When starting out as a first-time angel investor, one of the first pieces of information learned pertains to the success rate of an overall portfolio.
Simply put, angel investing is a numbers game.
If you invest in ten different early-stage companies, conventional wisdom suggests six or seven will fail to reach an exit. Two to three of those ten will provide modest returns on investment – or simply return the initial investment.
However, you could invest in nine absolute duds, but if the tenth company turns out to be the next Uber, those outsized gains outpace any losses from the rest.
That principle has guided the strategies of angel investors and venture capital firms for years.
But technological advancements could change these expectations altogether.
PitchBook, an online database for global capital markets that tracks investments and more, recently revealed it’s developed an AI-powered tool to help determine a prospective startup’s likelihood of reaching an exit.
Dubbed the “VC Exit Predictor,” this tool has been back-tested and trained to identify certain traits common to successful startups to find the next generation of winners.
“The VC Exit Predictor was developed using a proprietary machine learning algorithm developed by PitchBook’s quantitative research team, trained exclusively on data available within the PitchBook platform including deal activity, active investors and company details,” PitchBook product manager of market intelligence McKinley McGinn told TechCrunch. “To ensure accuracy, predictions are made for venture-backed companies that have received at least two rounds of venture financing deals.”
And initial results are particularly encouraging. Using a historical set of companies with known exits, VC Exit Predictor was successful in, well, predicting an exit, 74% of the time.
That said, there remain some flaws that will take time to correct – and given that machine-learning depends on the environment in which it exists, biases remain.
For example, in almost every case, a successful exit presupposes successful rounds of funding. We have written extensively about the gulf in funding committed to companies led by white men compared to members of other genders or races.
Similarly, certain qualities deemed favorable by this AI tool – such as an MBA or prior experience at a tech giant – are often reserved for those from more privileged backgrounds.
There will inevitably be kinks to be ironed out. Part of the refinement of these technologies depends on trial and error.
That said, the introduction of such tools is one more piece of evidence toward an altered approach to investment moving forward.
And for angel investors and VC firms, these innovations represent another tool at our disposal to help inform our decision making.
While there’s no word on if or when VC Exit Predictor will be made available to the public, it will almost surely not be the last innovation in this mold.
The future is brimming with advancements. That’s exactly why we invest in early-stage companies.