The developers that thing coding is hard are the ones that absolutely love AI coding. It's changed their world because things they used to find hard are now easy.
Those that think coding is easy don't have such an easy time because coding to them is all about the abstractions, the maintainability and extensibility. They want to lay sensible foundations to allow the software to scale. This is the hard part. When you discover the right abstractions everything becomes relatively easy. But getting there is the hard part. These people find AI coding a useful tool but not the crazy amazing magical tool the people who struggle with coding do.
The OP is definitely in the second camp since they could spot and realise the shortcomings of the AI. They spotted the problem, and that problem is that the AI can't do the hard bit.
PMs can now cross reference and organize tickets with just a few keystrokes. Organisational knowledge, business knowledge, design systems and patterns, etc all of it is encoded in LLM consumable artefacts. For PMs it is the same switch - instead of having to do it by hand you direct lower level employees to handle the details and inconsistencies and you just do vibe and vision.
When all of the pieces successfully connect and execute reliably, what is left for humans to do? Just direct and consume?
And AI companies with their huge swaths of data are soon gonna be in the situation of being able to do the directing themselves
The first group are still thinking fairly deeply about design and interfaces and data structures, and are doing fairly heavy review in those areas. The second group are not, and those are the ones that I find a bit more worrisome.
I can't speak for others, but I'd go further and say that LLMs allow me to go deeper on the design side. I can survey alternative data structures, brainstorm conversationally, play design golf, work out a consistent domain taxonomy and from there function, data structure and field names, draft and redraft code, and then rewrite or edit the code myself when the AI cost/benefit trade off breaks down.
I’m not making a judgement call about which is better, but it was widely accepted in tech before the advent of LLMs that you just fundamentally lack a sense of understanding as a reviewer vs an author. It was a meme that engineers would rather just rewrite a complicated feature than fix a bug, because understanding someone else’s code was too much effort.
I find it useful to not listen to people who just talk.
It's the same thing here. AI has dropped the cost of software development, so developers are now fooling themselves into producing low or zero value software. Since the value of the software is zero or near zero, it doesn't really matter whether you get it right or not. This freedom from external constraints lets you crank up development velocity, which makes you feel super productive, while effectively accomplishing less than if you had to actually pay a meaningful cost to develop something.
Like, what is the purpose of Gas Town? It looks to me like the purpose of Gas Town is to build Gas Town.
I worry about the first group too, because interfaces and data structures are the map, not the territory. When you create a glossary, it is to compose a message, that transmit a specific idea. I find invariably that people that focus on code that much often forgot the main purpose of the program in favor of small features (the ticket). And that has accelerated with LLM tooling.
I believe most of us that are not so keen on AI tooling are always thinking about the program first, then the various parts, then the code. If you focus on a specific part, you make sure that you have well defined contracts to the orther parts that guarantees the correctness of the whole. If you need to change the contract, you change it with regard to the whole thing, not the specific part.
The issue with most LLM tools is that they’re linear. They can follow patterns well, and agents can have feedback loop that correct it. But contracts are multi dimensional forces that shapes a solution. That solution appears more like a collapsing wave function than a linear prediction.
I follow the plan -> red/green/refactor approach and it is surprisingly good, and the plans it produces all look super well reasoned and grounded, because the agent will slurp all the docs and forums with discussions and the like.
Trouble is once it starts working there would inevitably be a point where the docs and the implementation actually differ - either some combination of tools that have not been used in that way, some outdated docs, or just plain old bugs.
But if the goals of the project/feature are stated clearly enough it is quite capable of iterating itself out of an architectural dead end, that is if it can run and test itself locally.
It goes as deep as inspecting the code of dependencies and libraries and suggesting upstream fixes etc. all things that I would personally do in a deep debugging session.
And I’m supper happy with that approach as I’m more directing and supervising rather than doing the drudgery of it.
Trouble is a lot of my team mates _dont_ actually go this deep when addressing architectural problems, their usual mode of operandi is “escalate to the architect”.
This will not end up good for them in the long run I feel, but not sure what they can do themselves - the window of being able to run and understand everything seems to be rapidly closing.
Maybe that’s not super bad - I don’t exactly what the compiler is doing to translate things to machine code, and I definitely don’t get how the assembly itself is executed to produce the results I want at scale - that is level of magic and wizardry I can only admire (look ahead branching strategies and caching on modern cpus is super impressive - like how is all of this even producing correct responses reliable at such a a scale …)
Anyway - maybe all of this is ok - we will build new tools and frameworks to deal with all of this, human ingenuity and desire for improvement, measured in likes, references or money will still be there.
You need to be checking every thing it does.
This is also why I don't see it getting better anytime soon. So many people ask me "how do you get your claude to have such good output?" and the answer is always "I paid attention and spotted problems and asked claude to fix them." And it's literally that simple but I can see their eyes already glazing over.
Just as google made finding information easier, it didn't fix the human element of deciphering quality information from poor information.
You can skip that and go directly to writing code. But that meant you replaced a few hours of planning with a few weeks of coding.