What AI startups need to achieve before VCs will invest
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What AI startups need to achieve before VCs will invest
David Blumberg
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David Blumberg is founder and managing partner of early-stage venture capital firm Blumberg Capital.
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Funding of artificial intelligence-focused companies reached approximately $9.3 billion in the U.S. in 2018, an amount that will continue to rise as the transformative impact of AI is realized. That said, not every AI startup has what it takes to secure an investment and scale to success.
So, what do venture capitalists look for when considering an investment in an AI company?
What we look for in all startups
Some fundamentals are important in any of our investments, AI or otherwise. First, entrepreneurs need to articulate that they are solving a large and important problem. It may sound strange, but finding the right problem can be more difficult than finding the right solution. Entrepreneurs need to demonstrate that customers will be willing to switch from what they’re currently using and pay for the new solution.
The team must demonstrate their competence in the domain, their functional skills and above all, their persistence and commitment. The best ideas likely won’t succeed if the team isn’t able to execute. Setting and achieving realistic milestones is a good way to keep operators and investors aligned. Successful entrepreneurs need to show why their solution offers superior value to competitors in the market — or, in the minority of cases where there is an unresolved need — why they’re in the best position to solve it.
In addition, the team must clearly explain how their technology works, how it differs and is advantageous relative to existing competitors and must explain to investors how that competitive advantage can be sustained.
For AI entrepreneurs, there are additional factors that must be addressed. Why? It is fairly clear that we’re in the early stages of this burgeoning industry which stands to revolutionize sectors from healthcare to fintech, logistics to transportation and beyond. Standards have not been settled, there is a shortage of personnel, large companies are still struggling with deployment, and much of the talent is concentrated in a few large companies and academic institutions. In addition, there are regulatory challenges that are complex and growing due to the nature of the technology’s evolutionary aspect.
Here are five things we like to see AI entrepreneurs demonstrate before making an investment:
Demonstrate mastery over their data and its value: AI needs big data to succeed. There are two models: companies can either help customers add value to their data or build a data business using AI. In either case, startups must demonstrate that the data is reliable, secure and compliant with all regulatory rules. They must also demonstrate that AI is adding value to their own data — it must explain something, derive an explanation, identify important trends, optimize or otherwise deliver value.
With the sheer abundance of data available for companies to collect today, it’s imperative that startups have an agile infrastructure in place that allows them to store, access and analyze this data efficiently. A data-driven startup must become ever more responsive, proactive and consistent over time.
AI entrepreneurs should know that while machine learning can be applied to many problems, it may not always yield accurate predictions in every situation. Models may fail for a variety of reasons, one of which is inadequate, inconsistent or variable data. Successful mastery of the data demonstrates to customers that the data stream is robust, consistent and that the model can adapt if the data sources change.
Entrepreneurs can better address their customer needs if they can demonstrate a fast, efficient way to normalize and label the data using meta tagging and other techniques.
Remember that transparency is a virtue: There is an increased need in certain industries — such as financial services — to explain to regulators how the sausage is made, so to speak. As a result, entrepreneurs must be able to demonstrate explainability to show how the model arrived at the result (for example, a credit score). This brings us to an additional issue about accounting for bias in models and, here again, the entrepreneur must show the ability to detect and correct bias as soon as they are found.