From the dawn of human existence there has been a core problem that has dictated and shaped human behaviour. Our resources are limited, however, human needs and wants are infinite. We always want more.

Capitalism is the tool we’ve used for centuries to shift wealth around to its most productive use. If someone can use the resources we have to satisfy consumer needs and wants better than anyone else, they will be able to make a profit. Investors will seek out this profit and capital will flow to that business.

The first companies in the 18th century made great use of capitalism, aggregating capital from numerous investors to ignite the economy, employing millions and producing assets that no individual could of dreamed of creating. Where there is opportunity, capital will flock. Capitalism isn’t perfect though, distributing wealth using profit as a carrot-on-a-stick can have some unintentional consequences.

The most notable of these consequences is the accumulation of wealth in the hands of the very few. Wealth inequality has been highlighted by society in recent years due to the rapid accumulation of wealth by the highest income earners in the world.

Why is Inequality Getting Worse?

Opinions differ on this subject, but I believe that it’s fairly simple economics at play that drives inequality. Companies have a fiduciary duty ( to their owners. This means that companies are ethically bound to act in their owners best interests. Who are the owners and what are their best interests? For companies listed on the stock exchange the owners are the shareholders. Shareholders are usually (but not always) motivated by one thing, profit. If dividends aren’t being paid and the stock price isn’t going up, shareholders will simply sell and move their money to a more profitable investment.

Following the above logic; companies are ethically bound to act in the owners’ best interest and the owners’ best interest is the profitability of their company. This creates a razor sharp focus on a company’s financial results, systematically doing anything they can to increase profits. Assuming that your market is fixed (i.e. the customer base remains the same and you’re competing against other businesses for the pool of customers) then the only way to increase your profit is to lower your costs and increase your market share. This competition is the foundation of capitalism. Cutting your costs and maintaining your profit means you’re deploying capital more efficiently- able to produce more overall. Competing for market share should hypothetically result in better, more effective products produced more efficiently. To a degree, these competitive forces do work, but what happens in the context of inequality?

Cost Cutting and Automation

When trying to increase profitability, businesses will often turn first to cost cutting. Technological advances are giving companies the ability to cut costs like never before. There is a word looming over most businesses employing large numbers of people in the world right now and that word is “automation”.

Human labour in most developed countries is expensive. It’s the reason that over the last few decades we’ve seen a massive outgoing of manufacturing and administration jobs to lower-wage economies such as India and Asia. Technology is paving the way for an even cheaper method however and that is to simply replace human labour with machines. Machines have no minimum wage and they don’t have the additional complications that humans have such as getting sick and going on holiday. Automation has already been taking place for a long time, however the technological strides being made in computer and AI technology are going to change the world very soon.

Automation has been going on since people realised windmills and waterwheels could be used to grind wheat and bovine could be used to plough fields. The earliest and most easily automated jobs were those that consisted of predictable physical labour. A study by McKinsey & Company claims that predictable physical labour makes up around 20% of all jobs in the US. This number is likely a lot higher in developing countries where people are typically employed in more primary industries.

This is what automation looked like until very recently. As automation technology become cheaper and more effective than human labour, business would buy machines and the need for employees would reduce. The rise of computer technology has also paved the way for the automation of more virtual tasks such as data collection and processing. McKinsey research shows that across all occupations, workers spend around one third of their time collecting and processing data. This has been the extent of automation for some time. The remaining jobs in the workforce require a more dynamic approach, assessing an individual situation in real-time and incorporating that feedback into the response. For want of a better word, they require “thinking”. Most jobs have situational elements that require a tailored response: a surgeon will have to assess the particulars of a patient’s condition before operating, a mechanic will need to observe a car before understanding how best to repair it, a psychologist needs to understand how their patient is feeling before making a diagnosis. For as long as these kinds of tasks have been performed, they have been the domain of humans. But not for long.

Machine Learning and How it Will Change the World.

What differentiates predictable physical labour and data collection from more unpredictable elements of work? As mentioned before, the advantage that humans have over machines is the ability to think, learn and apply our knowledge to a unique situation. So can we automate thinking?

