The skeptic's guide to Venture Capital

patagonia vest

Networking is broken.

Common knowledge early on in a VC career focuses on events: panels, discussions, fireside chats, conferences, you name it. But these are really wasteful.

You rarely meet quality contacts in the audience, so you're after the speakers. But at 2-3 hours per event, getting to speakers can cost you up to 45 minutes per speaker (depending on how many there are).

I found a different method much more effective: just message people! If you write a thoughtful, personalized cold email – or engage effectively on Twitter – then your chances of a quality meeting are much higher than bugging someone after their panel. It's pretty easy to guess email addresses and find handles, and it's way more efficient.

Time to get in contact:

Turns out people respond to messages if you write them with care. I started out by emailing 1-2 people each at the 50 top funds in NYC, and more than 70% responded within a few weeks. Those meetings created relationships as real as any other method of getting your foot in the door.

This is not a brag: it's a knock on the traditional way of meeting people. In an age dominated by digital communication and amazing opportunities for interest-based connection, there's a better way to network. But I think that high response rate was likely also due to another factor: there's way more capital in the ecosystem these days.

Data is transforming the ecosystem.

Data is becoming more ingrained in pretty much everything, and early stage investing is no exception. It can aid in:

  • Sourcing better, more relevant companies faster
  • Automating the diligence process by predicting startup outcomes
  • Scraping the web to help portfolio companies recruit

The transition, though, hasn't been so smooth. In the early stage community, leaders are still skeptical about using data to invest.

Here's how the debate holds up (click to toggle):

The Case For Data

Data might not be very helpful right now, but the future rarely resembles the past.

  • Algorithms are improving exponentially in many areas
  • We'll soon be able to understand people and personality algorithmically
  • "It's too personal and not scalable" is always the excuse for industries before they get disrupted

The industry is investing heavily in data, and it's only a matter of time until business models shift.

The Middle Ground

Investors can astutely combine their personal expertise with algorithms.

  • Most of the great Machine Learning solutions out there combine the algorithmic and subjective
  • Data doesn't need to totally take over, just contribute
  • We should be prepared whenever strides in VC data get made

The answer doesn't need to be binary. What wins is an openness to new developments combined with a respect for the traditional way of doing things.

The Case Against Data

Early stage investing is too qualitative for predictions to be accurate.

  • Investing is a people game, and people can't be quantified
  • Data quality and availability is ABYSMAL and most of the time useless
  • Everything can't be vectorized: some industries just need to work a certain way

If we see evidence of meaningful progress through data, we'll get on board. Right now it's just not clear that it's going to work.

When I was finishing up school in early 2017, I wrote a paper about how to integrate data into a few different parts of the VC "stack" – and it was not easy to find funds to interview. That's changing quickly:

"Data + VC – Quantifying The Qualitative"

My (now outdated) college paper about how VCs can use data to their advantage

As a Data Scientist, I'm biased towards data having a positive impact on the venture community. I'm also biased towards this future because I think venture is a supply game, not a picking one.

Is operating experience overrated?

It's assumed that you need some sort of experience working at or starting a startup ("operating") in order to be a supremely successful investor. On paper, it makes a lot of sense:

  • You'll have seen good leadership and operations, and can "pattern match" to it
  • To help your portfolio companies succeed, you need to have been in their shoes
  • The best founders want to work with investors that have done it before
  • The overwhelming majority of successful VCs have operating backgrounds

Then again, lots of things make sense on paper. It's my strong suspicion that operating experience is a perhaps helpful but entirely non-essential part of being a great investor.

I think most of the current narrative is historically arbitrary: over the past 20 years, the classic "road to VCdom" has always been successful founders becoming investors. Those people set the tone, and it's hard for human beings to imagine paths to success other than their own.

"I got here this way, so you need to also"

 

- Everyone who makes a lot of money

Question your assumptions. For those who draw a straight line from operating to investing, I'd consider a few things:

  • Does pattern matching really exist, given the average 80%+ failure rate?
  • Do founders really percieve investors as providing valuable portfolio help?
  • Do all funds across all investments need to beat out competition, and is there only one way to do that?
  • Does the fact that most VCs have operating experience imply causation, or just correlation? Haven't they also all gone to Ivy League schools?

You can make thoughtful suggestions, offer meaningful help to your investments, and be someone who entrepreneurs want to work with without having been in their shoes. There are plenty of big name VCs who have done that, and there will be plenty more when it's all said and done.

That's it! I hope you liked this, and feel free to share it if you did.

Sharing is caring 👉 twitter reddit linkedin hackernews