Common wisdom suggests that a typical seed-stage venture capital fund will invest in about 30 portfolio companies. Samir Kaji estimates that over “65% of seed funds” have some kind of construction based around the 30-company model.

Now, 30 stocks in a public equities portfolio could be sufficient to provide diversification within the stock market, but given the typically poor performance of a median venture capital fund [1], that scale of VC portfolio depth is clearly not enough. We know — or at least we intuitively suspect — that the venture capital space is capable of lucrative returns (after all, the recent phenomenon of crypto excluded, where else can an investor drop $10,000 and come back five years later to find $25 million dollars? [2]); thus, adequate diversification within early-stage venture capital should boost a fund into at least “decent” rather than “poor” performance.

The Underlying Math of Startup Outcomes

The question then is, “How many seed-stage startups should I [or insert an imaginary character of your choice] invest in (ignoring all the other strategic factors like deal access and informational advantage)?”

The answer is unfortunately complicated by the observation that startups’ outcomes seem to follow a power law, which Jerry Neumann recently experimented with in great detail. For the mathematically inclined, here’s the general formula Jerry (and I) use to determine the probability (noted as p(x)) of a startup achieving a particular return (noted as x):

The math reflects the typical VC rules-of-thumb about company outcomes, which says something like:

• 8 out of 20 startups (seed-stage!) are complete losses: a 0x return
• 8 out of 20 startups are salvaged (via asset sale, acqui-hire, etc.): a 1x return
• 3 out of 20 startups generate some return (decent acquisition by a bigger company): a 2x to 3x return
• 1 out of 20 startups will be a home run: a 20x or greater return

Based on this fuzzy heuristic, you could scroll to the data tables in Jerry’s post and agree with his experimental results: a VC fund should invest in as many startups as possible. It’s a nice TL;DR sound bite but…

… scroll a little further and you’ll realize that Jerry does not take his results as truth. The post is more inspiration than prescription.

That sentiment is a perfect segue to Gabriel Weinberg’s question:

[I managed to procrastinate for so long on this article that this tweet no longer exists in its full glory…]

Gabriel Weinberg on Twitter

@ganeumann Thank you for the thought-provoking post. Curious if you think things would substantially change under the following additional assumptions: 1) non-infinite mean/variance/tail by capping highest possible outcome as very high, but still finite.

In Jerry’s experiments, his power law is unbounded: a startup (say, Googuberlyftsnapbook) could enjoy a 100,000x return! That would be an IPO in the order of trillions of dollars!

Jerry’s implementation of a power law lacks a sensible average because of this mathematical construct. Instead, his average would be infinite, which explains why more investments experimentally results in better portfolio returns. After all, when someone has invested in a million companies, one of those companies could be a trillion-dollar winner.

While I place substantial faith in the ability of high-tech startups to develop into multinational giants, I find that Gabriel’s suggestion of capping the highest possible company outcome is a sensible call.

A Few Mathematical Tweaks

What should we cap our potential returns at? Google IPOed with a market cap of ~$15b (by Jerry’s estimate, a 10,000x return), while Facebook IPOed at a ~$80b market cap (by my estimate, a 15,000x return). I don’t believe any VC has pulled off a more spectacular exit, so I think it’s reasonable to cap our potential company returns at 20,000x.

With a capped power law distribution, we can run a sanity check by calculating the mean of our newly capped power law distribution. Updating some equations from Jerry [yes, a lot of this post is inspired by and built off Jerry Neumann’s writing — thanks!], I arrive to a mean through this process:

• The probability of a given startup achieving a particular return (once adjusted to accommodate the 0x — 1x return range is:

• The mean of this distribution is then:

• Taking the assumed a = 1.98; C = a-1 = 0.98, we can finish our calculation:

These assumptions suggest that the mean gross return of early-stage venture capital (as an asset class) is roughly 3.91x. We can reasonably expect a large enough portfolio (e.g. 1 million companies) to approach this return, which — by all standards — is strong, as far as venture capital returns go.

However, making this many investments in a single fund is a clearly impossible endeavor. A portfolio size of hundreds of investments, on the other hand, is within the realm of feasibility.

A Revised Simulation of Venture Portfolios

By constraining success cases to a set maximum, we now have a finite mean, which suggests that Monte Carlo simulation (use of repeated trials to understand the “expected” behavior of a scenario with randomness) is now valid.

Through a Python/Excel model, I simulated venture portfolios of 1000 companies each. Then, I took different cuts of these 1000-company portfolios: the first 50 went into a 50-company portfolio; the first 100 (inclusive of the first 50) went into a 100-company portfolio; and so on. For the sake of simplicity, I assumed that our sample funds would not reserve any capital for follow-on investments. Additionally, I took a fund-focused approach: there is a set pool of capital, which must be divided (in our simulation, equally) among the investments. Lastly, I assumed the outcomes that inspired our power law were inclusive of dilution, so no dilution factor was added in the model.

Through this approach, we can see the trade-offs (if any) of diversification in venture capital. By running through several Monte Carlo simulations of varying portfolio sizes, I surfaced a few trends, visible in this graph:

1. The median and the interquartile range (the “middle fifty”) rises steadily from 50 companies to 1000 companies.
2. Growth in expected performance decelerates as portfolio size increases. [I wonder what portfolio size would come close to the mathematical mean.]
3. The interquartile range doesn’t significantly tighten, unlike what would be expected under the law of large numbers.
4. The average is always greater than the median. Spectacularly performing outliers (more on this later) are responsible. The outliers get less spectacular at larger portfolio sizes, yet this doesn’t seem to noticeably affect the distance between average and median.

The answer is not to invest in 1000 startups though. The graph above only tells one side of the story. Let’s consider the following graph, which highlights the constraints, rather than the potential, of different portfolio sizes:

1. As a portfolio adds companies, its maximum portfolio return drops rapidly. If you’re looking to join the ranks of Lowercase Capital (boasting a 200x+ multiple), you can’t invest in many companies. While the specific maximums depend heavily on which Monte Carlo trial we look at, the general shape of the curve stands: it’s harder to hit a home run when the ball is heavier.
2. Similarly, when a portfolio grows in number, it needs to (overall) be more valuable to generate the same approximate return. This is a mostly linear relationship. Using purely illustrative numbers, a 50-company portfolio might need to exit for $500m to generate a 2x return, while a 200-company portfolio will need to exit for $2b for the same return. Sounds simple enough at smaller scales, but at large-scale, the question then becomes: do you believe you can find 5–10 unicorns where you’re looking? Is Uber really going to be in that pool of companies that you have access to?

Additionally, that “Combined Exit Value” is sensitive to entry price. My model assumed an uniform $5m post-money valuation, reflecting angel/pre-seed rounds. A seed-stage investor is likely to find deals closer to $12.5m in post-money valuation. The “Combined Exit Value” required for the same return shoots up by 2.5x.

