What's worth learning in an AI era?

I grew up when the internet in the US was just beginning its widespread adoption and I remember the first time we got high speed internet (we were the first on the block). It was unlike anything else we’d had, and put a trough of information quite literally at our fingertips.

The internet era question for skill building quickly became a question of ‘how do I Google to learn this?’. Those that are good at searching for the answer built a system where they can quickly sift through different materials, links in this case, to find the information they need. One of my favorite interview questions was asking someone how they search for something they don’t know, and seeing the thought process for how people reach the desired answer.

When you combine this with good rote understanding, you had a superpower for executing work. But in the AI era, how much of this is still relevant?

The key question now is ‘do I need to learn this’?

LLM’s can return responses back to us faster than anything to date, and in increasingly high reliability. I’d argue that the main question now isn’t ‘how do I find the information’ but ‘do I need to learn this information’? Being able to situationally apply this continues to show how high performers continue to outperform with AI, and it builds on the first two skillsets of architectural understanding, and the ability to know how to find the right information.

Take an example where you’ve asked an LLM to write a new feature for an application based on a prompt. There are hard skills we’re all continuing to learn about prompt and context engineering. But the real challenge comes in managing the response.

In the example prompt, we may ask the LLM to create a new login feature for a website. The LLM can build any number of pieces of this including authentication patterns, deployment patterns, and things like jwt tokens to make sure a user can call other services. But let’s assume we’ve never worked with login and auth before. What of this do we need to learn, if anything? This is the new challenge when we can create faster than ever.

My learning plan

Understanding what to learn is quickly becoming a new and desirable skillset, in particular when you have do this rapidly. Here are my thoughts on how I do this:

1. Being a consultant at heart, I’ve always valued the T shape of knowledge. If I’m doing something with an LLM, I usually try to minimally understand the high level concepts. So in this example above, I’ll read up on basic auth patterns or ask the agent to explain its coding behavior to me.

2. Next, I usually ask the question ‘do I need to do this once, or will I need to do this repeatedly’. This question helps me prioritize if I need to move on to increase my knowledge depth.

3. If I need to increase my depth, I’ll typically keep asking the agent or move back to ‘Googling that’ to find some informative articles. I leverage adjacent skills in my ‘T’ to increase my knowledge depth. For something like authentication, I will cross apply domain knowledge on testing to create different conditions.

4. If I need to increase by breadth in a topic that I don’t know but should, I find myself using more NotebookLM, or Youtube videos on in the background to read up. Some of these are fun topics like doing home work where I need to get better at breadth in order to improve my ability to prompt. The generalized T here does still go a long way in providing incremental value to LLM usage.

Applying this in your work

With AI I think it’s important to highlight that, just like ‘Googling that’, there’s no single strategy that is the best, although there are probably 5 to 10 that work well. A lot of this comes down to practice and repetition. In AI this can feel exhausting with the amount of information you have to go through in much shorter windows. It also feels somewhat evolutionarily opposed: we still need 30 days to build habits, so use it but remember this is a marathon, not a sprint.

Hope this is useful! Happy Tuesday!