Machines Like Us

If we don’t build it, China will.
That’s the rallying cry of the tech companies and governments racing to develop artificial intelligence as fast as humanly possible. The argument is that whoever reaches AGI first won’t just be dominant technologically, or economically – they’ll be the world’s next super power. But, if I’m being honest, I don’t know if that framing holds up. And part of the reason for that is that we don’t really understand China.
Enter Keyu Jin. Jin is a Harvard trained economist who splits her time between London and Beijing, and her book, The New China Playbook, is her attempt to “read China in the original” – to provide a firsthand look at the forces that shaped the country’s unprecedented rise. China’s success is a puzzle. How did one of the poorest nations on the planet become the second richest in less than a century? How did an economy without free markets birth a tech sector that rivals – and in some ways surpasses – Silicon Valley?
The answers to these questions aren’t academic. China became a global power without capitalism and without democracy, which means its success has profound implications for both.
And as Canada sets out to find its footing in a rapidly changing world order, one thing is abundantly clear: we need to start reckoning with the Chinese playbook. 
Mentions:
The New China Playbook, by Keyu Jin

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Nine months ago, Elon Musk said 2025 would be the year chatbots became smarter than humans. Sam Altman thought it would be the year fully autonomous AIs entered the work force. And Dario Amodei, the CEO of Anthropic, predicted that by the end of the year, AI would be writing 90 per cent of all software code.
We’re two weeks into the new year, and none of those things have happened. So, full disclosure: I have no idea if we’re going to reach artificial general intelligence or see the rise of humanoid robots this year. If the people at the centre of the industry can’t figure it out, I doubt I can.
But I do have some ideas about how AI could reshape our world over the next 12 months. I think we’re going to see a new political movement pushing back against AI adoption and leaning into our collective humanity. Democratic governments will defy an increasingly protectionist America and start taking digital regulation seriously again. And we’ll start establishing cultural norms about AI use – like whether you really need to respond to that AI-generated e-mail your colleague just sent.
On this episode, I turn the mics around and invite my longtime producer, Mitchell Stuart, to ask me about what’s actually in store for the year ahead.
Mentioned:
Trust, attitudes and use of artificial intelligence (2025), KPMG
Human-centric AI: Perspectives on trust and the future of AI (2025), Telus
Could an Alternative AI Save Us from a Bubble? (Gary Marcus), by Machines Like Us
GPT-5 System Card, OpenAI
Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support, by Mahmud Ohmar et al (Nature) 

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Jensen Huang is something of an enigma. The NVIDIA CEO doesn’t have social media and, until recently, rarely gave interviews. Yet he may be the most important person in AI.
Under his leadership, NVIDIA has become a goliath. Somewhere between 80 and 90 per cent of AI tools run on NVIDIA hardware, making it the world’s most valuable company. But unlike his contemporaries, Huang has been remarkably quiet about the technology – and the world – he’s building.
In his new book, The Thinking Machine: Jensen Huang, NVIDIA, and the World’s Most Coveted Microchip, journalist Stephen Witt pulls back the curtain. And what he finds is, at times, shocking: Huang believes there is zero risk in developing superintelligence.
So who is Jensen Huang? And should we worry that the most powerful person in AI is racing forward at breakneck speed, blind to the potential consequences?
Mentioned:
The Thinking Machine: Jensen Huang, NVIDIA, and the World’s Most Coveted Microchip, by Stephen Witt
How Jensen Huang’s Nvidia Is Powering the A.I. Revolution, by Stephen Witt (The New Yorker)
The A.I. Prompt That Could End the World, by Stephen Witt (New York Times)
Machines Like Us is produced by Mitchell Stuart. Our theme song is by Chris Kelly. Video editing by Emily Graves. Our executive producer is James Milward. Special thanks to Angela Pacienza and the team at The Globe and Mail.
Media sourced from the BBC.

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It was an idea that defied logic: an online encyclopedia that anyone could edit.
You didn’t need to have a PhD or even use your real name – you just needed an internet connection. Against all odds, it worked. Today, billions of people use Wikipedia every month, and studies show it’s about as accurate as a traditional encyclopedia.
But how? How did Wikipedia not just turn into yet another online cesspool, filled with falsehoods, partisanship and AI slop? Wikipedia founder Jimmy Wales just wrote a book called The Seven Rules of Trust, where he explains how he was able to build that rarest of things: a trustworthy source of information on the internet. In an era when trust in institutions is collapsing, Wales thinks he’s found a blueprint – not just for the web, but for everything else too.
Mentioned:
The Seven Rules of Trust by Jimmy Wales and Dan Gardner
A False Wikipedia ‘Biography’ by John Seigenthaler (USA Today)
Machines Like Us is produced by Mitchell Stuart. Our theme song is by Chris Kelly. Video editing by Emily Graves. Our executive producer is James Milward. Special thanks to Angela Pacienza and the team at The Globe and Mail.
Photo Illustration: The Globe and Mail/Brendan McDermid/Reuters

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Over the last couple of years, massive AI investment has largely kept the stock market afloat. Case in point: the so-called Magnificent 7 – tech companies like NVIDIA, Meta, and Microsoft – now account for more than a third of the S&P 500’s value. (Which means they likely represent a significant share of your investment portfolio or pension fund, too.)
There’s little doubt we’re living through an AI economy. But many economists worry there may be trouble ahead. They see companies like OpenAI – valued at half a trillion dollars while losing billions every month – and fear the AI sector looks a lot like a bubble. Because right now, venture capitalists aren’t investing in sound business plans. They’re betting that one day, one of these companies will build artificial general intelligence.
Gary Marcus is skeptical. He’s a professor emeritus at NYU, a bestselling author, and the founder of two AI companies – one of which was acquired by Uber. For more than two decades, he’s been arguing that large language models (LLMs) – the technology underpinning ChatGPT, Claude, and Gemini – just aren’t that good.
Marcus believes that if we’re going to build artificial general intelligence, we need to ditch LLMs and go back to the drawing board. (He thinks something called “neurosymbolic AI” could be the way forward.)
But if Marcus is right – if AI is a bubble and it’s about to pop – what happens to the economy then?
Mentioned:
The GenAI Divide: State of AI in Business 2025, by Project Nanda (MIT)
MIT study finds AI can already replace 11.7% of U.S. workforce, by MacKenzie Sigalos (CNBC)
The Algebraic Mind, by Gary Marcus
We found what you’re asking ChatGPT about health. A doctor scored its answers, by Geoffrey A. Fowler (The Washington Post)

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