Thinking Practically about Portfolio Construction

Portfolio construction is difficult because it is unintuitive and uncertain. However, my analysis suggests that there is mathematical validity to the oft-dismissed notion of “venture diversification”. I suspect that our graphed results above will shift significantly if we use different return spectrums. Different power law parameters reflect the varying quality of different VCs. For example, we might see the median portfolio return peak at 500 companies instead of 900 companies under tweaked power law parameters, reflecting a better or a worse VC firm.

At the end of the day, fund portfolio construction is really an exercise in being honest with one’s self. For fund managers, it starts with 1) figuring out what galaxy of startups you can reasonably discover and access; 2) understanding what level of risk and return you (and your LPs!) are comfortable with; and 3) sizing appropriately to those parameters. As Samir Kaji says, “one size certainly most certainly not fit all.”

Next step for me is trying out the same analyses under different assumptions for alpha. Maybe for the best VCs (low alpha), we will in fact find that the traditional wisdom (a concentrated portfolio!) holds.

Here is the Python code I used (unavailable on mobile):

import numpy as np
import powerlaw

ALPHA = 1.98
MAX_RESULT = 20000
FILE_NAME = "result.csv"

def vcdist(helper):
    # Variable "helper" will not be used; it's only present to trick np.vectorize 
    pl_dist = powerlaw.Power_Law(xmin=1.0, parameters=[ALPHA])
    
    pn = np.random.randint(0,3)
    if pn == 2:
        pn = pl_dist.generate_random(1)[0]
        while pn > MAX_RESULT:
            pn = pl_dist.generate_random(1)[0]
    return float(pn)

def main():
    v_vcdist = np.vectorize(vcdist)
    arr = np.ones(shape = [1000,1000])
    result = v_vcdist(arr)
    np.savetxt(FILE_NAME, result, delimiter=",")

[1] If you want a direct source, check out the Cambridge Associates benchmarks for early-stage venture capital: https://www.cambridgeassociates.com/benchmarks/

[2] Bonus points if you can figure out which company I’m talking about… this is a real life example!

More where this came from

This story is published in Noteworthy, where thousands come every day to learn about the people & ideas shaping the products we love.

Follow our publication to see more product & design stories featured by the Journal team.

Typically, we see a VC firm in the news (TechCrunch or Forbes, not CNN or Fox) when a startup announces that it has successfully raised a new financing round. These press releases and news articles put all the attention on why Famous VC Firm or Notable Angel Investor opted to make that particular investment, similar to judge deliberations on Shark Tank. These articles rarely cover the broad search for or the years of networking required to find that one startup. It’s never discussed how “writing the check” is not a culmination, but merely the end of the beginning of a multi-year engagement between a VC and a startup.

“Venture capital feels like consulting… vague, mysterious, but you know you want to be in it.”

- My friend, Christine (a consultant)

So to shed light on what a VC does, I’ve created a mental framework for VC functions. There’s four general functions.

• Construction
• Access
• Selection
• Sustaining

The four functions roughly follow the investment lifecycle: first, a strategy is developed, which defines how a VC portfolio should be constructed. After finding and investing in a startup, there’s work needed to support and manage the company. The section below dives deeper into each of these four functions, exploring how specific activities support that function.

Notably, I’ve left out fundraising and investor relations: both are crucial to a VC firm’s continued success, but feel inherently separate from the distinct act of “investing in companies”.

Construction

Setting a strategy and planning what the corresponding portfolio looks like (from multiple lenses, such as geography, industry, valuation, and % ownership).

It all starts with construction: thinking about how investments fit together sensibly as a single “product”, rather than piecemeal holdings. Savvy limited partners (LPs, the people who fund venture capital firms) say that good VCs pick a strategy based on their advantages. Then they set an appropriate fund size. A lot of aspiring VCs do the opposite, setting the fund size and then trying to force a strategy to justify that fund size. Adopting an LP’s perspective is crucial to effective construction: they’ll care about the timing of your capital calls (i.e. when you withdraw the money they’ve committed, as this impacts their IRR), about the ownership you anticipate holding in each company (they know the rarity of unicorns and want as much of any unicorn-sized pie as possible), and many other factors.

Activities include: researching where your strengths lie (see: access, selection), deciding how many portfolio companies to invest in, setting a reserve for follow-on investments, projecting cash flows and the capital calls necessary to cover those cash flows, and estimating startup risk/return given a company’s sector and valuation and geography.

Access

Finding great entrepreneurs and convincing them that you are worthy of investing capital in their startups.

Shows like Shark Tank give the impression that VCs have an endless flow of high-quality startups knocking on their door, falling over backwards to get some face-to-face time with a check-writer. While there’s some truth in that impression, there is a very high probability that the most impressive entrepreneurs never make it to your doorstep (or inbox or Twitter feed). High-quality deal flow is king in the industry; everyone wants it and everyone fights for it, tasking staff and capital solely to the purpose of finding the next Facebook.

But finding Zuckerberg 2.0 isn’t enough: you still have to convince him or her to take your investment dollars and not (for example) those of Sequoia or A16Z or any number of other investors that might be equipped with strategically beneficial skill sets and relationships. Accordingly, you build your own knowledge and network, offering those to entrepreneurs in a bid to win their favor.

Activities include: attending conferences, maintaining relationships with old classmates, making calls to industry analysts, doing other phone outreach, building an online presence (which can go from Twitter conversations to writing blog posts), and (very importantly) offering help to entrepreneurs.

Selection

Picking the best investments out of the available opportunities, keeping in mind limited capital and limited human bandwidth.

All the access in the world is for naught if you cannot effectively pick startups that are 1) poised to grow rapidly and defensibly or 2) growing rapidly and defensibly. Selection is where you can embrace your inner shark (or whatever the judges are called… I don’t watch the show). That being said, the analysis process is (for most firms) much more rigorous and time-consuming than a minutes-long deliberation based off a single interaction with the startup.

Diligence can stretch for weeks, with different VCs focusing on different investment criteria. For many early-stage investors, they focus almost entirely on the founding team. Others will concentrate on early traction metrics or unit economics. Still others construct well-researched theses that hold some opinion on the direction of the world.

Activities include: analyzing startup materials (including pitch decks, financial models, and operating metrics), asking questions of industry experts, reading reports about markets and verticals, texting friends at other VC firms for intel, writing investment memos, dissecting financial models, and launching novel analyses (e.g. epidemiology-inspired models of a company’s potential viral growth).

Sustaining

From keeping track of your investments to maximizing their chances of breakout success.

