I also agree that boring is good, but in our current society you won't get a job for being boring, and when you get a job, it's is guaranteed you are not being paid to solve problems.
> but in our current society you won't get a job for being boring,
One can argue that every other field of engineering outside of Software Engineering, specializes in making complex things into boring things.
We are the unique snowflakes that take business use cases and build castle in the clouds that may or may not actually solve the business problem at hand.
I think this is, essentially, a wishful take. The biggest barrier to models being able to do more advanced knowledge work is creating appropriately annotated training data, followed by a few specific technical improvements the labs are working on. Models have already nearly maxed out "work on a well-defined puzzle that can be feasibly solved in a few hours" -- stunning! -- and now labs will turn to expanding other dimensions.
I like this article, and I didn't expect to because there's been volumes written about how you should be boring and building things in an interesting way just for the hell of it, is bad (something I don't agree with).
Small models doing interesting (boring to the author) use-cases is a fine frontier!
I don't agree at all with this though:
> "LLMs are not intelligent and they never will be."
LLMs already write code better than most humans. The problem is we expect them to one-shot things that a human may spend many hours/days/weeks/months doing. We're lacking coordination for long-term LLM work. The models themselves are probably even more powerful than we realize, we just need to get them to "think" as long as a human would.
OT: Since the author is a former Apple UX designer who worked on the Human Interface Guidelines, I hope he shares his thoughts on the recent macOS 26 and iOS updates - especially on Liquid Glass.
I tend to think that the reason people over-index on complex use-cases for LLMs is actually reliability, not a lack of interest in boring projects.
If an LLM can solve a complex problem 50% of the time, then that is still very valuable. But if you are writing a system of small LLMs doing small tasks, then even 1% error rates can compound into highly unreliable systems when stacked together.
The cost of LLMs occasionally giving you wrong answers is worth it for answers to harder tasks, in a way that it is not worth it for smaller tasks. For those smaller tasks, usually you can get much closer to 100% reliability, and more importantly much greater predictability, with hand-engineered code. This makes it much harder to find areas where small LLMs can add value for small boring tasks. Better auto-complete is the only real-world example I can think of.
The author of "Choose boring technology" regretted the choice of the word "boring" [1].
Anyway, boring is bad. Boring is what spends your attention on irrelevant things. Cobol's syntax is boring in a bad way. Go's error handling is boring in a bad way. Manually clicking through screens again and again because you failed to write UI tests is boring in a bad way.
What could be "boring in a good way" is something that gets things done and gets out of your way. Things like HTTPS, or S3, or your keyboard once you have leaned touch typing, are "boring in a good way". They have no concealed surprises, are well-tested in practice, and do what they say on the tin, every time.
New and shiny things can be "boring in the good way", e.g. uv [2]. Old and established things can be full of (nasty) surprises, and, in this regard, the opposite of boring, e.g. C++.
between HTTP/1.1 to HTTP/2+ interoperability issues, HTTP/2+ configuration, TLS configuration, HTTPS is hardly boring and does what it says on the tin every time.
> Go's error handling is boring in a bad way. Manually clicking through screens again and again because you failed to write UI tests is boring in a bad way.
Go's error handling is useful if your system needs to be correct more than it needs to be available, and you should be manually clicking through screens even if you wrote UI tests.
Looking at why something is boring and who it's boring for can be helpful, especially in a team setting. It's entirely possible to automate away a bottleneck that exists for a good reason while sitting across from someone who'd love to handle the issue correctly.
I feel that with LLMs and AI, people are furiously trying to argue the reality they desire into existence. I've never read more articles predicting the future than on this topic (I am guilty of it, too.)
The investment fund that acquired the company that acquired our company requests all that all companies it owns go big on cloud and AI, no matter what, because this raises valuation and they can sell them for bigger profits.
I have nothing against cloud or AI per se, but I still believe in the right tool for the right job and in not doing things just for the sake of it. While raising valuation is a good thing, raising costs, delaying more useful features and adding complexity should also be taken into account.
LLMs are useful in contexts where fuzzy and hazily accurate is acceptable. A developer trying to hack together some solution through trial and error for example.
They are less useful in contexts where accuracy is expected or legally required. An audit log for example.
Many businesses have made bad judgements of where the distinction is, some don't even recognise a distinction. This will improve over time.
> Whenever there is hype, we shuffled into the easy path, forcing the tech into the product without understanding its weaknesses. We are more worried about being left behind than actually doing something of value.
> We keep asking them to do “intelligent things” and find out a) they really aren’t that good at it, and b) replacing that human task is far more complex than we originally thought
I never thought that. From the beginning on this were easy to uncover marketing statements.
The computer is not boring, per se, it's neutral when unplugged. The idea it accelerates binary primarily for prediction purposes in extracting values from the arbitrary, this is not only exciting, it's illusory.
The writer fails to grasp what rabbit hole we've gone down since the 70s/90s when we began applying the principles of prediction to computation, then horizontalized them via web. This was the most exciting time because it added a vast illusory value to the arbitrary, it's a time of massive piracy that posed as corporatocracy.
Once this prediction became automated by AI, yes, now the piracy becomes boring, and in turn reveals what was going on all along.
"This is mostly for proofreading and condensing my rambling voice notes. These rather boring uses have significantly reduced drudgery and improved the overall quality of my writing. Best yet, they work pretty well (well, most of the time) But I don’t ask them to do any of the writing."
I find I struggle with this. If an LLM is being used to improve the overall quality of my writing, I feel like it is then doing the writing and that doesn't sit well with me. Same with having it write my code (though I don't write code for a living).
