Compensation has always been one of the most confusing parts of management to me. Getting it right is obviously extremely important. Compensation is what drives our entire economy, and you could look at the market for labor as one gigantic resource-allocating machine in the same way as people look at the stock market as a gigantic resource-allocating machine for investments.
Let’s consider a toy model where you’re hiring for two things and that those are equally valuable. It’s not very important what those are, so let’s just call them “thing A” and “thing B” for now. For one set of abilities, the scatter plot looks like this:
When I started building up a tech team for Better, I made a very conscious decision to pay at the high end to get people. I thought this made more sense: they cost a bit more money to hire, but output usually more than compensates for it. Many fellow CTOs, some went for the other side of the spectrum. This was a mystery to me, until it all made sense to me.
I have done roughly 2,000 interviews in my life. When I started recruiting, I had so much confidence in my ability to assess people. Let me just throw a couple of algorithm questions at a candidate and then I’ll tell you if they are good or not!
I do a lot of recruiting and have given maybe 50 offers in my career. Although many companies do, I never put a deadline on any of them. Unfortunately, I’ve often ended up competing with other companies who do, and I feel really bad that this usually tricks younger developers into signing offers. On numerous occasions, I’ve gotten an email halfway through the interview process
The easiest way to be a 10x engineer is to make 10 other engineers 2x more efficient. Someone can be a 10x engineer if they do nothing for 364 days then convinces the team to change programming language to a 2x more productive language.
A motivated 10x engineer in one team could be a demotivated 0.5x engineer in another team (and vice versa).
A average 1x engineer could easily become a 5x engineer if surrounded by 10x engineers. Engagement and work ethics is contagious.
The cynical reason why 10x engineers aren’t paid 10x more salary is that there is no way for the new employer to know. There is no “10x badge”.
…but also, a 10x engineer can go to a new company and become an 1x engineer because of bad focus / bad engagement / tech stack mismatch.
So unfortunately there’s less economic rationality for companies to pay 10x salaries to 10x engineers (contrary to what Google or Netflix says)
There’s no such thing as a 10x engineer spending time on something that never ends up delivering business value. If something doesn’t deliver business value, it’s 0x.
If you build something that the average engineer would not have been able to build, no matter how much time, that can make you 100x or 1000x, or ∞x. Quoting Alexander Scott: There is no number of ordinary eight-year-olds who, when organized into a team, will become smart enough to beat a grandmaster in chess.
Most of the 10x factor is most likely explained by team and company factors (process, tech stack, etc) and applies to everyone in the team/company. Intra-team variation is thus much smaller than 10x (even controlling for the fact that companies tend to attract people of equal caliber). Nature vs nurture…
I’ve never met the legendary “10x jerk”. Anecdotally the outperforming engineers are generally nice and humble.
Don’t get hung up on the exact numbers here, it’s just for illustration purposes. I.e. someone introduced a bug in the trading system of Knight Capital that made them lose $465M in 30 minutes. Did that make it a -1,000,000x engineer? (and btw it had more to do with company culture). The numbers aren’t meant to be taken literally.
I haven’t mentioned what I’m currently up to. Earlier this year I left Spotify to join a small startup called Better. We’re going after one of the biggest industries in the world that also turns out to be completely broken. The mortgage industry might not be the #1 industry you pictured yourself in, but it’s an enormous opportunity to fix a series of real consumer problems and join a company that I predict will be huge.
Here’s a problem that I used to give to candidates. I stopped using it seriously a long time ago since I don’t believe in puzzles, but I think it’s kind of fun.
Let’s say you have a function that simulates a random coin flip. It returns “H” or “T”. This is the only random generator available. How can write a new function that simulates a random dice roll (1…6)?
Is there any method that guarantees that the second function returns in finite time?
Let’s say you want to do this $$ n $$ times where $$ n \to \infty $$ . What’s the most efficient way to do it? Efficient in terms of using the fewest amount of coin flips.
The first part is old, I think. The second and third part are follow up questions that I came up with.
