Honestly, I think that this was a horrid read. It felt so unfocused, shallow and at times contradictory.
For example, at the top it talked about how software implementation has the highest adoption rate while code review/acceptance has the lowest, yet it never really talks about why that is apart from some shallow arguments (which I will come back later), or how to integrate AI more there.
And it never reached any depth, as any topic only gets grazed shortly before moving to the next, to the point where the pitfalls of overuse of AI (tech debt, security issues, etc.) are mentioned, twice, with no apparent acknowledgement of its former mention, and never mentioned how these issues get created nor show any examples.
And what I think is the funniest contradiction is that from the start, including the title, the article pushes for speed, yet near the end of the article, it discourages this thinking, saying that pushing dev teams for faster development will lead to corner cutting, and that for a better AI adoption one shouldn’t focus on development speed. Make up your damn mind before writing the article!
I think that is the cause. Overall, the license doesn’t define what “commercial use” is, apart from the listed examples, which overall seem to avoid any sort of publishing cases, which makes forking even more risky. Sure, when you make money off it directly, that’s commercial, but does making money indirectly off it count as well? What about using your own fork of it for gaining goodwill and using it for PR, is that commercial use?
Generally creating a license is hard, as vauge terminology can scare off users from actually forking it due to the license being a liability or can render your license null and void if you’re not careful enough. That’s why it’s generally recommended to just use an existing licence model instead of trying to come up with your own.