There is a well-known (and old) paper called "The rise of worse is better". That paper describes some of the surprising ways in which technical adoption happens. It discusses the importance of simplicity vs completeness and correctness, making the case that something that is simple but not complete (and sometimes not correct) will often beat something that is "better", but more complex.
To a large degree, that pattern is about speed. It takes time to get something completely right, and it takes time for programmers to learn something more complex. Simple, gets the job done today, and maybe has problems to solve tomorrow, is often a winning pattern.
The tech industry is cyclic. We are in a part of the cycle when many new patterns and standards are being discovered and deployed. This is a "disruptive" phase rather than the "incremental" phase we are more used to. In this phase, speed is even more important than ever. It is often the case that the first person out the door with a new programming paradigm, app model, or business pattern is the long-term winner just because they got started first. Sometimes the difference is measured in weeks.
This is particularly true with things that have network effects. Networks are inherently exponential. Once a network effect starts, even at a small scale, it almost instantly becomes nearly impossible for someone else to disrupt it, unless they are able to immediately leapfrog with some other injection of use.
One good way to think about this is whether something can be fixed later or not. Some things (like minor syntax details) can be fixed later, others (like ecosystem momentum) can't. It's an absolutely fatal error in this moment to worry about things that can be fixed later, if that causes problems that can't.
This shows up differently for different teams and companies. It’s a hard mind shift to make, particularly after a long period of mostly incremental improvements that have valued caution and correctness. The tricky thing about this kind of moment is - you also don’t have much time to recognize and react to it.
Fast versus Right
As a professional who "grew up" in the world of government accounting, disruptive innovation has always been an uphill battle in this environment, and for clear reasons. With the recent revolutionary release of GPT and other NLP tech, I hope to work hard to inject some "speed" into our work, as the iterative "crawl" over the last half century has fallen too far behind, and now is a terrific time, in my eyes, to try and close the gap.