Comment by aleqs
18 hours ago
> should expect maybe 5x faster cycle in major software apps
To what end and what would that even look like though? Enshittifying everything at maximum speed? The apps/platforms I use regularly - GitHub, Spotify, Google maps (just to name a few), have gotten noticeably shittier in recent times.
Confirmation bias. The internet has complained about software updates decades before LLMs became ubiquitous. I made a career fixing human slop by domain experts.
We easily forget that the great majority of software engineering is fixing the mistakes of other highly capable software engineers.
It's just so easy to blame the machine instead of admitting no one here is an expert on anything and they count their hits and not misses. If they did, we would find the probability of making a mistake to be higher than a fronter coding agent.
It's a hard headed crowd and everyone, LLM pilled or not, suffers from the Dunning-Kruger. All of us.
Just look at the comments. Everyone is perfect when they do things themselves.
>GitHub, Spotify, Google maps (just to name a few), have gotten noticeably shittier in recent times.
What if AI lets you create new versions of those tools, but without the enshitification?
I say that being in the "soaking" stage of using AI to rebuild a shitty software project in 70KLOC over about 2 weeks of spare time, so this may not be as theoretical as you might think.
Oh I definitely agree that AI can and will help create great software.
It's just that creating great software isn't really the SV/VC/big tech business model or main goal.
> What if AI lets you create new versions of those tools, but without the enshitification?
I'm not sure I fully understand what you're saying here. Isn't the value of these tools almost entirely independent of their actual software? That is, we have many good open source, self-hostable forges (Forgejo, sr.ht, etc.), lots of great music player software (Jellyfin, Symphonium, etc.), and decent maps software (OsmAnd and Organic Maps). People use GitHub, Spotify, and Google Maps -- perhaps even _put up_ with their often bad/glitchy software -- because of network effects (all three) and content/licensing partnerships (Spotify/GMaps). That proprietary data isn't something AI can help you with, right?
It really depends on the use-case. For example, my most starred github repo is a tool to convert Spotify playlists to YouTube Music (that was done pre-AI). Github depends on what issues you have with it, what your use case is, and whether you can leverage some of the network effects via API from the github source. Maps, same story.
AI coders are great for making scrapers, possibly because AI companies use their own tools to make an awful lot of scrapers.