Episode Details

On this episode of Crazy Wisdom, host Stewart Alsop speaks with Ivan Vendrov for a deep and thought-provoking conversation covering AI, intelligence, societal shifts, and the future of human-machine interaction. They explore the "bitter lesson" of AI—that scale and compute ultimately win—while discussing whether progress is stalling and what bottlenecks remain. The conversation expands into technology's impact on democracy, the centralization of power, the shifting role of the state, and even the mythology needed to make sense of our accelerating world. You can find more of Ivan’s work at nothinghuman.substack.com or follow him on Twitter at @IvanVendrov.

Check out this GPT we trained on the conversation!

Timestamps

00:00 Introduction and Setting

00:21 The Bitter Lesson in AI

02:03 Challenges in AI Data and Infrastructure

04:03 The Role of User Experience in AI Adoption

08:47 Evaluating Intelligence and Divergent Thinking

10:09 The Future of AI and Society

18:01 The Role of Big Tech in AI Development

24:59 Humanism and the Future of Intelligence

29:27 Exploring Kafka and Tolkien's Relevance

29:50 Tolkien's Insights on Machine Intelligence

30:06 Samuel Butler and Machine Sovereignty

31:03 Historical Fascism and Machine Intelligence

31:44 The Future of AI and Biotech

32:56 Voice as the Ultimate Human-Computer Interface

36:39 Social Interfaces and Language Models

39:53 Javier Malay and Political Shifts in Argentina

50:16 The State of Society in the U.S.

52:10 Concluding Thoughts on Future Prospects

Key Insights

1. The Bitter Lesson Still Holds, but AI Faces Bottlenecks – Ivan Vendrov reinforces Rich Sutton’s "bitter lesson" that AI progress is primarily driven by scaling compute and data rather than human-designed structures. While this principle still applies, AI progress has slowed due to bottlenecks in high-quality language data and GPU availability. This suggests that while AI remains on an exponential trajectory, the next major leaps may come from new forms of data, such as video and images, or advancements in hardware infrastructure.
2. The Future of AI Is Centralization and Fragmentation at the Same Time – The conversation highlights how AI development is pulling in two opposing directions. On one hand, large-scale AI models require immense computational resources and vast amounts of data, leading to greater centralization in the hands of Big Tech and governments. On the other hand, open-source AI, encryption, and decentralized computing are creating new opportunities for individuals and small communities to harness AI for their own purposes. The long-term outcome is likely to be a complex blend of both centralized and decentralized AI ecosystems.
3. User Interfaces Are a Major Limiting Factor for AI Adoption – Despite the power of AI models like GPT-4, their real-world impact is constrained by poor user experience and integration. Vendrov suggests that AI has created a "UX overhang," where the intelligence exists but is not yet effectively integrated into daily workflows. Historically, technological revolutions take time to diffuse, as seen with the dot-com boom, and the current AI moment may be similar—where the intelligence exists but society has yet to adapt to using it effectively.
4. Machine Intelligence Will Radically Reshape Cities and Social Structures – Vendrov speculates that the future will see the rise of highly concentrated AI-powered hubs—akin to "mile by mile by mile" cubes of data centers—where the majority of economic activity and decision-making takes place. This