Not really. This is referring back to the 80's. People weren't even doing 'ML'. And back then people were more focused on teasing out 'laws' in as few data points as possible. The focus was more on formulas and symbols, and finding relationships between individual data points. Not the broad patterns we take for granted today.

I was a deep learning researcher. The problem is that accuracy (+ related metrics) were prioritized in research and funding. Factors like interpretability, extrapolation, efficiency, or consistency were not prioritized, but were clearly important before being implemented.

Dall-E was the only big surprising consumer model-- 2022 saw a sudden huge leap from "txt2img is kind of funny" to "txt2img is actually interesting". I would have assumed such a thing could only come in 2030 or earlier. But deep learning is full of counterintuitive results (like the NFL theorem not mattering, or ReLU being better than sigmoid).

But in hindsight, it was naive to think "this does not work yet" would get in the way of the products being sold and monetized.