Author made a couple of fundamental mistakes: the first is they assume employees are (or should be) paid according to how much they "individually" earned the company. Employers strive to pay employees the minimum they can bear, on employers terms. Those terms are information asymmetry and a Gaussian distribution. Fairness is the last thing one should expect from employers, but being honest about this is not good for morale, so instead, they rely on keeping employees uninformed, while the employers collude to gather everyone's remuneration history via the Work Number.

The second mistake they made is assume that companies would prioritize being lean and trimming the mediocre & bottom 5%. There are other considerations, combined productivity is more important than having individual superstars working on the shiniest features. How much revenue do you think a janitor or café staffer generates? Close to zero. The same goes for engineering. Someone has to do the unglamorous staff, or you end up with a dysfunctional company, with amazing talent (on paper).

Edit: there's an infamous graph that shows when aggregate worker productivity and average income. The two tracked closely, rising in tandem until the 1970s, where they got decoupled. With income becoming much flatter, and productivity continuing to rise. That's how the world has been for the past 50 years on the macro and the micro

From the perspective of a employee and/or human, that does seem like the most fair way of distributing what the company earns, sans the money that gets reinvested straight back into the business itself. But I'd guess that'd be more of a co-operative, and less like the typical for-profit company most companies are today.

Hipothetical two people cooperative that produces simple hammers. One specializes on wooden part, the other on metal part. How much each of them earned to the company? (Or producing and selling; or one spending his lifesavings to buy pricey hammer-making-equipment while other presses buttons on said equipment)

What you're describing, that money would go to whoever brings in revenue directly, is the myopic viewpoint of Sales with an emphasis on closing deals with nothing else. If it wasn't for the rest of the work, there'd be nothing to sell!

If you have 1000 possible IQ questions, you can ask a bunch of people those questions, and then pick out 100 questions that form a Gaussian distribution. This is how IQ tests are created.

This is not unreasonable... if you picked out 100 super easy questions you wouldn't get much information, everyone would be in the "knows quite a lot" category. But you could try to create a uniform distribution, for instance, and still have a test that is usefully sensitive. But if you worry about the accuracy of the test then a Gaussian distribution is kind of convenient... there's this expectation that 50th percentile is not that different than 55th percentile, and people mostly care about that 5% difference only with 90th vs 95th. (But I don't think people care much about the difference between 10th percentile and 5th... which might imply an actual Pareto distribution, though I think it probably reflects more on societal attention)

Anyway, kind of an aside, but also similar to what the article itself is talking about

Join those two, and the test only becomes reasonable near the middle. But the middle is exactly where the pick of questions makes the most difference.

All said, this means that IQ is kinda useful for sociological studies with large samples. But if you use it you are adding error, it's not reasonable to expect that error not to correlate with whatever you are looking at (since nobody understands it well), and it's not reasonable to expect the results to be stable. And it's really useless to make decisions based on small sample sizes.

To go from an IQ of 100 to 130 might require an increase in brainpower of x, and from 130 to 170 might require 3x for example, and from 170-171 might be 9x compared to 100.

We have to have a relative scale and contrive a Gaussian from the scores because we don’t have an absolute measure of intelligence.

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.

The analogy we used was a sports team. Pro sports teams have really good players and great players. Some people are superstars, but unless you're at least really really good you're not on the team.

Performance and compensation were completely separate, which was also nice. Performance evals were 360 peer reviews, and compensation was determined mostly by HR based on what it was costing to bring in new hires, and then bumping everyone up to that level.

So at least at Netflix 10 years ago, performance wasn't really distributed at all. Everyone was top 10% industrywide.

Another reason I really don't trust that to be true is that I've never seen a good way to measure who is a top performer and who is not. I don't think there's one, people are good in different things, even within the same job... for one assignment, Joe may be the best, but for another, Mary is the winner (but again, to measure this reliably and objectively is nearly impossible IMHO for anything related to knowledge work - and I've read lots of research in this area!).

Finally, just as a cheap shot at Netflix, sorry I can't resist as a customer: they absolutely suck at the most basic stuff in their business, which is to produce good content in the first place, and very importantly, NOT FREAKING CANCEL the best content! I won't even mention how horrible their latest big live stream was... oh well, I just did :D.

It's difficult to achieve, but it's not an unreasonable objective to have. After that there is a question of measurement. How do you measure that? Did they? What was their score? - and yes, until the evidence is released, they probably didn't. (But I would also cut slack on the measurement - it IS difficult to measure so a decent attempt - a top 10% attempt? - will do.)

Where the "top performers" meme obviously fails is when every new business and their sister claims the same thing. We are all winners here and all that.

It isn't that simple. Making money from content is not 1-to-1 related with the quality of the content. There are many examples of great content that doesn't make money, and many examples of content that makes a lot of money that isn't great. Also there are many differing opinions on what 'great content' even is.

