COVID-19 discussions for emerging GPs #OpenLP

I continue to be amazed at the content available for emerging GPs. This month, the content comes in the form of blog posts, but also zoom webinars and live audio talk shows.

I first heard Beezer Clarkson from Sapphire Ventures being interviewed on Semil’s Talkshow (listen here) on March 11th.  It was right after on Mar 14th that Samir Kaji posted his article “What I’m hearing in venture right now“, where he touches on fundraising for both startups and VCs. The next day he posted an article specific to Micro VC funding. Two days later, Lo Toney from Plexo Capital did his version of the Sequoia Black Swan article, but for GPs: The Economic Impact of  Coronavirus: What GPs Need to Know. Another two days later, Jim at SVB hosted a Zoom webinar:  Fundraising for Emerging VCs Post-Coronavirus, where he interviews three LPs – Joanna Rupp at University of Chicago, Michael Kim at Cendana, and Lindel Eakman at Foundry Group. I also enjoyed Michael Kim being interviewed on Strictly VC (here – Mar 20) and found Samir’s Emerging Manager Q&A mailbag from March 23rd a nice deep dive. Finally, Connie Loizos at Techcrunch shares wisdom from experienced GPs like Charles Hudson from Precursor, Eva Ho from Fika, and Aydin Senkut from Felicis: ‘A perfect storm for first time managers,’ say VCs with their own shops.

I know that’s a lot, so I’ll summarize a few take aways:

  • It’s a great time to invest. Great startups are founded during recessions. Congrats if you have dry powder.
  • Fundraising will be tough for GPs, especially new ones. Considerations for LPs include general uncertainty, desire for liquidity, allocation (esp. institutional), returning funds, and lack of in person meetings making it tough to build trust.
  • For existing funds, an important time to support portfolio companies, communicate with LPs (esp. around expected capital calls and distributions), and review reserve strategy (eg. what percent of your companies need help, and how many can and should you support if they need a life line).

LP 101: Intro to investing in venture funds

I’ve had the opportunity to recently talk to a few folks who are considering investing in a venture fund for the first time. While there are lots of resources for new angel investors, I’ve found there isn’t as many for new LPs.

#OpenLP is a great movement to get the voice of LPs into the market, and Origins by Notation Capital is a great podcast where they interview LPs. I highly suggest both of these, and there are other great resources out there, but they seem to focus more on providing LP insight into VCs.

I thought I’d share some articles that might help those considering investing in a venture fund for the first time. By no means do I guarantee these are the best resources, but it’s a good start – and I’ll keep adding.

For those who are very unfamiliar with venture capital, 16 Definitions on the Economics of VC on A16Z’s blog is good to get familiar with some of the terminology.

I’d then dig into VC Funds 101: Understanding Venture Fund Structures, Team Compensation, Fund Metrics and Reporting, which covers… a lot of stuff, as it says in the title.

For understanding expectation as an LP, Funding Math by @homanyuen is a good article that provides some data point around expected returns as an LP, and the power law dynamic at play in the underlying portfolio. To summarize:

  • LPs want to see 2-5x return (6%-15% annualized) depending on stage of investments.
  • 65% of startup investments see 0-1x.
  • 10% of startup investments see 5x+.

This Twitter thread by Benedict Evans has some great graphs that go further into explaining the power law of returns at the portfolio level and their impact on fund returns. To summarize:

  • Across the board, about 6% of deals done produce 60% of the returns.
  • For the best performing funds (that do >5x), ~20% of their deals produce 10x returns, providing ~90% of the fund returns.

