Crazy Wisdom
Episode #438: What If AI Is Just the Next Political Revolution?
- AI & Agents
- Money & Sovereignty
- Argentina & Latin America
- Geopolitics & Power
- AI
- intelligence
- the bitter lesson
- compute
- data
- scaling laws
- GPT-4
- language models
About this episode
Timestamps
00:00 Introduction and Setting
Key Insights
- 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.
- 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.
- 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.
- 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 could create a stark divide between AI-driven cities and rural or off-grid communities that choose to opt out. He draws a parallel to Robin Hanson’s Age of Em and suggests that those who best serve AI systems will hold power, while others may be marginalized or reduced to mere spectators in an AI-driven world.
- The Enlightenment’s Individualism Is Being Challenged by AI and Collective Intelligence – The discussion touches on how Western civilization’s emphasis on the individual may no longer align with the realities of intelligence and decision-making in an AI-driven era. Vendrov argues that intelligence is inherently collective—what matters is not individual brilliance but the ability to recognize and leverage diverse perspectives. This contradicts the traditional idea of intelligence as a singular, personal trait and suggests a need for new frameworks that incorporate AI into human networks in more effective ways.
- Javier Milei’s Libertarian Populism Reflects a Global Trend Toward Radical Experimentation – The rise of Argentina’s President Javier Milei exemplifies how economic desperation can drive societies toward bold, unconventional leaders. Vendrov and Alsop discuss how Milei’s appeal comes not just from his radical libertarianism but also from his blunt honesty and willingness to challenge entrenched power structures. His movement, however, raises deeper questions about whether libertarianism alone can provide a stable social foundation, or if voluntary cooperation and civil society must be explicitly cultivated to prevent libertarian ideals from collapsing into chaos.
- AI, Mythology, and the Need for New Narratives – The conversation closes with a reflection on the power of mythology in shaping human understanding of technological change. Vendrov suggests that as AI reshapes the world, new myths will be needed to make sense of it—perhaps similar to Tolkien’s elves fading as the age of men begins. He sees AI as part of an inevitable progression, where human intelligence gives way to something greater, but argues that this transition must be handled with care. The stories we tell about AI will shape whether we resist, collaborate, or simply fade into irrelevance in the face of machine intelligence.
Episode transcript
Welcome to the Crazy Wisdom Podcast. This podcast is for you. If you have an insane drive to find the truth of things, it's not the good answers that we seek, but the good questions. I interview a range of different guests from many different fields, all with the intention to uncover the simple truths that are hidden in plain sight. Most people don't want to go there. I go there, my guests go there, and you benefit. Please let me know if you enjoy these episodes and as always, subscribe on itunes, Spotify, or wherever you listen to the podcasts.
Welcome to the Crazy Wisdom Podcast. My guest today is Ivan Vendorf, and this is actually our second interview. And we just had a wonderful talk yesterday in Buenos Aires, and now we're in a park looking at some swans crossing the road, and we're about to go through a walk and do an interview. So welcome to the show, Ivan.
So what would you say is the most interesting thing you've learned about AI in the last week?
In the last week? I'm not sure I learned anything new in the last week. I feel like I've developed more confidence in some old ideas. Not. Not my ideas, other people's ideas.
The. Honestly, the oldest and most important one, still under underappreciated after 10 years, is the Bitter Lesson.
Right. Rich Sutton, the legendary reinforcement learning researcher from University of Alberta, wrote this essay a long time ago called the Bitter Lesson, which is like the main lesson of AI research for the last 70 years, is that general methods that leverage tons of compute and tons of data win out over everything else. So stop trying to impose your human concepts onto the system. Just let the information flow, let the compute flow that wins in the end. And Ilya Sutskever famously articulated this as like, if you make the network big enough at one of his Neurops presentations, if you make the network big enough, and if you train on enough data, success is guaranteed. Wow. And that sounded crazy back in, like, 2014, 2015, when he first said it. And now, actually, I think most people still haven't integrated that into their lesson.
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