Discover more from Matt Rickard
If you are to do important work then you must work on the right problem at the right time and in the right way. Without any one of the three, you may do good work but you will almost certainly miss real greatness.
Sequencing is doing things in the right order.
At a macro level, it's about inflection points – Uber couldn't have existed without Google Maps and consumer GPS. But the tougher to solve and more interesting type of sequencing is when the goal is obvious, but the path unknown.
It's difficult because you can't always mimic past successes – an olympian's workout plan might be optimal, but not for someone just starting out. It's also difficult because you can't even copy the order – the temporal aspect of "the right time" means that the "right order" is always changing.
Some examples of sequencing across different disciplines.
In education, learning from First Principles can be one of the best foundations for breaking down and solving complex problems. First-principles says you can't discover higher-order abstractions without first understanding the foundation.
In product management, figuring out the right order can optimize processes. What is the limiting step? What work can be done in parallel (also a good question to ask if you're a distributed systems engineer)?
In go-to-market strategy for startups, it matters what you do first. What technical wedge do you build first that catalyzes future expansion? What market niche do you win first that sets you up for the next, larger segment? For example, Amazon couldn't build "the everything store" on Day 1, so it started with books.
In technical infrastructure, it isn't easy to build products at a much higher abstraction than the current standards. Was Heroku too early? Too high up the stack compared to the basic IaaS primitives of 2007? Technical layers must be built in order.
In startup architecture, picking the right technology at the right stage. For example, Kubernetes may be the "correct" long-term answer, but don't use Kubernetes yet – teams should gradually make choices that bring them there.
In crypto, projects needed to start as decentralized and open as possible. How else could strangers trust each other? Now that the networks are larger and the technology more advanced, parts of it can be optimized through centralization. Products establish trust in alternative ways – through time in the market and past behavior.