20VC Why Historical Loss Ratios Are Simply Too High, Why Data Is The #1 Most Important Piece When Evaluating Effective Reserve Allocation & Why Nothing Is Truly Defensible Today with Jonathan Hsu, CoFounder and General Partner @ Tribe Capital

Summary Notes


In this episode of "20 Minutes VC," host Harry Stebbings interviews Jonathan Sue, co-founder and General Partner at Tribe Capital, a new venture fund in Silicon Valley. Jonathan shares his journey from a string theory PhD to leading Facebook's analytics and data science team, where he hired 200 top data scientists, to his role at Social Capital, and finally to founding Tribe Capital with partners Arjun Sethi and Ted Maidenberg. Tribe Capital focuses on leveraging data to augment investment sourcing, evaluation, and management, aiming to reduce historical loss ratios in venture capital. Jonathan emphasizes the importance of data in identifying product-market fit and assisting portfolio companies in scaling effectively. He also discusses the concept of "N of 1" markets, which refers to companies that achieve a unique, dominant market position akin to a monopoly but based on superior customer products rather than anti-competitive practices.

Summary Notes

Introduction to Jonathan Sue and Tribe Capital

  • Jonathan Sue is a co-founder and general partner at Tribe Capital, a new venture fund in Silicon Valley.
  • Prior to Tribe Capital, Jonathan was a partner at Social Capital.
  • He led the creation of the analytics and data science team at Facebook.
  • Jonathan has experience in hiring and managing a large team of data scientists and analysts.
  • Acknowledgments were given to Arjun Sethi and Matild Collin for their question suggestions.

"I'm thrilled to welcome Jonathan Sue, co-founder and general partner at Tribe Capital, one of Silicon Valley's newest funds on the block being founded by Jonathan, Arjun Sethi and Ted Maidenberg."

The quote introduces Jonathan Sue and highlights his current position as a co-founder and general partner at Tribe Capital, indicating the significance of his role in the venture capital industry.

Transition from Academia to Technology

  • Jonathan Sue transitioned from a PhD in string theory to a career in technology.
  • He did not want to pursue an academic career after his PhD at Stanford.
  • Instead of joining investment banks, which was common at the time, he chose to go into technology.
  • Jonathan landed a role at Microsoft as a product manager for web search, during the transition from MSN to Live Search, and before it became Bing.
  • He co-created one of the first social networking games on the Facebook platform, Super Poke, which led to an acquisition by Slide.
  • Post-acquisition, Jonathan worked at Slide before moving to Facebook in 2009 and later joined Social Capital.

"Towards the end of my PhD, it was just clear I didn't want to be an academic, and this was in 2006. So back then, everybody was trying to go to investment banks, but I wanted to go into technology somehow and ended up landing at Microsoft to be a product manager in the web search group."

This quote explains Jonathan's decision to pivot away from academia towards a career in technology, setting the stage for his later involvement in the venture capital sector.

Creation of Data Science at Facebook

  • Jonathan was not the first data scientist at Facebook but played a significant role in expanding the team.
  • He was initially brought into the marketing team for data support.
  • Challenges included the difficulty of counting and doing data science due to technological limitations at the time.
  • Jonathan helped consolidate various small data teams into a full-fledged data science and analytics team in 2011.
  • This team became the foundation for the extensive data science and analytics organization at Facebook today.

"Part of what was going on at Facebook in that era was that it was actually very hard to just count things right, like doing data science was actually difficult because the technology was hard back then."

The quote highlights the challenges faced in the early days of data science at Facebook, emphasizing the technological hurdles that had to be overcome to establish effective data practices.

Data Applied to Venture Capital

  • Jonathan discusses the application of data to the four key components of success in venture capital: sourcing, evaluation, winning, and managing.
  • He emphasizes that there is no single silver bullet in sourcing, which is a multifaceted problem requiring a combination of strategies.
  • Networking, brand building, and outbound strategies are essential for effective sourcing.
  • Data can power outbound strategies, with public signals from Crunchbase, panel data (app usage, credit card), and talent data (e.g., LinkedIn) being useful.
  • Jonathan suggests that while there are interesting data-driven techniques, it is too early to call them transformative solely for sourcing.

"Sourcing ends up being this multifaceted sort of problem where you need to be doing everything to make it happen, I think, successfully."

The quote conveys the complexity of sourcing in venture capital and the need for a diversified approach that includes both traditional methods and data-driven strategies.## Importance of Traditional Networking in Venture Capital

  • Traditional networking and brand reputation are vital in the venture capital ecosystem.
  • Established firms like Benchmark and Sequoia rely heavily on their brand and network for sourcing investments.
  • While data can enhance the process, lacking a strong network is a significant disadvantage in early-stage investing.

"But the traditional thing has to work. And then if you can layer on these data things, that's great. But if you start from a place where you don't have any of the traditional thing, and you try to go with the machine alone. It might be helpful, but you're going to be this massive disadvantage because fundamentally the early stage investing ecosystem is really well connected."

The quote emphasizes that although data can be beneficial, a robust traditional network is fundamental for success in early-stage venture capital.

Data's Role in the Evaluation Phase of Investments

  • Data science is crucial in the evaluation phase of venture capital.
  • The relevance of data varies across different stages of a company's lifecycle.
  • At the seed stage, limited data is available, and the focus is on scoring founder profiles and market potential.
  • In later stages (Series C and D), there is abundant data, and the focus shifts to valuing the business itself.
  • