You’ve most likely encountered various exaggerated forecasts regarding the transformative impact of technology on our globe. Saying our world will never remain the same! All of us will become unwanted! A group of robots will take over our work!
Both optimists and pessimists recognize the necessity for a change in course. However, what should this change entail?
This book aims to sift through the hyperbole and offer a clearer comprehension of automation: its nature, its implications for human societies, and, significantly, how we can harness it to cultivate a more promising future.
You will draw insights from both history and economics to understand the evolution of technological advancements. Additionally, you’ll explore a fresh approach that envisions a future where everyone can live content and fulfilling lives without the need to work.
In this book, you will discover
- why the future remains unpredictable;
- strategies to narrow the significant gap; and
- that Homer wrote about AI –in a figurative sense.
Chapter 1 – Machines will take over certain jobs – however, they will complement other jobs as well.
The narrative of machines taking over is a familiar one, isn’t it? It’s easy to understand why this stance arises – each year comes with new technological advancement. As computers and robots continue to get more intelligent, there’s a looming question: will human beings become outdated?
Reality is rarely easy; therefore, there is no need to worry. Machines will never entirely take over our jobs Their impact on the job market is far more delicate.
Dread of technological transformation is not a new thing. Centuries ago in Britain, during the onset of the Industrial Revolution, weavers damaged early machinery. These individuals, called Luddites, harbored fears about their job security. And they had valid concerns; fast technological transformations within their industry led to huge outbursts.
However, was this transformation completely negative?
While certain workers did face hardships, others reaped advantages. For instance, if a low-skilled worker acquired proficiency in operating the new machinery, their productivity increased, eventually translating into increased salaries.
New technology is usually complementing. While it may displace certain workers, it also enhances the productivity of others. How? By aiding them in tackling more complicated jobs.
For instance, algorithms capable of processing legal documents haven’t taken over from lawyers. Rather, they have given them more time for more creative tasks such as writing, problem-solving, and having one-on-one consultations with clients.
This heightened productivity brings about the second advantage of automation. Imagine a nation’s economy as a pie that everyone shares. While machines do alter how the slices of this pie are shared, they also significantly expand the size of the pie.
Still skeptical? Consider ATMs (automated teller machines). Initially, there were concerns that they would replace bank personnel when it first came out
However, let us look at what occurred. Over the last 30 years, the amount of ATMs in the USA has quadrupled. Simultaneously, the number of human bank tellers increased as well by approximately 20%. ATMs indeed replaced the role of dispensing cash; however, they also released humans to provide financial advice and personalized assistance.
As a result, the economy increased, and the overall demand for banking services as well as financial guidance grew too. In total, the average number of tellers in each bank decreased by around one-third over recent decades. However, the number of banks offering opportunities for tellers increased by as much as 43%.
Chapter 2 – Technological transformation poses a threat to all kinds of jobs.
Whose occupations are being threatened by machines? Is it those who work on assembly lines? Or supermarket cashiers? Could even brain surgeons be concerned about potential replacement by robots?
The advancement of technology will undoubtedly impact everybody. However, recent trends offer some insights into which segments of the workforce are more susceptible to automation.
In the past few decades, technology served as an advantage to highly skilled, formally educated employees compared to their less skilled counterparts. The reason? The reason is computers. Between the years 1950 and 2000, the power of computers increased by a factor of ten billion.
This rise led to a demand for highly proficient employees who are good with the use of new machines. As this demand increased, the supply increased as well – and more individuals learned how to operate the computers. As expected, this reduced wages. However, an interesting thing occurred: demand kept increasing and the salaries of highly skilled employees started to increase. By 2008, economists observed an unprecedented income disparity between US college graduates and individuals who had just completed high school.
Therefore, does this imply that technology consistently favors highly educated individuals? Not quite. In the past, the situation was reversed. Recall the Luddites? In eighteenth-century England, weaving was seen as a high-skill work. However, with the introduction of mechanical looms, advanced training became unnecessary for producing quality cloth. Consequently, low-skilled employees enjoyed the benefits.
Therefore, who stands to gain from automation in the future? Economists assert that technology is enhancing both low- and high-skilled occupations, while the middle class is experiencing setbacks. There’s a rise in the number of cleaners and lawyers; however, a lesser number of secretaries and salespeople.
One proposed reason for this situation comes from a group of MIT economists. Their theory posits that “routine” work is more susceptible to automation compared to “non-routine” task, which relies on judgment, creativity, interpersonal skills, or complicated manual labor.
Simple, routine tasks can be described, easily broken down, and converted into algorithms, making them manageable for computers. However, machines find it difficult with non-routine tasks, which are more complicated and challenging to explain.
For a long time, there was a belief that non-routine tasks were immune to automation since scientists couldn’t effectively program computers to perform them. But, this notion is starting to change gradually. As we will explore in the subsequent chapter, machines have already started the process of self-teaching.
Chapter 3 – The innovation in AI research occurred when computers stopped attempting to reason like human beings.
