I ve had a lot of discussions with peers about the possibility of us being replaced via ai agents and how to stay relevant, given a future where AI intelligence keeps improving to the point of being capable of replacing potentially any job.My personal strategy is specialization, because I ve noticed that I cannot work with AI in hard domains that I haven t got a background in. For example, I ve tried to use claude opus to understand a phd thesis in quantum physics from a friend. My background is in engineering and later compilers/static analysis. Despite opus helping a lot to give me the high level idea behind it, I could see a few problems with my lack of background: - I couldn t verify if opus was right or wrong in its explanations - Even when asking the AI to simplify explanations, there was so much prerequisite knowledge I needed to absorb that there was little point to bother with its output - It was very hard to collaborate with the AI to understand what predictions can be made from the PhD. The model could produce a lot of output but I didn t really understand its answers. Asking follow-up questions did not really solve the issue because it felt like I was missing a mental model.My thinking is that, no matter how much LLM intelligence grows, for sectors with inherent complexity, people will still need specialized expertise to understand, evaluate and use their outputs. I also doubt that the most efficient future is one where humans don t understand the outputs and delegate everything, because it will be both be hard to understand if the machines are aligned to the benefit of their users.So specialization sounds like a good strategy for the future.I d like to hear some opinions around this topic. Have you observed the same? Do you disagree based on other experiences/data?