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anecdotally i'm finally starting to see things actually ship in the past year that aren't just "use the AI to build more AI harness" and it's roughly these categories in order: 1. software made by single programmers with a cohesive vision, that would've previously taken a mid-size startup

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2. enterprise rewrites of already-working software, to get some enterprise-desirable property, that isn't immediately visible and therefore would've previously been unaffordable 3. software made by non-programmers (academics, hobbyists) that couldn't get programmer resources before

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4. rewrites and ports of legacy or even abandonware software that would've never happened otherwise 5. algoslop, like "claude make me a profitable saas app on the app store". exists but it's actually quite rare to *see* it because nobody's sharing this stuff

what I haven't seen as much of, is rapid buildouts to prove out new end to end research programs in software that unlock truly new paradigms in architecture, development, or deployment. that *aren't* just new Gastowns

another one i've wanted to build for a while is a compute environment based on hot reloaded wasm modules with fully qualified namespacing/content-addressing so you can have N branches of any given function or module within the binary and do things like parallel evaluation and shadowing

a few companies have tried something like this and they've all had to seriously cut down scope a human trying to build this by themselves would go insane but I don't think it would even be especially _hard_ for like $1mil tokens at this point

build seems to be an area ripe for total disruption in this space. imagine a Bazel-like where all components are distributed, mesh-based global caching, worktree oriented, agentic integration - that would've been an intimidating years long buildout. now it seems approachable for a small team

historically it's been hard for any meta-package-manager to keep up with all supported OSs and PLs, which are constantly changing but OSes and PLs won't be able to move anywhere near as fast as small tools/teams now can due to needing to keep a stable interface so that seems like a huge inversion

Also anecdotal, but I have been seeing that some companies evolve internal systems for better support of automation with human in the loop processes. This does show up in earnings reports when especially promising-looking, so not exactly secret.

I'd bet #3 is far and away the most common overall, but I supposed your software niche would determine what you see. I work in no-code automation so most of what I see is...you guessed it...no-code automation ("No-code is dead!" they all declare).