The Three Rules of AI Investing

A funny thing I’ve heard on more than one occasion was that accelerators were telling their startups to use AI buzz words like deep learning and machine learning in their pitches to investors. The idea, it goes, is that the unsophisticated investor, so impressed by these terms, would come knocking on the door with a term sheet, willing to shell out money to founders at any price.

While I exaggerate slightly here, the general notion holds true: with all this AI hype, it’s easy to get lost in this convoluted maze where AI is everywhere and everything. Accordingly, I’ve vowed not to be duped by anyone spouting the magical language of “AI.” Motivated partly out of intellectual curiosity but more likely out of chronic insecurity, I’ve developed three main rules to keep in mind when investing in AI. Today I share that with you:

You can divide AI companies roughly into two camps: the general-purpose AI tech company and the applied-AI company. A general-purpose AI company is one that provides a general AI technology (e.g. neural nets or some other new deep learning algorithm) that can be generalized across all fields of study and must be trained for implementation into an actual product. These are companies that tend to have great technology but often lack a specific application for their technology. By contrast, an applied-AI company is one that uses AI technology as a means to serve a specific business or product problem. These are companies that have seen a need in the market, such as autonomous driving, and are using AI techniques to answer that need.

As a general rule of thumb, I only look at applied AI companies. These are the only ones that I consider “venturable” for a couple of reasons:

1. A scalable business model: In comparison with applied AI companies, general-purpose AI technology companies tend not to have a product they can sell and make money in a scalable way. No potential customer is going to license such a company’s algorithms if they have not been trained to meet a specific business need. With such a company, what you have in the end is a bunch of code that still needs a lot of customization, and this is not the type of scalable product that makes a good venture case.

2. More favorable exit prospects: Acquisition opportunities for general-purpose AI technology companies are small and will get smaller as corporations build out their AI divisions. In the beginning of the AI hype curve, we may have seen a few big exits of general-purpose AI companies. The most notable was Google’s $400M acquisition of the neural-net company DeepMind, which ultimately became Google’s deep learning R&D outfit. Such early acquisitions were effectively R&D investments or acquihires. As big corporates implement their AI strategies, they will become less likely to pay big dollars for such expertise alone; they will instead seek to acquire applied AI companies that they can integrate into their existing products, leaving acquisitions of general-purpose AI technology companies to the wayside.

That said, general-purpose AI companies may still be a good bet if you invest early, such as at a seed stage. In these cases, an acquihire is the most likely exit event for such companies, so you better make sure you invest in a team with a lot of PhD’s. Acquihires tend to be smaller exits, so making a very early investment likely would be necessary to see any return at the end.

So remember: it’s all about applied AI!

Ok, so now that we have this great applied AI company, how do we know if it’s the best in its field? Some people will say it’s the algorithms! Others will say it’s the data! Both are wrong. A major conundrum in AI is that the most capable of such technologies — namely deep neural nets — are effective black boxes, so convoluted and unintelligible that mere mortals with decades of related experience cannot understand them. Accordingly, in the end, the only way to see if any such technologies comes down to one thing: how they perform in the real world.

So remember: in AI, a crappy algorithm trained on good data can beat out a super algorithm trained on bad data. What truly matters is how the system as a whole performs.

A few weeks ago, I was at a talk by AI guru Andrew Ng who told a story of how a former group of his students built their company. One day, these students, a group of Stanford engineers, ended up at some farm in Northern California and began taking pictures with their cellphones of heads of lettuce. One head there. Snap! One head here. Snap! Another head over there. Snap! Over time, the students amassed the greatest collection of lettuce-head images in the world. They founded the company, Blue River Technology, which manufactured agricultural robots using high-precision herbicide spray technology trained on their lettuce head images. The robots eliminated 90% of the herbicide use on farms, saving farmers money and fortifying against resistant weed growth. In October 2017, less than a decade later, John Deere bought Blue River for $305 million.

The reason behind Blue River’s success can be distilled into a matter of AI strategy: It built an empire in a specific market niche where it acquired more relevant data than any of its competitors to train its AI systems in the most meaningful way. In this brave new world of AI, data means everything. To train any algorithm to solve a given problem, you need to train it with data, particularly high-quality data and lots of it. The more data you have, the more accurate your AI system can operate. What this means for companies is that data, and naturally, the strategic use of it, can mean all the difference in obtaining an edge over one’s competitors. In this manner, Blue River collected the biggest repository of lettuce-head images to train its robots to recognize the difference between a good and bad lettuce head, which enabled it to optimize its herbicide-spraying robot. With every lettuce head its robot encountered, its algorithms became further refined, more powerful and more accurate. Blue River’s lettuce-head treasure trove, and the AI system on top of which it was built, became its competitive moat. Once the company built out its database of lettuce-head images, it became difficult for any other competitors to catch up — both in terms of data and the efficacy of their AI systems trained on that data.

This strategy — building a data empire in a niche area — is the same reason why you should not expect to see any emerging upstarts topple the empires of AI monoliths like Facebook and Google any time soon. Google, for example, has collected over two decades of data about every click, view, and search of yours to deliver to your screens the most optimized search results in existence today. Such vast empires will endure, gluttonizing on their bacchian feasts of data to the extent no other competitors can catch up.

So remember: Lettuce-head empires pay off. Build your startup empire in a specific market niche that no other player has a data monopoly over. Then sit back and let the lettuce heads roll.

There will always be, I admit, exceptions to these rules. However, these lessons have served me well, and I hope they will serve you well too. Happy startup-ing.

Principal at BMW i Ventures. VC trends. AI themes. Social commentaries. A personal blog bridging tech, business, and human issues by a curious mind.