“The distinction between work and learning might need to become more amorphous,” says Chowdhry. “We currently have a dichotomy where those who work need not learn, and those who learn do not work. We need to think about getting away from the traditional five day working week to one where I spend 60% of my time doing my job and 40% learning on a regular basis.”

For the majority of us, this could be a crucial switch in our thinking.

Research by management consultants McKinsey and Company suggests that fewer than 5% of occupations can be entirely automated by existing technology. The reason – our jobs are simply too varied and changeable for robots to take on all the tasks.

Instead, they predict around 60% of occupations could see a third of the activities they currently do being farmed out to machines. This will mean that most of us will probably be able to cling onto our jobs, but the way we do them is going to change significantly.

Robots will complement you, not replace you

Learning how to work alongside robots could be essential, too.

“We can have cases where machines pick up some of the repetitive work to free up humans to do other more rewarding aspects of their job,” explains James Manyika, senior partner at McKinsey who has led much of their research into the impacts of automation. “This could put a massive downward pressure on wages because the machine is now doing all the hard work. It could also mean more people could do that job aided by the technology, so there is more competition.”

There are wider issues at stake here too. With lower incomes and potential unemployment looming for middle-income workers, governments themselves could face some fundamental problems, like lost taxes and dissatisfied voting classes.

Luckily, there are some things humans can do that machines just can’t right now.

One good example of this comes from some work by researchers in Singapore, who are attempting to teach two autonomous robotic arms to assemble a flat-packed Ikea chair. Despite using some of the most advanced equipment around, the machines struggle with the most basic tasks.

Even identifying different objects from a chaotic mixture of parts is a major challenge for robots. In a recent test, it took the two robots more than a minute and half to successful insert a piece of dowelling into one of the chair legs.

And that's just one piece of furniture. “The real challenges occur when you want that robot to assemble several items of furniture,” Hawes explains. “A robot might be able to put together an Ikea chest of drawers, but it will struggle to then do a wardrobe from the same line, as the pieces will be different, even if some of the assembly steps are the same. Humans don’t have that problem.”