For those who want to dig in further on return expectations, this Twitter thread is a fairly biased thread that makes a great case on investing in venture capital. To summarize:

  • At the top quartile, VC has historically outperformed other asset classes (like PE, RE).
    • Note: VC is the most volatile, so this makes sense.
  • Even median funds in the 2009-2012 vintages are showing 9%-21% returns depending on vintage year.
  • VC has low top negative correlation with other major asset classes.
    • Specific to the S&P 500, VC returns have negative correlation.
  • On liquidity:
    • Avg time to M&A transaction: 5.5 years
    • Avg time to IPO: 8.5 years
    • Avg time to exit: 6.5 years

Slightly different, but if you want a better understanding of how many startups VCs meet before investing – while just one data point – Satya Patel at Homebrew was kind enough to share some of their metrics around this in their blog post: Homebrew’s 1%: The VC Metrics Behind Investing in One of Every 100 Companies We Meet.

Change is slow

When looking to permanently change the physical shape of hard materials, it often requires an additional ingredient to be added first (eg. heat, water), then you work the material slowly and gradually, and finally let it rest. If you skip adding the necessary ingredient, work the material too fast, or don’t let it rest, it can break or bounce back.

I often think about how this applies to all forms of change, whether that be in self improvement or social culture.

When I feel impatient about something changing, I remind myself that slow change tends to be more permanent.

Launching – a search engine that only shows results from trusted VCs

When asked questions by founders, I often send them articles from VCs who’ve said it better than I could. I always wished there was an easy way to search just content from blogs by VCs.

When James Augeri told me about his new startup Jingle last Thursday, I asked if he could help set this up, and we did it in a weekend! What’s more amazing is that our Thursday call was the first time we ever talked, after connecting on the Techstars Connect platform online.

This is a true #domorefaster #givefirst project.

Without further ado…

We’re excited to share – a search engine that only shows results from trusted VCs like Brad Feld, David Cohen, and Fred Wilson.

It’s great for searching startup terminology like “option pool”, “founder compensation”, or “valuation”. Check out some of the industry based searches that show interesting results like “autonomous vehicles” or “blockchain”.

No matter what you search, the results are only from a list of 100 or so VC blogs we’ve indexed for this search engine.

We hope founders and VCs alike find this a helpful tool to quickly find trusted information when they need it most.

Tweet us with questions or comments!

How to find Angel Investors using LinkedIn

I found a way for founders to find angel investors in their network using LinkedIn, and since sharing this with a couple founders and getting feedback – I’m excited to tell you – it works (for some, at least).

It’s simple.

Go to LinkedIn. Search for “angel investor”, and filter down to “people” who are within 1 or 2 degrees with you.

Depending on your network, this could be a huge list (mine shows 31,205). It’ll have noise, because this includes people who work with angels investors, etc.

Alternatively, you can remove the keyword search, and search for folks who have or have had the role “angel investor”. You can find this in advanced filters.

If the search result is too large, you should filter down to locations close to you so you can fundraise with minimal travel.

The key here is your mutual relationships with these angels.

At this point, I suggest setting up an Airtable. As much as I love Google Sheets, for what I want you to do, you’ll need the “Linked Table” feature from Airtable.

First, you’ll create the table “Angel Investors”. First column will be their name. You’ll then create a “Linked Column” titled “mutual connections” and create a new table called People. This is where you’ll put people in your mutual relationships.

As you go through the search results in LinkedIn, look at the profile of each search result and determine whether you feel they are worth reaching out to. Part of this is looking at the mutual connections and seeing if you know them well enough to ask them to forward an email along.

(I’m not going to go into forwardable emails here. Read Alex Iskold’s post if you’re not familiar with it)

If they seem like a fit, and you have good mutual connections, add them to the “Angel Investor” table, and in the “Mutual Connection” column, add the person or people you might try to reach them through.At the end of this exercise, you not only have a big list of angel investors in your network, but have tracked your mutual connections with them. You can then look at your “People” table, add a column that counts “Angels” they know – and sort by it. Now you know that Susan who you volunteered with happens to know 5 angel investors in your city.

You then ask your friends if they would mind looking through (or hearing) a short list of names, and if they know any of them well enough – if they could forward along an email to them.

The original list of angels you created will pare down fairly quickly, as you realize many of the LinkedIn relationships are not strong (eg “I don’t recall that name, must have met them at a conference”), but that’s part of the process.

