In this conversation, Salvador Carranza and Leo Park explore the complexities of investing in emerging technologies, particularly large language models. They discuss the challenges of business models in tech, drawing parallels with the airline industry, and the commoditization of AI technologies. The importance of evaluating model performance and the landscape of AI applications is emphasized, alongside a philosophical discussion on the nature of intelligence in AI and language models. Listen to the entire podcast and learn even more!

Takeaways

Investing in tech startups is uncertain and risky.

The airline industry serves as a cautionary analogy for tech businesses.

Large language models are becoming commoditized, affecting their profitability.

Evaluating model performance is crucial for application developers.

Domain expertise can create defensible business models in AI.

AI applications need to differentiate themselves in a crowded market.

Understanding the nature of intelligence in AI is complex and evolving.

Multimodal models challenge our understanding of thought and language.

The rapid pace of AI development makes it hard to keep track of innovations.

Consumers need better education to navigate the AI landscape.