If you listened to our last couple of episodes, you’ll have heard some pretty skeptical takes on AI. But if you look at the stock market right now, you won’t see any trace of that skepticism. Since the launch of ChatGPT in late 2022, the chip company NVIDIA, whose chips are used in the majority of AI systems, has seen their stock shoot up by 700%. A month ago, that briefly made them the most valuable company in the world, with a market cap of more than $3.3 trillion.

And it’s not just chip companies. The S&P 500 (the index that tracks the 500 largest companies in the U.S.) is at an all-time high this year, in no small part because of the sheen of AI. And here in Canada, a new report from Microsoft claims that generative AI will add $187 billion to the domestic economy by 2030. As wild as these numbers are, they may just be the tip of the iceberg. Some researchers argue that AI will completely revolutionize our economy, leading to per capita growth rates of 30%. In case those numbers mean absolutely nothing to you, 25 years of 30% growth means we’d be a thousand times richer than we are now. It’s hard to imagine what that world would like – or how the average person fits into it. Luckily, Rana Foroohar has given this some thought. Foroohar is a global business columnist and an associate editor at The Financial Times. I wanted to have her on the show to help me work through what these wild predictions really mean and, most importantly, whether or not she thinks they’ll come to fruition.

Mentioned:

“Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity” by Daron Acemoglu and Simon Johnson (2023)

“Manias, Panics, and Crashes: A History of Financial Crises” by Charles P. Kindleberger (1978)

“Irrational Exuberance” by Robert J. Shiller (2016)

“Gen AI: Too much spend, too little benefit?” by Goldman Sachs Research (2024)

“Workers could be the ones to regulate AI” by Rana Foroohar (Financial Times, 2023)

“The Financial Times and OpenAI strike content licensing deal” (Financial Times, 2024)

“Is AI about to kill what’s left of journalism?” by Rana Foroohar (Financial Times, 2024)

“Deaths of Despair and the Future of Capitalism” by Anne Case and Angus Deaton (2020)

“The China Shock: Learning from Labor Market Adjustment to Large Changes in Trade” by David H. Autor, David Dorn & Gordon H. Hanson (2016)

Further Reading:

“Beware AI euphoria” by Rana Foroohar (Financial Times, 2024)

“AlphaGo” by Google DeepMind (2020)

Douglas Rushkoff has spent the last thirty years studying how digital technologies have shaped our world. The renowned media theorist is the author of twenty books, the host of the Team Human podcast, and a professor of Media Theory and Digital Economics at City University of New York. But when I sat down with him, he didn’t seem all that excited to be talking about AI. Instead, he suggested – I think only half jokingly – that he’d rather be talking about the new reboot of Dexter.

Rushkoff’s lack of enthusiasm around AI may stem from the fact that he doesn’t see it as the ground shifting technology that some do. Rather, he sees generative artificial intelligence as just the latest in a long line of communication technologies – more akin to radio or television than fire or electricity.

But while he may not believe that artificial intelligence is going to bring about some kind of techno-utopia, he does think its impact will be significant. So eventually we did talk about AI. And we ended up having an incredibly lively conversation about whether computers can create real art, how the “California ideology” has shaped artificial intelligence, and why it’s not too late to ensure that technology is enabling human flourishing – not eroding it.

Mentioned:

“Cyberia” by Douglas Rushkoff

“The Original WIRED Manifesto” by Louis Rossetto

“The Long Boom: A History of the Future, 1980–2020″ by Peter Schwartz and Peter Leyden

“Survival of the Richest: Escape Fantasies of the Tech Billionaires” by Douglas Rushkoff

“Artificial Creativity: How AI teaches us to distinguish between humans, art, and industry” by Douglas Rushkoff” by Douglas Rushkoff

“Empirical Science Began as a Domination Fantasy” by Douglas Rushkoff

“A Declaration of the Independence of Cyberspace” by John Perry Barlow

“The Californian Ideology” by Richard Barbrook and Andy Cameron

“Can AI Bring Humanity Back to Health Care?,” Machines Like Us Episode 5

Further Reading:

“The Medium is the Massage: An Inventory of Effects” by Marshall McLuhan

“Technopoly: The Surrender of Culture to Technology” by Neil Postman

“Amusing Ourselves to Death” by Neil Postman

It seems like the loudest voices in AI often fall into one of two groups. There are the boomers – the techno-optimists – who think that AI is going to bring us into an era of untold prosperity. And then there are the doomers, who think there’s a good chance AI is going to lead to the end of humanity as we know it.

While these two camps are, in many ways, completely at odds with one another, they do share one thing in common: they both buy into the hype of artificial intelligence.

But when you dig deeper into these systems, it becomes apparent that both of these visions – the utopian one and the doomy one – are based on some pretty tenuous assumptions.

Kate Crawford has been trying to understand how AI systems are built for more than a decade. She’s the co-founder of the AI Now institute, a leading AI researcher at Microsoft, and the author of Atlas of AI: Power, Politics and the Planetary Cost of AI.

Crawford was studying AI long before this most recent hype cycle. So I wanted to have her on the show to explain how AI really works. Because even though it can seem like magic, AI actually requires huge amounts of data, cheap labour and energy in order to function. So even if AI doesn’t lead to utopia, or take over the world, it is transforming the planet – by depleting its natural resources, exploiting workers, and sucking up our personal data. And that’s something we need to be paying attention to.

Mentioned:

“ELIZA—A Computer Program For the Study of Natural Language Communication Between Man And Machine” by Joseph Weizenbaum

“Microsoft, OpenAI plan $100 billion data-center project, media report says,” Reuters

“Meta ‘discussed buying publisher Simon & Schuster to train AI’” by Ella Creamer

“Google pauses Gemini AI image generation of people after racial ‘inaccuracies’” by Kelvin Chan And Matt O’brien

“OpenAI and Apple announce partnership,” OpenAI

Fairwork

“New Oxford Report Sheds Light on Labour Malpractices in the Remote Work and AI Booms” by Fairwork

“The Work of Copyright Law in the Age of Generative AI” by Kate Crawford, Jason Schultz

“Generative AI’s environmental costs are soaring – and mostly secret” by Kate Crawford

“Artificial intelligence guzzles billions of liters of water” by Manuel G. Pascual

“S.3732 – Artificial Intelligence Environmental Impacts Act of 2024″

“Assessment of lithium criticality in the global energy transition and addressing policy gaps in transportation” by Peter Greim, A. A. Solomon, Christian Breyer

“Calculating Empires” by Kate Crawford and Vladan Joler

Further Reading:

“Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence” by Kate Crawford

“Excavating AI” by Kate Crawford and Trevor Paglen

“Understanding the work of dataset creators” from Knowing Machines

“Should We Treat Data as Labor? Moving beyond ‘Free’” by I. Arrieta-Ibarra et al.