I am thrilled to announce the release of the third episode of our podcast series. This episode, entitled “Universal Basic Irrelevance – UBI, AI, and Capitalism,” offers an in-depth exploration of a highly debated topic at the intersection of technology, economics, and social philosophy. Listen to the episode here on the website or on your favorite podcast player. The full transcript and links to sources are below. Enjoy!
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Hello and welcome to another episode of Blueberry Thoughts, a podcast at the intersection of human creativity and artificial intelligence. I’m your host, Ivor J. Burks, and in this episode, we’re tackling a rather contentious topic that has been central to discussions in both economics and technology for some time now. Let’s embark on a journey through what I have poetically called ‘Universal Basic Irrelevance’.
Today’s intersection of technology and economy is rich with both inspiring opportunities and daunting challenges. As we witness the accelerating pace of automation, especially with the advent of advanced AI systems, concerns about job displacement mount. Could Universal Basic Income, or UBI, provide a solution to this pressing issue?
In this episode, we delve into various UBI trials conducted worldwide, their impact on local communities, and how this might serve as a buffer against the potential effects of AI-driven job displacement. We’ll explore the role of Generative AI and its transformative influence across various industries, looking at some numbers from a recent report by Accenture and other sources. We will even try and question the concept of work itself and challenge the fusion of A.I. and capitalism with some help from a well-known science-fiction writer.
Let’s begin by examining some recent UBI experiments. The concept of UBI is not novel, but it has gained considerable traction in recent years as a potential remedy for income inequality and job displacement due to automation.
Over the past few years, we’ve seen a surge in the number of UBI trials globally. For instance, in the United States, the city of Compton in California launched the Compton Pledge, providing a guaranteed income to 800 residents for two years, based on the “guaranteed income” ideology of Dr. Martin Luther King. The initiative resulted in reduced income volatility, improved mental health, and an increase in full-time employment.
A similar experiment in Stockton, California, provided $500 a month for 24 months to 125 residents. Preliminary results indicated that recipients were less anxious and depressed, both overall and compared to a control group.
Back in the 1970s, the towns of Winnipeg and Dauphin in Manitoba, Canada, conducted a UBI experiment dubbed ‘Mincome,’ which showed promising outcomes. Despite the project’s premature termination, subsequent analyses found it led to an 8.5% decrease in hospitalizations.
On an international scale, Germany’s UBI experiment saw 120 individuals receiving 1200 Euros per month for three years. Initial results showed that recipients maintained or even increased their work input and reported improvements in their quality of life. Finland conducted a two-year UBI trial from 2017 to 2018. While the trial didn’t significantly boost employment as anticipated, it did enhance the mental and financial well-being of participants.
A notable trial in South Korea’s Gyeonggi province gave 13 million residents UBI in the form of local currency, leading to increased consumption, particularly within local businesses, thus invigorating the local economy.
These examples share a common thread: UBI, contrary to popular criticism, doesn’t discourage work. Instead, it offers a safety net, empowering people to make more confident decisions about their employment, spending, and overall well-being. Implementing UBI on a grand scale does present challenges, including cost, potential disincentives to work, and the risk of inflation.
But why are we discussing UBI now? The answer lies in the rapid advancement of AI and automation technologies. They’re transforming the way we work, and the effects are not always beneficial…
The rise of AI and automation has already disrupted many industries. Yet, with this power comes great responsibility and significant challenges. As AI evolves and becomes more advanced, concerns about job displacement affecting both white-collar and blue-collar workers grow.
In 2023, major tech companies such as Microsoft, Google, Meta, Amazon, Yahoo, and Zoom, along with many startups, announced substantial layoffs. Driven by multiple factors, including the macroeconomic environment and the pursuit of profitability, by May 2023, an alarming total of 193,860 employees had been laid off from 671 tech companies, as reported by layoffs.fyi
This striking figure signifies a broader trend. Advanced AI systems, like Generative AI, aren’t just automating repetitive, mundane tasks; they’re also performing complex language tasks, decision-making, and problem-solving – areas once considered exclusive to human intelligence.
According to a report by Accenture titled “A new era of generative AI for everyone”, Gen AI is poised to disrupt work as we know it, creating a new dimension of human and AI collaboration. The report suggests that almost every role in an enterprise has the potential to be reinvented, with most workers having an AI ‘copilot’.
