Thesis: Interesting work is less amenable to the use of AI

A Danish audio newspaper host / podcaster had the exact apposite conclusion when he used ChatGPT to write the manuscript for one his episodes. He ended up spending as much time as he usually does because he had to fact check everything that the LLM came up with. Spoiler: It made up a lot of stuff despite it being very clear in the prompt, that it should not do so. To him, it was the most fun part, that is writing the manuscript, that the chatbot could help him with. His conclusion about artificial intelligence was this:

“We thought we were getting an accountant, but we got a poet.”

Frederik Kulager: Jeg fik ChatGPT til at skrive dette afsnit, og testede, om min chefredaktør ville opdage det. https://open.spotify.com/episode/22HBze1k55lFnnsLtRlEu1?si=h...

The one thing AI is good at is building greenfield projects from scratch using established tools. If want you want to accomplish can be done by a moderately capable coder with some time reading the documentation for the various frameworks involved, then I view AI as fairly similar to the scaffolding that happened with Ruby on Rails back in the day when I typed "rails new myproject".

So LLMs are awesome if I want to say "create a dashboard in Next.js and whatever visualization library you think is appropriate that will hit these endpoints [dumping some API specs in there] and display the results to a non-technical user", along with some other context here and there, and get a working first pass to hack on.

When they are not awesome is if I am working on adding a map visualization to that dashboard a year or two later, and then I need to talk to the team that handles some of the API endpoints to discuss how to feed me the map data. Then I need to figure out how to handle large map pin datasets. Oh, and the map shows regions of activity that were clustered with DBSCAN, so I need to know that Alpha shape will provide a generalization of a convex hull that will allow me to perfectly visualize the cluster regions from DBSCAN's epsilon parameter with the corresponding choice of alpha parameter. Etc, etc, etc.

I very rarely write code for greenfield projects these days, sadly. I can see how startup founders are head over heels over this stuff because that's what their founding engineers are doing, and LLMs let them get it cranking very very fast. You just have to hope that they are prudent enough to review and tweak what's written so that you're not saddled with tech debt. And when inevitable tech debt needs paying (or working around) later, you have to hope that said founders aren't forcing their engineers to keep using LLMs for decisions that could cut across many different teams and systems.

If AI can do the easiest 50% of our tasks, then it means we will end up spending all of our time on what we previously considered to be the most difficult 50% of tasks. This has a lot of implications, but it does generally result in the job being more interesting overall.

There's a hundred ways to use AI for any given work. For example if you are doing interesting work and aren't using AI-assisted research tools (e.g., OpenAI Deep Research) then you are missing out on making the work that more interesting by understanding the context and history of the subject or adjacent subjects.

This thesis only makes sense if the work is somehow interesting and you also have no desire to extend, expand, or enrich the work. That's not a plausible position.

I don't have LLM/AI write or generate any code or document for me. Partly because the quality is not good enough, and partly I worry about copyright/licensing/academic rigor, partly because I worry about losing my own edge.

But I do use LLM/AI, as a rubber duck that talks back, as a google on steroids - but one who needs his work double checked. And as domain discovery tool when quickly trying to get a grasp of a new area.

Its just another tool in the toolbox for me. But the toolbox is like a box of chocolates - you never know what you are going to get.

The vast majority of any interesting project is boilerplate. There's a small kernel of interesting 'business logic'/novel algorithm/whatever buried in a sea of CRUD: user account creation, subscription management, password resets, sending emails, whatever.

But... agentic changes everything!

yes

[flagged]

It's definitely real that a lot of smart productive people don't get good results when they use AI to write software.

It's also definitely real that a lot of other smart productive people are more productive when they use it.

These sort of articles and comments here seem to be saying I'm proof it can't be done. When really there's enough proof it can be that you're just proving you'll be left behind.