Plan Your Career Around Problems

I see a lot of people who want to work in cybersecurity. I said the same when I got started, but now I think this is the wrong way to frame things, especially because of AI.

My thinking now is that working in an “industry” is too vague and unfocused to give you any stability in a world where AI can do most jobs.

I think the only way to get stability in this future—or at least as much as possible—is to be very talented at working on really hard problems.

Crucially, that requires that you can clearly articulate those problems, and describe how your approach and results are superior to alternatives.

My case

The past version of myself would’ve said I wanted to be a security expert and have a long, fruitful career in security.

I would’ve said something like:

And then my career would sort of accidentally fall into that direction, and I would hopefully become known for those things.

That path worked for me over the last couple of decades, but I don’t think it would work for me again. Which is why I don’t recommend it.

A different model

I think what people should say today, is that I am fascinated by all sorts of security problems, but my favorites include the Many Eyes problem within open source, the time delay in Attack Surface Management, and the problem of not knowing who is doing what in a world of nonhuman identities, or the problem of establishing trust in a world where anything could be deepfaked, i.e., how do you know if you’re actually interacting with a person you think you are interacting with?

These are still security. They’re still cyber. And in some ways it’s no different than where I started with my legacy narrative. But there is a crucial difference in that this doesn’t lead with “wanting to be in security”, and then rattling off some potential, ambiguous interests.

Instead, this narrative says I like security problems, and here are some examples of ones I want to work on.

I think this approach is going to be far more robust in competition with other job-seekers, and with AI. And perhaps even more importantly, it’s a way to clarify direction for those entering the field.

Get fascinated by problems.

• That fascination leads to curiosity

• That curiosity leads to work

• That work leads to skill

• And that skill over time leads to competence

Comparing old vs. new narratives

Here are some examples.

The difference between these two is small but massive.

To a hiring manager, the OLD version sounds like someone who needs guidance and handholding, which virtually no company has time to give anymore.

The NEW versions capture someone who is self-motivated by purpose, and who is using their skills in a tangible way to solve real problems.

That’s someone to hire. And it’s also someone to replace last with AI.

Summary

This is an extraordinarily bad time to not know what you want to do with your career. AI is coming for those people first.

This is why I’m so obsessed with questions and problems. They provide clarity, and they focus curiosity and talent to an edge that produces results.

So that’s my advice. Don’t think about entering an industry.

Think about problems that you want to solve because they fascinate you, and articulate/pursue the different ways that you intend to address them.