A wire transfer from a VC’s coffers to a startup’s bank account signifies the beginning of a relationship, one that should last for years and can persist for over a decade. Many investors are activists, offering their help to the startups that they back. That help can take the form of strategic advice, vetting key hires, making introductions, and much more. Given the unicorn-chasing nature of venture economics, VCs will try to maximize the probability and magnitude of success for their top companies. Even passive VCs remain involved by keeping regular tabs on their investments: this information can flow into many things, including LP reports and financial statements.

Activities include: observing board meetings, making introductions for a portfolio company, undertaking projects for a portfolio company (this can be anything from marketing to programming), recording information from newsletters to investors, and providing metric benchmarks.

I hope this breakdown was helpful for you, whether you’re an aspiring VC trying to break into the industry, a new VC searching for a more comprehensive look at the industry, an entrepreneur, or a general partner (GP) wondering how to integrate quantitative tools/machine learning/automation into your operations [more on that in a future post; or if you’re really curious, email me at kendrick.g.kho@gmail.com]!

I’d love to hear your thoughts in the comments. Thanks!

When my first intern started his first week at Hone Capital, he heard me and my coworker chatting about Ethereum (and PepeCash, but that’s a different story). Not wanting to miss out on the “fun”, he opened a Coinbase account and bought some Ether. GDAX featured heavily in his browser’s history after that.

Naturally, when my second intern walked through the door and settled into her desk right beside Intern #1, she was fascinated by GDAX’s charts. She soon became an investor in Ethereum as well.

Hearing excited/panicked reports about Ethereum’s price on a daily (hell, near-hourly) basis, I began to notice a trend: Ethereum seemed up near the weekend and down near the middle of the week. Wednesday? ETH at $225. Saturday? ETH at $275. Was this actually a trend or was I making patterns from noise?

Hypothesis

I believe that sentiment/casual dabbling is the main driver of significant price movements for “mainstream” coins (applicable even before November’s cryptocurrency market cap boom). It’s not hard to see how — on Friday and Saturday nights — a buddy talks to his friend, a lady chats with her significant other, people gossip over dinner, and we end up with more casual money being pumped into Coinbase and all its listed currencies.

Alternatively, people hear crypto stories all week at work and then remember on Saturday to finally set up that Coinbase account.

Point is, my hypotheses were that:

1. Price increases of BTC, ETH, and LTC tend to be greater on Friday, Saturday, and Sunday (when casual interest gets spread and converted into action).
2. Price increases of BTC, ETH, and LTC tend to be smaller on Tuesday, Wednesday, and Thursday.
3. Price swings (in general) will be greater on Friday, Saturday, and Sunday (sentiment being magnified by casual investors).

Findings

After crunching the numbers (nothing too complex; the methodology is in the Data below), it turns out I was mostly wrong on all three hypotheses.

Chart 1 (just below) shows the median price change per day of the week. Sunday, at least at the median, has the worst drops across all three currencies on Coinbase. Conversely, the end of Sunday is the best time to buy. On the flip side, prices consistently rise between Sunday and Monday. Maybe people get excited about cryptocurrency during the weekend and use their Monday procrastination to buy some coins?

At least my hypotheses partly survived the experiment: Saturday is a usually good day for cryptocurrency prices, while Wednesday tends to be a bad day (unless you’re a Litecoin enthusiast).

Median price movements only tell part of the story though. Here’s Chart 2, the average price change per day of the week. Try overlaying this chart with Chart 1.

When factoring in outliers, which would be captured in an average, the weekend looks really bad. One interpretation of this data is that casual trading still happens on Friday, Saturday, and Sunday, but affects downward price pressure disproportionately. In other words, casual traders freak out about BTC, ETH, and LTC much more than they get irrationally hyped up about those. If you’re looking to flip weekly, the trend still holds: buy at the end of Sunday. Sell sometime before the end of Wednesday.

That guideline holds at the median and at the average, but we all know cryptocurrencies have too much daily volatility to consider just those data points. Chart 3 is a set of three box plots (minus the whiskers, which distort the appearance of the graphs dramatically) that highlight the interquartile range of daily price changes for each cryptocurrency.

If you are looking to create a day-dependent trading strategy for a particular cryptocurrency, you’d probably start with these charts. From visual inspection, BTC and LTC tend to exhibit the same behaviors, with the notable exception of Thursday and Friday being flipped. ETH, however, has its own set of tendencies. Many reasons could explain ETH’s deviance from the norm established by the other two coins, including:

• Differences in demographic mix, including age, occupation, investment size, and nationality
• Impact of mainstream endorsements in amplifying or dampening opinion among the demographic mix (and when those endorsements are typically announced)
• Magnitude of market manipulation by whales or institutions

These are all factors that could feed into a cryptocurrency weekly trading strategy. I’ve never tested the efficacy of bots or other programmatic methods to extract sentiment/price signal from public data, but such tools could be deployed according to the factors listed above.

If you came to the conclusion that BTC and LTC were highly correlated (a widely-held assumption that I’d like to add nuance to in a future Medium post) from these charts, no one would blame you. However, these charts are aggregations of hundreds of price movements: a third-quartile price increase in LTC on Monday one week might be matched by a third-quartile price increase in BTC on Monday the next week, rather than occurring on the same exact day. Actionable advice? Treat trading decisions (at least those decisions that are based off weekly cyclicality) of each cryptocurrency separately.

Another method of representing the magnitude of price swings is through standard deviation, which I’ve included in Chart 4.

My third hypothesis was also off the mark: Sunday is actually the most “boring” day of the week across all three coins. You’re most likely to be on the crypto rollercoaster on Thursday, though there’s still decent odds of a wild ride on Wednesday and Friday.

Between these various statistical measures, I think the building blocks of a sentiment-inspired trading strategy are available. You might get more mileage through the use of other forms of sentiment (e.g. Google search trends), rather than pure technical analysis, but this will hopefully suffice as a start. I’m certain that you will come to your own conclusions from all the charts and numbers above, but if you wanted to gather the data and do additional manipulations, my methodology is below.

Data

Fortunately, daily data on BTC, ETH, and LTC are publicly available and easily accessible. With a simple copy-paste of historical data from CoinMarketCap into Excel, I had all the data I needed.

To compare price changes, I calculated daily percent change in the close price during the date range of January 1, 2017 — December 20,2017 (year-to-date). Possibilities to extend this type of analysis:

• Investigate the bottom and top deciles (i.e. the top and bottom 10% of percent changes in price).
• Measure the absolute value of percent changes in price, considering just the magnitude of movement.
• Add nuance to correlations between cryptocurrencies: for example, are large movements in prices correlated differently than small movements in prices?

I’d love to hear your thoughts on cryptocurrencies! Leave a comment below or send me an email at kendrick.g.kho@gmail.com. Occasionally, I post pithy blockchain/cryptocurrency musings on my Twitter as well.

