AI Moats

FourWeekMBA

AI Moats Timeline:

This timeline focuses on the evolution of AI business models and competitive strategies as discussed in the provided text.

Early December 2022:

• Text Authored: The provided text, analyzing the developing AI industry and the potential for building competitive moats, is written.
• Central Question Posed: How can companies build a lasting advantage ("moat") in the AI industry, especially when building upon existing foundational models like ChatGPT?
• Three Layer Model Proposed: The text introduces a three-layer model for understanding the AI business ecosystem:
• Foundational Layer: General-purpose AI engines (GPT-3, DALL-E, etc.)
• Middle Layer: Specialized AI engines built upon the foundational layer, focusing on specific tasks or industries.
• App Layer: Applications built on top of middle-layer AI engines, targeting user growth and engagement.

Late November 2022:

• ChatGPT Released: The release of ChatGPT sparks the author's in-depth consideration of AI industry competition and the potential for establishing moats.

Ongoing & Future:

• Arbitrage Opportunities Shrink: The text notes that opportunities to quickly capitalize on the emerging AI landscape are diminishing as the technology advances.
• Multimodal Models Dominate: Foundational models are becoming increasingly multimodal (handling text, images, video, etc.), raising barriers to entry for competitors.
• OpenAI's Potential Dominance: The author speculates that OpenAI, due to its control over powerful models like GPT-3, could establish a dominant position similar to Apple's App Store, capturing value through APIs or AI application marketplaces.
• Data as a Moat: Leveraging data for integration, curation, and fine-tuning of AI models is deemed crucial for creating valuable, differentiated AI applications.
• Prompt Engineering's Significance: The emergence of "prompt engineering" (using natural language to control AI models) is highlighted as a potential core value driver and a new form of "coding."
• Network Effects in AI: The author draws parallels to the internet era, arguing that AI companies can leverage network effects and fast iteration loops to build moats, similar to companies like Netflix and TikTok.
• Workflow as a Differentiator: The efficiency and effectiveness of an AI company's workflow for developing, deploying, and iterating on AI applications is positioned as a significant barrier to entry.
• Brand & Distribution Remain Key: Building strong brands and securing strategic distribution partnerships with major tech players will remain critical for success in the AI industry.

Cast of Characters:

The Author:

• An individual deeply engaged in analyzing the AI industry, particularly the business models and competitive dynamics.
• Believes that understanding how to build defensible moats in AI is essential for long-term success.
• Draws comparisons between the evolving AI landscape and the strategies of successful internet-era companies.

OpenAI:

• A leading AI research and deployment company.
• Developer of powerful foundational AI models like ChatGPT and DALL-E.
• Positioned as a potential dominant force in the AI industry, potentially shaping the market through its technology and partnerships.

Microsoft:

• A major technology company that has formed a strategic partnership with OpenAI.
• This partnership is highlighted as an example of how distribution and technology will be intertwined in the AI industry.

Other Companies/Entities Mentioned:

• DeepMind: An AI subsidiary of Google, mentioned in the context of AI partnerships.
• Stability AI: An open-source AI company known for its work on Stable Diffusion, mentioned as partnering with Apple.
• Apple: A tech giant highlighted for its potential partnership with Stability AI.
• Amazon AWS: Highlighted for leveraging its existing cloud infrastructure for AI.
• Meta (Facebook), Google, Netflix, TikTok: Mentioned as examples of companies that have successfully employed network effects and AI to achieve scale and dominance.