The funding environment for AI startups remains robust—and some of the rounds have been substantial. Just recently Dataiku, which operates a machine learning platform, announced a $100 million Series D investment.
AI truly represents a transformation in the tech world. “AI is making software much more dynamic and improves as it understands user behavior,” said Gordon Ritter, who is the founder and General Partner at Emergence.
OK then, what are VCs looking for when evaluating an AI deal? What should be in your pitch deck?
Well, to answer these questions, I talked to a variety of VCs. Here’s what they said:
Sri Chandrasekar, partner at Point72 Ventures:
A slide on Why Now. What technology has recently been developed that has made solving this customer problem now possible. It might be “speech-to-text technology has gotten good enough to use in 95% of a call center’s communications.” Or “recent deep learning focused processors have made it possible to do computer vision on the camera instead of in the cloud.” It’s rare for people to identify a customer problem that nobody has heard about before–usually, what creates the potential for a large new company is that technology is now available to solve that problem in a new or differentiated way.
I also want to see a slide about solving the “Cold Start” problem. This matters most for the earliest stage companies, but AI companies need access to data to start training their algorithms. I like to see that they’ve thought about this problem and have a clear way to get access to enough data to build their business. The answer can be anything from buying data to partnering with an ancillary business to “faking it until they make it,” where they deliver the product or service with humans until they have enough data to build the AI model.
Mark Rostick, who is a Vice President and Senior Managing Director of Intel Capital:
When looking at a presentation of a potential AI deal, we look closely at the specific problem in AI/ML that they are solving and why solving that problem is important enough to build a company—not just a feature or tool. We also take a look at why the team is uniquely positioned to understand the problem they are trying to solve and how they are equipped to execute on it. The team must have line-of-sight to an economic model they can create that is capable of driving growth at “venture scale”.
Jake Saper, a partner at Emergence :
When evaluating companies that use AI to augment workers, I like to see charts that show the percentage of AI-generated suggestions that are taken by the user over time. For strong companies, this portion may start relatively low as the model is training and the UI is being tweaked. As both improve, you want to see the “coaching acceptance rate” improve to >75% and stay consistent.
Kenn So, a venture capitalist at Shasta Ventures:
There are a couple of slides I like to see:
#1: A high level architecture/diagram of how the data flows from source to training to AI predictions to the product that the users interact with. This helps brush away some of the AI pixie dust.
#2: Quantifying the value of the product or model for the user. For example, radiologists save 1 hour per day because the AI automates report writing. Only 10% of radiologists make adjusts to what the AI writes
#3: Defensibility of the data now or in the future. There are different ways to achieve this from proprietary data rights agreements to data network effects. In the end, ML is all about data. One thing to note is that data defensibility is just a minimum and not a sufficient condition of defensibility for AI companies.
Jeremy Kaufmann, a principal at Scale Venture Partners:
One of the most valuable metrics to show investors when pitching an AI company is how the accuracy rate of the underlying algorithm is improving over time. It’s important that investors see this improvement over time, particularly if humans are in the loop, as this analysis points to the fundamental solvability of the underlying problem. Investors are scared of the potentially asymptotic nature of AI algorithms (that they will never get good “enough”), so it’s very important to define “good enough” in a business context (what a business user will accept in terms of error rate) and then overlay this underlying expectation with the quantitative measure of how an algorithm is performing over time.