The fish we eat usually die slowly, suffocating after they’re pulled out of the water. Saif Khawaja is the co-founder and CEO of a company called Shinkei. Saif's problem is this: Can he build a machine to efficiently kill fish in a way that is more humane?

In this episode, Saif explains: 

• How he came to believe that our current fishing practices are inhumane 
• How he adapted the Japanese technique of  ikijime for use at scale 
• Why Shinkei’s process results in better texture and longer shelf life than commodity fish.
• Why Shinkei is doing its own technology development, fish processing, and consumer branding. 

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Catherine Nakalembe is an assistant professor at the University of Maryland and the head of the Africa program at NASA Harvest, which uses satellite images to track agriculture around the world. Catherine’s problem is this: How do you predict when a famine is coming – and how do you convince leaders to act in time?

In this episode, Catherine explains: 

• How satellite data can predict crop failure months in advance
• Why phots can convince decision-makers faster than maps or statistics
• Why imaging systems developed for US farmland often don’t work in Africa
• Why a half-page memo moves food trucks faster than a scientific paper

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When Dr. Lara Jehi began treating epilepsy patients in the 2000s, critical surgical decisions were driven more by clinician intuition and expertise than data. Today, she is a leader of IBM and Cleveland Clinic’s Discovery Accelerator, using advanced AI and quantum computing to transform how researchers analyze data, simulate molecules, accelerate drug discovery, and develop more precise treatments. Malcolm Gladwell talks with Dr. Jehi about how quantum computing is changing biomedical research, and what these breakthroughs could mean for the future of healthcare and life sciences.

This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.

Visit us at https://www.ibm.com/think/podcasts/smart-talks

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Nir Barzilai is a physician and a researcher at the Albert Einstein College of Medicine in New York, where he runs the Institute for Geroscience. Nir’s problem is this: How can we use lifestyle changes and drugs to slow the process of aging?

In this episode, Nir explains: 

• The search for biological markers of aging
• The existing drugs that might reduce the risk of diseases associated with aging
• The four key lifestyle factors that every researcher in the field agrees on
• What he learned by studying the genes of people who lived to 100
• What “geroscience” means, and why it doesn’t have a Wikipedia page 

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Helen Pearson is the author of the book “Beyond Belief: How Evidence Shows What Really Works.” Helen's problem is this: How do you get people to make important decisions based on high-quality evidence, instead of on hunches or habits?

In this episode, Helen explains: 

• How a doctor's fatal mistake sparked the evidence-based medicine movement
• Why management is so resistant to evidence-based practices
• How systematic reviews changed the way she works as a science journalist
• Why global trust in science remains high – outside the US
• What a randomized trial of pint glasses revealed about preventing violent injuries.

Check out Helen’s book here. 

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In 2016, Geoffrey Hinton, one of the inventors of modern AI, predicted that AI would replace radiologists within years. Ten years later, we have a shortage of radiologists. AI has yet to revolutionize the field. What happened, and what does the story of AI and radiology tell us about AI and the future of work?

Saurabh Jha is a radiologist at the University of Pennsylvania. He's written about how he uses AI in his practice, and why radiologists have been slow to adopt AI. Saurabh has also traveled to India and Nepal to understand the very different ways AI is being adopted for radiology in the developing world.

In this episode, Saurabh explains: 

• Why AI still hasn't made radiologists faster or more productive
• The risks of novice radiologists using AI versus the benefits to experts
• Why a successful AI model would need to be nearly perfect 
• How AI is being used to spot tuberculosis in India
• Why diagnosing a normal scan is harder than diagnosing disease

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After spending decades as marginal players in the global auto industry, Chinese carmakers have become the world’s biggest exporters of cars. Chinese cars are some of the best cars on the planet, and they’re available almost everywhere – except in the US.

Michael Dunne is the CEO of Dunne Insights, a company that consults on the future of cars. Michael has been working with the Chinese auto industry since 1990. I talked with Michael about how China did it. The problem we discussed was: How can the United States catch up with the Chinese auto industry?

In this episode, Michael explains: 

• How China went from making terrible cars in 1990 to dominating global exports today
• Why China bet big on electric vehicles when nobody wanted them
• How Tesla's arrival in China revolutionized their EV industry 
• Why losing the car industry would be a national security problem for the US
• Why the future of a successful American auto industry is on the West Coast

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Phages are viruses that attack bacteria. People have been using phages to treat infections for over a hundred years, but in most cases they just don't work as well as antibiotics. 

Paul Garofolo is the co-founder and CEO of Locus Biosciences. His problem is this: How do you genetically engineer phages to be more effective at curing disease? 

In this episode, Paul explains: 

• Why wild phages never completely wipe out a bacterial infection
• How to engineer phages with CRISPR to kill bacteria more effectively
• The clinical trial testing phage therapy for chronic, recurrent UTIs
• Why persistent bacteria may contribute to autoimmune conditions like Crohn's disease
• How to use robots and AI to create phage cocktails from wastewater

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David Autor is a labor economist at MIT. He has written about how the arrival of personal computers increased inequality among American workers. But when it comes to AI, David thinks there could be a different outcome. 

Read David’s article about AI in Noema Magazine. 

In this episode, David explains: 

• How computers increased the wage gap between college and non-college educated workers
• Why AI’s ability to supplement expertise may open up expert fields to more people 
• How AI could make healthcare and legal services cheaper
• What fields will still experience rapid devaluation of skills due to AI 
• The “no regrets” policies that the US could enact to help ease the impact of AI  

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John Mills is the CEO and co-founder of a nonprofit called Watch Duty. His problem is this: How do you build an app to warn people when they are in immediate danger from a natural disaster? Watch Duty has millions of users and played a key role in the Los Angeles fires of 2025. In the show, John talks about how he built his app with the help of an army of volunteers, and why the government hadn’t already built something like Watch Duty.

In this episode, John explains: 

• Why one-quarter of Los Angeles residents downloaded Watch Duty during the 2025 fires
• Why the all-clear message matters almost as much as the initial warning
• How they are expanding into flood warnings
• How to decode firefighter’s radio chatter, and his favorite bit of jargon 

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