Venture Capital and AI: Navigating Investments in a Complex Landscape

In 2018, Venture Capital (VC) firms invested over $9 billion in AI related startups. It used to be that putting “Blockchain” in your startup name would guarantee your series A funding. Now switch the name to “Anything AI” and you’ll have similar success. Joking aside, the enthusiasm for AI-related startups is approaching that of internet and software companies in the early 2000’s and that should be cause for concern.

AI is an incredibly complicated topic. It is highly technical and it often takes a PHD in Mathematics to decipher the complex algorithms involved. Any time you pair a complex topic with enthusiasm from a non-technical investment community, a ‘speculative bubble’ appears. Think to the most recent financial crisis of 2008. It is partially attributed to investors placing large sums of money into financial derivatives that they did not fully understand. Once it was discovered that the underlying quality of the investments was poor, the bubble popped and the stock market spiralled out of control.

When investors see large dollars flowing to a single technology or sector they tend to follow along to avoid missing out on the opportunity. The most famous example of investors abandoning reason to make a quick buck is “Tulip Mania” of the 1600’s. The Dutch were buying and selling tulip bulbs like stocks. At its peak, a rare tulip bulb could go for several thousand dollars or about 10 times the annual salary of a skilled craftsman at the time. This bubble also popped and many lost everything they owned. This is all to say that when investors see large sums of money flowing into AI, they may invest without understanding the technology or completing an assessment of whether it will provide investment returns over time.

The above is meant to be a warning; not a recommendation against investments in AI. $9 billion dollars is a lot of money and there are many savvy investors who understand AI and have completed the required due-diligence before putting their capital at risk. Instead of avoiding AI investments altogether, VCs and other investors should be asking “How can I de-risk my investment with an understanding of the technology and its potential for future returns?”

Reducing the risk of AI investments and making strong, research-based decisions should follow the same decision-making process of any other investment. An investigation of the product, how it benefits customers, what are its competitive advantages, what are the risks of fast-paced growth, and other typical investment questions are still relevant. The difference is the product is an algorithm. Or it’s a new way of providing a service with machine intelligence as the driving force, and it takes highly specialized domain expertise to properly assess the opportunity.

To understand an AI investment opportunity, VCs could attempt to hire a PHD in Computer Science or Machine Learning and leverage their expertise. Unfortunately, this task is becoming more and more difficult. Element AI, an AI startup, estimates that there are 144,000 AI-related job openings in the US and only 26,000 qualified workers applying for AI-related positions.

With firms seeking talent to help them maximize returns on AI investment this is the time for business professionals to begin diving in and getting a more complete understanding of the technology. Many are worried about AI disrupting the workforce, and this is true, but those who face the challenge head-on will be able to adapt, understand the technology, and make an impact on the new economy. Check out these resources to get started:

• Coursera: Machine Learning
• Udemy: Machine Learning
• Masters of Management in Artificial Intelligence