Say goodbye to your job; and hello to the innovation that took it

As a young adult, I've been told to get a job more times than I'd like to admit, and I'm sure at one point in your life you have been or will be told the same. But for the first time in human history, we almost have an excuse to say "I'm unemployed".

Meet Steve

The new financial analyst of the company you just got fired from. Steve is a model employee; he

• almost never makes mistakes
• is fast
• never stops working
• doesn't complain
• skips lunch to photocopy your first quarter SWOT analysis without waiting in line.
• can probably take over the world

It may be funny to think that a box of silicon can one day replace the carbon-based man, but if I where you, I'd reconsider the outlook.

Why I'm shaken to the core

Throughout history, advancement has followed a simple and sequential formula

1. Innovation made life easier
2. Having easier lives led to higher productivity
3. Higher productivity led to more innovation

This cycle is why humanity went from agriculture to production to the service industry at an exponentially growing rate, and this is where we face a major problem.

Innovation no longer means more jobs.

What does this mean? Put simply, modern-day corporations no longer need vast numbers of employees to make huge profits, all thanks to automation. While automation has always existed, companies like Google, Facebook, and even Tesla are innovating at higher speeds but they are not creating enough jobs to compensate for the industries they are replacing and for the exponential population growth. The first example that comes to mind is Blockbuster and Netflix. Netflix is over 1/18 times smaller in terms of employees, yet still made 9 billion dollars in revenue, outshining the 6 billion dollar annual revenue of Blockbuster.

Automation has finally entered a new stage in its development. As A.I snowballs into a global phenomenon, machine learning has allowed for even complex jobs to be replaced faster than can be imagined. Machines can replace man because of a concept known as specialization. Over the course of human history, people have specialized in their own sectors; jobs have become more and more narrow to compensate for the hundreds of other employees. This has allowed for machines to take over complex jobs by breaking them down into its simpler components and solving each operation in the correct order. By 2020, 5 million jobs could be destroyed.

Among the most likely to fall are

• Accountants
• Auditors
• Tellers
• Clerks
• Data Entry Keyers

Essentially, machine learning is an algorithm's capability to acquire new skills and information by analyzing and applying data. By identifying relationships within the data, they can become better at recognizing that relationship in another, possibly larger or more complex data set. This means with every set of data we feed machines, we're making it better and with the amount of surprisingly personal information companies have collected over the years, data is far from scarce.

So to recap,

• Programs can learn faster than you can
• Programs rarely make mistakes
• Programs get faster and make fewer mistakes over time
• Programs don't have to stop learning
• Programs can be duplicated for no cost

What this means for us

There are (or at least where) three clear patterns in the job market

1. The number of jobs is decreasing
2. The population is rising
3. Productivity rises the standard of living

Today, the first two patterns remain true, but the third is no longer valid. What this means is if we worked 10 hours 20 years ago versus 10 hours today, the percent output would be higher today. How does this make sense? The number of worked hours no longer correlates with productivity. Despite higher levels of output, new jobs being created, and the population rising, the number of hours worked hasn't risen.

Despite the growing number of postgraduates, 40% of fresh-out-of-school students are forced to take on jobs that don't require a degree. What Nadeem Nathoo told my cohort on the first day of TKS resonated with me deeply, as a grade 12 student looking into university prospects. What we learn in University might not be able to set us up for a job in that field anymore.

We have two paths ahead

1. A world where machines do the work for us reducing problems like poverty (through universal income) and social inequality
2. A world where the .1% of the richest minority and most powerful people control all the machines that rule above us

While A.I and automation will continue to grow, we as members of society have the responsibility and indeed the capacity to make the right choices that lead us down the better path. As Navid Nathoo told my TKS cohort during our last meeting, it is possible for a company to make profit and help improve the world. I highly recommend reading this Forbes article to find out more about the future of work and if you've got time, watch this Kurzgesagt (In a Nutshell) video for an in-depth look at automation.