The olden Greek poet, Homer is best known for his epic works like The Iliad and The Odyssey. However, it’s fascinating to know that his writings were beyond stories of heroes and warfare. Homer also talked about what we now refer to as artificial intelligence (AI). In The Iliad, Homer mentions “driverless” three-legged stools that respond to their owner’s call, similar to today’s autonomous vehicles.
While Homer might not have envisioned self-driving vehicles when narrating that story. However, it mentions a significant key point: humanity has envisioned autonomous machines for centuries. Recent innovations are now turning these dreams into reality.
To know the capabilities of AI, it’s important to examine its history. Early endeavors to create artificial intelligence came about in the mid-twentieth century, the same time when computers were emerging. Early AI researchers sought to emulate human thought processes.
For instance, in creating chess software, engineers consulted grandmasters to describe their strategic thinking about the game. Then the engineers attempted to teach computers the exact processes.
However, by the late 1980s, progress in this method had hit a roadblock. Whether it pertained to chess, translation, or object recognition, early AI struggled to surpass human capabilities by mimicking human thought processes. Therefore, what can be done? Thus, researchers recognized the need for a different approach – relying solely on copying human thought processes could only take computers so far.
The subsequent phase of AI research embraced a more practical methodology. Scientists assigned machines specific tasks and instructed the software to achieve them by any means necessary, even if the methods didn’t make sense to humans. Rather than employing standard chess or translation methods, new AI programs were fed vast amounts of data and tasked with identifying patterns within it.
Due to this, it marked a significant advancement in AI research. In 1997, IBM’s Deep Blue defeated Garry Kasparov, the world chess champion. AI is not only succeeding in chess; contemporary image recognition programs, for instance, consistently surpass humans in contests.
These developments are crucial in knowing the potential impact of automation on the future of work. Previously, economists believed that computers would always require human guidance to function effectively. However, machines are now able to devise non-human answers to jobs and problems. Also, this shows that one day, they may eventually acquire non-routine skills previously deemed beyond their abilities.
Chapter 4 – Machines are improving in various works, yet technological advancement will vary across different contexts.
William Gibson, a Science fiction writer said one time, “The future is here, it’s not distributed equally.” This quote serves as a valuable reminder when talking about automation. In terms of what computers are capable of doing, the future is already upon us. AI demonstrates superior performance over humans in a growing number of jobs, ranging from lie detection to the production of prosthetic limbs.
However, the change that “AI can do it” to “AI will do it” represents a significant leap, and this difference varies from one nation to another.
As technological abilities continue to advance, automation will change all industries. Take agriculture, for instance. The farmers we have presently utilize driverless tractors, facial recognition systems for livestock, and automated sprayers. In Japan, drones handle 90% of crop spraying. Even works requiring fine motor abilities have been automated; robots can harvest oranges by shaking oranges off trees.
What about sectors demanding more complicated thinking? Well, industries like finance, law, and medicine have witnessed the introduction of software capable of analyzing vast amounts of data surpassing human capacity. AI excels at identifying important patterns and referencing past cases.
For instance, a diagnostic system devised by Chinese technology company Tencent in collaboration with a Guangzhou hospital draws upon over 300 million medical records to assess patients.
Even roles requiring emotional intelligence can now be carried out by machines. Certain facial recognition systems already outshine humans in determining real or fake smiles.
The emergence of “social robots,” designed to perceive and respond to human feelings, is anticipated to turn into a $67 billion industry. These robots are highly prevalent in the healthcare industry; for example, a humanoid robot named “Pepper” has been integrated into several Belgian hospitals. Pepper’s responsibilities include welcoming patients and guiding them through the complex layout of corridors and facilities.
However, simply because more jobs can be automated doesn’t entail that they will be. The prerequisites and expenses associated with automation differ from one region to another, meaning nations won’t progress at the same rate.
For instance, Japan has a large elderly population but faces a shortage of nurses. Therefore, hospitals in Japan have a compelling motivation to automate care tasks. Conversely, nations with younger demographics and a lot of individuals ready to undertake lower-paying work may have less reason to automate healthcare. There might even be political pressure to resist the introduction of robo-medics.
Chapter 5 – The growing proficiency of machines will significantly reduce employment opportunities.
Searching for work isn’t particularly fun, is it? Now picture being jobless because your work has been automated. How do you go about finding work when a machine has taken over your role? This dilemma is likely to impact millions of individuals.
In the former chapters, we discussed how automation will broaden the scope of economic opportunities. Essentially, machines will bring about new employment opportunities, countering unemployment. However, the question is will people take these new roles? The answer to this is not simple. Numerous challenges stand in the way, such as skills mismatch.
For instance, if the majority of new positions demand high skills—such as AI management specialists—this wouldn’t benefit low-skilled factory workers.
Think about the geographical mismatch. Would you be willing to relocate hundreds of miles for a job? While the internet has facilitated remote work, geographical location still plays a significant role. Take Silicon Valley, for example, which attracts a lot of tech firms. The amount of skilled programmers in the area often leads startup founders to relocate there in search of talent and networking opportunities.
Economists call these hindrances “frictions.” Scientists think that these hindrances are temporary and will eventually resolve over time. However, there’s a persistent issue that remains: the structural transformation of the labor market.
