When I left Microsoft in 2012 and returned to India, my goal was to build a career management practice and eventually a career management product for IT professionals. I talked to almost a thousand engineers and engineering students during my journey to understand the needs and solutions that might work. My takeaway was this:

Engineers do not understand that careers need to be managed, and the solution for every career problem is not ‘job change’. This is probably because IT industry in India has not seen a downturn.

It was clear I couldn’t make headway.

So I came back to the IT industry so that I could survive and live to try it again when the timing is right!

As I see the AI wave decimating the dreams and careers of so many students and early-career engineers, I am reminded of my futile attempts at teaching strategies for career management to engineers and students, which would have been helpful at this juncture.

In a perverse way, the current situation makes me happy that hopefully now people can focus on managing their careers better and avoid getting surprised by changes like these. AI is hugely disruptive as well as transformative for careers. AI is also being driven by people who believe in mixing marketing with tech liberally, and so we have a lot of noise, falsehoods, and paranoia that need to be cut through if any serious conversation about durable careers needs to be had.

So I dusted off my career management toolkit and tried to apply it to the current scenario of AI-infused software engineering and what it can do to the IT industry in future.

I recently had the opportunity to deliver this material in a talk to two groups of working professionals who are pursuing work-integrated learning programmes with BITS Pilani.

The talk was focused on identifying how software engineering and the industry will evolve over the next several years, spaces where AI will prove to be a powerful adversary and spaces where AI will be weak, and humans can do well (with or without AI assistance). I tried to identify areas where AI will continue to perform poorly, and skills to build in these areas so we can have a great career in the age of AI (of course, while leveraging AI as an assistant).

5 competency areas I talked about:

• Being precise while operating in ‘vague’ territory (articulation in natural language)
• Being able to think and communicate in abstract
• Being fluent in higher-order competencies (evaluate, analyse, create)
• Being world-class in our quality by working in areas we are passionate about

Feel free to go through the deck I used for the session.