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Agreed with everything @kevinriggle wrote here. Another angle on this to try with people who simply do not understand what software engineering •is•: “What’s the impact on the other 7/8?”
AI can generate code fast. Often it’s correct. Often it’s not, but close. Often it’s totally wrong. Often it’s •close• to correct and even appears to work, but has subtle errors that must be corrected and may be hard to detect.
All the above is also true (though perhaps in different proportions) of humans writing code! But here’s the big difference:
When humans write the code, those humans are •thinking• about the problem the whole time: understanding where those flaws might be hiding, playing out the implications of business assumptions, studying the problem up close.
When AI write the code, none of that happens. It’s a tradeoff: faster code generation at the cost of reduced understanding.
2/
The effect of AI is to reduce the cost of •generating code• by a factor of X at the cost of increasing the cost of •thinking about the problem• by a factor of Y.
And yes, Y>1. A thing non-developers do not understand about code is that coding a solution is a deep way of understanding a problem — and conversely, using code that’s dropped in your lap greatly increases the amount of problem that must be understood.
3/
Increase the cost of generating code by a factor of X; increase the cost of understanding by a factor of Y. How much bigger must X be than Y for that to pay off?
Check that OP again: if a software engs spend on average 1 hr/day writing code, and assuming (optimistically!) that they only work 8 hr days, then a napkin sketch of your AI-assisted cost of coding is:
1 / X + 7 * Y
That means even if X = ∞ (and it doesnt, but even if!!), then Y cannot exceed ~1.14.
Hey CXO, you want that bet?
4/
This is a silly thumbnail-sized model, and it’s full of all kinds of holes.
Maybe devs straight-up waste 3 hours a day, so then payoff is Y < 1.25 instead! Maybe the effects are complex and nonlinear! Maybe this whole quantification effort is doomed!
Don’t take my math too seriously. I’m not actually setting up a useful predictive model here; I’m making a point.
5/
Though my model is quantitatively silly, it does get at the heart of something all too real:
If you see the OP and think it means software development is on the cusp of being automatable because devs only spend ≤1/8 of their time actually typing code, you’d damn well better understand how they spend the other ≥7/8 of their time — and how your executive decisions, necessarily made from a position of ignorance* if you are an executive, impact that 7/8.
/end (with footnote)
* Yes, executive decisions are •necessarily• made from a position of ignorance. The point of having these high-level roles isn’t (or at least should not be) amassing power, but rather having people looking at things at different zoom levels. Summary is at the heart of good management, along with the humility to know that you are seeing things in summary. If you know all the details, you’re insufficiently zoomed out. If you’re zoomed out, you have to remember how many details you don’t know.
Yes, this from @sethrichards is what I’m talking about:
https://mas.to/@sethrichards/113642032055823958
I had a great moment with a student the other day dealing with some broken code. The proximate problem was a Java NullPointerException. The proximate solution was “that ivar isn’t initialized yet.”
BUT…
…It wasn’t initialized because they weren’t thinking about the lifecycle of that object because they weren’t thinking about when things happen in the UI because they weren’t thinking about the sequence of the user’s interaction with the system because they weren’t thinking about how the software would actually get used or about what they actually •wanted• it to do when it worked.
The technical problem was really a design / lack of clarity problem. This happens •constantly• when writing code.
A good point from @rgarner, looking at this through the lens of Brooks’s Law:
https://mastodon.social/@rgarner/113642040777621582
@inthehands @rgarner image that through an act of god (or the courts …) you are suddenly in possession of a large, working code base. Say, Windows. Or FaceBook. But you have access to none of the engineers. Imho you might as well ignore the code, there is basically nothing you can do before you go insane. Bug fixing? Minor things, yes. Larger-scale refactor: out of the question. Having AI generate a non-trivial code base is like that. Plus it may not actually work.