1. An agent is not going to talk to the “business” and solve XYProblems, conflicting agendas, and deal with strategy. I’ve had to push back on people in my own company that want to give customers “questionnaires” to fill out pre engagement and I refuse to do it on any project I lead. An agent can tell facial expressions, uncertainty etc.

2. AI is horrible at system design. One anecdote. I was vibe coding an internal website that will at most be used by 7 people in total. Part of it was uploading a file to S3 and then loading the file into an Postgres table. It got the “create pre-signed S3 url and upload it directly to that instead of sending it to the API” correct (documented best practice). But then it did the naive “upload the file from S3 and do a bulk sql insert into the database”. This would have taken 20 minutes. The optimized method that I already knew was just to use the Postgres AWS extension to load it directly from S3 - 30 seconds. I’ve heard from a lot of data engineers run into similar problems (I am not one. I play one sometime).

6. Involves talking to the customer and UX.

7. Moving to production doesn’t take AI. Automation, stage deployments, automated testing and monitoring, blue /green deployments etc is a solved problem.

8. Monitoring is also a solve problem pre AI. It’s what happens after a problem is what you need people for.

So yes 1,2 and 7 are high value, high touch. If you look at the leveling guidelines for any BigTech company, you have to be good at 1 and 2 at least to get pass mid level.

Then there is always “0” pre-sales. I can do inbound pre-sales (not chase customers). It’s not that much different than what I do now as the first technical person who does a deep dive strategy conversation