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Will we have Superintelligence by 2028? With Anthropic’s Ben Mann

2025-06-12 · 41 min · episode 118 · 12 entities

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Asserted relationships

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    Will we have Superintelligence by 2028? With Anthropic’s Ben Mann
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  • → hosted by Conviction company
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Entities found in this episode

concepts 8

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  • mentioned Technology concept
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  • mentioned Business concept
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  • mentioned Entrepreneurship concept
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  • discusses Science concept
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  • discusses Technology concept
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    evidence rules-v4
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  • discusses Business concept
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  • discusses Entrepreneurship concept
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    evidence rules-v4
    Feed category: Entrepreneurship

companys 2

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    Feed author/publisher: Conviction
  • hosted by Conviction company
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    Feed author/publisher: Conviction

persons 1

  • mentioned Anthropic’s Ben Mann person
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    Will we have Superintelligence by 2028? With Anthropic’s Ben Mann

podcasts 1

Episode description as stored
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.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @8enmann Links:  ai-2027.com/  Chapters: 00:00 Ben Mann Introduction 00:33 Releasing Claude 4 02:05 Claude 4 Highlights and Improvements 03:42 Advanced Use Cases and Capabilities 06:42 Specialization and Future of AI Models 09:35 Anthropic's Approach to Model Development 18:08 Human Feedback and AI Self-Improvement 19:15 Principles and Correctness in Model Training 20:58 Challenges in Measuring Correctness 21:42 Human Feedback and Preference Models 23:38 Empiricism and Real-World Applications 27:02 AI Safety and Ethical Considerations 28:13 AI Alignment and High-Risk Research 30:01 Responsible Scaling and Safety Policies 35:08 Future of AI and Emerging Behaviors 38:35 Model Context Protocol (MCP) and Industry Standards 41:00 Conclusion