“didn’t really trust Sam even back then, they just thought the option was signing the letter or the company dying, and they really didn’t want the latter.”
I personally knew a bunch of people at the time who didn’t fully support the letter but signed it anyways because they felt overwhelming pressure to do so. I’d guess these people are a minority of signatories, but it’s hard to know for sure. (The kinds of people who would sign out of fear of retaliation are probably unlikely to reveal this to people they don’t know super well.)
Also, yeah, financial/livelihood motivation were not the only factor (the board had just taken this huge drastic action that made no sense and then refused to explain why! that comes off pretty shady!) but obviously a huge factor. Few people in the world are indifferent to suddenly losing their job, let alone seeing millions of their own dollars evaporate before their eyes.
a simple elegant intuition for the relationship between SVD and eigendecomposition that I haven’t heard before:
the eigendecomposition of A tells us which directions A stretches along without rotating. but sometimes we want to know all the directions things get stretched along, even if there is rotation.
why does taking the eigendecomposition of ATA help us? suppose we rewrite A=RS, where S just scales (i.e is normal matrix), and R is just a rotation matrix. then, ATA=STRTRS, and the R’s cancel out because transpose of rotation matrix is also its inverse.
intuitively, imagine thinking of A as first scaling in place, and then rotating. then, ATA would first scale, then rotate, then rotate again in the opposite direction, then scale again. so all the rotations cancel out and the resulting eigenvalues of ATA are the squares of the scaling factors.