After yesterday's blog post, The Laws of Subtraction, where I talk about how AI has ultimately helped me do more by crashing together seemingly discordant concepts and ideas, I was asked:

So let's get into it. Here's how to teach yourself AI (or anything, really) to build up a sense of technological agency in your life.

IRL Class Update: For anyone who happens to be in NYC, I'm teaching a "Prompts for Parents - How to AI-ify Your Home Operating System" course IRL next Friday, February 7. Details here.

It might seem like a weird place to start, but I've really found that unless you're actually curious to learn about a new thing, motivating yourself to put in the time and effort to learn about it will be close to zero. So you need to first need to figure that out for yourself. Are you feeling FOMO? Feeling the pressures of job or financial insecurity? Feeling old and want to prove you can still learn new tricks? Those might be really powerful motivations, you just need to tap into the right one.

In this podcast interview with Diana Chen from Rehash DAO, I shared more about my own so-called "learning stack" and a few tricks I learned about how to motivate myself to learn something new (in this case, crypto). But I've since applied this same framework in how I've been learning AI. Tune in at minute ~37 to hear me talk through this curiosity and motivation-based learning framework a bit more.

"I would use AI, I just don't know what tools to use." This is often the thing that people share with me as their biggest barrier to not trying. I see this as a pretty weak excuse (just type that question into google, or any LLM, and you'll see hundreds of tools to try), but I do empathize with the problem of not knowing which tools to trust, or where to start.

I'll share some tools to try (because you asked). But I'll also note three important caveats:

1. This list of tools will surely go out of date. With the pace that AI is moving, the half life of this list might be only one week.

2. You still need to put in the time and the energy to play around with a tool. The tools don't matter so much as your willingness to commit to using at least 1 or 2 of these really, really deeply.

3. Tools are just a proxy for discovery and tinkering. If you really want to learn a thing, you need to learn how to find your way to new tools on your own, too.

Pick one from each column. Go deep. Have fun.

This is often the reason why people fall off in the learning curve of anything new: They don't find a relatable problem or application to apply that new thing. They just think that they should learn something. But as it turns out, that's not a sticky enough reason to learn

For instance, when I worked at a VC firm, I got in my head that I needed to level up on finance, so I signed up for an 8-week evening crash course on corporate finance. I did not have fun in that crash course. Nor do I remember anything that I learned, except for how to read through a public company Form 10-K (which, actually is pretty helpful).

The reason that nothing stuck was that I had no direct application for my learning. Flash forward to last year, when I suddenly needed to help one company allocate a $30M grants budget and another figure out their runway and revenue projections for the next 18 months. Turns out, that was a pretty good forcing function for me to finally pick up a few best practices on budgeting and P&Ls.

If you want to stick with learning and applying your learning with AI, you need to ask yourself:

That problem could be work-related or home-related. It can be personal or professional. But you need to start from a problem-solving lens. I talk more about how to think about defining a so-called "AI-shaped problem" here.

For instance, right now I've been thinking a lot about how to AI-ify things in my home operating system, like streamlining the annual planning work that my husband and I do together. So I'm teaching a class on this next week. (You're welcome to come if you're IRL in NYC.)

Once you figure out one round through a learning cycle, you just do it all again to learn the next thing. Have fun. Remember: Learning is the ultimate infinite game.