I’m sure this will work better for some than others – if you try this, I’d love to know how it goes. You can leave a comments or just tweet me (@yoheinakajima).

Graduation Rate of Seed to Series A in Major US Cities

I randomly became curious about the graduation rate of startups from seed round to Series A round, specifically around the context of cities. I thought this might actually be more interesting to look at than total $ invested, or total number of rounds.

For the analysis, I looked at 6 major cities: SF, LA, Seattle, Boston, Chicago, NY.

I used Crunchbase data to pull all startups who raised a seed round in 2014, 2015, or 2016. I picked these years pretty randomly, but mostly because it felt recent enough, but not too recent.

I defined graduation rate as raising a Series A, anytime between their seed round and today.

Total companies in this list were 5114, with an average graduation rate of %20.3.

Here’s the chart:

Interestingly, Seattle and SF are leading the pack. LA was significantly lower.

Not sure why, but it’s interesting to see this.

Hands-on with Magic Leap

I had a chance to try Magic Leap last night. I’d read various reviews that the Magic Leap didn’t live up to the hype, so my expectations were low. I was pleasantly surprised with the experience. I haven’t tried other AR headsets so I can’t compare, but here are some thoughts.

When you first put the headset on, it asks you to scan the room by following an arrow and stopping at targets. It gets you to really look around the room, up, down, left, right, diagonal, and so on. As you scan the room, it creates a grid that shows you where it’s identified walls, floors, ceiling, furniture, etc. This itself was a pretty fun experience.

I tried the “Create” app, which is supposedly the most compelling one available. It’s more of a demo that allows you to draw and drop objects into your environment, as opposed to a full fledged game – but it was lots of fun. I first drew a red tornado spiraling around me, walked over to another part of the room and drew a blue 3d fish. I then dropped a ball on a couch and saw it bounce around a bit before nestling into the corner. It really felt like the items were interactive with the environment I was in.

It was fun to hand the goggle to others and have them see what you’d drawn.

The field of view is something that had been mentioned by others, and yes, it is small. That being said, I felt like the “screen” covered most of the available field of view, which I think is better than the “screen” being smaller than the lens – if that makes sense. Rather than objects disappearing off the “screen”, they disappear off the lens, which is a more natural experience.

The lens felt dark. While you could definitely see your environment, it kind of felt like I was wearing shaded snowboard goggles.

The scanning was  pretty accurate. It was up to about one inch off, which wasn’t noticeable until I tried to “trace” the couch. Didn’t notice this when I was dropping objects on it.

I could have played with this one app for hours, probably getting lost drawing some crazy environment.

IMPO, the technology is there. Now we need some creative geniuses to come up with “killer” use cases and games that everyone would want to play while looking silly wearing glasses like this.

An exercise in understanding the impact of new technologies.

The effect of new technologies are difficult to understand. I think we’re still learning a lot about how social media influences the world today, which is an extension of how the internet affects us – both of which might not seem that new to us. Today, some of the newest topics we’re discussing are blockchain, artificial intelligence, and robotics. We as a society agonize over the impact of these technologies.

When trying to understand something new, it’s a good exercise to think of ways that it’s not new – specifically, look for something that’s been around for a while with lots of parallels.

Let’s take Bitcoin as an example, what is it similar to? One example I can think of is the Gold Standard. They’re clearly very different, but there are some similarities:

– Both tie government issued currency to the value of something not (directly) controlled by one government.
– Both are only as valuable as we consider it to be.
– Both have a limited supply.
– Both can be “mined” by those with enough of the appropriate resources and know-how.

Based on this exercise, what are some theories we can come up with on the effect of Bitcoin?

– It won’t replace government issued currency.
– It will continue to have value that fluctuates.

Looking at the differences, for example one being digital while the other is not, we can already see today how Bitcoin could be accepted as currency at most merchants while Gold cannot (or at least not easily). This may or may not affect our previous theories. What do you think and why?

