Posted by lukeio 12/11/2025
When quartz watches came up the makers of mechanical watches struggled. Quartz watches are cheaper, more accurate in many cases and servicing is usually restricted to replacing a battery. However some people appreciate a good mechanical watch (and the status symbol aspect of course) and nowadays the mechanical watch market is flourishing. Something similar happened with artificial fabrics (polyester, acrylic) and cheap made clothes, there’s a market for handmade clothes that use natural fabrics.
Nobody (well, barring a few HN readers) will ever care if the software was written by people or a bot, as long as it works.
That's how it works for me. I'm currently turning a lot of raw data into a map of Berlin rents. I spend less time figuring out the map API, and more time polishing the interesting parts.
I don't care if a craftsman used hand tools or a CNC to build beautiful furniture. I pay for taste, not toil.
Emphasis mine:
> there won’t be a niche
"Mechanical watches" also aren't exploding at all. When people cite this, they're citing the overall watch market growing, because the market for million dollar watches is being driven by a very small group of collectors. Its also not sustainable, and will die down in ~10-20 years when these old guys finish dying. The average not rich person could not give less of a damn about mechanical watches. There's no great comeback on the horizon
That is probably true. But all evidence to date is that if the software is written by a bot, it won't work. That is why people will care.
> more accurate in many cases
It's laughable that LLMs can be considered more accurate than human operators at the macro level. Sure, if I ask a search bot the date Notre Dame was built, it'll get it right more often than me, but if I ask it to write even a simple heap memory allocator, it's going to vomit all over itself.
> Nobody [...] will ever care if the software was written by people or a bot, as long as it works
Yeah.. wake me up when LLMs can produce even nominally complex pieces software that are on-par with human quality. For anything outside of basic web apps, we're a long way off.
With both of you doing research in your own ways, you'll get it right more often (I hope).
In the comparison I was making with respect to accuracy was that the bot is much more likely to accurately answer fact-based queries, and much less likely to succeed at any tasks that require actual 'thinking'. Especially when that task is not particularly common in the training set, such as writing a memory allocator. I can write and debug a simple allocator in half an hour, no worries. I'd be surprised if any of the current LLMs could.
If you look up the factual question in a quality source, you'll be more accurate than the bot which looked at many sources. That's all I meant.
Good luck for your new project!