Two kinds of AI users are emerging

Perhaps the wildest thing to me is how you'll have senior leaders in a company talking about innovation, but their middle managers actively undermine change out of fear of liability. So many enterprise IT employees are really just trying to avoid punishment that their organization cannot try new things without substantial top-down efforts to accept risk.

https://github.com/day50-dev/

I have been meaning to put up a blog ...

Essentially there's a delta between what the human does and the computer produces. In a classic compiler setting this is a known, stable quantity throughout the life-cycle of development.

However, in the world of AI coding this distance increases.

There's various barriers that have labels like "code debt" where the line can cross. There's three mitigations now. Start the lines closer together (PRD is the current en vogue method), push out the frontier of how many shits someone gives (this is the TDD agent method), try to bend the curve so it doesn't fly out so much (this is the coworker/colleague method).

Unfortunately I'm just a one-man show so the fact that I was ahead and have working models to explain this has no rewards because you know, good software is hard...

I've explained this in person at SF events (probably about 40-50 times) so much though that someone reading this might have actually heard it from me...

If that's the case, hi, here it is again.

The word "thinking" can be a bit nebulous in these conversations, and critical thinking perhaps even more ambiguously defined, so before we discuss that, we need to define it. I go with the Merriam-Webster definition: the act or practice of thinking critically (as by applying reason and questioning assumptions) in order to solve problems, evaluate information, discern biases, etc.

LLMs seem to be able to mimic this, particularly to those who have no clue what it means when we call an LLM a "stochastic parrot" or some equally esoteric term. At first I was baffled that anyone really thought that LLMs could somehow apply reason or discern its own biases but I had to take a step back and look at how that public perception was shaped to see what these people were seeing. LLMs, generative AI, ML, etc are all extremely complex things. Couple that with the pervasive notion that thinking is hard and you have a massive pool of consumers who are only too happy to offload some of that thinking on to something they may not fully understand but were promised that it would do what they wanted, which is make their daily lives a bit easier.

We always get snagged by things that promise us convenience or offer to help us do less work. It's pretty human to desire both of those things, but proving to be an Achilles Heel for many. How we characterize AI determines our expectations of it; so do you think of it as a bag of tools you can use to complete tasks? Or is it the whole factory assembly line where you can push a few buttons and an pseudo-finished product comes out the other side?

That's the type of input I give to Claude / Codex. Works for me.