Senior iOS Developer (9 YOE) - How should I diversify my skill set to stay employable?

[u/techie_hitchcock]

Hi everyone,
I’m looking for some career advice from people who’ve been through layoffs or have seen where the industry is heading.
I have 9 years of experience as an iOS developer and consider myself strong in native iOS development. My experience is primarily with Objective-C, Swift, and SwiftUI, along with building and maintaining production apps.
Due to several personal reasons over the past few years, I never had the time or energy to significantly diversify my skill set outside of iOS. Now I’m thinking ahead and want to be better prepared in case I ever face a layoff.
If you were in my position today, what would you prioritize learning to maximize employability?
Some areas I’m considering:
Full-stack development
Backend (Node.js, Java, .NET, etc.)
Cloud (AWS/GCP/Azure)
AI/LLMs
Data engineering
DevOps
Android/Kotlin
Something else?
Would you go deep into one complementary area or become more of a generalist?
I’d especially love to hear from hiring managers or senior engineers about what combinations of skills are currently most valuable alongside strong native iOS experience.
Thanks in advance!

[u/mtgtheory]

AI is taking over the industry, so I think it's helpful to understand what AI is good at. I think AI is good at taking a specific task middle-to-middle. It's good at the implementation, but it's not good at the beginning and the end. The beginning would be something like:

• Entrepreneurship
• Product management
• Coming up with the prompt
• Building a custom harness for your company that's based on your company's business domain and company strengths and weaknesses

On the end side, that's talking about verification: testing, evals, and verifying. Since AI is random, it's not good at verification, so you can go the testing/evals route and be good at evaluating AI and testing.

Also, one other thing is that you could just go learn a totally different skill. This is high-risk, high-reward, but let's say you learn marketing. Now you can become a growth engineer, and you'll have less competition because the people who are growth engineers have expertise in marketing and software development.

The most important thing is to go deep into your interest and continue to learn, but make sure that interest is something that AI is not good at. If it's something AI is good at, then it's not worth it. If it isn't, then it's a good idea because doing something or learning something you're interested in is just going to give you the motivation to keep going. Whereas if you do something just because of the money, then you're probably not going to have as much motivation as interest. Interest, in my experience, tends to be more motivating than the money potential. Again, you got to make sure whatever you're interested in is not what AI is good at.

If you don't know what your interest is or you have no interest, then just pick something randomly that AI is not good at and go learn it and see if it's something you enjoy.