Fifteen years ago I was on a 24/7 on-call rotation at IBM, pager always within reach. When it went off past midnight, I opened my laptop and started reading logs. No AI. No assistant. Nobody else awake. That investigation ran for as long as it took, sometimes until mid morning.
Last week, I hit a bug in code I was writing for Db2. The same category of problem that once kept me up until mid morning. I shared the error with an AI coding agent. Root cause identified. Fix applied. Done. Under two minutes.That is not a small improvement. That is a different kind of work.
Last Saturday I shared both of those stories with 35 university students from India, most of them computer science majors, preparing for careers in AI engineering. Then I showed them exactly what sits between those two moments, the tools, the habits, and the mindset shifts that made the difference.
In the session we covered three things. First, how I currently hold a full-time role as AI Architect at IBM Db2 and complete a PhD in AI at the same time, using AI tools to run both tracks in parallel. Second, three prompting habits that immediately change what you get back from any AI tool, including how I use AI when reading something new. Third, two free tools you can open today: Claude and NotebookLM.
The session also opened up questions from the audience that made it richer. Someone in the audience asked how a student studying neuroscience, not computer science, can start using AI. We talked about how AI makes you a sharper learner, not just a faster coder. And we closed with three specific tasks you can act on this weekend.
If you are an engineering student preparing for a career in AI, this session was made for you.
