Hey, wait – is employee performance Gaussian distributed?

> I would expect only a minority of developers would be willing to try working there, despite the awesome rewards.

So much this. OP's description of the work environment is stressing me out and I don't even work there.

At best a strategy like the one described above will get you the top 10% of people who are willing to put up with that kind of work environment, which means you might get the top 10% of single, childless 20–35-year-olds—people who are motivated first and foremost by ego and pay and don't value stability and work-life balance. But in the process you're more or less explicitly saying that you're not interested in people who are further along in their lives and value stability and reliability more than ego and raw paycheck size.

This means that you're missing out on the top 10% of 35–65-year-old engineers who are now parents with responsibilities outside of their career, even though the top 10% of that bracket would typically be "better" by most metrics than the top 10% of the younger bracket you're pre-filtering down to.

In a startup environment this might be a perfectly rational tradeoff—you want to filter for people who don't have much else to do and can give you a huge amount of unpaid overtime in exchange for you stroking their ego—but past a certain size and market share you need the stability offered by mature, experienced professionals.

If Netflix failed to get over that hump, it's not so surprising after all that they fell so hard in the last 10 years.

It's not so much that managers need a performance review per se, but they need training and useful feedback.

If you've ever worked in tech management, your experience likely was "IDK, you're senior, you vaguely have an idea what we should do, here, go manage a few folks".

No training, or minimal training. Often with an expectation that of course you can still be a strong technical contributor, because how much time could managing folks possibly take. And then mostly being evaluated based on how your reports delivered.

As long as we follow that approach, we'll struggle with managers doing the right thing, because they neither have learned it, nor have they seen it modelled.

Sure, that expresses in bad manager performance, but often nobody can really see it or tell people what they should do better. Performance review is too late to fix that. (This is, btw, mostly true for employees as well - if you only talk about performance 1-4 times a year, people are being set up to fail)

> It would be a monumental achievement if computer science ever advances to the point where we have a mathematical way of determining the minimum absolute intelligence required to solve a given problem.

While that would be nice, it's likely a pipe dream :( There's a good chance "intelligence" is really a multi-dimensional thing influenced by a lot of different factors. We like pretending it's one-dimensional so we can sort folks (and money reinforces that one-dimensional thinking), but that means setting ourselves up for failure.

It doesn't help that the tests we currently have (e.g. IQ) are deeply flawed and taint any thinking about the space. (Not least because folks who took a test and scored well are deeply invested in that test being right ;)

I was really giving them the benefit of the doubt. I don’t think Netflix had anything special above and beyond any other Silicon Valley software company. They just pushed this narrative and nobody questioned them.

Netflix as a business isn’t even way ahead of competition anymore. It’s not better than Hulu or Max or anything else.

Netflix’s platform crumbled handling live streaming a boxing match, while Amazon and the rest of the legacy media companies have no issues streaming NFL games every weekend, and I’m supposed to believe that Netflix engineers are better than the ones at Paramount+ who never made me wait for a buffer to watch Premier League or NFL on CBS.

The author looks at "observables" of performance without considering whether there might be confounders such as those discussed in great nuance here https://onlinelibrary.wiley.com/doi/full/10.1111/joes.12328 .

He cites similar work by William Shockley who taught both electrical engineering and scientific racism at Stanford https://en.wikipedia.org/wiki/William_Shockley (no swipe at the author, just pointing at the biased motiviations of some of the researchers foundational to the idea of "high performers").

In general, when you see pareto structures or power laws, you should think of compound or cascade effects, which in human structures generally means some form of social mediation. Affinity for a desireable skill might be gaussian, but the selection process means that the people who _get_ to do that skill might become pareto shaped because if you aren't much better than the next guy, you wouldn't stably stay at the top. Similar logic can hold for other expressions.

In general, I wish more people would read https://blackwells.co.uk/bookshop/product/Causality-by-Judea... or at least the more accessible https://mixtape.scunning.com/ before starting to conjecture from data about social systems - the math will tell you what you can and cannot speculate on.

(fun exercise: draw the causal models of IQ in https://dagitty.net/ and ponder the results)