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hypotheses we have about the space and the company. We work to figure out
the gap analysis between where the company is and what we think this next
check should enable them to accomplish. We also start to do homework with
the team around the company and around the space to quantify what those
gaps are and whether or not the amount of money they're looking to raise is
appropriate. A lot of times we'll find out—when we're digging into a company
and helping out—that their financial model shows that they are generally
underestimating the amount of resources they'll need to hit key milestones
to prove what they need to prove to raise to the next round. Around this
time the company will come in to meet all three of us, and get to know us as
people and as a partnership. We then figure out if we want to work together
as partners to build a great business.
One of the things I've learned is that the worst thing you can do is undercapital-
ize a business, especially in an environment where it's very hard to raise follow-
up financing. Again, we're talking about the very early stages, so this isn't the
issue of writing a $30 million check and overfunding their company. The issue
at this stage is whether you raise 1.5 million or 2.5 million dollars. That's the
order of magnitude we're talking about. Sometimes that extra half million or
million, which provides an extra six months of runway, can mean the difference
between having a super-successful Series A or “Oh my God, we need to do a
bridge because we haven't proven enough.” The latter type of conversation is
never a fun conversation. This happens quite frequently at the seed stage, so
we work hard to try and avoid this by properly financing the plan upfront..
Gutierrez: How do you manage the data you're generating about these
investment hypotheses?
Ehrenberg: We have a very, very structured file-sharing system. We're very
careful about categorization. This makes it very easy for us to access our data.
Then, given the fact that we're running a fairly concentrated investment port-
folio, we aren't burdened by the fact that we don't have a hundred portfolio
companies or more. We're not a micro VC. We are a classic, conventional,
old-style VC, which makes our data very concentrated. So we are pretty good
at retrieving that data and leveraging it again and again and again. Even though
we leverage this data frequently, we are constantly refining how we analyze
companies and the manner in which we engage with companies. At this point,
I don't feel like we've reached steady state.
Gutierrez: You've previously talked about finding people who are data-cen-
tric and have data-centric DNA. How can you tell?
Ehrenberg: We can tell by going through the process with them. Let's say
there's a team operating in a space that we find intriguing, and we have two to
three meetings with the founders. It's super easy to tell at that point whether
or not they are metrics-driven, where they are thoughtful about architecting
the business to best leverage the data that they are collecting. It's something
in the natural course of due diligence that just becomes apparent.
 
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