I have been at Privy for a year. I’m proud of the team and product we’ve built, and I was excited to sit down and make a list of some of the new things I learned during my time here. Then I realized that most of these “lessons” would’ve been covered if I had just re-read everything ever written by Fred Brooks, Martin Fowler and Eric Ries…but that doesn’t make a good blog post.
So that got me thinking about the things I already sorta-knew that had been validated. Perhaps there was some pattern there. And so I made my first order list, which I present below.
I have learned virtually nothing about…
- Using a stack in the middle of the adoption curve: Ruby on Rails.
- Ruby/MRI is between 2 and 50x slower than running a static language on the JVM, but even a slight increase in developer productivity more than makes up for the operations cost.
- The advantage of using a really fancy stack (more cool factor for recruiting, etc) really doesn’t seem to compare favorably to the disadvantages (more uncertainty, smaller pool of technical talent).
- The evidence that startups regularly die due to technology stack is vanishingly flimsy, so no need to dwell here.
- Building a local team.
- Geographically distributed teams and getting on the bandwagon of “work anywhere cuz we have Slack lol” seems all the rage today, but the early team is more important than the early product, and the best teams are in the same place every day.
- Resisting the urge to go remote has been something of a useful filtering mechanism: does this individual believe enough in our vision to consider moving here for the job?
- Having some really solid cultural values (or aspirations, as they may be) that aren’t totally groundbreaking.
- It’s more important that we live up to great values than come up with amazing ones. I’ll leave the latter to the management consultants.
- Using traditional engineering management.
- We basically do agile: there are weekly-ish sprints; we do higher level planning on a monthly basis; a couple times a year we work on a strategic roadmap. We write software specifications before we code, and we ship daily with continuous integration and lower test coverage than I’d like to admit. Yawn.
- We don’t use “flat” organizations or Holacracy or whatever trendy hipster management structure is in vogue. What the hell kind of problem is this trying to solve anyway? My theory is it’s got something to do with cool factor for recruiting, but I have a feeling the people trying this are no more certain than I am.
What’s the big meta lesson here?
If anything, it probably goes a little bit like this: the available levers to pull in a startup are numerous, but there are only a few that make a measurable difference. The things that are most likely to kill us are the things that kill most startups: having a subpar team, building a product that nobody wants, executing poorly on feedback loops, that kind of thing.
These are the things that, in Paul Graham terminology, make you “default dead” until you figure out how to get them right. And it’s critically important to realize that things like “what do we build?” and “who do we sell it to?” are the things that startups are doing “wrong by default” and need to diagnose and fix as quickly as possible.
But then there are the other things, like “how do we write a scalable system to respond to HTTP requests?” or “how should we manage engineering teams?” in which there are essentially no forced errors, and where (barring a well-articulated exception) the correct answer is the default one. So almost all of the risks here seem to be to the downside, and any upside is probably insignificant compared to the scale and difficulty of the hard problem: building a novel product under uncertainty.
There are certainly going to be exceptions to this. There are going to be teams that have figured out how to deviate from orthodoxy and are reaping benefits from it. I’m OK with this, and my theory is that it either doesn’t matter (e.g., they were going to be a success anyway) or it won’t rescue them (they’re doomed and they didn’t differentiate in a way that mattered).
 This isn’t all roses, since it biases us significantly towards younger folks who don’t have as many attachments, the net effect of which is…debatable, but obviously not lethal in a vibrant tech city like Boston.
 Example: One excuse I’ve used to provision real hardware in a real datacenter as opposed to just spinning up an EC2 instance is “I’ve done the math and TCO in AWS is literally 25X more expensive.”