Where Are the Opportunities in AI? Here's Where to Look Generative AI opens the way to create valuable products, especially in the application layer.

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AI Fund's process for building startups

Dear friends,

I recently spoke about “Opportunities in AI” at Stanford’s Graduate School of Business. I'd like to share a few observations from that presentation, and I invite you to watch the video (37 minutes).

AI is a collection of tools, including supervised learning, unsupervised learning, reinforcement learning, and now generative AI. All of these are general-purpose technologies, meaning that — similar to other general-purpose technologies like electricity and the internet — they are useful for many different tasks. It took many years after deep learning started to work really well circa 2010 to identify and build for a wide range of use cases such as online advertising, medical diagnosis, driver assistance, and shipping optimization. We’re still a long way from fully exploiting supervised learning.

Now that we have added generative AI to our toolbox, it will take years more to explore all its uses. (If you want to learn how to build applications using generative AI, please check out our short courses!)

Where do the opportunities lie? With each new wave of technology, entrepreneurs and investors focus a lot of attention on providers of infrastructure and tools for developers. The generative AI wave has brought tools from AWS, Google Cloud, Hugging Face, Langchain, Microsoft, OpenAI, and many more. Some will be huge winners in this area. However, the sheer amount of attention makes this part of the AI stack hypercompetitive. My teams (specifically AI Fund) build startups in infrastructure and tools only when we think we have a significant technology advantage, because that gives us a shot at building large, sustainable businesses.

But I believe a bigger opportunity lies in the application layer. Indeed, for the companies that provide infrastructure and developer tools to do well, the application companies that use these products must perform even better. After all, the application companies need to generate enough revenue to pay the tool builders.

For example, AI Fund portfolio companies are applying AI to applications as diverse as global maritime shipping and relationship mentoring. These are just two areas where the general-purpose technology of AI can create enormous value. Because few teams have expertise in both AI and sectors like shipping or relationships, the competition is much less intense.

If you’re interested in building valuable AI projects, I think you’ll find the ideas in the presentation useful. I hope you’ll watch the video and share it with your friends. It describes in detail AI Fund’s recipe for building startups and offers non-intuitive tips on the ideas that we’ve found to work best.

Keep building!



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