1. Most of these companies are AI companies & would want to say that to promote whatever tool they're building
2. Selection b/c YC is looking to fund companies embracing AI
3. Building a greenfield project with AI to the quality of what you need to be a YC-backed company isn't particularly "world-class"
3. You are absolutely right. New startups have greenfield projects that are in-distribution for AI. This gives them faster iteration speed. This means new companies have a structural advantage over older companies, and I expect them to grow faster than tech startups that don’t do this.
Plenty of legacy codebases will stick around, for the same reasons they always do: once you’ve solved a problem, the worst thing you can do is rewrite your solution to a new architecture with a better devex. My prediction: if you want to keep the code writing and office culture of the 2010s, get a job internally at cloud computing companies (AWS, GCP, etc). High reliability systems have less to gain from iteration speed. That’s why airlines and banks maintain their mainframes.
"4.1. Generally. Customer and Customer’s End Users may provide Input and receive Output. As between Customer and OpenAI, to the extent permitted by applicable law, Customer: (a) retains all ownership rights in Input; and (b) owns all Output. OpenAI hereby assigns to Customer all OpenAI’s right, title, and interest, if any, in and to Output."
It wouldn't be OpenAI holding copyright - it would be no one holding copyright.
Can you replay all of your prompts exactly the way you wrote them and get the same behaviour out of the LLM generated code? In that case, the situation might be similar. If you're prodding an LLM to give you a variety of resu
But significantly editing LLM generated code _should_ make it your copyright again, I believe. Hard to say when this hasn't really been tested in the courts yet, to my knowledge.
The most interesting question, to me, is who cares? If we reach a point where highly valuable software is largely vibe coded, what do I get out of a lack of copyright protection? I could likely write down the behaviour of the system and generate a fairly similar one. And how would I even be able to tell, without insider knowledge, what percentage of a code base is generated?
There are some interesting abuses of copyright law that would become more vulnerable. I was once involved in a case where the court decided that hiding a website's "disable your ad blocker or leave" popup was actually a case of "circumventing effective copyright protection". In this day and age, they might have had to produce proof that it was, indeed, copyright protected.
Public domain in, public domain out.
Copyright'd in, copyright out. Your compiled code is subject to your copyright.
You need "significant" changes to PD to make it yours again. Because LLMs are predicated on massive public data use, they require the output to PD. Otherwise you'd be violating the copyright of the learning data - hundreds of thousands of individuals.
Patent trolls will extort you: the trolls will be using AI models to find "infringing" software, and then they'll strike.
¡There's no way AI can be cleanroom!