This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic. There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.

The brilliant bit here is that each of the trackers has a bit of data about you, but not all of it, because not every tracker is on every web site. But on the other hand, cross-referencing individuals between trackers is kinda hard, because none of them wants to give away their secret sauce. So each ad seller tries their best to cross-reference the data from all the tracker data they buy, but it mostly doesn't work. Let's say there are 25 trackers each tracking a million users, probably with a ton of overlap. In a sane world we'd guess that there are, at most, a few million distinct users. But in an insane world where you can't prove if there's an overlap, it could be as many as 25 million distinct users! The more tracker data your ad network buys, the more information you have! Probably! And that means better targeting! Maybe! And so you should buy ads from our network instead of the other network with less data! I guess!

None of this works. They are still trying to sell me car insurance for my subway ride.

When I heard this was also when I learned the word "satisficing," which essentially means searching through sludge not for the best option, but for a good enough option. Nowadays Netflix isn't about finding the best movie, it's about satisficing. If it has the choice between an award-winning movie that you 80% might like or 20% might hate, and a mainstream movie that's 0% special but you 99% won't hate, it will recommend the second one every time. Outliers are bad for business.

under the influence of captured parties and the military’s ubiquitous and extravagantly funded public relations apparatus, America has itself redefined the “nature of war.” Armed conflict has gone from being an occasional unpleasant political necessity to the core product line of the American corporation. Wars are what we make, and like blue jeans or Louisville Sluggers, we build them to last, with Afghanistan the prime example.

Twenty years. All that exists of this century. As I have written elsewhere, the war was a total failure, by any measure, perhaps the great failure of our age, the nexus of all the malignant forces precipitating American decline—End of History delusions, failed leadership, the ascendant Human Rights NGO complex, public-private self-dealing—venal, arrogant, brain-dead, sclerotic, unaccountable in every aspect. We will likely learn nothing from this catastrophe. Our leadership class will not be chastened by it. They will not reconsider their magical thinking. They will simply redirect it elsewhere. Probably at home. There is already talk of forgetting the terrorists in Afghanistan so we can fight the terrorists on our own soil.

In case there is any doubt, they are referring to you and me. They are referring to anyone who doubts their mandate to rule over us. What comes next will likely be far worse than what preceded it, if equally deranged. And though the architects of this failed war might not take any lessons from Afghanistan, we can. The Pashtun persevered because they had the will to do so. Because they had God on their side.

Hold the line. Call their bluff. They will blink first. The weaker hand always does.

Another doctrine, which was invented by a combination of the courts and executive agencies, is disparate impact. So if you give standardized tests, Grigg vs. Duke Power Company, this was a case early after the Civil Rights Act. It said if you give an IQ test and it has a disparate impact between groups… you can still use it but it’s a little complicated, it has to be related to the work, but it becomes harder. Everything you do that has a racial disparate impact, and by the way everything in the world has a racial disparate impact, if you find something that doesn’t I’ll be surprised, they can come after you for it either through the government directly coming after you or through people suing you.

It says you can’t discriminate in government and you can’t discriminate in private business. And most people at the time thought that basically meant you couldn’t put up a sign that says no black people. Even the gender thing they say was added as a joke actually. Somebody was trying to kill the bill, they didn’t want the racial equality parts. They said, “it would be so absurd to have a society where you didn’t discriminate based on sex” so they put sex in there hoping to kill the bill. And it ended up passing.

The fact that it was vague and there were potentially substantial penalties sort of put business on edge. You needed a full-time bureaucratic class to interpret the laws and what was going on.

So the DEI industry is derived off of the rise of human resources. So you know, the way people see woke institutions today, “well they’re just deciding to be woke, there’s just a class of people deciding to take the leftwing issue on anything related to race and gender,” and some of that is obviously right. But you’re ignoring that basically legally you’re only allowed to be on one side of the culture wars.