That's true if you're optimizing for start-up success. The odds of a startup succeeding are pretty low and mostly tech is not the cause of failures though. When people learn a new stack for a startup they're optimizing for the startup failing, so they can still derive some value out of the failure.
This just comes down to a napkin calculation of expected value of building a startup given P(success). If your startup is looking like a real business out of the gate, using the simple stack you know is the way to go, and if it's more of a hobby project that you'd like to monetize eventually, learn something new.
This lines up with how I make tech stack decisions for my own projects. But I think it's not always obvious going into something if it's going to end up being a money-making endeavor or just an educational project in the end, so I'll fall somewhere in between.
What makes the most sense to be is to be really selective about what new technologies to use, and try to really learn ~one thing per project. E.g. my current project is a small search engine, and I've spent a lot of time exploring / figuring out how to use LLM Embedding models and vector indices for search relevance (vs. falling back on using ElasticSearch the same way we use it at work), but I'm using tools that are familiar to me for the UI/db/infrastructure.
This just comes down to a napkin calculation of expected value of building a startup given P(success). If your startup is looking like a real business out of the gate, using the simple stack you know is the way to go, and if it's more of a hobby project that you'd like to monetize eventually, learn something new.