It is a combination of publisher lock in and folks attempting wild new stuff that breaks out of what AI stuff typically produces.
Earlier this year with a lot of luck, the Canadian duo Angine de Poitrine suddenly got discovered because they are doing stuff that falls outside of conventional music styles.
They aren't unique in the experimental nature they are exploring but it has highlighted an hunger from audiences to find stuff outside of the median. Folks like Frank Zappa had to relentlessly advocate for themselves as they figured there was a middle ground between these two thing.
On a similar note I recently deleted a whole bunch of automated tests because if the AI is going to write most of the code then I should test it to make sure it's good! This won't work for all projects, but for my indie games it's a good idea.
> I recently deleted a whole bunch of automated tests because if the AI is going to write most of the code then I should test it to make sure it's good!
??
You say you deleted the tests, because you "should test it"? The logic seems inconsistent.
Sanity checking LLM-generated code with LLM-generated automated tests is low-cost and high-yield because LLMs are really good at writing tests.
I think LLMs are really bad at writing tests. In the good old days you invested in your test code to be structured and understandable. Now we all just say "test this thing you just generated".
I shipped a really embarrassing off-by-one error recently because some polygon representations repeat their last vertex as a sentinel (WKT, KML do this). When I checked the "tests", there was a generated test that asserted that a square has 5 vertices.
But LLMs let you skip all the boring parts - setting up harness, writing some initial inputs, adding asserts for every output. And then _you_ get to do actually important stuff, like ensuring square has 4 vertices.
I suppose that my generalization was too broad and that LLMs can be either good or bad at writing tests depending on your workflow and expectations.
I'm closely supervising the LLM, giving it fine-grained instructions — I generally understand the full interface design and most times the whole implementation (though sometimes I skim). When I have the LLM write unit tests for me, it writes essentially what I would have written a couple years ago, except that it tends to be more thorough and add a few more tests I wouldn't have had the patience to write. That saves me quite a bit of time, and the LLM-generated unit tests are probably somewhat better than what I would have written myself.
I won't say that I never see brain-dead mistakes of the "5-vertex square" variety (haha) — by their nature, LLMs tend towards consistency rather than understanding after all. But I've been using Claude Opus exclusively for while and it doesn't tend to make those mistakes nearly as often as I used to see with lower-powered LLMs.
I think DJs with even a light catalog of their own original music will become some of the most important artists instead. Nobody has any interest in going back to the old system.
As an user I wouldn't mind as long as it's attributed and I can skip it.
Pisses me off on YouTube - it's really hard to find something genuine in the sea of the AI written, AI subbed, AI generated and AI published - it's a scourge not because it's there, but because the channels are lying about it AND because 99.99999% of what I encountered it's not worth the waste heat processing a "publish 100 catchy videos about current affairs".
Oh yes, for sure, but it's not more training that is gonna make it sound like Mozart. There is no soul behind, it can only be bland and robotic, even if it sounds polished and well-produced, if you can call it like that.
