The Circuit
The Circuit
Ben Bajarin and Jay Goldberg
Episode 52: AI and Outsized Expectations
41 minutes Posted Feb 4, 2024 at 10:43 pm.
Introduction and Early Adopters Day
Outsized AI Expectations
The Difficulty of Writing about AI
The Impact of AI on Stock Market Reactions
Overcorrection of AI Expectations
The Challenges of Modeling AI Growth
The Example of AMD's MI300
The Arms Race of Analyst Estimates
The Importance of Realistic Expectations
The Gains from AI in Small Places
The Additive Nature of AI to the TAM
The Difficulty of Calculating AI TAM
The Challenge of Charging More for AI Features
The Premium Tier of AI Products
The Expectations for AI PCs
The Hope of Shortening Refreshment Cycles
The Unlikelihood of AI Accelerating Refreshment Cycles
The Small Gains of AI in Software
The Realistic Expectations for AI
The Experimentation Stage of AI
Measuring Expectations and Being Reasonable
The Excitement of Apple's Vision Pro
Conclusion
0:00
41:31
Download MP3
Show notes

Summary

In this episode, Ben Bajarin and Jay Goldberg discuss the outsized expectations for AI growth and the impact on stock market reactions. They explore the challenges of modeling AI growth and the difficulty of charging more for AI features. They also discuss the potential for AI to accelerate refreshment cycles and the importance of realistic expectations. The conversation highlights the small gains of AI in software and the experimentation stage of AI. They conclude by emphasizing the need to measure expectations and be reasonable in the AI industry.

Takeaways

  • Outsized AI expectations have led to negative stock market reactions, as companies have not met ambitious growth models.
  • Modeling AI growth is challenging due to variables such as product availability and demand.
  • Charging more for AI features is difficult, as customers may not be willing to pay a premium.
  • AI may not accelerate refreshment cycles, as the average consumer may not see significant improvements that warrant more frequent upgrades.
  • The gains from AI are often small and incremental, but still important in improving efficiency and productivity.