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Eight Myths on Software Engineering and GenAI

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Re: Eight Myths on Software Engineering and GenAI

#71

Post by AdieuToLogic »

simonw wrote: post #4

> We already know developers don’t actually spend most of their time writing code, with studies at Microsoft and elsewhere showing it’s closer to 14 percent. Anyone else finding they're spending more time writing code (or at least driving agents to write code) now? 14% used to feel about right for me - I'd spend the rest of the time researching approaches and libraries, planning things out in issues, or sometimes jus…

>> We already know developers don’t actually spend most of their time writing code, with studies at Microsoft and elsewhere showing it’s closer to 14 percent.

> Anyone else finding they're spending more time writing code (or at least driving agents to write code) now?

Not really, as once it is time to write code, the problem has been defined/understood (to the degree possible with knowledge acquired at the time), and encoding it is largely an exercise in typing along with verifying assumptions via test suites.

Does GenAI quicken some portions of the above workflow? Sure, in the same way IDEs with contextual code snippet suggestions can make encoding faster.

Re: Eight Myths on Software Engineering and GenAI

#72

Post by 01100011 »

kylecazar wrote: post #7

I don't understand Myth 1 (Developers Spend Most of Their Time Writing Code). They quote a study in which developers report to spend 11-14% of their day coding. The rest is stuff like solution design and meetings. The insinuation is that AI can at most automate 14% of your day. The problem with this argument is that once you have code, some (not all) of the precursors to code go away.

Also AI is now drafting design docs, generating PLC work products, entering it all in Jira, characterizing and root causing bugs... It's speeding up the 86% of my job that isn't coding. The article is a bit myopic and frankly contradicts itself.

Re: Eight Myths on Software Engineering and GenAI

#73

Post by 01100011 »

unknownfuture wrote: post #9
kylecazar wrote: post #7

I don't understand Myth 1 (Developers Spend Most of Their Time Writing Code). They quote a study in which developers report to spend 11-14% of their day coding. The rest is stuff like solution design and meetings. The insinuation is that AI can at most automate 14% of your day. The problem with this argument is that once you have code, some (not all) of the precursors to code go away.

Okay. Show me the evidence that AI has an impact on productivity when doing design work. Or reducing meeting load. My own experience is that AI doesn't tighten the design cycle, and in fact might extend it by encouraging gold plating.

It helps me. Case in point, I recently had a API refactoring project that was 80% busywork and only a small amount of code. It's crap work, but it has to be done and it's part of my role. I told AI to generate the initial design doc. It took it upon itself to reference the local headers on my system and found some APIs that should be included that I hadn't even considered would also need to be touched for consistency. It saved me hours typing up the doc, requirements, test plan, etc and then saved me embarrassment by preventing me from making a stupid oversight on a tedious and boring task.

Re: Eight Myths on Software Engineering and GenAI

#76

Post by thewhitetulip »

01100011 wrote: post #73
unknownfuture wrote: post #9

Earlier quoted context omitted.

Okay. Show me the evidence that AI has an impact on productivity when doing design work. Or reducing meeting load. My own experience is that AI doesn't tighten the design cycle, and in fact might extend it by encouraging gold plating.

It helps me. Case in point, I recently had a API refactoring project that was 80% busywork and only a small amount of code. It's crap work, but it has to be done and it's part of my role. I told AI to generate the initial design doc. It took it upon itself to reference the local headers on my system and found some APIs that should be included that I hadn't even considered would also need to be touched for consistency…

AI is helpful in such projects. Less so in other projects where design is heavy

Re: Eight Myths on Software Engineering and GenAI

#77

Post by the_af »

simonw wrote: post #40
unknownfuture wrote: post #15

Earlier quoted context omitted.

Self-reported or observational data capturing time spent for categories of task ala every other similar study in this space? This isn't exactly novel territory, here, Simon. Let's not pretend I'm asking for something strange, unprecedented, or unreasonable.

How's this? https://simonwillison.net/2026/Jul/13/datasette-code-frequen... I have similar charts across my three main open source projects: https://github.com/simonw/datasette/graphs/code-frequency https://github.com/simonw/llm/graphs/code-frequency https://github.com/simonw/sqlite-utils/graphs/code-frequency

Isn't this falling into the "lines of code" trap TFA mentions?

More code written is not a good measure of productivity. It could be garbage, or redundant code, or simply not addressing the real or more pressing needs, it could be building the wrong thing, etc.

Like TFA mentions, it's been known for decades LoC is a misleading metric for productivity. It's one of the lessons of software engineering.

Re: Eight Myths on Software Engineering and GenAI

#78

Post by saghm »

a_bonobo wrote: post #16

>On my visits to the Bay Area, I would ask AI researchers or interns why they are doing their current research or projects, when in a year or three agentic LLMs could probably do them; This is such a weird point to make that doesn't become correct just because everyone makes it, all the time. Why clean the ocean if some magic future tech will clean them? Why save the world now if some benevolent AI is 'just around th…

It also seems kinda tone deaf. If someone basically told me I was wasting my time and asked what I would do in the future, I would not bother giving them a particularly thoughtful answer because trying to spend effort justifying my life choices to them would be the actual waste of time.

What kind of answers were they expecting to get?

Re: Eight Myths on Software Engineering and GenAI

#79

Post by laichzeit0 »

I don’t trust point estimates like 14%. It’s like calculating an average salary and saying it’s $120k. Completely meaningless. What does the actual distribution look like that this was pulled from? No standard deviation. Is it even symmetric? What’s the 10th and 90th percentiles? Just giving a statistic on its own tells me nothing.

Re: Eight Myths on Software Engineering and GenAI

#80

Post by fhub »

fhub wrote: post #22
a_bonobo wrote: post #16

>On my visits to the Bay Area, I would ask AI researchers or interns why they are doing their current research or projects, when in a year or three agentic LLMs could probably do them; This is such a weird point to make that doesn't become correct just because everyone makes it, all the time. Why clean the ocean if some magic future tech will clean them? Why save the world now if some benevolent AI is 'just around th…

> I, as the human, still have to do the thinking as Claude still 'can't jump' I still have to do quite a bit of thinking but the amount of of thinking I do per task is trending down. I agree LLMs are not good at abduction but very few humans are either and very few jobs/tasks require it. I can't talk for researchers jobs though. But perhaps fewer researchers would be desired by these labs (not none).

"Abduction" being the way the article used it as in "explanatory reasoning in justifying hypotheses". https://plato.stanford.edu/entries/abduction/
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