The most common mistake people make in mid-career transition to AI

 AI will impact many jobs  and COVID has only accelerated this trend   

 

Based on my teaching at the University of Oxford for AI, we see a big demand for people wanting to transition to AI due to the current environment. 

If you are at the early stage of your career (less than two years out of Uni), a transition to AI is relatively easier

But you may need a different strategy for a mid career transition to AI

Why?

In the early stage of your career, all things being equal,  you would add more value than the company pays you

But about 4 plus years into your career, that changes 

At that time, you are moving away from core development (directly creating value) and also your are being paid more  – leading to a decline in value in pure economic terms 

(I know this is a generalization – it does not apply to everyone but it is valid overall)

Hence, it is at this point that people explore alternate careers paths especially to AI 

But mid-career transition to AI is a bit different

The general thinking goes like this

You focus on learning Python and equate knowledge of coding with AI

Coding is indeed necessary but is not a sufficient in itself

Because ultimately to add value, you would need to more than coding alone

So, now let’s consider a different starting point

You learn Python but you also consider what you know

To give you an example, suppose you are an engineer

The good thing is, data science will be in every field

Image source: General Assembly – United Nations