The answer is yes, however it takes an astounding amount of computer processing power. The colloquial term you’ve probably heard for this type of technology is Artificial Intelligence (AI). Thinking, in its purest form and for the most part, is simply considering the outcome of previous events and incorporating this feedback into future actions. Some tasks can have an effectively infinite number of variables that influence the correct response. This means that depending on how many variables there are in a given task, the resulting algorithm will have to be that much more complex. The amount of coding, memory and processing power required to bring this level of automation to reality is immense. Historically the technical capability to pull off machine learning has not been possible. This is changing fast

Memory prices have come down dramatically over the years; processing power too has increased substantially. There are no signs to indicate that this trend will do anything but continue. This means that the level and scope of automation will continue to increase and automation costs will continue to come down. There will be several ‘breaking points’ where the cost of automating an activity is cheaper than employing labour to do it. The further the technology develops the less human labour we will need.



The Universal Basic Income

“I think that there’s a pretty good chance we end up with a universal basic income, or something like that, due to automation.  I’m not sure what else one would do.” – Elon Musk

The concept of a Universal Basic Income (UBI) has taken off in recent years. In basic terms, the UBI was theorised as a way to deal with the negative consequences of automation on employment and capitalism.

If robots take our jobs, we won’t make money. If we don’t make money, we don’t spend it on goods and services that the robots make. If we can’t buy goods and services, why would we bother having the robots make them? If no one is making money, where does the tax needed to pay for our public services come from? This is the automation dilemma.

What if everyone was paid despite not having to work? The only thing we need to keep the wheels of capitalism turning is an income; we don’t necessarily need to work. The concept of UBI is to provide everyone with a certain level of income without the need to work. Imagine an unemployment benefit for everyone on the planet. The idea is that humans could go on consuming and machines could go on producing without the need to employ human labour. There is a deeper idea behind the UBI about what humans could do with their freed up labour. Bill Gates predicts that this excess time will allow us to: “do a better job of reaching out to the elderly, having smaller class size, helping kids with special needs. And all of those are things where human empathy and understanding are still very, very unique, and we still deal with an immense shortage of people to help out there”.

How do we Pay for a Universal Basic Income?

One of the core questions around the UBI is how would we pay for it? Bill Gates has an idea about this too. If we taxed humans based on the income they made from employment, why don’t we tax machines for the work they perform? “Right now if a human worker does, you know, $50,000 worth of work in a factory, that income is taxed. If a robot comes in to do the same thing, you’d think that we’d tax the robot at a similar level”. This idea might seem strange at first, but it does make a lot of sense. The money to fund the UBI has to come from somewhere. If it’s not coming from human labour, why not let it come from machine labour? This tax would not only be used to fund the UBI, it would also serve to limit the advantage the machines have over human labour. However, in most instances, not having to pay machines an income would still make them the most attractive option.

Bill Gates argues that if we can tax machines and allow humans the freedom to specialise in activities that they excel at, we can be “net ahead” as a society. Perhaps automation taking our jobs away from us isn’t such a bad thing after all.

There are of course a lot of unanswered questions: how much should the UBI be? Are there jobs we shouldn’t automate? What will humans do with their free time? Will they spend it productively, destructively? There is a raft of economic and social consequences stemming from implementing something as radical as the UBI.

What to do in the Meantime:

Automation is inevitability. Fortunately for us however, it won’t happen overnight. While the UBI presents a type of solution to the problem of automation, it is currently nothing more than a theory. Inequality and the competitiveness of labour markets is likely to get worse before a solution is implemented. So what can we do to stay competitive in the market?

  • Develop Skills in Areas Not at Risk of Automation
    MckInsey Global Institute put together the following chart based on a study of automation on various industries. The chart demonstrates that while predictable physical labour and data processing have a large scope for automation, managing others and applying expertise are at a far lower risk.

    If we want to have jobs in the future, it would be wise to think about carefully diversifying the skills we have to match the changing landscape automation presents.

  • Derive Income Outside of Employment
    The other option of course is to create income outside of employment. Employment is just one type of income. When we own a business we can earn a profit. We can also earn passive income, royalties, rental income, investment returns, the list goes on.As we go about our day to day lives, we can sometimes forget that there are ways to make a living outside of employment. If we’re able to start a business, which thanks to the internet has never been easier, we can even use robots to our own advantage; earning income from machine labour.Like all changes, there will be those that will be able to adapt and take advantage of new opportunities and there will be those that don’t. Those that are able to loosen their dependency on employment and explore other opportunities are those that will be able to avoid the perils of automation.