Netflix burns customers when they cancel beloved shows, and they constantly have to experiment.

They now have a bazillion competitors who are ramping up comparable businesses. There's no moat or secret sauce competitive advantage. Customers are free to switch at no cost.

Bigger tech companies are using media content as simply a fringe benefit or commodity to enhance their platform offerings.

YouTube, on the other hand, is already starting to eclipse the entire Netflix business model. YouTube is a monster with a huge and enviable moat, and it's only going to continue growing. It's a much stronger business model and they have a sticky and growing user base.

In my experience, these labels in corporate environments often correlate more with social dynamics and political acumen than actual work output. People who are less socially connected or don't engage in office politics may find themselves labeled as 'low performers' regardless of their actual contributions, while those who excel at workplace networking might be deemed 'top performers'.

The interview process of these kind of companies also often falls into a problematic pattern where interviewers pose esoteric questions they've recently researched or that happen to align with their narrow specialization from years in the same role. This turns technical interviews into more of a game of matching specific knowledge rather than evaluating problem-solving abilities, broader engineering competence or any notion of 'performance'.

Let's be honest: how many people can truly separate personal feelings from performance evaluation? Even with structured review processes in place, would most evaluators give high marks to someone they personally dislike, even if that person consistently delivers excellent work?

Could you please describe how the unlimited vacation policy worked? How did people feel about it and whether they were anxious regarding using it (afraid that it will reflect on them badly when they take "too much" time off)?

Huh? How is that nice? Does performance and compensation not correlate in your ideal world, or am I misunderstanding it?

Here's a thought experiment: pretend that Netflix is lying and that their employees are not actually made up of the top 10% of talent industrywide. Let's for this thought experiment assume the realit is that they have slightly above average talent because Netflix pays slightly above industry average.

But now they've convinced those employees that they're not just slightly above average, they are like elite NFL players. And that means they have to work like elite NFL players. Netflix convinces their employees to work XX% harder with longer hours than the rest of the industry because they think they are elite.

"Only amazing pro athlete geniuses can work here" is way more motivating than "You have to work yourself to death with extra hours to make quota or you're fired!" because it's a manipulation of the ego.

I think this thought experiment is closer to reality than Netflix or their kool-aid-drunk employees will admit, and that Netflix's "pro athlete" culture is worker-harming psychological manipulation.

Also, since when is telling people they're among the best at what they do "worker-harming psychological manipulation?"

I'm not saying that everyone on a 360 review process does that. But the incentive is there and it's working against fair reviews.

Even if human talent have a Pareto distribution (which is not clear), the people employed by a company are a selected sub-set of that population, which would likely have a different distribution depending on how they are selected and the task at hand.

I think that any of these simplified distributions are likely not generalizable across companies and industries (e.g. productivity of AWS or Google employees are likely not distributed like employees of MacDonalds or Wal*Mart because of the difference in hiring procedures and the nature of the tasks.)

Get hard data within the companies and industry you are in and then you can make some arguments. Otherwise, I feel it is too easy to just be talking up a sand castle that has no solid footing.

This exact statement applies to the practice of Gaussian performance ranking. It is pure corporate politics, it isn't founded in sound statistics.

The present author at least provides multiple sources of statistical evidence for their beliefs, if you read the footnotes.

IQ is famously Gaussian distributed... mainly because it's defined that way, not because human "intelligence" (good luck defining that) is Gaussian.

If you look at board game Elo ratings (poor test for intelligence but we'll ignore that), they do not follow a Gaussian distribution, even though Elo assumes a Gaussian distribution for game outcomes (but not the population). So that's good evidence that aptitude/skill in intellectual subjects isn't Gaussian (but it's also not Pareto iirc).

Elo ratings for active players are close to Gaussian, but not quite, they show a very clear asymmetry, especially for OTB old school Elo (compared to online Glicko-2).

The active players restriction is a big one and one I didn't assume I in my original statement.

100%. I was going to write something similar.

> If you look at board game Elo ratings (poor test for intelligence but we'll ignore that), they do not follow a Gaussian distribution, even though Elo assumes a Gaussian distribution for game outcomes (but not the population). So that's good evidence that aptitude/skill in intellectual subjects isn't Gaussian (but it's also not Pareto iirc).

Interesting, yeah, Elo is quite interesting. And one can view hiring in a company as something like selecting people for Elo above a certain score, but with some type of error distribution on top of that, probably Gaussian error. So what does a one sided Elo distribution look like with gaussian error in picking people above that Elo limit?

It's basically a Gaussian with a very long right tail.

Big caveat here is that these are the ratings of weekly active players. If we instead include casual players, I suspect we'd have something resembling a pareto distribution.

