
Sign up to save your podcasts
Or


Since the release of ChatGPT, huge amounts of attention and funding have been directed toward chatbots. These A.I. systems are trained on copious amounts of human-generated data and designed to predict the next word in a given sentence. They are hilarious and eerie and at times dangerous.
But what if, instead of building A.I. systems that mimic humans, we built those systems to solve some of the most vexing problems facing humanity?
In 2020, Google DeepMind unveiled AlphaFold, an A.I. system that uses deep learning to solve one of the most important challenges in all of biology: the so-called protein-folding problem. The ability to predict the shape of proteins is essential for addressing numerous scientific challenges, from vaccine and drug development to curing genetic diseases. But in the 50-plus years since the protein-folding problem had been discovered, scientists had made frustratingly little progress.
Enter AlphaFold. By 2022, the system had identified 200 million protein shapes, nearly all the proteins known to humans. And DeepMind is also building similar systems to accelerate efforts at nuclear fusion and has spun off Isomorphic Labs, a company developing A.I. tools for drug discovery.
Demis Hassabis is the chief executive of Google DeepMind and the leading architect behind AlphaFold. So I asked him on the show to talk me through how AlphaFold actually works, the kinds of problems similar systems could solve and what an alternative pathway for A.I. development could look like.
Mentioned:
“The Curse of Recursion” by Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross Anderson
“DeepMind’s CEO Helped Take AI Mainstream. Now He’s Urging Caution” by Billy Perrigo
Book Recommendations:
The Fabric of Reality by David Deutsch
Permutation City by Greg Egan
Consider Phlebas by Iain M. Banks
Listen to this podcast in New York Times Audio, our new iOS app for news subscribers. Download now at nytimes.com/audioapp
Thoughts? Guest suggestions? Email us at [email protected].
You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.
This episode of “The Ezra Klein Show” was produced by Rogé Karma. Fact checking by Michelle Harris. Fact checking by Michelle Harris with Rollin Hu. Our senior engineer is Jeff Geld. The show’s production team also includes Emefa Agawu, Annie Galvin and Kristin Lin. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero.
Unlock full access to New York Times podcasts and explore everything from politics to pop culture. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify.
By New York Times Opinion4.3
1322913,229 ratings
Since the release of ChatGPT, huge amounts of attention and funding have been directed toward chatbots. These A.I. systems are trained on copious amounts of human-generated data and designed to predict the next word in a given sentence. They are hilarious and eerie and at times dangerous.
But what if, instead of building A.I. systems that mimic humans, we built those systems to solve some of the most vexing problems facing humanity?
In 2020, Google DeepMind unveiled AlphaFold, an A.I. system that uses deep learning to solve one of the most important challenges in all of biology: the so-called protein-folding problem. The ability to predict the shape of proteins is essential for addressing numerous scientific challenges, from vaccine and drug development to curing genetic diseases. But in the 50-plus years since the protein-folding problem had been discovered, scientists had made frustratingly little progress.
Enter AlphaFold. By 2022, the system had identified 200 million protein shapes, nearly all the proteins known to humans. And DeepMind is also building similar systems to accelerate efforts at nuclear fusion and has spun off Isomorphic Labs, a company developing A.I. tools for drug discovery.
Demis Hassabis is the chief executive of Google DeepMind and the leading architect behind AlphaFold. So I asked him on the show to talk me through how AlphaFold actually works, the kinds of problems similar systems could solve and what an alternative pathway for A.I. development could look like.
Mentioned:
“The Curse of Recursion” by Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross Anderson
“DeepMind’s CEO Helped Take AI Mainstream. Now He’s Urging Caution” by Billy Perrigo
Book Recommendations:
The Fabric of Reality by David Deutsch
Permutation City by Greg Egan
Consider Phlebas by Iain M. Banks
Listen to this podcast in New York Times Audio, our new iOS app for news subscribers. Download now at nytimes.com/audioapp
Thoughts? Guest suggestions? Email us at [email protected].
You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.
This episode of “The Ezra Klein Show” was produced by Rogé Karma. Fact checking by Michelle Harris. Fact checking by Michelle Harris with Rollin Hu. Our senior engineer is Jeff Geld. The show’s production team also includes Emefa Agawu, Annie Galvin and Kristin Lin. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero.
Unlock full access to New York Times podcasts and explore everything from politics to pop culture. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify.

91,066 Listeners

8,849 Listeners

38,494 Listeners

6,755 Listeners

3,879 Listeners

10,741 Listeners

1,500 Listeners

9,480 Listeners

2,069 Listeners

143 Listeners

87,379 Listeners

112,601 Listeners

56,441 Listeners

1,513 Listeners

12,632 Listeners

307 Listeners

7,081 Listeners

12,261 Listeners

468 Listeners

51 Listeners

2,319 Listeners

380 Listeners

6,683 Listeners

5,471 Listeners

1,500 Listeners

10,886 Listeners

1,562 Listeners

3,436 Listeners

11 Listeners

537 Listeners

23 Listeners

0 Listeners