Learn or leave the market

PROFESSIONS OF THE PAST: HOW 
TO KEEP UP
McKinsey Global Institute estimated that over 800 million people 
will lose their jobs to robots by 2030. About 2.2 million people will 
be replaced by machines in retail and wholesale alone, with 1.5 million 
more being replaced in administration and maintenance. China plans 
to automate 77 % of jobs altogether.

---

The Future of Professions. Chapter 2.
26
By 2055, machines will be able to perform so many different tasks that 
practically half of today’s occupations will be on the verge of extinction. 
Let’s have a look at who else should consider additional education and 
look into new job opportunities:
Why do stores need 
cashiers? They scan barcodes and count 
out change when customers pay in cash. However, as payment 
systems develop, customers become capable of scanning 
barcodes and paying by themselves. Therefore, the need for 
cashiers is vanishing. Online purchasing is becoming more 
and more popular. For example, people are now able to order 
and try on clothes without even leaving their homes. For that 
reason, cashiers should consider retraining to become online 
sales managers.
Owning a car does not come cheap. This includes the expenses 
on maintenance, gas, repairs, etc. That is why many have 
already given up their private cars in favor of car-sharing 
or taxis. Entire companies now employ these services, thanks 
to special corporate rates. You may have already noticed new 
types of public transport, some of which are powered by elec-
tricity, while others no longer even require a human driver. 
An example of this is the subway in Dubai, United Arab Emirates. 
What is left for 
drivers to do then? They might want to consider 
retraining to become road safety risk assessment engineers, 
operators or technicians.
Translators are also at risk, due to the development of neural 
networks. Neural networks keep learning and their translations 
get better each day, as users upload new texts for translation, 
correct errors and suggest new translations. The efficiency 
of neural networks is undeniable: since Google first started 
using them for translation in 2016, the number of translation 
errors has been reduced by 60 %. The advantage of machine 
translation is its integrated approach to text processing, while 
people translate each word separately. Nowadays, machines 
can be trusted with translation, as they have access to a huge 
number of possible contexts and meanings stored in their data-
bases. It is also faster than manual translation.