Both for sure. If you don't practice you will never rise much about bad. But if you are bad and not progressing you won't play much because it isn't rewarding to lose.

One needs to almost figure out those with low ELO ratings, what is their history compared to the number of games played and see if they were following an expected ELO progression.

I wonder if you can estimate with any accuracy where a player will eventually plateau given just a small-ish sampling of their first games. Basically estimate the trajectory based on how they start and progress. This would be interesting. Given how studied Chess is, I expect this is already done to some extent somewhere.

E.g. if there are N loci, and each locus has X alleles, and some of those alleles increase the trait more than others, the trait will ultimately present in a Gaussian distribution.

i.e. if there are lots of genes that affect IQ, IQ will be a Gaussian curve across population.

Doesn't this assume a linear relationship between relevant alleles and the given trait though?

> But there are low-performing employees at large corporations; we’ve all seen them. My perspective is that they’re hiring errors. Yes, hiring errors should be addressed, but it’s not clear that there’s an obvious specific percentage of the workforce that is the result of hiring errors.

I think it is clear that we expect a certain percentage of hiring "errors". And that they are not binary but rather a continuum. And that there are lots of other factors like employees who were great when they were hired but stopped caring and are "coasting" or just burnt out, who got promoted or transferred when they shouldn't have been and are bad at their new level/role, and so forth.

The Pareto distribution isn't particularly relevant here, because a hiring process isn't trying to get a whole slice of the overall labor market with clear cutoffs. For any position, it's trying to maximize the performance it can get at a given salary, and we have no reason to expect the errors it makes in under- and over-estimating performance to be anything but relatively symmetric.

So a Gaussian distribution is a far more reasonable assumption than a slice of the Pareto distribution, when you look at the multiplicity of factors involved.

It's not an assumption. See the evidence referenced in the footnotes.

When A doesn't like B it doesn't mean A or B are necessarily unfit to work at the company, but it generally results in the subordinate being framed as underperforming or not being given the resources to perform.

In a previous job I modelled this and concluded that due to measurement error and year-over-yead enrichment, Welchian rank-and-yank results in firing people at random.

His performance at GE was 100% fueled by financial leveraging that blew up in 2009, basically killing the company. Nobody should be taking management lessons from this guy.

Rank and yank is simply about lowering labor costs, once the business has achieved a significant moat and no longer needs to focus solely on growing revenues. A negotiating tool for the labor buyer, due to the continuous threat of termination.

I found that team composition and role assignment matters a lot, at least if you hire people who are at least above a certain bar. Match a brilliant non-assertive coder with someone who is outgoing and good at getting along and at least decent coder, and the results from the two outperform generally either of them individually.

You can bring out the best of your employees or you can set them up against each other. This either brings everyone up or brings everyone down.

But I think it is also cheaper to make great teams rather than just doing brutal firings all the time. But it may be a micro-optimization?

A researcher friend at a previous job once mentioned that in grad school he and several other students were assisting a professor on an experiment and each grad student was given a specific molecule to evaluate in depth for fitness for a need (I forget what at this point) and one of the students had a molecule that was a good fit while the others did not - that student was credited on a major research paper and had an instant advantage in seeking employment as a researcher while the other students did not. That friend of mine was an excellent science communicator and so fell into a hybrid role of being a highly technical salesperson but tell me - what metrics of this scenario would best evaluate the researchers' relative performance? The outcome has a clear cut answer but that was entirely luck based (in a perfect world) - a lot of highly technical fields can have very smart people be stuck on very hard low margin problems while other people luck into a low difficulty problem solution that earns a company millions.

Ignoring luck or trying to control for it would be a mistake.

Performance is "visibly doing the things that the company rewards during the performance review process".

Theoretically, each role at a company should have a set of articulated accomplishments that are expected. (This is sadly often not the case.)

But you're right that the subjective nature of "performance", and the lack of a clear numerical scale, are a difficulty of the entire process!

The amount of money the manager is willing to match is the perceived value to the company. This is how the company actually behaves (we know for sure whether they match the offer or not) and that behavior implies a value to the company, regardless of what anyone says in performance review season.

This assumes the manager is irrelevant here. But we all know that different managers (or non-managers) can communicate value differently for the same employee. So this metric can't be solely measuring the value of the employee.

The action to not match an offer implies that the company believes the employee adds less value than their new offer. If the company believed the employee was adding more value than their new offer, they would match the offer to keep the employee.

A company isn't a single rational agent. It's made up of people performing different functions. But behaving irrationally is a categorically bad thing for the company to do, and the leadership has a fiduciary duty to prevent the company from acting irrationally or otherwise not in its own self interest.

The manager may matter here, but the leadership is supposed to be creating a management structure such that the company acts rationally to make progress towards set goals.

