I get frustrated by this sort of unthinking thought piece.
To pick one example:
"Rogers proposed a bell curve with five distinct adopter categories: innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%), and laggards (16%). This distribution of adopters remained remarkably consistent, regardless of the particular innovation being studied, from agricultural practices to technological advancements."
The distribution of adopters was defined, not observed. There's nothing remarkable about it staying the same when the same definition was applied to different products, any more than it would be noteworthy that the first fifth of the people to arrive at a party make up 20% of the attendees.
I’d also note the lack of editing in parts of the article. There’s a sentence that starts with “[Something like] <text of draft sentence>”.
This might be what you're saying, and it's been a long time since I read Rogers' original work, but IIRC Rogers was following the standard deviations of the normal distribution but wasn't without merit in finding common characteristics within said group.
But def incorrect as written in the article.
It's funny: The article talks about GPT and diffusion in the context of technological innovations, and then getting to AI/LLMs. But "diffusion" and "GPT" have some very different meaning in the context of AI/LLMs, referring to specific kind of models.
That said, I don't really get the conclusion of the article. We should put more effort for productization of LLMs? But isn't that exactly what the big companies are trying to do already?
I wonder if it's some sort of search engine hack or something? The diffusion of "General Purpose Technologies" seems like a slightly strained way to phrase it, and should have struck the author as awkward given the name space collisions. Unless it's an attempt at humor?
It is indeed an attempt at humor, but it comes near the end:
> when a GPT finds a form factor that allows it to fulfill a real need. Whether it be celestial navigation, steam power or, indeed, the Generative Pretrained Transformer.
Some things diffuse faster/slower than others, for various reasons. I only skimmed the article, but I see no dilemma or enlightenment here. Growth teams worry about these things.