Do you know if there's any post-mortems about what went wrong there?

I mean, I have the gist. Intel bet big on Xeon Phi, but that didn't seem to offer enough performance. Intel then switched over to this GPU-strategy (see Intel Xe), but that required them to rework virtually everything from scratch.

No one said anything in my previous paragraph. But its blatantly obvious: the Xeon Phi was being advertised very strongly by Intel up to the point that Aurora's design was reworked. Suddenly, Xeon Phi was cut, and Intel started talking about Xe (including OneAPI, and all sorts of stuff the HPC market cares about). I'm confident enough at reading between the lines that I'm willing to assume this in the absence of hard facts :-)

I'm kind of curious on the play by play, if at all possible. What test showed up that made Argonne National Laboratory decide that the Xeon Phi model wasn't going to work? Was it possible to come to this conclusion any sooner? I realize this sort of stuff is rarely published, but I guess that's what makes me interested in those juicy details.

My friends were at more junior levels so I don't know the full details too. However know this, a leadership class computing purchase by a national lab is a very complicated thing. Many people have their hands in the decision making pie - all the way up to Secretary of Energy.

Secondly, DOE has a strategy (mostly rightly in my opinion) of not putting all its' computation eggs in a single companies' basket. Thus national labs compete between themselves as do companies. Notice the cadence of computing purchases: IBM/Nvidia -> Intel/Intel -> AMD/AMD.

Thirdly, Intel had promised that using OneAPI existing GPU optimized simulation codes could be translated to Intel GPUs with minimum effort. The idea had merit, back in 2016-2017. I have a CUDA based simulation code - it's almost a matter of recompiling to Intel, with some minimal effort on my part. That it didn't work out - well hindsight is 20/20