In case the SEC is reading this: the data provided in this post is for information purposes only and should not be construed as investment or tax advice, nor as a recommendation to buy, sell, or hold any particular coin/token/security.

Analysis

How can a truly “free” state exist if the individual citizen is enslaved to the forceful will of others? It cannot. This right to self-protection is a cornerstone principle essential to the American experiment and critical to maintaining life, liberty and the pursuit of happiness. — Tiffany Madison

Though this research investigates only one case study of crypto-anarchism, I argue that the use of digital technology to undermine gun control efforts comprises a representative (i.e. typical) case. The premise of Defense Distributed’s efforts strongly fits the standard model of anarchism: rebellion against government control; the “enemy” is not an amorphous media-industrial complex, but rather, quite clearly the state. The character of Defense Distributed’s efforts similarly adopts an anarchist flavor. Consider the musings of Cody Wilson during an interview: “Imagine an insurgent scenario. People could be replicating printers in their neighborhoods, out of site. Anywhere there’s a computer and an Internet connection, there would be the promise of a gun… that has to change how the state treats citizens”17. Wilson’s statement exudes a challenge and a warning to the state that posits both agency and ability among common people. Most importantly, the incorporation of technology into subverting gun control matches two crucial criteria: 1) an anarchist technological arrangement and 2) other contemporary deployments of technology by anarchists.

To the first point, the model of creating and widely distributing 3D blueprints — artifacts that grant physical and political power — for free is consistent with the anarchist embracing of “decentralization and local empowerment” (Gordon). Defense Distributed’s actions capitalize on the Internet’s “anarchist attractions” by developing a “a communistic informational economy” (Gordon). Further, the crypto-anarchist opposition to gun control appropriates the Internet — a system that is organized in a decentralized manner, but is materially constrained to be centralized and large-scale — and intentionally maximizes its nonhierarchical potential. Similarly, these crypto-anarchists intentionally invalidated the hierarchical power granted to those who control the Internet’s material infrastructure by placing information in “an electronic commons”, impossible to feasibly halt or monitor.

To the second point, crypto-anarchist opposition to gun control has used technology in remarkably similar methods to several other crypto-anarchist efforts including: 1) Bitcoin, a “cryptocurrency” outside any governmental authority, which relies on distributed processing, storage, and confirmation of transactions and balances; 2) peer-to-peer storage and sharing of media to avoid federal law, including the Digital Millennium Copyright Act (DMCA); and 3) Anonymous, a “hacktivist” group that has most recently targeted the National Security Agency (NSA) PRISM surveillance program. I have established the crypto-anarchist opposition to gun control as fundamentally anarchist by delving into its identity components — who, what, how, and why. The use of 3D blueprints to oppose gun control stands as a representative case of crypto-anarchism in practice. While I am not equipped with the argumentative scope to generalize any findings from this case study to the entirety of crypto-anarchism, this analysis remains relevant and informative to other “typical” incidents of crypto-anarchism.

Prior to this research, I posited the following theoretical proposition: as technology is harnessed by anarchists for political purposes, technology will, in turn, shape anarchism in a qualitatively significant fashion. I hypothesized that the label of crypto-anarchism would mask a deeper ideology that strayed from anarchist principles. Private capitalist benefit, anarcho-capitalism, and communist tendencies were all likely candidates. However, from the example of Defense Distributed and Cody Wilson, I could not uncover a political undercurrent that deviated from classically anarchist tenets. A deconstruction of the political agenda of Defense Distributed begins with its actions. Yet, from designing a firearm that could be manufactured using 3D printers around the price range of US$1000 to creating a free, online download repository for these blueprints, Defense Distributed has leveraged the “nonhierarchical structure, low transaction costs, global reach… [and] censorship-foiling” characteristics of the Internet to spread politically potent material (Gordon). A key observation is that these crypto-anarchists did not price their product, nor restrict access to anyone; unfettered access transformed the scope of their efforts from the local to the global, the ultimate expression of “decentralized, democratic… self-management” (Winner).

I have a single contradiction to consider: Defense Distributed emphasizes that their actions remain within the law. In addition, the organization underwent efforts to remain within an unarbitrarily clear purview of law by initiating dialogue and cooperating with the requests of the ATF and the State Department. Defense Distributed’s website publicly states that its purpose is “to defend the civil liberty of popular access to arms as guaranteed by the United States Constitution and affirmed by the United States Supreme Court… in promotion of the public interest”. Defense Distributed’s statement surpasses an obligation to recognize the validity of the state and to adhere to the state’s standards. It embraces the United States’ political foundation (i.e. the United States Constitution and the United States Supreme Court), a notion that is decidedly unanarchist. Rather, this statement implies a libertarian slant, as defined by Peter Marshall as “one who takes liberty to be a supreme value and would like to limit the powers of government to a minimum compatible with security”. However, Marshall acknowledges “the line between anarchist and libertarian is thin… the terms have often been used interchangeably”. Due to this ambiguity, I refrain from making a definitive statement as to whether, in this case, crypto-anarchism is a wrapper for libertarian impulses, which are more accommodating of government than anarchism.

Instead, I turn to another component of Defense Distributed’s public statement of purpose: the organization’s justification of development and distribution of 3D firearm blueprints as for the “public interest”. However, as crypto-anarchists have bypassed gun control, they have circumvented any public dialogue on 3D firearm blueprints. Instead, they have decided for society and have constructed “the kinds of reasoning that justify the adaptation of social life to technical requirements” (Winner). One reasoning that crypto-anarchists use to justify 3D firearm blueprints centers of safety; yet Kim Dotcom and Senator Schumer feel that public safety is threatened by the release of these blueprints. Conflicting opinions of safety yield insights into how “freedom” and “liberty” are differentially defined on both sides of the gun control debate. Drawing from the thoughts of Second Amendment defenders and anarchist principles, I create a simplified sentiment for crypto-anarchists in the context of gun control: individuals should be free to possess (i.e. should have the positive liberty to possess) firearms, in order to safeguard society from impositions on positive and negative liberty. From the oft-expressed arguments against gun control, I again formulate a simplified sentiment: government should protect the natural right to life and guarantee the peace-of-mind connected to individual safety by creating limitations on positive and negative liberty. To rephrase, one side declares that “freedom” is not just the “freedom to bear arms”, but the “freedom to regulate one’s self”; the other demands the “freedom to live in an environment of personal safety”. Defining and elaborating on differing concepts of “freedom” and “liberty” reveal that, despite a shared basic understanding of what these terms entail abstractly, people can hold vastly incompatible perspectives on what these terms imply for our lives.

