How to Build AI Startups Part 1: Hard Tech

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Four airbaloons and one rocket in the sky

Dear friends,

AI Fund, which I lead, is a venture studio that works with entrepreneurs to build companies rapidly and increase their odds of success. We’ve evaluated a lot of AI startup ideas. There’s no one-size-fits-all template for building businesses, but we’ve fine-tuned our recipes. In this and subsequent letters, I’ll share some of the patterns I’ve seen.

AI businesses differ from traditional software startups in important ways. For instance, technical feasibility isn’t always clear, product specification is complex, and data is necessary to train and test the system.

One important factor is whether a startup focuses on hard tech (sometimes called deep tech). A hard-tech company:

  • Relies on advanced, better-performing technology that significantly improves the customer experience or business efficiency.
  • Requires highly skilled teams that are capable of building materially better technology.

In determining whether a business requires hard tech, the key factor is whether best-in-class technology will make the difference between success and failure.

For instance, speech recognition based on deep learning was hard tech 10 years ago. Only a handful of teams were able to build highly accurate systems and put them into production at scale. Higher accuracy greatly improved the user experience, and that drove adoption. Competitors had a hard time catching up.

Another example is online advertising. Building a system that selects the most relevant ad within hundreds of milliseconds is very challenging. Showing better ads results in more revenue per page view. More revenue not only improves the bottom line but makes it possible to afford higher costs to acquire users (say, by paying a maker of web browsers to feature one search engine over another). This, in turn, makes it harder for rivals to compete.

What once was hard tech often becomes easier to build over time. For example, as speech recognition became commoditized, more teams were able to build useful systems. When this happens, having the best tech is much less critical to success. Other factors can have a bigger impact such as superior product design, a skilled sales team, bundling with other services, or an efficient supply chain.

I enjoy working on hard-tech businesses — and many AI Fund companies fit that description — because the quality of the technology really matters. A hard-tech company has an incentive to build the best possible team, because the finest team can significantly outperform competitors.

Of course, AI businesses that aren’t hard-tech can be very meaningful, too. There are many, many exciting applications, across all industries, yet to be built using established technology. We need developers going at these problems, too.

Keep learning!

Andrew

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