The best way to play AI is to buy the big hyperscalers, says Lead Edge Capital's Mitchell Green

Key Concepts

AI, Hyperscalers, Retail Investors, OpenAI, Anthropic, Microsoft, SoftBank, Dilution, Stock Options, AI Engineers, Capital Expenditures, Earnings Multiples, ROI, Internet Bubble, Market Correction.

Investing in AI: A Strategic Approach

Mitchell Green discusses the current AI landscape and offers advice on how to invest in this rapidly evolving sector. He suggests that for retail investors, the best approach is to invest in "hyperscalers" like Facebook (Meta), Amazon, and Google, as they are dedicating massive amounts of capital to AI development. These companies have the resources and infrastructure to compete effectively in the long run.

• Hyperscalers' Advantage: Green emphasizes the difficulty of competing with these giants, citing their extensive investments and established infrastructure. Amazon's planned $60 billion investment in the second half of the year and Google's projected $85 billion expenditure are mentioned as examples.
• Alternative Investment Vehicles: For those seeking exposure to companies like OpenAI, Green suggests investing in Microsoft (due to its significant investment in OpenAI) or SoftBank. However, he cautions that SoftBank offers exposure to a broader range of assets.
• Private AI Companies: Dilution Concerns: Green expresses concern about the dilution of stock in private AI companies like OpenAI and Anthropic due to constant fundraising. He notes that while early investors may have profited, the long-term impact of dilution is a significant risk.

The Talent War and Stock Option Dilution

The conversation highlights the intense competition for AI talent and the role of stock options in attracting and retaining engineers.

• Engineer Compensation: Green compares the compensation packages offered to top AI engineers to those of NBA players, emphasizing the significant amounts of cash and equity involved.
• Stock Option Dilution: He points out that the massive stock option grants in Silicon Valley lead to substantial dilution for existing shareholders, a factor often overlooked in discussions about AI innovation.
• Retention Strategy: Companies are using stock options as a primary tool to retain talent, offering engineers a significant stake in the company's success.

The AI Bubble and Long-Term Potential

Green draws parallels between the current AI hype and the internet bubble of the late 1990s, cautioning against overestimating the short-term impact while acknowledging the long-term transformative potential.

• Internet Bubble Analogy: He suggests that we are currently in the "1999, 2000" phase of the AI revolution, implying that a market correction or "pop" is possible.
• Uncertainty of Winners and Losers: Just as it was unclear which internet companies would succeed in the early 2000s, Green argues that it is difficult to predict the ultimate winners and losers in the AI space today. He mentions early search engines like Lycos and AltaVista as examples of companies that initially showed promise but ultimately faded.
• Long-Term ROI: Despite the potential for a bubble, Green acknowledges that some companies are starting to see real ROI from their AI projects, albeit in a limited and gradual manner. He cites a conversation with a retired CEO who serves on the boards of several large companies.

The Question of Who Pays

The discussion touches on the economic implications of AI adoption, particularly the question of who will bear the costs of implementing and maintaining AI systems.

• Cost Allocation: An example is given of a healthcare company considering having doctors contribute to the cost of AI agents designed to improve their efficiency, raising concerns about cost allocation and potential resistance.
• Commoditization and Pricing: The potential for AI to become commoditized is raised, along with the question of how companies will monetize AI services and whether consumers will ultimately bear the costs through higher prices.
• Google's Model: Google's advertising-based revenue model is presented as an example of how a company can successfully monetize a technology platform, with advertisers (and ultimately consumers) bearing the costs.

Market Sentiment and Potential Correction

Green concludes by discussing the current bullish market sentiment and the potential for a market correction.

• Contrarian Investment Strategy: He references a strategy advocated by Steve Cohen, which involves betting against the prevailing market consensus.
• Unforeseen Catalyst: Green suggests that a market "pop" is likely to be triggered by an unforeseen event that is currently unknown.

Conclusion

Mitchell Green advises retail investors to focus on investing in hyperscalers due to their significant investments and established infrastructure in the AI space. He cautions against the dilution risks associated with private AI companies and draws parallels between the current AI hype and the internet bubble, suggesting a potential market correction. He also raises important questions about the economic implications of AI adoption, particularly the allocation of costs and the potential for commoditization. The key takeaway is to approach AI investments strategically, considering both the long-term potential and the inherent risks.