Conclusion

The crypto-anarchist campaign against gun control is a living creature; it is currently interacting with public opinion and forcing political responses. It is not the only crypto-anarchist issue within our lives either: from movements toward decentralized digital currencies to criticisms of government “security” practices, crypto-anarchism is an increasingly relevant political ideology. Embedded within these movements will be questions about technological arrangements, political identity, and ultimately, personal values. Using the representative example of gun control and 3D printing, we, as a global society, will have to consider the implications of decentralized, distributed, nonhierarchical technological arrangements. Simultaneously, crypto-anarchism will stimulate a deeper discussion on the role of governments within our current social, technological, and cultural context: do we, as a collective, subscribe to libertarian or anarchist beliefs given abuses of government power in the 20th and 21st centuries? Essential to the previous question is a self-examination of our individual values: is security or liberty more valuable? Do I value individual worth or greater good more? These inquiries might seem like cliché “life questions”, but problems and solutions created by technology will allow government to interact with society on grander, more intimate scales than has ever been previously possible.

One of the things that we’re going to have to discuss and debate is how were we striking this balance between the need to keep the American people safe and our concerns about privacy, because there are some trade-offs involved.. — President Barack Obama, June 7, 2013

Case Study

How can we use the Internet and productive capital to dis-intermediate State actors and expand free spheres of action? The entire market, even for currencies, should become black. — Cody Wilson, representative of Defense Distributed

The privilege of owning firearms is embedded into the history preceding and formation of the United States. During the formation of the United States, political consensus held that “the main danger to the republic was tyrannical government and the ultimate check on tyrannical government was an armed population”. That prevailing opinion likely arose from the perceived tyranny of English colonies in North America by the Parliament of Great Britain and the subsequent revolution of said colonists during the American Revolution of the 18th century. To guarantee “certain [natural] rights”, a bill of rights was drafted and ultimately ratified by the United States in 1791. The Second Amendment of the United States Bill of Rights specifically states: “A well regulated militia being necessary to the security of a free state, the right of the people to keep and bear arms shall not be infringed”.

In the following centuries, the United States has released numerous pieces of legislation that refine this foundational privilege. In 2008, the United States Supreme Court, the highest court of the nation’s judicial branch, ruled in District of Columbia v. Heller that, although the “Second Amendment right is not unlimited”, the District of Columbia’s “total ban on handgun possession in the home” is a violation of the Second Amendment. In summary, this decision guaranteed the individual right to possess firearms in federal territories. Two years later, the Supreme Court ruled that the Second Amendment extended to state and local gun control laws. Despite these setbacks, support for gun control remains strong among the American public (on April 29, 2013, 65% of Americans believed that the US Senate “should have passed the measure that would have expanded background checks for gun purchases”) and politicians, including United States President Obama. Several recent gun violence incidents — many of which involved assault rifles — have driven the issue of gun control of the forefront of political and public debate in the United States. Notable mass shootings withing the past year include the Sandy Hook Elementary School shooting (December 14, 2012) and the Santa Monica shooting (June 7, 2013).

While the 21st century debate on gun control was occurring, 3D printing was rapidly evolving from a tentative experiment to a commercial, yet potentially open-source, realization. Some individuals, including the now-famous Cody Wilson, saw the potential in 3D printing to completely circumvent the gun control debate. In August, Cody Wilson, a self-described crypto-anarchist, banded with some like-minded individuals to create Defense Distributed. Soon after, Defense Distributed created an Indiegogo crowd-sourced fundraising campaign to raise funds necessary to “design and release blueprints for a plastic gun anyone can create with an open-source 3D printer”. While Wilson, a law student at the University of Texas School of Law, insisted this project was legal, Indiegogo administrators took down the fundraising page and disallowed donations. Despite this obstacle, Defense Distributed continued to accept donations through alternate means.

Through their funding, Defense Distributed leased a commercial 3D printer from the company Stratasys. When Stratasys learned of Defense Distributed’s plans to manufacture and test a 3D firearm, the company quickly retracted their lease and immediately sent contractors to retrieve the printer from Wilson’s apartment. Following the incident, which occurred in September 2012, Wilson released a statement claiming that “nothing we do violates the law”; while United States federal law allows individuals to “manufacture a gun at home without a license” under certain restrictions, the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) considers 3D-printed guns to “fall into a regulatory gray area”. In response, Defense Distributed applied for a federal firearms license in October 2012.

Despite these setbacks, Defense Distributed continued with its project. In February 2013, the group printed and tested a 3D-printed “lower receiver” (i.e. the component of firearms that contains its operational parts) within the body of a conventionally manufactured AR-15 semi-automatic rifle. While their previous attempts at printing a lower receiver were largely unsuccessful, this attempt yielded a lower receiver that could withstand the stresses of firing more than 650 rounds. It should be noted that, by the ATF’s definition, for the AR-15, the “lower receiver” is the regulated component; in essence, Defense Distributed successfully printed the essential (and the regulated) component of a firearm. Shortly after their successful test, the group released the 3D blueprints for the AR-15 lower receiver on the Internet for free. In March 2013, the group’s application for a federal firearm license to manufacture firearms was approved. This license allows Defense Distributed to transport and sell a variety of firearms.

Following the acquisition of their license, Defense Distributed tested and released 3D blueprints for the Liberator, a single-shot firearm made almost completely from plastic. The final component in the Liberator was a metal nail (which can be purchased at any hardware store) that acted as the firing pin. While Defense Distributed used a relatively expensive, commercial 3D printer to test the Liberator, they are also working on refining their 3D blueprint to work with printers meant for home use, particularly RepRap. With the successful operation and free digital distribution of blueprints for a working firearm, worry about 3D-printed firearms has renewed. In response to the Liberator, Senator Charles Schumer of New York called for legislation banning 3D-printed firearms, which he stated could be used to evade walk-through metal detectors and which could be created by “printers that cost as little as $1,000”. In California, the legislature response was much more profound.

Fear of 3D-printed firearms extends beyond typical gun control demographics. For example, in December 2012, Thingiverse, an free online library for user-uploaded 3D blueprints, removed all designs for weapon components. Even champions of open source and free content like Kim Dotcom have denounced 3D-printed firearms: “I think it’s a serious threat to the security of the community… it’s scary”. In response to the censorship of 3D firearm blueprints on many popular 3D blueprint libraries, Defense Distributed created its own online library — DEFCAD — for 3D blueprints of all types, controversial or not. In May 2013, the State Department removed DEFCAD files “from public access”. Before the State Department could act, however, the Liberator blueprints had been downloaded over 100,000 times; as of May 31, 2013, “over 4,000 computers all around the world” were distributing the Liberator blueprints over peer-to-peer networks.

