When we look around in our field, everyone in Tech seems to focus on one thing: "How can we adopt AI in our tooling and in our processes?"

So it is a proof of a bubble. Everyone is enthusiasts but it doesn't solve real use cases.

A rightful question can be: "How can we set up our engineers for long-term career success?"

Jens Meiert ask pertinent questions to solve this big up question.

What can be done reasonably well with AI today? (And tomorrow? And the day after tomorrow?)
How are our engineers affected by AI?

• Are our engineers using AI?
• How are our engineers using AI?
• What are realistic expectations for our engineers in terms of AI use and proficiency?
• Are we setting clear expectations for use of and proficiency with AI in our job descriptions as well?
• Do we document and anchor these expectations in our competency and skill matrixes?
• Are we watching the AI market, and are we evaluating tooling?
• While the AI market is in flux—which it may be for some time—, do we have enough flexibility (budget, processes, room for errors) to test AI tooling?
• If our engineers leave the company, would they find a new job—or would their profile make them less interesting?
• If they would not necessarily find a new job, what extra skills and experience do they need?
• How can we make our engineers ready for the AI age?

As you can tell, we cannot have all those answers yet—this is precisely why this is so important to get on top of, and it’s also the reason why I say “start answering.”