All of the above is clearly oversimplification, but I present this method merely as a thought exercise.

Creating Wealth Through Sustainability

In 2017 alone, Bitcoin created massive value for early investors in it – billions of dollars. This made me think – did Bitcoin really create that much value for society? Where does this wealth come from? Can wealth be created?

Or is wealth a zero sum game? My gut says it is.

I leaned on my trusted friend, the internet, and found some good arguments for why wealth is not a zero sum game, that wealth grows – but I found this leaned on two basic premises, that wealth is perceived, not material, and can only be acquired by humans.

Let’s talk about perception first.

One argument for wealth creation goes like this. I have two eggs and you have two apples. We’re likely to trade because I value one apple (which I don’t have any of) more than one egg (which I have two of), and vice versa. Argument goes that wealth is created through this trade on both ends.

Another argument goes like this. If I paint a beautiful painting, you’re likely to pay more for it than if I were to simply sell you the paint and canvas. Thus, I must be creating wealth through my skills.

In both cases, there is no increase in material wealth. In the first case, we start with and are left with two apples and two eggs. In the second case, we start with and are left with paint and a canvas. That being said, there’s no question that perceived wealth does increase.

Now let’s talk about wealth only being acquired by humans.

One argument for wealth creation goes like this. If I walk into a park and find a beautiful rock, pick it up, and take it home, I’ve created wealth. While if you look at me alone, or human society as a whole, I’ve indeed created wealth, but the idea that I’ve actually created wealth assumes that the park cannot possess any.

Another argument is a little more complicated, but goes like this. I’m a farmer, and I start using pesticides that increases the yield of my crop by 20%. This increases wealth as I’m able to feed more people with the same amount of work. I’m not going to pretend I can quantify the value of life to an individual insect, the value of the insect to a frog, or the total effect of pesticide creation on who knows what, but simply stating that I’m creating wealth through increased yield by using pesticides doesn’t sit well with me.

My argument is that if you look at wealth as material, not perceived, and if wealth is not just acquired by humans but can be acquired but all entities, then wealth may very well be a zero sum game. Wealth, like mass, can neither be created nor destroyed – simply altered in form.

The reality though, is that wealth is perceived. The value of an object, physical or digital, is the value we as society place on it.

Perhaps, though, when we use words like wealth, we might consider it as something not only acquired by humans, but by all entities in the universe and the universe itself. This might encourage us to recycle more, eat organic food, and value (or at least consider) sustainable activities not often associated with wealth creation.

The Role of Venture Capital

I like to view the world through different “lenses” to help me understand it. It’s a means to generalize the world and help me consider patterns. Thinking of the world simply as a collection of matter and space is what I call the “physicist lens”, a collection of incentives is the “economist lens”, a collection of energy is the “spiritualist lens”, and so on. Today, I spent some time thinking of the world as a collection of data, which I’ll call the “data scientist lens”.

Through this lens, I saw that human society is doing one big complicated data science project. It can be broken down into many smaller projects, but they’re intertwined as every piece of data can be connected to another through some series of nodes.

The process of a data science experiment roughly goes like this: you collect and store data, you process it (clean, merge, etc.), analyze the data, extract insight, create an action plan based on the insight, execute the action plan, reflect on the process, and repeat the process.

Similarly, in society, we’re constantly going through this cycle. We do this as individuals daily, as groups of people (companies, families, social groups), and as a society at large. Like complex organisms, society becomes efficient by allowing smaller groups within to specialize, in the type of data it deals with and in the phase of the process.

As a Venture Capitalist, our role is in the reflection phase of the process, specific to new data (social data, voice data, health data, etc.) and new processes (cloud storage, distributed ledgers, machine learning, etc.). We reflect on the past and allocate resources that human society has trusted in us to control – through a network of complicated financial institutions.

As individuals, we have the desire to both serve ourselves and serve society at large. As Venture Capitalists, our job is to see what’s working to create value (desired change in data – wealth, health, and happiness) for individuals, both people and business, and for society at large.