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Post-ChatGPT Reality – an update to the 2023 Essential Reading List
I love good old wine 🍷 and hung meat 🥩. One side dish to those pinnacles of hedonic well-being is an enlightening, intense, controversial discussion on life, the universe, and all the rest 🤓 which is well covered by the best book in the universe known to me: The Hitchhiker’s Guide to the Galaxy. Do not dive into anything without this preparation. 😉
But here’s what changed since 2023: We’re no longer theorizing about AI. We’re living with it.
Two years ago, I recommended Nick Bostrom’s “Superintelligence” to prepare for the AI future. Today, I see executives deploying AI systems that struggle with simple tasks, like understanding children’s nursery rhymes, while claiming these systems are ‘ready for production’ and laying off many employees. Meanwhile, Apple’s researchers published a fascinating paper on the limitations of large reasoning models in complex reasoning tasks.
It’s striking that books like Superintelligence offer critical business insights for navigating AI’s future, yet I understand busy leaders may lack time to read more than one. Having studied these works extensively, I’m confident I can help you apply their lessons — let’s connect to discuss how. 😉
The Foundation Layer: Still Required Reading
Thinking, Fast and Slow
by Daniel Kahneman
If there’s only one book you can read, it’s still this one. But now it’s not just about human cognitive bias – it’s about understanding why AI systems exhibit similar biases at scale.
2025 Reality Check: LLMs amplify human biases from training data. Understanding System 1 vs System 2 thinking is now essential for AI deployment strategy.
Cosmosapiens: Human Evolution from the Origin of the Universe
by John Hands
Even more relevant now that we’re creating artificial minds. You cannot understand what we’re building without understanding what we are. Hands’ challenge to orthodox paradigms feels prophetic as we watch AI assumptions crumble in real-time.
2025 Connection: Every AI system embeds assumptions about intelligence, consciousness, and reality. Most of those assumptions are wrong.
The Cynefin Framework
by Dave Snowden
More critical than ever. Most AI failures happen because leaders apply “Clear” domain solutions to “Complex” domain problems.
2025 Application: If your AI strategy assumes predictable outcomes, you’re operating in the wrong Cynefin domain.
The AI Reality Update: Essential 2025 Additions
Human Compatible: Artificial Intelligence and the Problem of Control
by Stuart Russell
Russell moved beyond Bostrom’s theoretical concerns to practical alignment challenges. Written before ChatGPT, but every prediction is playing out in real-time.
Why Now: Russell understands that alignment isn’t a future problem – it’s happening in every AI deployment today.
PS: You have already read and understood Kurt Russel’s: Artificial Intelligence: A Modern Approach, I assume and hope.
Life 3.0: Being Human in the Age of Artificial Intelligence
by Max Tegmark
Tegmark bridges the gap between cosmic perspective and immediate AI challenges. Essential for understanding how current developments fit into longer timescales.
2025 Insight: We’re not just building tools. We’re potentially creating the next phase of life itself.
The Alignment Problem: Machine Learning and Human Values
by Brian Christian
The most practical book on AI safety. Christian explains why getting AI systems to do what we want is exponentially harder than it appears.
Real-World Relevance: Every “hallucination,” every biased output, every unexpected AI behavior is an alignment problem in miniature.
The European and Global Reality: New Required Reading
The Age of AI: And Our Human Future
by Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher
Geopolitical implications of AI development. Essential for understanding why the EU AI Act matters beyond compliance.
European Context: As AI becomes a geopolitical weapon, European regulatory frameworks become competitive advantages or disadvantages.
Race After Technology: Abolitionist Tools for the New Jim Code
by Ruha Benjamin
Critical for understanding how AI systems perpetuate and amplify social inequalities. Especially relevant as European markets grapple with algorithmic accountability.
2025 Urgency: The EU AI Act’s bias requirements aren’t just legal compliance – they’re business survival as automated discrimination becomes legally actionable.
The Weaponisation of Everything: A Field Guide to the New Way of War
by Mark Galeotti
no comment 🙊🙉🙈
The Complexity Addition: Systems Thinking for AI Age
Antifragile: Things That Gain from Disorder
by Nassim Nicholas Taleb
More relevant than ever. AI systems are complex, unpredictable, and often fail in unexpected ways. Organizations need antifragile approaches to AI deployment.
Practical Application: Build systems that get stronger when AI fails, not systems that break when AI behaves unexpectedly.
Thinking in Systems: A Primer
by Donella Meadows
Essential for understanding why AI initiatives fail at the organizational level. Most AI problems aren’t technical — they’re systemic.
System Insight: Changing the AI tool without changing the system just creates expensive technical debt.
The Duck Test Addition: AI Deployment Reality
Click here for 👉 The 🤖 AI 🦆 Duck 👁️🗨️ Test
AI Superpowers: China, Silicon Valley, and the New World Order
by Kai-Fu Lee
Understanding global AI competition is essential for European business strategy. Lee’s insider perspective on both Chinese and American AI development is invaluable.
Strategic Relevance: European companies need to understand the competitive landscape they’re entering with AI deployment.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
by Cathy O’Neil
O’Neil’s work predicted many of the AI bias problems we’re experiencing today. Essential for understanding why algorithmic accountability matters.
EU AI Act Connection: The regulatory framework isn’t academic – it’s responding to real harms O’Neil documented.
