legibility. When you have a complex system, whether that's a company with thousands of engineers or a world with many billions of dollars going to aid work, the system is too complex for any decision maker to really understand, whether that's an exec at a company or a potential donor trying to understand where their money should go. One way to address this problem is by reducing the perceived complexity of the problem via imagining that individuals are fungible, making the system more legible. That produces relatively inefficient outcomes but, unlike trying to understand the issues at hand, it's highly scalable, and if there's one thing that tech companies like, it's doing things that scale, and treating a complex system like it's SimCity or Civilization is highly scalable. When returns are relatively evenly distributed, losing out on potential outlier returns in the name of legibility is a good trade-off. But when ROI is a heavy-tailed distribution, when the right person can, on their paternity leave, increase company revenue of a giant tech company by 0.7% and then much more when they work on that full-time, then severely tamping down on the right side of the curve to improve legibility is very costly and can cost you the majority of your potential returns.
Thanks to Laurence Tratt, Pam Wolf, Ben Kuhn, Peter Bhat Harkins, John Hergenroeder, Andrey Mishchenko, Joseph Kaptur, and Sophia Wisdom for comments/corrections/discussion.
A friend of mine recently told me a story about a trendy tech company where they tried to move six people to another project, one that the people didn't want to work on that they thought didn't really make sense. The result was that two senior devs quit, the EM retired, one PM was fired (long story), and three people left the team. The team for both the old project and the new project had to be re-created from scratch.
It could be much worse. In that case, at least there were some people who didn't leave the company. I once asked someone why feature X, which had been publicly promised, hadn't been implemented yet and also the entire sub-product was broken. The answer was that, after about a year of work, when shipping the feature was thought to be weeks away, leadership decided that the feature, which was previously considered a top priority, was no longer a priority and should be abandoned. The team argued that the feature was very close to being done and they just wanted enough runway to finish the feature. When that was denied, the entire team quit and the sub-product has slowly decayed since then. After many years, there was one attempted reboot of the team but, for reasons beyond the scope of this story, it was done with a new manager managing new grads and didn't really re-create what the old team was capable of.
As we've previously seen, an effective team is difficult to create, due to the institutional knowledge that exists on a team, as well as the team's culture, but destroying a team is very easy.
I find it interesting that so many people in senior management roles persist in thinking that they can re-direct people as easily as opening up the city view in Civilization and assigning workers to switch from one task to another when the senior ICs I talk to have high accuracy in predicting when these kinds of moves won't work out.
On the flip side, there are managers who want to maximize the return to their career. At every company I've worked at that wasn't a startup, doing that involves moving up the ladder, which is easiest to do by collecting as many people as possible. At one company I've worked for, the explicitly stated promo criteria are basically "how many people report up to this person".
Tying promotions and compensation to the number of people managed could make sense if you think of people as mostly fungible, but is otherwise an obviously silly idea.[return]
There are advantages to a system where people don't have power, such as mitigating abuses of power, various biases, nepotism, etc. One can argue that reducing variance in outcomes by making people powerless is the preferred result, but in winner-take-most markets, which many tech markets are, forcing everyone lowest-common-denominator effectiveness is a recipe for being an also ran.
A specific, small-scale, example of this is the massive advantage companies that don't have a bureaucratic comms/PR approval process for technical blog posts have. The theory behind having the onerous process that most companies have is that the company is protected from downside risk of a bad blog post, but examples of bad engineering blog posts that would've been mitigated by having an onerous process are few and far between, whereas the companies that have good processes for writing publicly get a lot of value that's easy to see.
A larger scale example of this is that the large, now >= $500B companies, all made aggressive moves that wouldn't have been possible at their bureaucracy laden competitors, which allowed them to wipe the floor with their competitors. Of course, many other companies that made serious bets instead of playing it safe failed more quickly than companies trying to play it safe, but those companies at least had a chance, unlike the companies that played it safe.[return]
I'm generally skeptical of claims like this. At multiple companies that I've worked for, if you tally up the claimed revenue or user growth wins and compare them to actual revenue or user growth, you can see that there's some funny business going on since the total claimed wins are much larger than the observed total.
Just because I'm generally curious about measurements, I sometimes did my own analysis of people's claimed wins and I almost always came up with an estimate that was much lower than the original estimate. Of course, I generally didn't publish these results internally since that would, in general, be a good way to make a lot of enemies without causing any change. In one extreme case, I found that the experimental methodology one entire org used was broken, causing them to get spurious wins in their A/B tests. I quietly informed them and they did nothing about it, which was the only reasonable move for them since having experiments that systematically showed improvement when none existed was a cheap and effective way for the org to gain more power by having its people get promoted and having more headcount allocated to it. And if anyone with power over the bureaucracy cared about accuracy of results, such a large discrepancy between claimed wins and actual results couldn't exist in the first place.
Anyway, despite my general skepticism of claimed wins in general, I found this person's claimed wins highly credible after checking them myself. A project of theirs, done on their paternity leave (done while on leave because their manager and, really, the organization as well as the company, didn't support the kind of work they were doing) increased the company's revenue by 0.7%, robust and actually increasing in value through a long-term holdback, and they were able to produce wins of that magnitude after leadership was embarrassed into allowing them to do valuable work.
P.S. If you'd like to play along at home, another fun game you can play after figuring out which teams and orgs hit their roadmap goals. For bonus points, plot the percentage of roadmap goals a team hits vs. their headcount growth as well as how predictive hitting last quarter's goals are for hitting next quarter's goals across teams.[return]
I've seen quite a few people leave their employers due to location adjustments during the pandemic. In one case, HR insisted the person was actually very well compensated because, even though it might appear as if the person isn't highly paid because they were paid significantly less than many people who were one level below them, according to HR's formula, which included a location-based pay adjustment, the person was one of the highest paid people for their level at the entire company in terms of normalized pay. Putting aside abstract considerations about fairness, for an employee, HR telling them that they're highly paid given their location is like HR having a formula that pays based on height telling an employee that they're well paid for their height. That may be true according to whatever formula HR has but, practically speaking, that means nothing to the employee, who can go work somewhere that has a smaller height-based pay adjustment.
Companies were able to get away with severe location-based pay adjustments with no cost to themselves before the pandemic. But, since the pandemic, a lot of companies have ramped up remote hiring and some of those companies have relatively small location-based pay adjustments, which has allowed them to disproportionately hire away who they choose from companies that still maintain severe location-based pay adjustments.[return]