[00:00:01] Hey there. This is Mark and welcome to the learning machine podcast. As the title suggests, the whole focus of this podcast is becoming a better learner. And while some of the episodes will be very tactical, this first one is very strategic. It's about how to decide where to invest your learning time. I want to share an analogy that my old roommate and I used to talk about actually. It's called engines versus power ups.
[00:00:29] So, if you ever played racing games as a child, you may have played somewhere you can buy power-ups or you can get new features or get a new car between levels depending on what you earned from each race before and there is often a choice of upgrading the engine of the car which will make it run better and faster all the time or getting power-up like getting some nitro that will give you an incredible boost when you want it. But after a short time, the boost is used up and then you're back to where you were.
[00:01:08] So in this analogy, learning something like mathematics or a human language is definitely upgrading your engine. If you learn Spanish now, that skill will still be useful in 50 years. Whereas the other extreme if you learn something like a very specific front end framework, like say you learned Backbone.js in 2013 which I did, then you'd discover as I have that that skill has much less value and 2019 and will probably have close to none in 2030. The same thing would happen if you'd focused on any specific skill with a short shelf-life. Especially if you learn something very detailed in a fast moving industry, or if you learn something that's news-dependent or dependent on a specific technology which gets replaced, that will happen. On the other hand, if you had been studying say pure math the entire time you, would just be accumulating more and more skill in that domain which has almost no shelf-life.
[00:03:02] In contrast, imagine if I had spent all of that time that I spent learning Backbone.js, learning about compiler construction or learning about statistics. Well in both of those fields, there are great jobs but there are also people who have been doing them for 15 years and accumulating more and more relevant experience that no newcomer could really compete with. So, I would have to come in at a probably much worse salary and have to have more impressive credentials even to get started. And something like say learning Java, the programming language, is kind of in between, like programming languages definitely don't move as quickly as frameworks but they're also not they're not more or less eternal like math is, or like very low level science related things or even computer science.
[00:03:57] So my investment in what I call "nitro" in this case, was very useful. It enabled me to make a career change but that's not the only time where it would make sense. There are a lot of times when there's a skill that's highly relevant or highly sought after or under-supplied where if you spend time learning it you can actually make a career change or you can widen your social circle to people that otherwise you would never have connected with. So there is value in pursuing skills with the short shelf life. That's why so many people do it and why there's so many courses for people doing it.
[00:04:35] So here's what I think is the optimal long term strategy: Of the time and money that you plan to invest into learning, invest the vast majority of it into building evergreen skills that will help you decades from now. But when you see an opportunity to make a discontinuous jump in your career, in your social circle, in your relationships, in your health or in anything that would similarly benefit your life as a whole, then shift your focus away from the engine and invest in however much nitro it takes to successfully make the jump up to that next level. And once you're secure in that situation, then shift back to building that engine. I hope you find this analogy useful and thank you for listening to the learning machine.