Don't get me wrong, DALL-E is remarkable. But it's remarkable in almost the exact same way ELIZA was remarkable in 1966, and Markov Chain generative models were in the 1990s. All of these, given the context computational powers at the time, were both miracles and parlor tricks at the same time.
The trouble is that these demos are impressive, but not much has changed in how the world works. I work in AI/ML and every places I've seen the practical applications of these techniques has ranged the equivalent to adding sprinkles on a cake, to completely useless engineering nightmares that would ultimately create more value with their removal.
Yea, a 3.5 billion parameter model is neat, but we know that in 1970 when we couldn't imagine training such a thing. The problem is that we've made essentially no progresses other than showing when you pour incredible human and physical resources into a pot the result looks cool.
But when you do the accounting, and ask yourself "what has really changed with all these fantastic innovations in machine learning" the answer is surprisingly little.
Dall-E 2 is the type of cool that will be figure 12-3 in a undergrad textbook in 20 years. Students will go "oh that's cool" and turn the page.