The conversation around AGI and ASI is louder than ever—but the definitions are often abstract, technical, and disconnected from what actually matters. In this episode, I break down a human-centered way of thinking about these terms, why they’re important, and a system that could help us get there. I talk about: • A Better Definition of AGI and ASI Instead of technical abstractions, AGI is defined as the ability to perform most cognitive tasks as well as a 2022 U.S.-based knowledge worker. ASI is intelligence that surpasses that level. Framing it this way helps us immediately understand why it matters—and what it threatens. • Invention as the Core Output of Intelligence The real value of AGI and ASI is their ability to generate novel solutions. Drawing inspiration from the Enlightenment, we explore how humans innovate—and how we can replicate that process using AI, automation, and structured experimentation. • Scaling the Scientific Method with AI By building systems that automate idea generation, recombination, and real-world testing, we can massively scale the rate of innovation. This framework—automated scientific iteration—could be the bridge from human intelligence to AGI and beyond. Subscribe to the newsletter at: https://danielmiessler.com/subscribe Join the UL community at: https://danielmiessler.com/upgrade Follow on X: https://x.com/danielmiessler Follow on LinkedIn: https://www.linkedin.com/in/danielmiessler
Chapters: 00:00 - Why AGI and ASI Definitions Should Be Human-Centric 01:55 - Defining AGI as a 2022-Era US Knowledge Worker 03:04 - Defining ASI and Why It’s Harder to Conceptualize 04:04 - The Real Reason to Care: AGI and ASI Enable Invention 05:04 - How Human Innovation Happens: Idea Collisions and Enlightenment Lessons 06:56 - Building a System That Mimics Human Idea Generation at Scale 09:00 - The Challenge of Testing: From A/B Tests to Biotech Labs 10:52 - Creating an Automated, Scalable Scientific Method With AI 12:50 - A Timeline to AGI and ASI: Predictions for 2027–2030
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