Posted by TheEdonian 18 hours ago
> Yes, AI can generate code quickly (whether that’s a good thing is open for debate), but that doesn’t mean it’s generating the correct code.
It really depends on what you asked it to do. Add a new feature? I wouldn't touch that code with a 10 foot pole. Create a service with an example of another service in your project that does something similar? It is going to nail that pretty much every time in 2026.
Someone else put it really well: use LLMs as a fast typer, not a fast thinker. Don't have it generate any code you can't verify at a glance. Call in small completions that don't span more than a couple files, everything else is vibe coding.
To some extent, we tell as many lies as we can get away with. Some answers are more convenient then others.
"Why" this is taking so long, like "why did this fail?" are prone to broadly agreed lies. Sometimes this is for obvious blame liability reasons. Often, this is because the lie conflicts with some "meta."
One such fallacy is the idea that software=value. Code= money, because it cost money to write. Features=revenue. Etc.
Irl.. startups produce features very quickly because they actually need features. They start with zero features.
But... LinkedIn, visa or even Facebook.... What they are short on is opportunities to develop code with value. Ie... Something that will increase revenue.
FB aren't resource constrained. They're demand constrained. If there were a "write code, make revenue" opportunity available... they'd have taken it already.
This totally conflicts with the experience of working somewhere. That's because you have wishlists, road maps and deadlines.... and it always appears that demand for code is sky high.
The human their cumulative experience over a career of the nuances behind every decision and their evolved context at their given company. This context allows them to take that one-line spec and extract tons of detail from it by knowing who wrote the ticket, what was the "trigger" for the ticket, what other work is being done in tandem that might need to be incorporated, etc.
LLMs can be given this context but it's a manual process of transcription into its prompt/memory/skills and that content must be continually updated and refined. It just pushes lots of work to spec writing from the more intuitive nature of feature development a lot of us have a level of mastery over. Then you must constantly have a back-and-forth to refine the output.
Any senior engineer knows that a lot of that communication is wasted energy. If I have a good idea of what I'm building I can develop the feature in a focused flow of output that I refine in an almost unconscious way because I don't need to translate intent into words, just code, and that process is incredibly automatic after years of developing software.
When all the effort is placed into writing specs, re-prompting and then reviewing (often over and over again), that intuitive and automatic ability to build software degrades. Think of a time when you were mostly focused on PR reviews and not contributing to a project. You may have been able to help developers build better code, but if you were to jump into that project to contribute, there would be a real and painful effort to re-familiarize yourself and reconstruct that intuitive familiarity of the project.
LLMs have many very useful qualities but so far I fear an over reliance on them can be more a hinderance than a benefit.
If you don't like the state of technology with AI tools, just wait a few weeks. Things are still changing at a quite rapid pace. The scope of what is possible seems to shift regularly. A lot of what I did in the last weeks was complete science fiction even a year ago.
This article makes a few good points though. AI won't magically make processes faster. You might actually have to change the process. A lot of processes in companies are about people and how they communicate. The more people you have, the more communication you get. It's an exponential. Using AI in that context just adds to the communication noise.
But if you restructure your processes you might get different results. Most companies have not really gone through that process yet. It's too early to call success or failure. And especially non technical people have mostly not yet experienced any agentic tooling at all. We've yet to see how that will change companies. My guess is that some companies will be better at this than others. And we'll see a bit of darwinism play out.
The primary issue is simply that developers are the most immediately impacted by this technology. The combination of being able to adopt, willing to adopt, and the tech actually being incredibly good at developer related concerns is unique. The rest of the business will eventually catch up. I'm watching it happen in real time. It is agonizingly slow in most places, but it is happening.
The developers being able to drain a one year long work queue in an afternoon is meaningless if the rest of the business cannot absorb the effects of that work in the same timeframe. The business will not leave your idle work queue on the table for long though. Keep pulling a vacuum on them and they will fill the space eventually.
Once tooling (e.g. agent harnesses, external tools) becomes more mature and consistent, the other 2 will become less of a bottleneck.
If I were to take a gamble here, I would argue that development will at one point reach the more ideal scenario, whereas the project planning, the scoping, will become longer. Also, the documentation section will take almost the same as the development, slightly longer at the edges.
The new ai-assisted era will most likely push companies to adopt a Waterfall management, rather than an Agile one.
Another option is that lower software costs would significantly reduce the cost of whatever non-software product the software supports (manufactured good, electricity, services, telecom etc.) but I don't know in which industry the cost of software is a large portion of the overall product cost.
And there's another thing. A company that makes tractors can't produce food without land. A company that makes metal machining equipment can't make cars without the raw materials. But a software company that makes software that automatically makes software could just produce the result software itself rather than sell the software-making software. If AI ever reaches the point it makes software at a marginal cost that's not much higher than the cost of the AI itself, what would be the incentive of selling that AI?