But in my experiance employee perf evals are more political than data based.

At the end of the day a lot of mgmt at BigCo, esp these days, wants that 10% quota for firing as a weapon/soft layoff and the "data" is a fig leaf to make that happen. More generously it's considered a forcing function for managers to actually find underperformers in their orgs, even if they don't exist. Either way it's not really based on anything other than their own confirmation bias.

IME the scrutiny of perf evaluation is basically tied to the trajectory of the company and labor market conditions. Even companies with harder perf expectations during the good times of ~2021 relaxed their requirements.

What's interesting is that school grades often doesn't follow a normal distribution, especially for easier classes. I suspect that getting an "A" was possible for 95%+ of students in my gym class and only 5-10% of the students in my organic chemistry class.

In the same way, some jobs are much easier to do well than others.

So we should expect that virtually all administrative positions will have "exceptional" performance, which is to say that they were successful at doing all of the tasks they were asked to do. But for people who's responsibility-set is more consequential, even slightly-above average performance could be 10x more meaningful to the company.

Another example where this analogy stops working is that in school the students usually get the same/comparable assignments, that is somewhat the point of those. As the goto hard-problem-person at my current workplace I am pretty sure that it is absolutely impossible to compare my work to the work of my collegue who just deals with the bread and butter problems, it isn't even the same sport. How would you even start doing a productivity comparison here, especially if you understand 0 about the problem space

Would that be cool? We could posit the implications of all sorts of improbabilities. But I feel more strongly about how cool it would be that P = NP.

All this aside, being laid off sucks - being pushed out, even when you're a high performer, sucks even more. The truth is that "data science" does not help you process grief the way reading Dostoevsky does, so maybe getting an A in your liberal arts education is valuable even when you are working as a software developer.

There are certainly times that you would want them included, but those can be classified under "budgeting," not gaining insight on a workforce.

1. There is a certain skill in communicating all the important things you've done, we shall lump likability + politicking into this one for convenience.

2. There is a premium that is placed on shiny new features and saving the day heroics. A lot less priority is placed on refactoring and solving the problems before they require heroics.

3. Finally there are individual's technical and self-management skills. I.E. it's important to work on important things and be good at it.

If the company would be dysfunctional without that janitor or software engineer, and not bring in as much revenue as a result, it sounds like the model that attributes close to zero revenue to them is already dysfunctional. If the company can't function without the janitor, then a significant portion of the revenue of the company should be attributed to them.

If you are in a hiring freeze or not promoting, most of the curve should shift right, assuming you are hiring great people. They will probably perform better quarter after quarter. Some might counter-argue that if everyone performs better, this should be the "new expectation," but I disagree: the market sets expectations.

If you have someone at a senior level with expectations of staff, for example, they won't be in the company for long. I hired many great engineers who later said they only looked for a new job because they were never promoted despite being overperformers.

Height is generally not considered to be Gaussian and this is exactly the kind of statistics mistake the author seems to be accusing employers of. Adult height is somewhere between Gaussian and bimodal.

Perhaps better stated as "adult human height is approximately Gaussian for a given biological sex", with an asterisk that environmental factors stretch the distribution.

I love the anecdote that people born in the American colonies came back to England to visit family, and were remarkably taller compared to their cousins due to environmental factors.

The problem, from a company's perspective, is you probably need to retain everyone at least five years, and actually give them a wide variety of assignments in that time, to really get any usable data about their long-term prospects.

The only people who benefit from performance reviews are shareholders whose price pops when layoffs happen, and those who game the system for their own political ends. Top talent never really thrives in these, because they’re too busy doing actually meaningful and important work.

In case people want to read more about this:

https://www.essentiallysports.com/nba-active-basketball-news...

https://marginalrevolution.com/marginalrevolution/2024/08/go...

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In a properly functioning team, people perform different, discrete roles which are probably not entirely understood by other team members or management.

2) the are also not aligned with the replacement cost of employees because the religion of management is that labor is effortlessly replaceable and low value

3) employee retention is not aligned with corporate performance in Machiavellian middle management, it is aligned with manager promotion for things like loyalty and maintaining fiefdom power, budgetary size, headcount, etc

4) there are no absolute or ever directly derived metrics in software development that have ever worked, to say nothing of other positions

Those are off the top of my head.

That being said, I like to think that startups growing into large corporations have an opportunity to be better when it comes to things like performance management.

Most of the big companies just throw endless interviews, high pressure firings, and a lot of money at the problem and make the people below them solve the rest of the problems.

They see how much they are paying for the mess, but any medium term effort is torpedoed because of all the other things the business focuses on (lack of resources for the process and training), and other powerful individuals who want to put their own brand on hiring and firing who have significantly more ego than sense.