This is a nice article (not sure how I stumbled upon it a month later) I directionally agree with it in that: ✅ I have a massive bias for sl…

This is a nice article (not sure how I stumbled upon it a month later) I directionally agree with it in that: ✅ I have a massive bias for slope, grit, and scrappiness in candidates vs. pure experience. During interviews I often ask the candidates (across eng, gtm, and others) ad-hoc problems to test how they would reason about new situations. The people that can learn the quickest are those that can use AI to their advantage. ✅ In the pre-AI world of work, I would say 80%+ of time on the job is spent doing routine tasks and <20% is actually learning new skills. When I was a ML researcher, 80% of my time was actually programming PyTorch (repetitive) and <20% was thinking. So the actual amount of pure learning a junior worker needs to get to the senior worker's level of output is probably quite low. And that's shrunk even more with AI. In general, high-slope will win out vs. experience, especially in the current volatile market. Experience may not be as important, but imo learning and understanding is important. Based on this, some pushbacks: * Actual learned experience helps you use AI better. When you are a senior/staff-level engineer, you know what prompts to use to write higher-quality, maintainable code. * For the junior worker to ramp-up quickly, they actually need to use AI to learn and not just produce. it is easy to give the illusion of producing a lot of output when most of it is slop.

Source: Jerry Liu (X) | 2026-05-17