In recent years, much ado has been made of the autonomous economy. Websites predicting the likelihood that robots will replace you in a workplace are a dime a dozen. With arguments that self-driving cars are mere years away, it is time to reassess the inevitability of this autonomous future. If autonomy lies ahead, what can be expected by such change? Would this change be inherently wrong? This article will consider these questions.
We live in a smart economy that is fundamentally human-driven with aid from technology. While algorithms perform the most meaningless tasks, humans still make the most critical decisions. For example, many human managers control, and decide on, investment strategies of portfolios. In recent years, the economy has begun to transform, through a silent revolution, into an IT-driven economy; an economy that does not require human support. We have increasingly delegated complex tasks, that were performed by humans, to machines. For example, credit card applications are now decided by algorithms, which has made their decisions fairer and quicker.
A change to an autonomous future has already started, but it has been notoriously difficult to pin-point when this change will fully flourish. Growth is infamously difficult to predict because economists have, in the past, seen innovation as something that operates in a vacuum of its surroundings. Economists should recognise innovation as a force that interacts with, shapes and, in turn, moulds its surroundings. An increase in the adoption of technology brings down the cost, making it more economical and inevitable. The feedback loop that is important in this model shows why humans are an inferior judge of innovative progress, as innovations tend to create a volatile snowball effect. The snowball effect of automation has already started and has gone beyond the point of no return.
However, this development is being held back by four factors: trust, supply chain, platform, and regulations. Trust will be the most difficult to overcome and has been eroded over the years by the apparent failure of capitalism. The majority of people could view automatisation as surrendering control of the economy to those in charge of algorithms without any way of getting back. To overcome the 'fear of losing control', there needs to be increased transparency and a way to return control to humans. The overhaul of the supply chain will take time and vision, and should be straightforward to achieve as companies have already started their move towards automated systems because of its savings opportunities. Platforms need to shift from a central to decentralised state, and regulation needs to catch up. Despite this, we have crossed the tipping point, and the snowball has begun cascading into our life with no way back. So, we must look at what this future would mean and how our needs can reflect better in it.
To understand what an autonomous economy will look like, we must look at current innovations. We are hurtling towards the Fourth Revolution, but what shaped the third economic revolution? Integrated circuits shaped the first stage (from the 1970s to 1980s) which brought increased computational powers to computers. This helped companies to data crunch big data. The second stage (from the 1990s to 2000s) led to the connection of digital processes, which allowed people from distant corners of the globe to work in teams and collaborate on projects. A more appropriate name might be the ‘communication revolution’, allowing companies and organisations to talk seamlessly to one another, increasing efficiency. Ideas and wealth spread much faster in this new, interconnected economy. The third stage began in the 2010s and brought an influx of cheap sensors. These sensors have influenced the new autonomous economy by bringing forth a new age of external intelligence. They are used by programmes to detect changes and react autonomously like a neural network with other computers. The result is 'invisible strings' of computers with the potential to run the economy. Businesses are using virtual structures as building blocks of new organisational models, such as blockchains to create smart contracts, to cut time and costs. These contracts could bind global commerce, making business more efficient.
To highlight the potential of automation to change industries, I will focus on the financial industry. These 'invisible strings' are already shaping the industry, turning it outside in. In 2015, the FinTech sector grew to $12.5 billion from $5.6 billion. Finance has migrated online, and these online platforms are increasing disintermediation, eroding our need for 'brick and mortar' institutions. This process has accelerated throughout the current pandemic because people are increasingly using online replacements; this has called into question the need for in-person alternatives by consumers. It is not only consumer habits that have changed, but the sector has also realised that online options are cheaper. There has been a disruption in industries that were considered 'safe' and a change in ways that we invest and buy insurance. Insurance providers have progressively turned to data to assess their decision, modelling and algorithms to ensure that decisions are uniform. While these changes have already gained a foothold in the industry, they will quickly become even more pronounced as other branches of finance look to utilise advanced technologies. An innovation that may increasingly become commonplace in insurance is the ‘pay-as-you-live’ cover which tailors insurance to individual needs. These changes are becoming increasingly feasible through deep learning techniques and big data. The role of insurance providers is shifting away from ‘detect and repair’ to ‘predict and prevent’.
The change has also affected the way society purchases and invests in the stock market. Online only investing platforms that use algorithms to invest savings in index funds automatically are more common now and buying stocks has become much more straightforward with online programs. With these technologies, more people than ever have become stock owners, with the percentage of individual stockholders climbing from 10.6% in 2012 to 13.5% in 2018 (Summers, 2018). As these changes mature, we can assume that more individuals will become invested in stock markets through increasingly complicated financial products. Such changes would be brought forth though increase in wage, which in turn would allow people to invest in such products through higher disposable income. These investments will push the stock markets to greater heights. Algorithms will also hold greater sway in stock markets through ‘high-frequency trading’, it will improve market liquidity but cause unpredictable changes in the stock market that can either hurt or help small investors. So far, they seem to be a force for good; they have increased efficiency and reduced implicit execution costs. Previously, only big investment institutions employed these algorithms, but they are gradually becoming commonplace. Connected by the 'invisible string', these algorithms have bound us, underpinned by our dependence on the stock market.
Is such a change desirable? Keynes (1932) argued that, in concurrence with growth in efficiency, an increase in ‘technological unemployment’ will occur. People will become redundant to make way for more efficient machines. In recent years, economic discourse on ways this will affect the population has become more diverse. The divide between 'pessimists’ and ‘optimists' closely mirrors the split of economists during industrialisation. Optimistic economists have argued that the vanishing of jobs in one sector creates jobs in others. This argument, in hindsight, stands up to criticism. When automated switchboards were invented, many switchboard operators lost their jobs but retrained and found work elsewhere. Pessimistic economists have argued against this, stating that there is always a victim of economic progress. While the claims made by pessimistic economists are highly exaggerated, the shrinking job market will outpace job creation. In the new world, dominated by machines, it is realistic that human labour will become mostly redundant. While this appears unfavourable at first, the shift would improve living standards for most people. If pointed in the right direction, the 'new dawn' of the economy would bring unrivalled prospects for everyone. However, there is a real danger that the changes will bring forth more significant inequality. As seen during the Industrial Revolution, nations may divide into two classes, or as Disraeli (2008) puts it, 'The Two Nations'.
The changes will either strengthen or destroy capitalism. The new offering from stagnating industries will benefit consumers as the competition takes hold of sectors. It will renew hopes in capitalism by reminding people of its ability to better lives. Welfare gains from the utilisation of machines and data could be the saving grace for capitalism, following its recent failings that have dominated political discourse. Automation will cut costs, making business more consumer centric. In a world where production costs continue to fall, and goods are created quickly, the economy's central problem would shift from production to distribution. This raises further philosophical questions on these issues, such as who deserves to partake in the fruit of such change. Sandler's "The Tyranny of Merit" serves as a springboard into questions about our expectations of a future society run by values of merit. However, the scope of current discourse is limited to nations. To fully understand these changes, we need to look at global changes. Universal basic income works well to reduce disparity at a national scale but does little to settle the nerves of developing nations. These nations, already burdened by the weight of development, would find it hard to match developed countries' steps. If the answers to these questions do not satisfy most people, society might have to look beyond the restrains of capitalist theories.
In conclusion, we live in a world where the 'invisible strings' of computers continue to increase connectedness through an invisible neural network. This neural network will cut time and costs, but also lead to job losses. Economics, as a discipline, needs to pivot to answer these questions and provide a viable way to reduce the inequalities that plague the system currently. If not, we will see inequities heightened, potentially leading to a ‘class war’.
