Build Broadly Applicable Skills
At each stage of life, I used my current skill and applied it in a new domain to learn another skill. I never started completely at zero; I was always building from a previous skill.
For example, I naturally inclined toward math and science in school, which led me into academic science. In academic science, I learned the skill of giving slide presentations. This translated well into making pitch decks and fundraising as an entrepreneur. That skill translated into evaluating pitch decks as an investor and a venture capitalist because I knew what went into them.
The academic computer code I wrote for my research was the skill foundation for writing bioinformatics code at my startup. That gave me the ability to architect a commercial system. Each step was like a lily pad hop where I leveraged everything I already knew. I never had to jump all the way to something totally unfamiliar.
Each step leads to the next. You bring immediate value from what you already know and build up skill in what you don't know that well yet. At each step, you have to be ambitious but not unrealistic. Be your own biggest fan and your own biggest critic. Have an incredible degree of realism about your own strengths and weaknesses, and work with others who complement your strengths and weaknesses.
As an engineer, scientist, investor, or entrepreneur, your theories are constantly tested against physical laws or the market. It’s bracing and humbling.
I tend toward discussing theory, but let's talk tactics and next steps because we must also be practical enough to get things done.
The ideal is you are a full-stack engineer and full-stack creator. That's using both your right brain and left brain. For engineering, that means you master computer science and statistics. Knowing physics and continuous math is also good. That's actually quite valuable, and you might need to use continuous math with AI nowadays.
Every domain has algorithms and data structures, which means computer science and stats are useful anywhere. You can walk into Walmart and start writing code for shopping carts or basket pricing. You can walk into American Airlines and write code for flight scheduling. You can walk into Pfizer and start on drug preparation and pharmaceutical manufacturing.
A productive mental model just keeps chugging out result after result. If you're good at math, you can do a lot of physics. You can go into fluid mechanics, electrodynamics, or astrophysics and start generating useful results.
Of course each area has domain knowledge, but math, computer science, and statistics are universal languages. I don't mean just learning programming and how to invoke library calls; I mean understanding the fundamental concepts of computer science and really wrestling with them.
That base of computer science and stats is really strong if you understand the theory as well as the practice. You need both, because you need to understand basic stuff like big-O notation and all your probability distributions. Now you can collect data and analyze it, right?
Computer science is theory; software engineering is practice. You could argue probability and statistics are theory, and data science is practice.
Computer science and stats are today what physics was to the early twentieth century. In the heyday of physics, physicists could go kick in the doors of any discipline and be like, “Hi, I'm gonna write down some equations and change your life.”
Becoming a full-stack creator is also important. Social media is about to become far, far, far more lucrative and monetizable than people realize. They think it's over or stagnant. But we’re just getting started. Many who want to build billion-dollar companies will have to also build million-person media followings.
Establish your broad skill and knowledge foundation, then find an area you want to work in. To choose your specific domain, pick an area you really care about for some reason. Genomics, robotics, crypto, permanent relocation; that’s what I did, anyway.
I'm super bullish on engineering. I think parents should be praised for putting massive emphasis on early engineering education.
Literacy means read access. Computer literacy means write access. Learning to code is more like learning to write than learning to read. Everyone should learn to code because: (1) it’s not that hard to learn the basics, (2) it’s useful even if just doing Excel macros, and (3) it’s valuable in every country. Not everyone is Turing, just like not everyone is Tolstoy. But universal computer literacy is like universal literacy.
For those who aren't good at engineering—well, get good at content. Content is as important as engineering nowadays. Every new company could have a founding influencer on par with the founding engineer. Today, a founding engineer and a founding influencer are building a company. Tomorrow, a founding influencer and a founding engineer may be building a country.