Literature Review

To familiarize myself with intellectual frameworks useful in analyzing this debate, I conducted a preliminary literature review. Isaiah Berlin, one of the most influential philosophers on liberty, separates liberty into two “concepts”: positive liberty and negative liberty (1958). Positive liberty, Berlin writes, is “the freedom which consists in being one’s own master”; negative liberty is “the area within which a man can act unobstructed by others”. There exists a subtle distinction between these two concepts which, in the political context, manifests as such: political rights (e.g. the right to bear arms) typically create positive liberty, in that these rights allow people to extend decision-making power and control within their own lives. On the other hand, laws (e.g. restrictions on bearing arms) are political obstructions that act to decrease negative freedom, in that laws interfere with freedom of choice and the pursuit of desire. However, Berlin emphasizes that, though the two are “profoundly divergent and irreconcilable attitudes”, governments must balance both positive and negative liberty for both represent “an ultimate value… classed among the deepest interests of mankind”. Gun control efforts are, under this basic definition, invasions into citizens’ negative liberty.

Given the centralized, hierarchical nature of current gun control efforts, I dare to assign James Scott’s label of “state-initiated social engineering” to government gun control campaigns. Scott deconstructs these state-orchestrated social engineering projects into four elements, three of which are particularly relevant for my investigation (1998). The first relevant element is “a high-modernist ideology” representative of desires to “bring about huge, utopian changes in people’s work habits, living patterns, moral conduct, and worldview”. High-modernist ideologies gain potency when coupled with the second relevant element: an “authoritarian state” that can “use its coercive power” to achieve the realization of this ideology. While government-imposed gun control satisfies the first two elements necessary for a successful state social engineering project, it lacks the final element: “a prostrate civil society that lacks the capacity to resist these plans”. With the advent of 3D printing, opponents will only grow in their capacity to resist gun control.

Gabrielle Hecht’s argument that “national identity discourse constructs a bridge between a mythologized past and a coveted future” provides a cultural understanding of resistance to gun control (1998). While the state can attempt social engineering to provide “cultural legitimacy” to gun control efforts, ethos within communities (the most notable example in my mind being the distrust of government and confidence in armed resistance in the early United States) supplies inertia.

At this point, I am equipped to question the motivations behind creating and resisting gun control. Because gun control represents a limitation upon the range of actions a person is allowed to engage in, I will challenge its foundational reasoning. Proposed and existing restrictions on firearms typically act as system-wide limitations with small exceptions for “valid” uses; this organization presumes firearms are inherently dangerous, rather than acknowledging firearms as technologically neutral. However, Pinch and Bijker refute this technologically determinist view of material artifacts (1984). An extension of Pinch and Bijker’s arguments would situate the safety of firearms not within the technology, but within the social and cultural contexts surrounding the technology. Yet Langdon Winner’s theorizing on the politics of artifacts does not fully coincide with Pinch and Bijker’s claims (1986). Winner reasons that a material artifact has politics inherent to its design and “technical arrangement”. Winner classifies two extremes to the “technical arrangements” of socio-technological systems: large-scale and centralized vs. small-scale and distributed. Given these extremes, I observe an incoherency between the design of gun control (centralized and hierarchical) and the material manifestation of firearms (small-scale, requiring little expertise, and infrastructurally inexpensive). This dichotomy qualitatively reflects the state-against-anarchist struggle.

By these same frameworks, 3D printing capability is not necessarily anarchist by technological function, but has been arranged in distributed and digital formats, which both potentially support anarchist goals. Howard and Hussain examine the political consequences of this digital context (2013). Similar to the Arab Spring demonstrations, anarchists have utilized the Internet as a “means and medium for political resistance”. Howard and Hussain note that “the Internet has changed the way political actors communicate with each other”; this insight, which recognizes the power to “realize shared grievances and nurture transportable strategies for mobilizing against dictators”, is a factor to consider when investigating the role of technology in anarchism. Contemporary examples of political power through digital presence suggest that, currently, digital media steer towards democratization and decentralization.

Despite this suggestion, Uri Gordon asserts that “contemporary anarchist practices display a strong ambivalence toward technology, with active resistance residing alongside active use and development” (2009). Gordon’s analysis leads to the realization that anarchists “judge technologies according to their promotion of hierarchical or nonhierarchical social practices”. In turn, anarchists respond in three general ways: “abolitionism, guarded adoption, and active promotion”. Application of Gordon’s approach and conclusions will provide a larger intellectual discourse on anarchism with which to compare crypto-anarchism. The work of Uri Gordon supplies a launching point from which to closely consider how, once used by anarchists, technology might in turn influence and shape anarchism.

Introduction

The philosophy of anarchism is included in the word “Liberty”… therefore, it has but one infallible, unchangeable motto, “Freedom.” Freedom to discover any truth, freedom to develop, to live naturally and fully. — Lucy Parsons, “The Principles Of Anarchism”

Anarchism — defined by Random House Dictionary as “a doctrine urging the abolition of government or governmental restraint as the indispensable condition for full social and political liberty” — has a rich history dating back to the 19th century. With the creation and proliferation of the Internet, the struggle between the state and the anarchist has evolved. To combat the supposed invasion of political freedoms by governments, anarchists have utilized “the computerized encoding and decoding of information”, otherwise known as cryptography. The use of cryptographic techniques to subvert state authority has been termed “crypto-anarchism”.

With anarchism’s opposition to state restraint, it is only natural that anarchists oppose gun control laws and policies. Several developed countries — including Japan, the United Kingdom, Australia, and Canada — have adopted strict gun control laws; these countries have much lower gun homicide rates than the United States. Gun control supporters use similar observations to justify government restrictions on firearm ownership.

However, governments are anticipating that gun control will be impossible to enforce in the near future due to digitally distributed firearm blueprints to be used with 3D printers. Behind the creation, experimentation, and release of these firearm blueprints are self-proclaimed crypto-anarchists. Using this conflict between gun control advocates and crypto-anarchists as a case study, I will investigate how technology can interact with the nature, the practice, and the philosophy of contemporary anarchist movements. Thus, this research will consider how technology has created a crypto-anarchist concern (e.g. gun control) and a crypto-anarchist solution (e.g. digital firearm designs). In the course of exploring this debate, I will observe how the notions of liberty and freedom are differentially defined by the opposing parties.

Because my employment comes bundled with a long commute behind the wheel of my car (typical Bay Area), I consume a fair enough of podcasts, which led me to notice that there’s a plenitude of “top/essential newsletters for VCs” lists, but very few (if any) for podcasts. So I decided to make my own: here’s my list of top podcasts for both aspiring and official VCs. Please feel free to make recommendations as well!

a16z: Choose any field of interest (quantified self, eternal life, national security, etc.) and a16z probably has a world-class network of subject-matter experts in that field. Each conversation — all are centered around a particular topic — is surprisingly extensive, containing personal insight from multiple angles.

