But, to the claim of how to think in the age of LLMs, I think it’s worth contrasting the other approach, which is a dialog with the LLM; two things are needed IMO to keep things honest:
First, you need to keep asking the LLM to explain things, until you understand what it did. Lots of angles here; have the LLM make a design/product doc, write one-off architecture explainers, write throwaway data-viz tools to explain the data distribution. Then ask a fresh copy to adversarially review the core concepts. Try asking a different frontier model too.
Second, you need to push for “proof”, in other words, ask for demos and try it yourself. Imagine yourself a CEO or VC; how do you know if the output of an entire org is vaporware? One thing to beware is launching into a field where you don’t have expertise and can’t actually validate the output. This takes time of course, but for example if you write a stress test script and get CPU/QPS numbers, these complex validators are IME quite reliable when you apply the same rigor from 1) above to their construction.