The Personal Development Layer: Leading Through AI Transition
Range: Why Generalists Triumph in a Specialized World
by David Epstein
As AI handles more specialized tasks, human value increasingly comes from synthesis, pattern recognition across domains, and adaptive thinking.
Leadership Insight: The leaders who succeed in the AI age will be those who can navigate complexity across multiple domains.
The Innovator’s Dilemma
by Clayton Christensen
AI is the ultimate disruptive technology. Christensen’s frameworks for understanding disruption are essential for navigating AI transition.
2025 Application: Most AI initiatives fail because they apply sustaining innovation frameworks to disruptive technology.
The Philosophical Depth: Updated for AI Age
I Am a Strange Loop
by Douglas Hofstadter
Even more profound now that we’re creating artificial loops. Hofstadter’s exploration of consciousness and self-reference is essential for understanding what we might be building.
AI Connection: LLMs exhibit strange loop behaviors. Understanding consciousness helps evaluate what’s really happening in AI systems.
The Beginning of Infinity: Explanations That Transform the World
by David Deutsch
Deutsch’s work on the nature of knowledge and progress provides philosophical grounding for evaluating AI capabilities and limitations.
Knowledge Framework: AI systems process information. Only humans (so far) create knowledge through conjecture and criticism.
The Wild Card Philosophy: Science Fiction That Predicted Reality
The World of Null-A
by A.E. van Vogt
This 1948 novel explored non-Aristotelian logic systems and multi-valued logic decades before AI researchers rediscovered these concepts. Van Vogt imagined minds that could process contradictory information without breaking – exactly what modern AI alignment researchers struggle with.
2025 Relevance: LLMs often generate contradictory outputs. Van Vogt’s exploration of non-binary logic systems offers frameworks for understanding AI behavior that classical logic cannot explain.
Deep Connection: The book’s premise that “the map is not the territory” (based on Korzybski’s General Semantics) is fundamental to understanding why AI systems hallucinate and why simple tests like the Duck Test reveal complex truths.
Dune
by Frank Herbert
Herbert’s universe is built on the aftermath of the Butlerian Jihad – humanity’s war against “thinking machines” that ended with the commandment: “Thou shalt not make a machine in the likeness of a human mind.” The result? Humans developed extraordinary mental capabilities to replace artificial intelligence.
2025 Relevance: Herbert imagined a post-AI society where humans became Mentats (human computers), Navigators (prescient space pilots), and Bene Gesserit (masters of human psychology). Instead of being replaced by machines, humans evolved beyond their original limitations.
Strategic Insight: The Mentat discipline – “It is by will alone I set my mind in motion” – offers a framework for human-AI collaboration. Mentats use spice to enhance computation while remaining fundamentally human. Perhaps the future isn’t AI replacement, but AI augmentation of irreplaceable human capabilities.
Do Androids Dream of Electric Sheep?
by Philip K. Dick
Dick’s exploration of what makes someone “human” when artificial beings become indistinguishable from natural ones. The Voight-Kampff test that distinguishes humans from replicants parallels modern AI detection challenges.
AI Reality Check: We’re already living in Dick’s world. The Turing Test, AI alignment problems, and questions about AI consciousness are straight from his paranoid imagination. Dick understood that the real threat isn’t AI becoming too different from humans – it’s becoming too similar.
Solaris
by Stanisław Lem
Lem’s masterpiece about humanity’s encounter with a truly alien intelligence that can’t be understood through human categories. The ocean-planet Solaris creates perfect replicas of humans from memory, raising profound questions about consciousness and reality.
AI Parallel: Current LLMs might be like Lem’s ocean – alien intelligences that create convincing simulations of human thought without actually understanding what they’re doing. We project human-like reasoning onto statistical pattern matching, just as Solaris’s researchers projected human psychology onto an incomprehensible alien mind.
Deep Warning: The book’s central insight – that we may be fundamentally incapable of understanding non-human intelligence – applies directly to AI systems. We assume they think like us because their outputs resemble human language.
The 2025 Meta-Lesson: Integration Over Isolation
The biggest change since 2023 isn’t new books – it’s the recognition that AI deployment is simultaneously:
- Philosophical (What is intelligence? What are human values?)
- Technical (How do these systems actually work?)
- Organizational (How do humans and AI systems work together?)
- Regulatory (How do we ensure beneficial outcomes?)
- Geopolitical (How does AI affect global power structures?)
- Mentally (SF prepares us as a society for the future – it is of course to read SF as preparation once its prediction is reality)
No single book covers everything. But reading across these domains creates the intellectual foundation for navigating AI transition successfully.
The Anti-Hype Reading Strategy
- Avoid: Books that promise AI will solve everything or destroy everything with certainty.
- Seek: Books that help you think clearly about complex, uncertain situations.
- Remember: The goal isn’t to predict the future. It’s to build mental models that help you navigate whatever future emerges.
Next Steps: From Reading to Application
- Start with Russell or Christian if you’re deploying AI systems now
- Read Kahneman first if you’re new to thinking about thinking
- Add Snowden’s Cynefin for decision-making frameworks
- Choose domain-specific books based on your particular challenges
The reading list is a foundation. The real learning happens when you apply these concepts to actual AI deployment challenges.
Remember: In 2023, this was preparation. In 2025, this is survival equipment.
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