We’ve all seen both sides of the argument spectrum, with “Let them atrophy, LLMs are the future, just look at the abacus!” to “I don’t use LLMs, they make mistakes and get in the way”. But the reality for many is that LLMs give a real performance boost and take on many tasks, even if they do make mistakes and need babysitting.
I fall on the side of caution in letting skills fall behind, there are too many unknowns in how much LLMs will drive workplaces in the medium to long term and what skills will be used, so I’m wondering how you keep your existing skills from atrophying when tempted with the “make it so” button?
What I'm exploring now:
1. How to convert tokens to value more efficiently
2. How to orchestrate a large LLM team instead of babysitting one session
3. How to parallelize work and make sure nothing blocks others
4. How to accelerate both productivity and quality control
5. How to make the system evolve itself
To achieve these, it requires much more skill and knowledge, not less.
Enterprise development has been commoditized for well over a decade. This is where most developers work - enterprise dev. Why am I going to waste time keeping skills relevant that companies don’t value unless you go into BigTech or adjacent (been there find that) where most developers will never get .
Anecdote: I worked for a company based in Knoxville with a satellite office in Atlanta where I worked from 2014-2016. They paid developers between $115K - $135K in Atlanta then. They just posted a job on LinkedIn with the same requirements as they were back then for $140K fully remote.
Just to keep up with inflation that should be over $180k.
Just to quick Google search shows that is still average in Atlanta.
I’m finding the pressure at work to be faster and more productive gets in the way of actual learning.
I’m starting to believe that code will not matter soon, as long as it works, then everything will just be a single natural language interaction to make a change.
Allows Claude to create code tours I can navigate with the Agent, give feedback, and iterate together. It's a nice inner-loop step to internalize what's changing and why.
https://www.rxjourney.net/how-artificial-intelligence-ai-is-...