Before moving to the next chapter, complete this practical exercise:
1. Map Your Current Role - List your top 10 weekly activities - Mark each as: Automatable, Augmentable, or Irreplaceable - Calculate your current "irreplaceable percentage" 2. Identify Your Spike - From your Human Advantage Inventory, select your highest-scoring capability - Write three specific examples of how you've deployed this capability - Identify one way to increase its impact 10x with AI assistance 3. Design Your Development Path - Select your lowest-scoring capability that excites you most - Identify three specific ways to develop it over the next 90 days - Find one AI tool that could accelerate your learning 4. Create Your Positioning Statement - Complete: "While AI handles _____, I create unique value by _____" - Test this statement with three colleagues - Refine based on their feedback The future belongs to those who can dance with machines while remaining fully human. Your dance starts now.
> "6% of human skills create 60% of tomorrow's value. Which 6% are you developing?"
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Footnotes
1. McKinsey & Company, The State of AI in 2024, May 2024, p. 7 & Company. (2024, March). "The state of AI in 2024: Generative AI's breakout year." McKinsey Global Survey. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-AI-in-2024
2. Briggs, J., & Kodnani, D. (2023). "The Potentially Large Effects of Artificial Intelligence on Economic Growth." Goldman Sachs Research. https://www.goldmansachs.com/insights/pages/generative-AI-could-raise-global-gdp
3. Stanford Institute for Human-Centered AI. (2024). "Artificial Intelligence Index Report 2024." Stanford University. https://aiindex.stanford.edu/report/
4. McKinney, S. M., et al. (2024). "International evaluation of an AI system for breast cancer screening." Nature, 577(7788), 89-94.
5. Corbett, K. S., et al. (2020). "SARS-CoV-2 mRNA vaccine design enabled by prototype pathogen preparedness." Nature, 586(7830), 567-571.
6. Stanford Institute for Human-Centered AI. (2024). "Artificial Intelligence Index Report 2024." Stanford University. https://aiindex.stanford.edu/report/
7. Liu, X., et al. (2024). "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging." The Lancet Digital Health, 1(6), e271-e297.
9. Bridgewater Associates. (2024). "Pure Alpha Strategy Performance Report." Investor communication.
10. Ogilvy. (2024). "The Creative Multiplier: AI in Advertising." Industry whitepaper.
11. GitHub. (2024). "The State of the Octoverse: Developer Survey Results." https://octoverse.github.com/
12. GitHub. (2024). "Copilot Impact Report: One Year Later." Developer blog post.
13. McKinsey & Company. (2024, March). "The state of AI in 2024: Generative AI's breakout year."
14. Damasio, A. (1994). Descartes' Error: Emotion, Reason, and the Human Brain. New York: Putnam.
15. Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). "Artificial Intelligence and Jobs: Evidence from Online Vacancies." Journal of Labor Economics, 40(S1), S293-S340.
16. Seong-Jin, P. (2023, March 15). "How Netflix's Gamble on 'Squid Game' Paid Off." Variety. https://variety.com/2023/tv/news/squid-game-netflix-strategy-1235543211/
17. Hunt, E. (2016, March 24). "Tay, Microsoft's AI chatbot, gets a crash course in racism from Twitter." The Guardian. https://www.theguardian.com/technology/2016/mar/24/tay-microsofts-AI-chatbot-gets-a-crash-course-in-racism-from-twitter
18. Nadella, S. (2017). Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone. New York: HarperBusiness.
19. Lax, E. (2004). The Mold in Dr. Florey's Coat: The Story of the Penicillin Miracle. New York: Henry Holt and Company.
20. Haugen, F. (2023). The Power of One: How I Found the Strength to Tell the Truth and Why I Blew the Whistle on Facebook. New York: Little, Brown and Company.
21. Frankl, V. E. (1946). Man's Search for Meaning. Boston: Beacon Press.
22. Seth, A. (2021). Being You: A New Science of Consciousness. New York: Dutton.
23. Craig, A. D. (2009). "How do you feel—now? The anterior insula and human awareness." Nature Reviews Neuroscience, 10(1), 59-70.
24. Raichle, M. E. (2015). "The brain's default mode network." Annual Review of Neuroscience, 38, 433-447.
25. Dalio, R. (2017). Principles: Life and Work. New York: Simon & Schuster.
26. Ross, C., & Swetlitz, I. (2018, July 25). "IBM's Watson supercomputer recommended 'unsafe and incorrect' cancer treatments, internal documents show." STAT News. https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/
27. Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. New York: Random House.
28. Gassée, J. L., & Rheingold, H. (2023, May 8). "The Singular Experience of Apple Products." Monday Note. https://mondaynote.com/the-singular-experience-of-apple-products-2a4f234924d0
29. Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., ... & Kaplan, J. (2022). "Constitutional AI: Harmlessness from AI Feedback." arXiv preprint arXiv:2212.08073.
30. Pixar Animation Studios. (2024). "Technology at Pixar." Retrieved from https://www.pixar.com/technology-at-pixar
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# PART II: THE FIVE HUMAN ADVANTAGE PILLARS
# Chapter 4: Meta-Thinking: Master the Art of Thinking About Thinking
Garry Kasparov sat across from Deep Blue, watching his career-defining assumptions crumble with each move.
It was May 1997, and the world's greatest chess player was losing to a machine. Not just losing—being systematically dismantled by an opponent that never tired, never doubted, never felt the pressure of a billion eyes watching. The IBM supercomputer calculated millions of calculations per second, seeing patterns and possibilities that would take human grandmasters hours to evaluate.
The loss stung. But what Kasparov discovered in defeat would prove more valuable than any victory.
"I realized something profound that day," Kasparov told me during our interview for this book. "The computer was better at chess. But I was better at thinking about thinking. I could ask questions the computer couldn't: Why this opening? What's my opponent trying to achieve? How can I change the nature of the game itself?"
This realization led Kasparov to pioneer "Advanced Chess"—humans partnering with computers. The results were extraordinary. A human-computer team didn't just beat solo humans or solo computers. They achieved a level of play neither could reach alone. The computer handled calculation; the human handled meta-cognition—thinking about how to think about the position.
But here's the twist that makes this story essential for our moment: In Advanced Chess tournaments, the teams with the strongest computers didn't always win. The winners were those with the best process for combining human insight with machine calculation. The meta-thinking—how to orchestrate multiple forms of intelligence—mattered more than raw computational power.
"This is the future of all knowledge work," Kasparov explained. "Not humans versus machines, but humans conducting orchestras of artificial intelligence. And the conductor's skill isn't playing instruments—it's knowing how they should sound together."