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The Evolving Landscape of AI Investment
As a leading thought investor, the value in the incredible AI value chain is not equally distributed across the different layers. Each layer presents its own unique challenges and opportunities that require careful consideration.
Hardware Infrastructure: Chips, Data Centers, and Power
The hardware infrastructure, including chips, data centers, and power, is undoubtedly a crucial component of the AI ecosystem. The demand for these resources will continue to grow exponentially in the next decade. However, the progress and financing of this infrastructure can be tricky. The history of the internet has shown that the bottleneck can shift from one area to another, and the same pattern is emerging in the AI landscape.
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For instance, the price of Nvidia chips has dropped significantly this year, indicating a potential data bottleneck. Similarly, power and cooling challenges are likely to arise as the demand for computational resources increases. Investors need to be mindful of these shifting bottlenecks and ensure that the financing of the hardware infrastructure is well-structured to avoid potential pitfalls.
Foundation Models: The Backbone of AI
The foundation models, such as those developed by Anthropic, OpenAI, and Gemini, are the backbone of the AI software market. These models are ubiquitous, with nearly 80% of the software market relying on them. While the revenue in this space is growing rapidly, the price competition is intense, with token prices falling hundredfold in the last two years.
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This dynamic suggests that the market is approaching an asymptotic limit, where further improvements in intelligence may come at a much slower pace. This could lead to architectural changes, and new players may emerge, similar to the way Google emerged from the 37 search engines that existed before it.
Applications: The Race to Market
The application layer is where the real value can be captured, but it's also the most competitive. Large companies can optimize their products very quickly using AI, posing a challenge for new entrants. The race is on to get to market faster than the big companies can develop their own AI-powered solutions.
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In some cases, this can present the biggest private equity opportunity of all time. The example of the dev tool company that grew from 0 to 40 million in revenue in just 3 months illustrates the potential for rapid market capture. However, this is not the case for all applications, and investors need to carefully assess the competitive landscape and the ability of new entrants to outpace the incumbents.
In conclusion, the AI value chain is a complex and rapidly evolving landscape, with each layer presenting its own unique challenges and opportunities. Investors need to be strategic and adaptable, constantly monitoring the shifting bottlenecks and competitive dynamics to identify the most promising investment opportunities.