Exponent: Whenever I listen to Ben Thompson and James Allworth talk on Exponent, I think “these guys have no fear”. They consistently dive deep into the subject of the day, delivering master classes in bold, strategic analysis.

Exchanges at Goldman Sachs: A glimpse into how the rest of the world (i.e. non-Silicon Valley tech) is viewing markets and scientific/technological trends. Exchanges translates macro forces (e.g. policy) to tangible impacts (e.g. capital investment slowing), helping me build my global awareness.

EconTalk: I come from an engineering/sociology background, so to hear economic thinking applied so thoroughly to any topic (be it labor market decision-making or the wider social impact of luxury) is fascinating. I believe broadening one’s own mental toolkit is always valuable.

The Twenty Minute VC: The quintessential venture capital podcast where you can listen to VCs of different industry/stage/thesis foci talk about their experiences and perspectives. Host Harry Stebbings asks focused questions, usually around a particular theme.

Venture Studio: Though also focused on the tech/venture capital ecosystem, Venture Studio episodes tend to be more long-form and exploratory, giving a more holistic and personal view of each guest. For those of us based in the West Coast, Venture Studio delivers valuable perspectives into the East Coast zeitgeist.

How I Built This: I love this podcast because — not only is each episode a fantastic story — it reminds me that there’s plenty of great businesses that exist beyond the reach and scope of venture capital.

The power to vote, political participation, government by the people — these phrases surround the modern visualization of democracy. From these principles, the city-state of Athens, the Roman Republic, and England’s Parliament are governments, among others, that are typically considered the origins of democracy. Timothy Mitchell, however, in his book Carbon Democracy: Political Power in the Age of Oil, argues that this notion is wholly incorrect, that these early representative governments were “oligarchic alternative[s]” to democracy (17). Instead, Mitchell insists that mass democracy came from “a new kind of political power”, a power supplied by the sudden importance of coal (19). Because of coal (and the need to transport and process coal), workers could bring industry and society to a screeching halt. With workers able to “sabotage” coal, truly democratic politics finally appeared (22).

Carbon Democracy is not a study of “democracy and oil”, but of “democracy as oil” (5). Mitchell treats fossil fuels not as a commodity, but as an industrial complex interacting with social and technological components. This perspective guides Mitchell in his exploration of fossil fuels’ impact in current political systems. For example, the economy — “the central object of democratic politics in the West” — emerged as an abstraction, escaping the limits of the material universe (125). Oil, with its seemingly infinite supply, allowed for economy to “increase in size without any form of ultimate material constraint” by transitioning to a “system of monetary circulation” (143, 234). The evolution of the economy into the abstract separated politics and nature. In turn, the economy shaped a political dynamic in which “experts began to displace democratic debate and whose mechanisms set limits to egalitarian demands” (143). Dominion over oil, Mitchell suggests, is dominion over democracy.

Likewise, Mitchell argues that the infrastructure of oil — oil wells and pipelines, and also “oil agreements and oligarchies” — must not be seen as only a mechanism to ensure the supply and pricing of oil, but also as a manifestation of empire and political power (143). Throughout the 19th and early 20th centuries, the prolonged campaign of oil firms to limit oil production and slow the development of the petroleum industry (in short, to build a American-European monopoly) also “impeded the ability… to build effective methods for advancing egalitarian political claims” in colonies throughout the world (86). Restrictions on oil production and development in these colonies resulted in populations unable to enjoy the wealth of their natural resources and unable to exercise political will through the power of sabotage. Europeans justified their control over colonies by claiming that colony populations were “in need of development”(83). Behind this rhetoric was the “need for materials unavailable in the industrial regions”, as well as the need for secure supplies of petroleum for transportation (85); these requirements of sustained empire were made clear by the industrialized warfare of World War I.

The rise of independent states in the Middle East changed the dynamics of democratic politics in those countries. Popular support and strength wrestled political influence away from European countries, allowing “local disputes and disruptions to be built into something more effective,” in service of “larger political purposes” (144). While these countries were constantly struggling to reduce the control of foreign oil firms (who legally owned large resource claims and infrastructure), this period resulted in “the growth of a more assertive Arab nationalism” (144). To rally government support for business and economic interests, foreign oil companies and government officials framed the importance of oil in terms of “strategic importance,” necessitated by the Cold War (122). In the 1970s, Middle Eastern countries (assembled into OPEC) fought to wrestle control of their oil fields away from foreign oil companies. Mitchell argues that, faced with “the loss of their control of [Middle Eastern] oilfields”, foreign oil companies sought methods of “generating a large increase in the price of oil”, which would facilitate petroleum development in alternate areas (170). By controlling and manipulating flows of oil and finance, these foreign firms were, according to Mitchell, able to engineer the 1973–1974 oil crisis.

An analysis of oil and politics would be incomplete without considering how oil interests have shaped contemporary politics of the Middle East, and on this topic, Mitchell explores the deficiency of capitalism to create its own political order to secure profits (230). Instead, the actions necessary to collect “rents on the world’s most profitable commodity” were always executed through the “political forces, social energies, forms of violence and powers of attachment” of others (230). In oil’s latest chapter, the international oil industry turned to McJihad, adopting the social and political energy of Islam to secure future profits by ensuring a scarcity of available oil. Oil companies fabricated the notion of national energy security, thereby securing government cooperation in their quest to control the supplies and flow of oil. Mitchell asserts that “the more closely a government is allied with Washington, the more Islamic its policies” (201); such countries publicly claim “an Islamic authority”, becoming more Islamic in “political rhetoric and modes of legitimation.” To Mitchell, the American occupation of Iraq, for example, has not been conducted with the intention of democratization (evidenced by the “brutality of America’s enormous military power”, American rule through “an unelected governing council”, and the retained “outlawing [of] independent labour unions in the public sector”) (224–225). While the United States turns to “conservative forces of tribal leaders and Islamist parties to help it keep control,” it has wrestled away the control of oil supplies and oil infrastructure necessary for Iraqis to demand democratic government and it has facilitated the rebuilding of the oil industry in the interests of international oil firms (226).

This final accusation represents perhaps the most contentious aspect of Carbon Democracy. The claims of collusion between the international oil industry and Western governments might strike some as conspiracy theory. Mitchell’s assertion that, in the 1950s, American labor rights activists were jailed not for a communist agenda, but for threatening to disrupt the functioning of the oil industry with strikes is one notably disputable claim. Criticisms of Mitchell’s work as conspiracy theory are grounded in his failure to explicitly establish the motivations behind government support of oil interests, particularly in a modern context.