We’ve observed how technology enhances productivity and output at large. Yet, as technology grows, it will eventually get to a stage where human involvement becomes unnecessary. Take the example of cars: at first, GPS technology complemented human beings’ by enabling taxi drivers to choose more efficient directions. But presently, the introduction of driverless cars threatens to entirely replace human drivers. Even if there’s a surge in demand for taxi services, it won’t translate into more job opportunities for humans; instead, firms will simply produce more driverless vehicles.
This change won’t occur instantly. As said by Roy Amara, a researcher at Silicon Valley “We often overestimate the impact of a technology in the short run and underestimate the effect in the long run.”
However, what exactly constitutes the “long run”? These impacts will unfold over decades, not centuries, and they will keep increasing as artificial intelligence becomes smarter. The trajectory is clear: despite the ongoing increase in output, there will be a diminishing need for human labor.
Chapter 6 – Automation has deepened inequality by widening the disparity in income levels across various occupations.
Throughout much of human history, we had difficulties with subsistence. This word, created by the renowned economist John Maynard Keynes, implied that the societies of humans were not making enough that everyone could depend on. This brought about concern for the distribution of resources.
Nowadays, all thanks to technological advancements, our production capacity has reached a point where there’s plenty for everyone around the world to live comfortably. The economic pie has expanded significantly; however, the question of how should we allocate it arises.
Analyzing economic data reveals a stark truth: in recent years, the distribution of pie has become increasingly unequal.
Consider the things we possess—such as our capital, in economic terms. This can be divided into two main types: traditional capital as well as human capital. Traditional capital comprises tangible items you can purchase such as equipment, land, and intellectual property, while human capital encompasses a broader spectrum, that contains our abilities and skills.
If everybody possessed an abundance of conventional capital, the advent of automation wouldn’t be something serious. But, the reality is that most individuals have minimal control over traditional capital. Rather, they rely on their human capital to amass wealth. When their work is automated and rendered obsolete, they lose that human capital. Do you see the issue?
The data vividly illustrates the extent of this problem. Before 1980, income growth was consistent among every American. But, between the years 1980 and 2014, a change happened. Lower-income earners experienced small, if any, income growth, while the 1 percent of the population earning high witnessed an increase in their earnings.
This trend is evident in affluent nations worldwide. For everyone but the most highly skilled employees, the value of human capital has diminished compared to before. In the USA, the poorest 50% of the population possesses a mere 2% of the nation’s wealth, while the wealthiest 1% owns about 40%.
There are several observations to see here. The most apparent one is that shifts in the future of the job market will bring about high inequality. Another pertinent question arises: How will society adapt when employment is no longer a necessity for individuals?
Chapter 7 – When automation disrupts the job market, the “Big State” needs to make sure that wealth is allocated
Typically, the majority of us had to work to earn our portion of the economic pie. However, what will be the case when automation destroys jobs? How can societies provide for individuals displaced by machines?
If the labor market fails to address this issue, another organization needs to intervene. The state is the only institution capable of doing that.
The majority of the developed nations have formed welfare states, an idea that traces back to the early twentieth century.
Yet, the traditional welfare state model requires transformation. Initially created to complement the labor market, its core principle was that those employed supported those without work. The norm was to anticipate employment.
However, in this era of an automated world, employment can no longer be the sole expectation. The welfare state must evolve into what the author terms the “Big State” – an institution that acknowledges that there won’t be adequate work for everybody.
The primary aim of the Big State entails levying taxes on the people gaining from automation and redistributing income to those adversely affected by it.
The Big State can tax various employees, such as software developers and managers in the tech industry. Additionally, it can collect revenue from owners of traditional assets such as land, machinery, or property rights. Furthermore, businesses, particularly those profiting more from increased automation, can also be subject to taxation.
As soon as the Big State has amassed these funds, the question arises: how should it allocate them? While some economists advocate for a Universal Basic Income (UBI) – providing cash payments to all individuals.
The author suggests a modification. He proposes a Conditional Basic Income (CBI), which would offer financial support to certain communities.
The Conditional Basic Income (CBI) evades a potential issue with Universal Basic Income (UBI) – the perception of unfairness. Distributing state funds to everyone could be seen as unjust by some individuals, leading to the risk of division within communities and potential conflict.
On the other hand, the author suggested CBI would exclusively benefit individuals who meet specific criteria. This arrangement would empower earners to direct their wealth toward people they genuinely wish to support.
This could lead to the emergence of a more improved and stable society – a society where individuals may have reduced workloads but still experience robust support from their communities.
A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind Book Review
Automation is said to transform the future of work in ways anybody could have thought. As computers teach themselves to do tasks once deemed beyond their capabilities, the labor market is undergoing significant transformation, potentially making human labor redundant. To help the individuals facing unemployment, it’s proposed that the government should redistribute revenue from top earners and capital owners to the broader population.
Attempts should be made to use technology to increase productivity.
Individuals doing high-skilled jobs, such as software engineers, usually utilize technology, including AI, to increase their productivity. However, opportunities to use technology for mundane, high-volume jobs exist across all sectors. For example, employing software for document scanning, data organization, or identifying patterns that you might miss without the assistance of computers.
