One of the hottest areas for VC investment at the moment is AI/machine learning that includes artificial intelligence algorithms, related machine learning systems, neural networks, and back-end processing to produce insightful and self-learning applications. As Nvidias CEO recently said:

Software may be eating the world, but AI is going to eat software

VC investment in AI has risen from $3.2 billion in 2014 to $9.5 billion for the first five months of 2017 annualized, with the number of funding rounds nearly doubling since 2015 to over 1,200 on an annualized basis so far this year. No wonder Frost & Sullivan calls AI the hottest investment trend of 2017:

Above: Source: Pitchbook

Investors piling into a space aim for multiple exits worth hundreds of millions of dollars. However, the pattern of AI exits is the opposite. Most successfully-exited AI companies sell for below $50 million after raising only a small amount of money. This works well for founders and small angel backers but not for VCs looking for exits well over $100 million.

Of 70 AI M&A deals since 2012, 75 percent sold below $50 million. These deals are often acqui-hires companies acquired for talent not business performance. The number of $200 million+ deals barely registers.

Above: Source: Pitchbook

The typical journey goes like this: A small team comes together around 1-2 individuals, they forge real advances on key use cases (voice recognition, visual/video tracking, fraud detection, retail consumer behavior, etc.), sign a handful of prominent customers, raise less than $10 million (often less than $5 million), then attract the attention of a major buyer looking to solve that problem set. These kinds of AI companies are often valued as an amount paid per engineer rather than on performance (revenue, growth, profits); the average price per employee is around $2.5 million:

Above: Source: Pitchbook

The other issue for VCs is that AI companies dont generally need to raise much money, even if they are valued far above $100 million. Argo, valued at $1 billion for a majority stake by Ford, was 20 people when bought. Our research from PitchBook shows the 10 most valuable AI M&A targets raised on average only $15-25 million; there was only room for 1-2 VC investors in each deal:

Sure there are larger AI companies still growing, such as Palantir, valued at $10 billion having raised over $500 million. But a few isolated cases of $1 billion+ unicorns created using significant VC money is hardly fertile ground for 1,200 VC investment rounds. The reality is, AI just isnt as rich a segment for VCs as investment activity suggests.

Once several VCs invest, an AI company can no longer entertain a $50-100 million M&A offer and must scale its team and product suite to ramp to a much higher valuation years further out; otherwise VCs cannot get the return they require. Heres why we think this is counterproductive:

For many AI founders, the best approach is raising little money, demonstrating they can solve hard problems, and waiting for the M&A phone to ring.

For VCs the best approach is often to look elsewhere.

Victor Basta is founder of Magister Advisors, a specialist bank focused on M&A exits and larger financing rounds.

Continued here:

For AI startups, more funding is often not the answer - VentureBeat