Maybe it would be similar to playing a guitar solo and then having an LLM fix all the missed notes. Is that still my solo? I tend to feel it isn't.
Just my personal struggle with this new, admittedly incredible, paradigm.
This might be slightly of topic but this article mentions the stat that keeps going around that refutes productivity gains given by LLM’s, but honestly I think this is because this is reported by companies that are either measuring time to deliverable completion, or value in dollars generated by their workers using the technology.
They always seem to miss another metric, saved human brain power. My experience hasn’t been that I can complete tasks faster from go to woe, it’s been that they’re far less mentally taxing and I can do other things whilst the ai churns some of the tedium away. When I’m feeling real tired, it’s a lot easier to review ai code than write my own from scratch. As any writer will tell you, editing is easier than writing from a blank page.
A good analogy might be that I can vacuum the house way faster than my robot vacuum can, probably in less than 1/3rd of the time! But letting the robot do it is still way better for quality of life and also it frees me up to do other things. This has been the true gain from using coding LLM’s in my lived experience.
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[ 2.6 ms ] story [ 54.3 ms ] threadThat's just your experience, based on your geolocation and chain of events.
One can argue that every other field of engineering outside of Software Engineering, specializes in making complex things into boring things.
We are the unique snowflakes that take business use cases and build castle in the clouds that may or may not actually solve the business problem at hand.
If he means they will never outperform humans at cognitive or robotics tasks, that's a strong claim!
If he just means they aren't conscious... then let's don't debate it any more here. :-)
I agree that we could be in a bubble at the moment though.
Small models doing interesting (boring to the author) use-cases is a fine frontier!
I don't agree at all with this though:
> "LLMs are not intelligent and they never will be."
LLMs already write code better than most humans. The problem is we expect them to one-shot things that a human may spend many hours/days/weeks/months doing. We're lacking coordination for long-term LLM work. The models themselves are probably even more powerful than we realize, we just need to get them to "think" as long as a human would.
https://jenson.org/about-scott/
If an LLM can solve a complex problem 50% of the time, then that is still very valuable. But if you are writing a system of small LLMs doing small tasks, then even 1% error rates can compound into highly unreliable systems when stacked together.
The cost of LLMs occasionally giving you wrong answers is worth it for answers to harder tasks, in a way that it is not worth it for smaller tasks. For those smaller tasks, usually you can get much closer to 100% reliability, and more importantly much greater predictability, with hand-engineered code. This makes it much harder to find areas where small LLMs can add value for small boring tasks. Better auto-complete is the only real-world example I can think of.
um, dynamo is a generator, it takes mechanical energy and turns into to electricity.
Anyway, boring is bad. Boring is what spends your attention on irrelevant things. Cobol's syntax is boring in a bad way. Go's error handling is boring in a bad way. Manually clicking through screens again and again because you failed to write UI tests is boring in a bad way.
What could be "boring in a good way" is something that gets things done and gets out of your way. Things like HTTPS, or S3, or your keyboard once you have leaned touch typing, are "boring in a good way". They have no concealed surprises, are well-tested in practice, and do what they say on the tin, every time.
New and shiny things can be "boring in the good way", e.g. uv [2]. Old and established things can be full of (nasty) surprises, and, in this regard, the opposite of boring, e.g. C++.
[1]: https://boringtechnology.club/#30
[2]: https://github.com/astral-sh/uv
Go's error handling is useful if your system needs to be correct more than it needs to be available, and you should be manually clicking through screens even if you wrote UI tests.
Looking at why something is boring and who it's boring for can be helpful, especially in a team setting. It's entirely possible to automate away a bottleneck that exists for a good reason while sitting across from someone who'd love to handle the issue correctly.
I have nothing against cloud or AI per se, but I still believe in the right tool for the right job and in not doing things just for the sake of it. While raising valuation is a good thing, raising costs, delaying more useful features and adding complexity should also be taken into account.
Many businesses have made bad judgements of where the distinction is, some don't even recognise a distinction. This will improve over time.
Shots fired
> We keep asking them to do “intelligent things” and find out a) they really aren’t that good at it, and b) replacing that human task is far more complex than we originally thought
I never thought that. From the beginning on this were easy to uncover marketing statements.
The writer fails to grasp what rabbit hole we've gone down since the 70s/90s when we began applying the principles of prediction to computation, then horizontalized them via web. This was the most exciting time because it added a vast illusory value to the arbitrary, it's a time of massive piracy that posed as corporatocracy.
Once this prediction became automated by AI, yes, now the piracy becomes boring, and in turn reveals what was going on all along.
I find I struggle with this. If an LLM is being used to improve the overall quality of my writing, I feel like it is then doing the writing and that doesn't sit well with me. Same with having it write my code (though I don't write code for a living).
Maybe it would be similar to playing a guitar solo and then having an LLM fix all the missed notes. Is that still my solo? I tend to feel it isn't.
Just my personal struggle with this new, admittedly incredible, paradigm.
They always seem to miss another metric, saved human brain power. My experience hasn’t been that I can complete tasks faster from go to woe, it’s been that they’re far less mentally taxing and I can do other things whilst the ai churns some of the tedium away. When I’m feeling real tired, it’s a lot easier to review ai code than write my own from scratch. As any writer will tell you, editing is easier than writing from a blank page.
A good analogy might be that I can vacuum the house way faster than my robot vacuum can, probably in less than 1/3rd of the time! But letting the robot do it is still way better for quality of life and also it frees me up to do other things. This has been the true gain from using coding LLM’s in my lived experience.
Yes I know, stable things are stable
because "for every 1% unemployment goes up 40,000 people die." bubbles bursting hurt people and there's tens of billions in this bubble.