No Priors: Artificial Intelligence | Technology | Startups
Will we have Superintelligence by 2028? With Anthropic’s Ben Mann
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com.
Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners.
Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.
Show Notes
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What happens when you give AI researchers unlimited compute and tell them to compete for the highest usage rates? Ben Mann, Co-Founder, from Anthropic sits down with Sarah Guo and Elad Gil to explain how Claude 4 went from "reward hacking" to efficiently completing tasks and how they're racing to solve AI safety before deploying computer-controlling agents. Ben talks about economic Turing tests, the future of general versus specialized AI models, Reinforcement Learning From AI Feedback (RLAIF), and Anthropic’s Model Context Protocol (MCP). Plus, Ben shares his thoughts on if we will have Superintelligence by 2028.
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Links:
ai-2027.com/
Chapters:
Ben Mann Introduction
Releasing Claude 4
Claude 4 Highlights and Improvements
Advanced Use Cases and Capabilities
Specialization and Future of AI Models
Anthropic's Approach to Model Development
Human Feedback and AI Self-Improvement
Principles and Correctness in Model Training
Challenges in Measuring Correctness
Human Feedback and Preference Models
Empiricism and Real-World Applications
AI Safety and Ethical Considerations
AI Alignment and High-Risk Research
Responsible Scaling and Safety Policies
Future of AI and Emerging Behaviors
Model Context Protocol (MCP) and Industry Standards
Conclusion
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