mars.
"But the fact that some geniuses were laughed at does not imply that all who are laughed at are geniuses. They laughed at Columbus, they laughed at Fulton, they laughed at the Wright brothers. But they also laughed at Bozo the Clown"
>The only difference is that the airplane creates a temporary, highly artificial "weather" environment. But I think it's important to include the physical interactions between that environment and the one outside of it, and not only account for interior conditions like air pressure, etc.
In other words, even you don't think it's "due to how unnatural it is to move so far in so short a time", and instead think it's something to do with the cabin conditions?
Can you feel the fact that in the sun's reference frame, your rotation speed around the sun is constantly changing? For about half the day, the earth is rotating in the same direction as the earth's orbit around the sun, adding to the speed, whereas for the other half of the day, the earth is rotating opposite the direction of the orbit, subtracting from the speed.
Cognitive researchers argue late teens to early 20s is the zeitgeist that is the hardest to shake.
That seems to explain the obsession with Reagan era economics and politics of the >50 crowd
Millennials have been in "learn new abstraction" mode for a couple decades regardless such output doesn't really move science forward. Is just learning a new state storage and state mutation syntax
End of the day its labeling some math with some biz edge case
I like the idea of such not being in the code at all. Taking anti-oop to the extreme; code need just be geometric functions to draw on the screen be it text shapes or video game entities
Then we can label on the presentation layer the human context
Then programming can go away.
I'm fairly certain it's the loudness and constant cabin vibration; not temporal mechanics.
Summary of the addiction management tips from the article.
1. Time-box your AI coding sessions with a clear goal and a hard end time.
2. Separate exploration (testing ideas) from execution (shipping code) to avoid losing focus.
3. Prioritize sleep, hard stops, and actual recovery as essential maintenance, not just wellness.
4. Invest in structured training to move from basic usage to advanced multi-agent workflows.
5. Personalize your AI workflow to fit your needs while actively avoiding common anti-patterns.
-
When a developer stops writing code and starts using Claude to handle multiple projects at once, they are essentially managing the outcomes.
They have become 10x engineering managers. The context strain and emotional strain is overwhelming.
> Nothing for others part of this, companies developing the software and organizations employing these tools.
Then it takes hours or days - sometimes weeks - to find and fix the AI-induced problems. If you very, very tightly constrain the AI by using structured processes and unit tests you can work wonders with it, but you do start to wonder to what extent this is better than if you had simply coded it yourself.
> The fix is deliberate habits, not restricted tools. Time-box sessions, separate exploration from execution, and treat recovery as maintenance.
Getting tired of AI slop telling me about AI.
One other aspect of LLMs that I do not enjoy when it comes to development is the fact that LLMs minimize my contributions. I do not feel like I can take credit for anything I create if I technically did not create it.
However, I absolutely adore LLMs for learning new concepts and for troubleshooting. To me, that is where they shine the brightest.
Where I do get value out of LLMs is in two main areas. One is generating short bits of code that I can more or less instantly recognize as correct. Bash scripts are a good example - I can read bash well enough but I'm not great at writing it, so Claude can generate a 20-line script very quickly and I can equally quickly understand the generated code. Writing such scripts would probably take me 15-20 minutes, so I'm not saving huge time, but it's there. The other use case I have is asking the LLM for code review on my personal projects. I don't let it write code (that would destroy the whole fun of the personal project, for one thing), but sometimes I have some code I'm pretty sure sucks and I'll ask ChatGPT to suggest better ways to accomplish the same thing. I learn a lot reviewing its suggestions.
if you're overloaded with PRs, build LLM-based systems to take the load off. don't be a senior engineer, be an engineering manager.