INSUBCONTINENT EXCLUSIVE:
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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The Ascent Of Early-Stage Venture
Capital
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,
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 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
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
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.
