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Your position is confusing to me as well.

> if you put the work in you can get to a point where you are fast enough at reading and reviewing code

Please correct me if I'm wrong, but "fast enough" here would still be slower than writing the code yourself (given equal amounts of practice at reading and writing code), right? To throw some made-up numbers around: if it would take me 20 minutes to write code to do X, it might take me 30 minutes to read/review code that does X written by somebody else (or an LLM), so I'm at a net loss of 10 minutes. Can you explain the mechanism by which this eventually tips into a productivity gain?

Personally, I think "reading code is harder than writing code" lacks nuance. While I believe it's true on average, the actual difficulties vary wildly depending on the specific changeset and the path it took to get there. For example, writing code can involve exploring many solutions before eventually discovering a concise/simple one, but when reading the final changeset you don't see all those dead-end paths. And reviewing nontrivial code often involves asynchronous back and forth with the author, which is not a factor when writing code. But please take the "reading code is harder than writing code" claim for granted when responding to the above paragraph.



Maybe you're imagining a team context and accounting for the total time spent by the entire team, and also imagining that LLM changesets aren't first reviewed by you and submitted in your name (and then have to be reviewed again by somebody else), but rather an agent that's directly opening pull requests which only get a single review pass?


It's more like it takes me five minutes to read code that would have taken me an hour to write.


Ah, then it seems like you don't agree that reading code is harder than writing code (for you). Or maybe you're decoupling hardness from time (so it's five difficult minutes vs an easy hour).


For the first 15 years of my career I found reading code much harder than writing code. Then I invested a lot of effort in improving my code reading and code reviewing skills, with the result that code reading no longer intimidates me like it used to.

That's why I think reading is harder than writing: it takes a whole lot more effort to learn code reading skills, in my experience.


Thanks, now I understand your perspective.

It seems like your answer to sarchertech's upthread "if you put in equal amounts of practice at reading and writing code you'll get faster at reading code than writing code" question might be "yes". Either that or you've intentionally invested more in your reading skills than your writing skills.


I'm not sure if "equal practice" is exactly right, but my opinion is that code reading and code review are skills that you can deliberately strengthen - and strengthening can help you get a lot more value out of both LLMs and collaborative development.


Oh course reading code is a skill that can be strengthened. Just like writing code can.

But if reading code is indeed harder than writing code, it stands to reason that if you put in equal effort to improving reading and writing abilities, your writing abilities would improve comparatively more.

If you spent all this time and effort learning to read code, such that you can read code 6x faster than you can write it, how do you know that you couldn’t have spent that effort improving you’re writing abilities such that you could write code 6x faster.

On the other hand if you did spend the same effort deliberately trying to increase you’re writing abilities as you did reading and the result is that you can read 6x faster than you can write, I’m unsure how you can support the conclusion that reading code is harder than writing it.

My gut feeling is that people on the far pro AI side of the spectrum tend to be people who are early in their career who don’t have strong writing or reading abilities (and so don’t really see the flaws) or people who have a reached a level where they aren’t really ICs anymore (even if that is their title). The latter have better reading than writing abilities because that’s what they spend all day doing.

Not that reading code is something that has an inherently higher skillcap than writing it.

I think there’s also a 3rd type of heavy AI proponent—people who spent most of their time cranking out MVPs or one offs that don’t require heavy maintenance (or they aren’t the ones doing the maintenance).

That’s not to say that I don’t think AI isn’t useful in those cases. I use AI pretty often myself when I’m writing in a language I don’t use everyday. Or when I’m doing something that I know has existing algorithmic solutions that I can’t quite remember (but I’ll know it when I see it) because it’s faster than googling. But I also recognize that there are many styles of programming, people, and domains where the productivity gains aren’t worth it.


No matter how hard I train my fingers will never be able to move fast enough to output 100 lines of code in 15 seconds.

When I get to writing actual production code with LLMs I treat them more as typing assistants than anything else: https://simonwillison.net/2025/Mar/11/using-llms-for-code/#t...


As someone who has had RSI issues in the past, I empathize with this, and I tend to use AI similarly.

But that’s not a fair comparison. You typed the equivalent of 15-20 lines of code to generate 100, and you also needed additional for reading/understanding that code.

I have no doubt that a programmer who worked with the relevant APIs frequently enough could have written that function faster than the total time it took you to do all those things.

Now that programmer in a less familiar domain could probably benefit from AI, but I get where people with different experiences are coming from.




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