No opinion on the specifics of this distinction, but it's worth noting that in research, an awful lot of successful projects have their origins in failed projects of decades ago...
My experience working in machine learning academia is an overfocus on failed projects from the early 00s to 90s that really only stopped in 2020+.
We can often trace back successful projects to failed precursors, but often the people behind the successful project are not even familiar with the failed precursor and the 'connection to the past' only really occurs in retrospect. See the 'adjoint state method' and connections with backprop.
This is sometimes true, sure. And often the older work has more entered the general consciousness than being chased down by searching specific cites. On the other hand, very little is truly new, and recency bias can lead you into all sorts of back-eddy's.
Once the dust has settled, there are often much clearer through lines than in looked like at the time. It's hard to see when you are on the moving front though.
We can often trace back successful projects to failed precursors, but often the people behind the successful project are not even familiar with the failed precursor and the 'connection to the past' only really occurs in retrospect. See the 'adjoint state method' and connections with backprop.