Managing Context Windows vs. Chunked Embeddings

In this episode of Crazy Wisdom, host Stewart Alsop talks with Kevin Smith, co-founder of Snipd, about how AI is reshaping the way we listen, learn, and interact with podcasts. They explore Snipd’s vision of transforming podcasts into living knowledge systems, the evolution of machine learning from finance to large language models, and the broader connection between AI, robotics, and energy as the foundation for the next technological era. Kevin also touches on ideas like the bitter lesson, reinforcement learning, and the growing energy demands of AI. Listeners can try Snipd’s premium version free for a month using this promo link.

Check out this GPT we trained on the conversation

Timestamps

00:00 – Stewart Alsop welcomes Kevin Smith, co-founder of Snipd, to discuss AI, podcasting, and curiosity-driven learning.
05:00 – Kevin explains Snipd’s snipping feature, chatting with episodes, and future plans for voice interaction with podcasts.
10:00 – They discuss vector search, embeddings, and context windows, comparing full-episode context to chunked transcripts.
15:00 – Kevin shares his background in mathematics and economics, his shift from finance to machine learning, and early startup work in AI.
20:00 – They explore early quant models versus modern machine learning, statistical modeling, and data limitations in finance.
25:00 – Conversation turns to transformer models, pretraining, and the bitter lesson—how compute-based methods outperform human-crafted systems.
30:00 – Stewart connects this to RLHF, Scale AI, and data scarcity; Kevin reflects on reinforcement learning’s future.
35:00 – They pivot to Snipd’s podcast ecosystem, hidden gems like Founders Podcast, and how stories shape entrepreneurial insight.
40:00 – ETH Zurich, robotics, and startup culture come up, linking academia to real-world innovation.
45:00 – They close on AI, robotics, and energy as the pillars of the future, debating nuclear and solar power’s role in sustaining progress.

Key Insights

1. Podcasts as dynamic knowledge systems: Kevin Smith presents Snipd as an AI-powered tool that transforms podcasts into interactive learning environments. By allowing listeners to “snip” and summarize meaningful moments, Snipd turns passive listening into active knowledge management—bridging curiosity, memory, and technology in a way that reframes podcasts as living knowledge capsules rather than static media.
2. AI transforming how we engage with information: The discussion highlights how AI enables entirely new modes of interaction—chatting directly with podcast episodes, asking follow-up questions, and contextualizing information across an author’s full body of work. This evolution points toward a future where knowledge consumption becomes conversational and personalized rather than linear and one-size-fits-all.
3. Vectorization and context windows matter: Kevin explains that Snipd currently avoids heavy use of vector databases, opting instead to feed entire episodes into large models. This choice enhances coherence and comprehension, reflecting how advances in context windows have reshaped how AI understands complex audio content.
4. Machine learning’s roots in finance shaped early AI thinking: Kevin’s journey from quantitative finance to AI reveals how statistical modeling laid the groundwork for modern learning systems. While finance once relied on rigid, theory-based models, the machine learning paradigm replaced those priors with flexible, data-driven discovery—an essential philosophical shift in how intelligence is approached.
5. The Bitter Lesson and the rise of compute: Together they unpack Richard Sutton’s “bitter lesson”—the idea that methods leveraging computation and data inevitably surpass those built from human intuition. This insight serves as a compass for understanding why transformers, pretraining, and scaling have driven recent AI breakthroughs.
6. Reinforcement learning and data scarcity define AI’s next phase: Stewart links RLHF and the work of companies like Scale AI and Surge AI to the broader question of data limits. Kevin agrees that the next wave of AI will depend on reinforcement learning and simulated environments that generate new, high-quality data beyond what humans can label.
7. The future hinges on AI, robotics, and energy: Kevin closes with a framework for the next decade: AI provides intelligence, robotics applies it to the physical world, and energy sustains it all. He warns that society must shift from fearing energy use to innovating in production—especially through nuclear and solar power—to meet the demands of an increasingly intelligent, interconnected world.