The Intersection of AI and Public Learning: Contrarian Theses for Early Stage Investors

The Intersection of AI and Public Learning: Contrarian Theses for Early Stage Investors

Introduction:
Artificial intelligence (AI) and public learning are two distinct fields that have the potential to shape the future of technology and innovation. While these topics may seem unrelated, they share common points that can provide valuable insights for early-stage investors. In this article, we will explore five contrarian theses that highlight the intersection of AI and public learning, and discuss actionable advice for investors looking to navigate these evolving landscapes.

Thesis 1: Horizontal LLMs will lose, but vertical LLMs will thrive:
The first thesis argues that horizontal large language models (LLMs) will eventually be displaced by new and better technologies. AI experts believe that LLMs alone will not lead us to artificial general intelligence (AGI). Instead, the next leaps in AI will likely come from novel approaches and not just more data. Additionally, it is suggested that vertical LLMs will outperform their horizontal counterparts in most applications. Rather than relying on a single universal LLM, utilizing multiple versions of vertical LLMs tailored to specific industries or processes may yield better results.

Thesis 2: AI will impact specific companies, not entire markets:
Contrary to previous technological waves, AI is expected to impact specific companies rather than entire markets. AI applies intelligence and learning to different steps in the corporate value chain. Thus, companies that have value chains conducive to leveraging AI will benefit the most. It is important to note that most companies aim to minimize the need for intelligence and learning in their workflows by systematizing processes. Therefore, the key factor for AI-driven success lies in identifying companies with similar value chain steps rather than focusing solely on the industry or customer base.

Thesis 3: AI will erode competitive advantage:
The third thesis posits that AI will eliminate traditional forms of competitive advantage. This is exemplified by Google's acknowledgment in their "no moat" memo. As AI becomes ubiquitous, organizations will need to find new ways to differentiate themselves. The ability to effectively leverage AI technologies will become a key factor in maintaining a competitive edge in the market.

Thesis 4: Bifurcation of the economy into real and AI worlds:
AI is predicted to create a division between the real world and the AI world. This thesis suggests that customer acquisition channels will collapse into agents, transforming the way we interact with technology. The rise of AI agents, such as AutoGPT, will lead to increased reliance on them for various tasks, potentially reducing the need for traditional software application interfaces. This shift may render certain growth strategies, such as product-led growth (PLG) or community-driven go-to-market (GTM) models, less relevant in the future.

Thesis 5: Learning in public cultivates collective intelligence:
Shifting gears to public learning, the fifth thesis emphasizes the importance of learning in public rather than privately. Overcoming the fear of judgment and seeking validation should not be the primary goal. Instead, the focus should be on tapping into the collective intelligence of one's network to create constructive feedback loops. By working on projects that you own and sharing your progress publicly, you can track your learning progress effectively. It is during this vulnerable stage of the project that learning becomes most valuable.

Actionable Advice:

1. Embrace emerging AI technologies: Early-stage investors should prioritize companies that are exploring new and innovative AI approaches rather than relying solely on LLMs. Identifying vertical LLMs tailored to specific industries can provide a competitive advantage.

2. Seek out companies with AI-friendly value chains: Look for companies whose value chains align with the application of AI technologies. Understanding how AI can enhance different steps in the value chain will help identify businesses with the potential for AI-driven growth.

3. Encourage public learning and feedback loops: Investors should support entrepreneurs and individuals who embrace public learning. Encouraging the creation of public logs and progress updates can foster a culture of collective intelligence, enabling faster learning and improvement.

Conclusion:
The intersection of AI and public learning presents unique opportunities and challenges for early-stage investors. By recognizing the potential of vertical LLMs, identifying companies with AI-friendly value chains, and promoting public learning, investors can position themselves to navigate these evolving landscapes successfully. As the fields of AI and public learning continue to advance, keeping an open mind and staying informed will be crucial for capitalizing on emerging trends and technologies.