[philipp-gayret]

Could you give some examples? I can only imagine completely proprietary technology like trading or developing medicine. I have worked in software for many years and was always paid well for it. None of it was particularly unique in any way. Some of it better than others, but if you could show that there exists software people pay well for that AI cannot make I would be really impressed. With my limited view as software engineer it seems to me that the data in the product / its users is what makes it valuable. For example Google Maps, Twitter, AirBnB or HN.

[nostrademons]

Were you around when any of Google Maps, Twitter, AirBnB, or HN were first released? Aside from AirBnB (whose primary innovation was the business model, and hitting the market right during the global financial crisis when lots of families needed extra cash), they were each architecturally quite different from software that had come before.

Before Google Maps nobody had ever pushed a pure-Javascript AJAX app quite so far; it came out just as AJAX was coined, when user expectations were that any major update to the page required a full page refresh. Indeed, that's exactly what competitor MapQuest did: you had to click the buttons on the compass rose to move the map, it moved one step at a time, and it fully reloaded the page with each move. Google Maps's approach, where you could just drag the map and it loaded the new tiles in the background offscreen, then positioned and cropped everything with Javascript, was revolutionary. Then add that it gained full satellite imagery soon after launch, which people didn't know existed in a consumer app.

Twitter's big innovation was the integration of SMS and a webapp. It was the first microblog, where the idea was that you could post to your publicly-available timeline just by sending an SMS message. This was in the days before Twilio, where there was no easy API for sending these, you had to interface with each carrier directly. It also faced a lot of challenges around the massive fan-out of messages; indeed, the joke was that Twitter was down more than it was up because they were always hitting scaling limits.

HN has (had?) an idiosyncratic architecture where it stores everything in RAM and then checkpoints it out to disk for persistence. No database, no distribution, everything was in one process. It was also written in a custom dialect of Lisp (Arc) that was very macro-heavy. The advantage of this was that it could easily crank out and experiment with new features and new views on the data. The other interesting thing about it was its application of ML to content moderation, and particularly its willingness to kill threads and shadowban users based on purely algorithmic processes.

[Toutouxc]

All it takes is a sufficiently big pile of custom features interacting. I work on a legal tech product that automates documents. Coincidentally, I'm just wrapping up a rewrite of the "engine" that evaluates how the documents will come out. The rewrite took many months, the code uses graph algorithms and contains a huge amount of both domain knowledge and specific product knowledge.

Claude Code is having the hardest time making sense of it and not breaking everything every step of the way. It always wants to simplify, handwave, "if we just" and "let's just skip if null", it has zero respect for the amount of knowledge and nuance in the product. (Yes, I do have extensive documentation and my prompts are detailed and rarely shorter than 3 paragraphs.)

[datsci_est_2015]

Everything I’ve ever worked on has been entirely greenfield in domains that had very limited prior development. Industrial applications of software (and data science).

Google Maps, Twitter, and AirBnb occupy a tiny fragment of the possible domain applications of software.

[krethh]

You know how whenever you shuffle a deck of cards you almost certainly create an order that has never existed before in the universe?

Most software does something similar. Individual components are pretty simple and well understood, but as you scale your product beyond the simple use cases ("TODO apps"), the interactions between these components create novel challenges. This applies to both functional and non-functional aspects.

So if "cannot make with AI" means "the algorithms involved are so novel that AI literally couldn't write one line of them", then no - there isn't a lot of commercial software like that. But that doesn't mean most software systems aren't novel.