Recall our previous episode where we discussed Microsoft’s Work Trend Index Annual Report for 2023. Microsoft refers to their new AI program as ‘Copilot’, emphasizing this new AI-employee alliance. This partnership between AI and humans is critical in shaping our future work landscape.”
The impact of this technology will be felt across industries, with varying degrees of automation and augmentation potential. For example, in the banking industry, 54% of tasks have high potential for augmentation by AI, while in the natural resources industry, the figure is just 20%.
Interestingly, language tasks, which account for 62% of total worked time in the US, have high potential to be automated or augmented by Large Language Models, or LLMs. This means that a substantial proportion of tasks currently performed by workers could be automated by AI in the future.
Despite the challenging picture this paints, those developing the technology assure us of a silver lining. The same technology that’s threatening jobs also holds the potential to create new ones. We can anticipate an increasing need for AI specialists to develop, maintain, and improve these systems. As AI takes over routine tasks, humans could concentrate more on high-level, strategic, and creative tasks.That sounds almost too good to be true.
First of all, this transition won’t be straightforward or seamless. It will require significant efforts in job redesign, task redesign, and reskilling of workers. Organizations that prepare for this future now will have a significant advantage. But doesn’t the concept of work itself also require rethinking? In his New Yorker piece, “Will AI Become the New McKinsey?”, Ted Chiang offers an intriguing perspective. He proposes a new metaphor for AI, comparing it to management-consulting firms such as McKinsey & Company, renowned for their influence on corporate strategies and executing mass layoffs.
Chiang highlights how AI, like McKinsey, can help companies boost profits while potentially sidestepping responsibility for decisions detrimental to the workforce or wider society. He discusses AI being used as a tool to avoid accountability, with companies shifting blame onto ‘the algorithm’. The risk, he notes, is AI becoming ‘capital’s willing executioners’, prioritizing company goals over employee welfare and societal balance. He further discusses the pursuit of profit above all else, arguing that most companies prioritize shareholder value, even if it means choosing an AI that follows these principles rather than one that considers broader social implications. He criticizes the concentration of wealth among a small group of individuals, facilitated by AI, as a damaging aspect of current capitalism.
As presently deployed, AI often replaces human labor for cost savings, a problem directly aligned with management’s interests. Chiang questions if AI could be developed to further the interests of workers instead of management. He compares the current trajectory of AI to accelerationism – a philosophy that endorses worsening conditions to incite drastic change. He cautions that the relentless pursuit of shareholder value could lead to societal collapse unless the government intervenes. He challenges the concept of universal basic income (UBI) as a solution, viewing it as passing the responsibility onto the government.
His arguments invite questions about the societal impact of AI and the necessity of public policy and regulation in managing AI’s socio-economic impacts. Chiang’s proposal that AI should empower workers and improve working conditions encourages us to consider how we can use technology to address social and economic injustices.
In this episode, we’ve taken a new good look at the rise of generative AI, its conceivably profound impact on jobs, as well as various experiments with Universal Basic Income worldwide, and their perceived outcomes.
Ted Chiang’s article leaves us thoughtful. He pushes the debate beyond the AI apocalypse some experts have been warning about recently, directing our focus instead toward the tangible social and economic issues that AI may create or exacerbate. Chiang emphasizes the need for regulatory oversight to ensure AI doesn’t unintentionally serve as a tool to avoid responsibility while perpetuating inequities and injustices. It seems that it’s up to us to avoid a future of universal basic irrelevance.
As we step into this AI-driven future, let’s not lose sight of the importance of regulations and proactive measures. We must work collectively – policymakers, corporate leaders, technologists – to guide AI and technology to benefit all of society, not just a select few. We should consider the potential implications of AI, not just in terms of the profits it may generate or the efficiencies it may create, but also in the broader context of social and economic justice.
In the next episode, we’ll revisit the basics and explore some of the technological advances that have made current generative AI possible. I will also try and explain how some of the Gen AI tools you may have heard of, such as ChatGPT or Stable Diffusion, actually work. Until then, continue to ponder at the intersection of human creativity and artificial intelligence, and remember to stay critical and ask hard questions.
Before I sign off, I wanted to let you know that we will be publishing the full transcripts as well as links to sources used in the podcast episodes on the blog at blueberrythoughts.com
That’s all for today. Thank you for tuning in, and we’ll catch you next time.
Until then, I’m Ivor J. Burks. Stay curious.