LLM Inevitabilism

In the 90s a friend told me about the internet. And that he knows someone who is in a university and has access to it and can show us. An hour later, we were sitting in front of a computer in that university and watched his friend surfing the web. Clicking on links, receiving pages of text. Faster than one could read. In a nice layout. Even with images. And links to other pages. We were shocked. No printing, no shipping, no waiting. This was the future. It was inevitable.

Yesterday I wanted to rewrite a program to use a large library that would have required me to dive deep down into the documentation or read its code to tackle my use case. As a first try, I just copy+pasted the whole library and my whole program into GPT 4.1 and told it to rewrite it using the library. It succeeded at the first attempt. The rewrite itself was small enough that I could read all code changes in 15 minutes and make a few stylistic changes. Done. Hours of time saved. This is the future. It is inevitable.

PS: Most replies seem to compare my experience to experiences that the responders have with agentic coding, where the developer is iteratively changing the code by chatting with an LLM. I am not doing that. I use a "One prompt one file. No code edits." approach, which I describe here:

https://www.gibney.org/prompt_coding

The thing is that the data from actual research doesn't support your anecdotal proof of quality:

- https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

- https://www.theregister.com/2025/06/29/ai_agents_fail_a_lot/

But more importantly, it makes you stupid:

- https://www.404media.co/microsoft-study-finds-ai-makes-human...

- https://archive.is/M3lCG

And it's an unsustainable bubble and wishful thinking, much like crypto:

- https://dmitriid.com/everything-around-llms-is-still-magical...

So while it may be a fun toy for senior devs that know what to look for, it actually makes them slower and stupider, making them progressively less capable to do their job and apply critical thinking skills.

And as for juniors — they should steer clear from AI tools as they can't assess the quality of the output, they learn nothing, and they also get critical thinking skills impaired.

So with that in mind — Who is the product (LLM coding tools) actually for, and what is its purpose?

I'm not even going into the moral, ethical, legal, social and ecological implications of offloading your critical thinking skills to a mega-corporation, which can only end up like https://youtu.be/LXzJR7K0wK0

These studies profoundly miss the mark and were clearly written for engagement/to push a certain view. It's abundantly clear to any developer who has used LLMs that they are a useful tool and have turned the corner in terms of the value they're able to provide vs their limitations.

Not to me. I have also not seen any signs that this technology has had macroeconomic effects, and I don't know of any developers in meatspace that are impressed.

To me it seems like a bunch of religious freaks and psychopaths rolled out a weird cult, in part to plaster over layoffs for tax reasons.

The anti-LLM crowd on HN is far more cultish. I don't know why some developers insist on putting their head in the sand on this.

The pro-LLM crowd on HN is just as cultish. The divide is as diverse as the work we do:

There is work that I do that is creative, dynamic and "new". The LLM isn't very helpful at doing that work. In fact it's pretty bad at getting that sort of thing "right" at all. There is also plenty of work that I do that is just transformational, or boiler plate or a gluing this to that. Here the LLM shines and makes my job easy by doing lots of the boring work.

Personal and professional context are going to drive that LLM experience. That context matters more than the model ever will. I would bet that there is a strong correlation between what you do day to day and how you feel about the quality of LLM's output.

What is the thing about glue code that people are rambling about? I’ve never seen such glue code that is tedious to write. What I’ve seen are code examples that I copy-pasted, code generators that I’ve used, and snippets that I’ve inserted. I strongly suspect that the tediousness was about making these work (aka understanding), not actually typing the code.

> I’ve never seen such glue code that is tedious to write.

Its a fair point, its not the writing per se thats tedious:

Fetch data from API 9522, write storage/trasformation/validation code, write display code. Test, tweak/fix, deploy.

Do you know how many badly designed and poorly documented API's I have had to go through in 25+ years? Do you know how many times I have written the same name/first_name/FirstName/First_name mapping between what comes in and what already exists. Today it's an old personal project, tommrow a client app, the day after home assistant (and templated yaml).

Why should I spend any time figuring out if the api doc is poorly or well written? Why should I learn what esoteric scheme of tokens you have chosen to put up the facade of security. Is mapping code fun to write? It's like the boiler plate around handling an error or writing a log message (things that you let autocomplete do if you can). Do you really want to invest in the bizarre choices of systems you USE but not often enough to make it worth your time to commit their goofy choices to memory (I'm looking at you templated yaml).

You are right that the "code is easy". It's the whole process and expense of brain power on things that are, in the long run, useless that makes it tedious. The study where people did not retain what the wrote/did with the LLM is a selling point not a down side. Tomorrow I have to do the same with API 9523 and 9524, and I'm going to be happy if it gets done and I retain none of it.