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.”
It's not the exact opposite*, the author said that if you're doing boilerplate _code_ it's probably fine.
The thing is that since it can't think, it's absolutely useless when it comes to things that hasn't been done before, because if you are creating something new, the software won't have had any chance to train on what you are doing.
So if you are in a situation in which it is a good idea to create a new DSL for your problem **, then the autocruise control magic won't work because it's a new language.
Now if you're just mashing out propaganda like some brainwashed soviet apparatchik propagandist, maybe it helps. So maybe people who writes predictable slop like this the guardian article (https://archive.is/6hrKo) would be really grateful that their computer has a cruise control for their political spam.
) if that's what you meant
*) which you statistically speaking might not want to do, but this is about actually interesting work where it's more likely to happen*
notachatbot123 · 45m ago
> It made up a lot of stuff despite it being very clear in the prompt, that it should not do so.
LLMs are not sentient. They are designed to make stuff up based on probability.
viccis · 58m ago
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.
mattmanser · 41m ago
I get what point you're trying to make, and agree, but you've picked a bad example.
That boilerplate heavy, skill-less, frontend stuff like configuring a map control with something like react-leaflet seems to be precisely what AI is good at.
ianbicking · 2h ago
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.
voxelghost · 2h ago
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.
aaronbrethorst · 3h ago
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.
rijoja · 53m ago
Yes so why would you spend tons of time and introduce a huge amount of technical debt by rewriting the boring parts, instead of just using a ready made off the shelf solution in that case.
You'd think that there be someone who'd be nice enough to create a library or a framework or something that's well documented and is popular enough to get support and updates. Maybe you should consider offloading the boring part to such a project, maybe even pay someone to do it?
bravesoul2 · 2h ago
Most places I worked the setting up of that kind of boilerplate was done a long time ago. Yes it needs maintaining and extending. But rarely building from the ground up.
forrestthewoods · 2h ago
This depends entirely on the type of programming you do. If all you build is CRUD apps then sure. Personally I’ve never actually made any of those things — with or without AI
PeterStuer · 1h ago
You are both right. B2B for instance is mostly fairly template stuff built from CRUD and some business rules. Even some of the more perceived as 'creative' niches such as music scoring or 3D games are fairly route interactions with some 'engine'.
And I'm not even sure these 'template adjacent' regurgitations are what the crude LLM is best at, as the output needs to pass some rigorous inflexible test to 'pass'. Hallucinating some non-existing function in an API will be a hard fail.
LLM's have a far easier time in domains where failures are 'soft'. This is why 'Elisa' passed as a therapist in the 60's, long before auto-programmers were a thing.
Also, in 'academic' research, LLM use has reached nearly 100%, not just for embelishing writeups to the expected 20 pages, but in each stage of the'game' including 'ideation'.
And if as a CIO you believe that your prohibition on using LLMs for coding because of 'divulging company secrets' holds, you are either strip searching your employees on the way in and out, or wilfully blind.
I'm not saing 'nobody' exists that is not using AI in anything created on a computer, just like some woodworker still handcrafts exclusive bespoke furniture in a time of presses, glue and CNC, but adoption is skyrocketing and not just because the C-suite pressures their serves into using the shiny new toy.
rijoja · 45m ago
> "And if as a CIO you believe that your prohibition on using LLMs for coding because of 'divulging company secrets' holds, you are either strip searching your employees on the way in and out, or wilfully blind."
Right so if you are in certain areas you'll be legally required not to send your work to whatever 3:rd party that promises to handle it the cheapest.
Also so since this is about actually "interesting" work if you are doing cutting edge research on lets say military or medical applications** you definitely should take things like this seriously.
Obviously you can do LLM's locally if you don't feel like paying up for programmers who likes to code, and who wants to have in-depth knowledge of whatever they are doing.
12 months ago called, they want their thesis back.
ssivark · 3h ago
Curious to see examples of interesting non-boilerplate work that is now possible with AI. Most examples of what I've seen are a repeat of what has been done many times (i.e. probably occurs many times in the training data), but with a small tweak, or for different applications.
And I don't mean cutting-edge research like funsearch discovering new algorithm implementations, but more like what the typical coder can now do with off-the-shelf LLM+ offerings.
NitpickLawyer · 49m ago
> Curious to see examples of interesting non-boilerplate work that is now possible with AI.
Oh it's feels like crypto again. Outlandish statements but no argument. "Few Understand" as they say.
rijoja · 1h ago
yes
bitwize · 3h ago
But... agentic changes everything!
darkxanthos · 1h ago
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.
bertman · 1h ago
>you're just proving you'll be left behind.
... said every grifter ever since the beginning of time.
“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 thing is that since it can't think, it's absolutely useless when it comes to things that hasn't been done before, because if you are creating something new, the software won't have had any chance to train on what you are doing.
So if you are in a situation in which it is a good idea to create a new DSL for your problem **, then the autocruise control magic won't work because it's a new language.
Now if you're just mashing out propaganda like some brainwashed soviet apparatchik propagandist, maybe it helps. So maybe people who writes predictable slop like this the guardian article (https://archive.is/6hrKo) would be really grateful that their computer has a cruise control for their political spam.
) if that's what you meant *) which you statistically speaking might not want to do, but this is about actually interesting work where it's more likely to happen*
LLMs are not sentient. They are designed to make stuff up based on probability.
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.
That boilerplate heavy, skill-less, frontend stuff like configuring a map control with something like react-leaflet seems to be precisely what AI is good at.
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.
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.
You'd think that there be someone who'd be nice enough to create a library or a framework or something that's well documented and is popular enough to get support and updates. Maybe you should consider offloading the boring part to such a project, maybe even pay someone to do it?
And I'm not even sure these 'template adjacent' regurgitations are what the crude LLM is best at, as the output needs to pass some rigorous inflexible test to 'pass'. Hallucinating some non-existing function in an API will be a hard fail.
LLM's have a far easier time in domains where failures are 'soft'. This is why 'Elisa' passed as a therapist in the 60's, long before auto-programmers were a thing.
Also, in 'academic' research, LLM use has reached nearly 100%, not just for embelishing writeups to the expected 20 pages, but in each stage of the'game' including 'ideation'.
And if as a CIO you believe that your prohibition on using LLMs for coding because of 'divulging company secrets' holds, you are either strip searching your employees on the way in and out, or wilfully blind.
I'm not saing 'nobody' exists that is not using AI in anything created on a computer, just like some woodworker still handcrafts exclusive bespoke furniture in a time of presses, glue and CNC, but adoption is skyrocketing and not just because the C-suite pressures their serves into using the shiny new toy.
Right so if you are in certain areas you'll be legally required not to send your work to whatever 3:rd party that promises to handle it the cheapest.
Also so since this is about actually "interesting" work if you are doing cutting edge research on lets say military or medical applications** you definitely should take things like this seriously.
Obviously you can do LLM's locally if you don't feel like paying up for programmers who likes to code, and who wants to have in-depth knowledge of whatever they are doing.
** https://www.bbc.co.uk/news/articles/c2eeg9gygyno
And I don't mean cutting-edge research like funsearch discovering new algorithm implementations, but more like what the typical coder can now do with off-the-shelf LLM+ offerings.
Previously discussed on HN - oAuth library at cloudflare - https://news.ycombinator.com/item?id=44159166
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.
... said every grifter ever since the beginning of time.