I recently finished the excellent book Kochland. This isn’t my first interest in Koch—I read The Science of Success by Charles Koch himself a couple of years ago.
Charles Koch inherited a tiny company in 1967 and turned it into one of the world’s largest ones. That’s impressive! Just a quick disclaimer just to get it out of the way. You may know the Koch brothers as the climate deniers who funded the Tea Party. I don’t understand this disconnect between being so brilliant in one field, and extremely ignorant in another. But my curiosity tells me there’s something worth learning from most notable people, despite what I may think of their opinions and Koch Industries turns out ot be a particularly interesting case study.
This is a blog post originally featured on the Better engineering blog. If you want to link to this article or share it, please go to the original post URL! Separately, I’m sorry it’s been so long with no posts on this blog. Between kids, moving, and being a startup CTO, I’ve been busy. I have a few posts coming down the pipe though, so stay tuned…
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.
Ok, so I have to first preface this whole blog post by a few things:
I really struggle with the term microservices. I can’t put my finger on exactly why. Maybe because the term is hopelessly ill-defined, maybe because it’s gotten picked up by the hype train. Whatever. But I have to stick to some type of terminology so let’s just roll with it.
This blog post might be mildly controversial, but I’m throwing it out there because I’ve had this itchy feeling for so long and I can’t get rid of it. I respect it if you want to disagree vehemently, and maybe there’s something both of us can learn.
I have a weird story. My first “real” company, Spotify, used a service-oriented architecture from scratch. I also spent some time at Google which used a service-oriented architecture. So basically since 2006 I’ve been continuously working in what people now call a “microservice architecture”. It didn’t even occur to me that some people might want to build things as monoliths. So I guess I’m coming at it from a different direction than many other. Either way, there were particular non-standard reasons why Spotify and Google had to do this that I’ll get back to later.
There are often close relationships between top level business metrics. For instance, it’s well known that retention has a super strong impact on the valuation of a subscription business. Or that the % of occupied seats is super important for an airline. A fun little toy model that I can up with generates a curious relationship between conversion rates and revenue.
How hard can it be to compute conversion rate? Take the total number of users that converted and divide them with the total number of users. Done. Except… it’s a lot more complicated when you have any sort of significant time lag.
I just realized last Thursday that I have spent two full years at Better, incidentally on the same day as we announced a $15M round led by Kleiner Perkins. So it was a good point to reflect a bit and think back – what the F led me to abandon my role managing the machine learning team at Spotify? To join some random startup in the world’s most boring industry? So here’s my justification why I love being where I am:
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
Here’s a conclusion I’ve made building consumer products for many years: the speed at which a company innovates is limited by its iteration speed.
I don’t even mean throughput here. I just mean the cycle time. Invoking Little’s law this is also related to the total inventory of features not being deployed yet.
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.