Carbon Democracy especially shines in its construction of rich and detailed historical narratives to support Mitchell’s claims. Furthermore, the book contains its own overarching narrative; this narrative contextualizes and relates Mitchell’s individual arguments to his thematic statement of “democracy as oil.” Through its self-proclaimed goal of “follow[ing] closely a set of connections that were engineered over the course of a century or more between carbon fuels and certain kinds of democratic and undemocratic politics,” Carbon Democracy concludes that future energy technologies (by extension, technological and social futures) are in no way deterministic (252); a societal decision to utilize coal did not necessarily imply a political transition to democracy, just as renewable energy does not guarantee the continuation of democracy. With this in mind, two issues are likely to determine our energy future: the “problems of peak oil and climate breakdown” (233). In the exploration of these two issues, the “politics of calculation” are clear (235): peak oil is filled with imprecision and uncertainty, while climate change is a science which, while buffeted by political wills, survived with its legitimacy intact (234). These problems, connected but treated differently in public opinion and approach, will lead to politics different from those of today. With oil scarcity and climate change posing unique anti-democratic challenges, only through deep reflection of the political impacts of fossil fuels may we avoid our earlier pratfalls and achieve “more democratic futures” (254).

The only prerequisite is that the company must have a product/business model that I can comprehend at a basic technical/consumer behavior/economic level. Unfortunately, this rules out a lot of biotech (at least, until I can educate myself further). Gaining an understanding through analogies just isn’t sufficient for me, for reasons that will soon become clear :)

Trend 1: Emerging Technologies

The first type of startup that I gravitate towards are ventures working on near-commercialization “emerging technologies” (technologies that are likely to reach mass market in roughly 3 to 5 years). If we look at the Diffusion of Innovation curve laid out by Everett Rogers, I’m drawn to companies that are refining their technologies and business models with “early adopters” and will likely cross the boundary to “early majority” in those 3 to 5 years.

For example, if we plot virtual reality on that graph, I would place it at the boundary of “early adopters” and “early majority”. Oculus has become a household name; many people of all ages have experienced being in a virtaul reality headset at least once. If you’re willing to believe a research firm’s market prediction, there’ll be roughly 12.8 million VR headsets sold this year. Another research firm’s market prediction is thinking that figure will be closer to 15.9 million VR headsets for 2016.

[Disclaimer: I used to work in virtual reality and have provided advice to VR projects/startups. I might be more bullish than I should be :)]

Rewind 3 to 5 years and it would have been the right time (at least for my investment thesis) to invest in VR hardware startups. As it stands, it is probably still a good time to invest in VR software startups: the software currently available are mostly cinematic or gaming-related in nature, leaving plenty of room for innovative and more complex applications.

Rogers’ diffusion of innovations curve doesn’t inherently tell us about timing though… but the Gartner Hype Cycle does. On this graph, we’re looking for startups that are dragging their technologies up the slope of enlightenment or driving those technologies across the plateau of productivity. It’s hard to plot each technology on that curve on our own, but fortunately, Gartner does this hard work for us.

Every year, Gartner puts out a Hype Cycles report, based on thousands of technology profiles. I don’t always agree with their placement of technologies, but their free reports certainly give some baseline that an investor is welcome to adjust. Let’s take a look at their 2012 Hype Cycle.

On some technologies, Gartner looks like they’ll be off on their timing (wireless power, gesture control, BYOD, HTML5), but on others, Gartner looks pretty damn accurate (predictive analytics, media tablets, cloud computing, big data). For fun, here’s the 2015 Hype Cycle summary.

While part of my gravitation to “emerging technologies” comes from my inner technophile, I have important investing-related rationale behind it. Most venture capital funds are structured to last 10 years. Investing in startups working with technology that will mature in 3 to 5 years provides enough runway (in terms of time, maybe not capital) to support a company if market adoption is a bit slower than expected. On the other end, if market adoption occurs within those 3 to 5 years, my (mock) venture capital firm does not have to rush an early exit.

Some might argue that my timeline is too short: such critics might say startups who have been working on a technology since the “technology trigger” (Gartner Hype Cycle) will have accumulated technical/market expertise and will have built a first-mover advantage. I would reply, however, that 1) first-mover advantage is negligible in nascent technologies (not to mention, many believe there exists a second-mover advantage), 2) accumulated technical/market expertise is often industry-wide (communal, as characteristic of the “innovators”, taking from Rogers again) and can be used as a resource for new entrants, and 3) investment opportunities will not be fully tapped because more conservative investors will be deterred by highly uncertain market potential and market timing.

Trend 2: Fundamental Advantages

The second type of startup that I am drawn towards are those with products/business models that have “fundamental advantages” over their competitors. A “fundamental advantage” is an advantage — be it cost-related, performance-related, design-related — that is inherent; an advantage that cannot be surpassed unless something radically different appears on the market or the product/business model is duplicated.

Let’s use an example. I used to work for a startup named Cogenra Solar (which has since been acquired by SunPower). Cogenra used active cooling to extract useful thermal energy (heat) from their solar modules. That heat might then be used to wash manufacturing equipment, or to warm up the occupants of a building, or to regulate the temperature of a swimming pool. Normal solar panels can’t provide that extra economic value to customers. Cogenra’s dual delivery of value is a fundamental advantage.

Yet people who are familiar with solar technology probably realize there’s more to this story. A solar panel’s efficiency drops when it heats up. By cooling its solar panels, Cogenra keeps the efficiency of its panels high, improving electric output over normal solar panels. In summary, by actively cooling its solar panels, Cogenra improves the amount of electricity its panels are creating and creates additional economic value from heat capture: two fundamental advantages that ordinary manufacturers cannot compete with, unless they duplicate Cogenra’s active cooling scheme or create something radically different.

One more example, taking my post last week on Motion AI: Motion AI’s graphical editing interface eliminates the need for programming aptitude when creating and deploying chatbots. Motion AI has a fundamental advantage over other startups offering prebuilt bots (lack of customization) or APIs (programming chops required!).

Note, network effects and network defensibility do not qualify as fundamental advantages! Fundamental advantages are inherent, not timing-dependent. And, in the interest of transparency, investing along this trend is bound to miss some homeruns: I don’t think Facebook, Twitter, Lyft, would have made the cut under this criterion, unless they were framed in a particular manner. Evaluating the quality of a venture’s fundamental advantages would be tied to 1) how related those advantages are to the venture’s core value proposition(s) to its stakeholders and 2) how difficult it is for competitors to counter (i.e. integrate the relevant features into their own products).

Summarized

Kendrick’s #fantasyportfolio fund looks to invest in ventures focused on bringing “emerging technologies” to mass market within 3 to 5 years and ventures whose value proposition is based on fundamental advantages.

[I’d love to hear your thoughts on my investment thesis! Feel free to drop a comment or send me an email: kendrick.g.kho@gmail.com]