One aspect AI is weak in is controlling complexity. If you tell it to implement something it will go ahead and implement it, without considering how much complexity it adds to the system or weighing alternatives. An experienced engineer on the other hand may decide the feature is too minor relative to the complexity it adds, and may decide to not do the feature. Or he may make some clever compromises to get most of the functionality while keeping the codebase simple. AI is weak in this judgement, it doesn't spontaneously exercise architectural restraint. As a result the code may progressively become too complex even for AI manage, and it becomes whack-a-mole where you can't make a change without breaking something.

With previous engineering trends like blockchains and microservices, you could choose not to jump on the bandwagon. However the coding agents trend is different and is changing the very fabric (sry for Claudeism) of software engineering, for better or worse. I do know we will never go back to mainly programming through code again, that’s for sure.

About "implementing by words bit": I don't believe English is a great language to program.

It's not type-safe, not object oriented, not functional. Has poor tools to highlight syntax or navigate through "wordbase", doesn't fail fast. It has no tests and has too large room for machine or other humans to interpret it.

Very often it's easier for me to express my thoughts in Java, which is ironically known to be a "wordy" language. But it's nowhere close to wordiness of English.

Coding with AI has now introduced feature dopamine. At times this results in the system being prone to more failures because AI may have missed edge cases. Also i am experiencing a decline in job satisfaction and i'm more prone to procrastination because I know the agents will do the work 10x faster than me. I am personally worried about this shift and I fear becoming less knowledgeable over time or not feeling the need to keeping up with new tech stack as agents do the work.

And you immediately give up your IP for someone else to use. The 4th option, if you have something in your mind worth building, is to just build the thing, without an LLM.

You have to be able to write the software yourself in order to judge the results and get good software out. Otherwise the system claims the goals are met, the tests pass, and the human driving the system puts up a new PR. If they don’t know any better it must seem like the AI system is better than them and knows what it’s doing.

All the loops and agents don’t protect you from generating garbage.

Which sucks because then how are you supposed to improve your skills when you’re just getting the answers all day… answers you can’t verify?

People are more confident than they ought to be. Always have been. But AI throws gas on that fire.

And yes, there is also another sane and rewarding option: write everything just by youserlf without any assistance. Let's not forget about that one, shall we?

It's kind of like folks wielding gen ai and calling themselves artists.

Questionable output, generally shunned by artisans.

But possibly good enough for some.

I'm not really sure what point this article is trying to make

My personal experience is the larger the task you ask it to do, the less attention it pays to the details - for a very large task it seems more prone to missing test coverage, writing duplicate code, not refactoring where it should etc. So I try to split into smaller tasks where possible (also makes it easier to review).

AI coding is not that efficient. 2x increase at best, depending on the usage. Doesn't seem like a lot is gonna change tbh.

> You can’t just give a 3000-word, 4-page detailed dense spec and expect it to follow everything, and the larger the codebase, the less it can pack everything in, nor are the vast documents you can feed it worthwhile.

This point seems lost on a lot of principals. I’ve had very little success with these grandiose designs and change requests from RFCs/specs. The context windows just can’t keep it all together and very quickly the approach unravels.

I posit that the further ICs were from writing code at this point in their career, the more they suffer from AI psychosis. It’s the same ivory tower they were already on, just a different order they’re giving.

The entire post reads like a tautology.

It really depends on how you use it. I’ve switched up how I interact with LLMs repeatedly over the years, as the technology has developed.

Now, it’s at the point where it’s like running a development team of very eager amnesiacs. I’ve found the trick is exhaustive documentation by a lead agent, and then having a fresh agent work as a coordinator across as many subtasks as the project sensibly allows. This way the individual components stay on spec, as does the ultimate integration. It’s only really this year that this workflow has started to actually function, and it still needs human supervision - but less and less over time.

I give it two years, tops, and everyone everywhere is building bespoke software because it’s trivially easy.

tl;dr - you still have to think when developing software

C in author's name stands for chu**

How can you assess the output of coding agents if you don't know about algorithms and other fundamentals of software engineering?

There is no such thing as "junior level algorithm".

And one does not memorize algorithms.