
Summary
Recent discussions surrounding artificial intelligence (AI) have highlighted significant concerns regarding the financial stability and ethical implications of major AI companies. Following a notable decline in AI and semiconductor stocks, which collectively lost over $1 trillion in value, skepticism is growing among investors about the sustainability of the current AI landscape. Cal Newport, a computer science professor at Georgetown University, emphasizes that the aggressive pursuit of artificial general intelligence by companies like Anthropic and OpenAI, often referred to as a "race to the top," raises questions about their financial strategies and long-term viability.
As lawmakers prepare for upcoming elections, Newport urges a more critical examination of the regulatory proposals being pushed by AI companies and their affiliated interest groups. He argues that current legislative efforts often lack transparency and are heavily influenced by the same entities that stand to benefit from lax regulations. Newport stresses the need for Congress to demand specific answers from these companies regarding their goals and the potential risks associated with their AI developments, rather than accepting vague promises of safety and innovation.
The conversation also touches on the disconnect between public sentiment and the priorities of tech companies. Many voters express frustration over the perceived focus on futuristic ambitions rather than addressing immediate societal needs. Newport suggests that a shift in how AI is conceptualized—moving away from the dominance of large language models and towards a broader understanding of AI capabilities—could pave the way for more responsible innovation and regulation. He advocates for leveraging existing product liability laws to hold companies accountable for their AI products, rather than allowing them to dictate the terms of regulation based on their own interests.
Show Notes
Everyone wants A.I. to be regulated—including the major A.I. companies like Anthropic and OpenAI. There’s just one problem… they want to have a say in what regulation looks like for their industry. Doesn’t that defeat the whole point?
Guests: Cal Newport, Full Professor of Computer Science at Georgetown University
Take our listener survey at slate.com/whatnextsurvey
Want more What Next? Subscribe to Slate Plus to access ad-free listening to the whole What Next family and across all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking “Try Free” at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.
Podcast production by Evan Campbell, Madeline Thames-Ducharme, Patrick Fort and Micah Peters. Need to set up your Slate Plus feed? If you subscribed through Slate.com, check out our FAQ at slate.com/podcastfaqs for easy instructions. Members subscribed via Apple Podcasts get automatic access—no setup required. Hosted on Acast. See acast.com/privacy for more information.
More from What Next: TBD | Tech, power, and the future
Perimenopause’s journey from little-understood phase before menopause to the subject of every ad on social media for women of a certain age was quick—and impossible to imagine without the internet.
An exchange of insults on X was all that director Alex Gibney got directly from the main character of his new documentary, “Musk.” But by interviewing his business partners, father, wives and girlfriends—and using a bit of movie magic—Gibney takes viewers inside Elon Musk’s head. Spoiler alert: it’s not pretty.
A.I. large language models are prompt, attentive, positive and frictionless—in short, much easier to deal with than humans with their own needs, wants, and moods. But, as long as humans aren’t social creatures by nature, this should be fine, right?
Artificial intelligence is pitched as a potential economic powerhouse—and also a potential bringer of the apocalypse. Can two economic and military rivals cooperate enough to fulfill the former (and former only)?
Why did the “Hugging Face Incident” scare A.I. researchers so much? And if they are so confident artificial intelligence needs to be slowed down, why aren’t they slowing it down?