#33: Regulating AI
November 10, 2026
Picking up a Minnesota Public Radio discussion with University of Minnesota guest Daniel Schwartz, Dustin and Andy examine why training an AI model differs from teaching a person. They discuss what could happen if models improve themselves faster than people can test them, and why that raises questions about oversight.
They also look at harms businesses and customers may encounter sooner: denied insurance claims, misleading offers, and more convincing scams. Their practical takeaway is to strengthen consumer protections while keeping room for open models and smaller organizations to innovate.
🔗 Resources and mentions from this episode:
- University of Minnesota
- Minnesota Public Radio
- Daniel Schwartz
- OpenAI
- Anthropic
- Adobe
- Claude
- Hugging Face
- Linux
- Microsoft
- Apple
- Siri
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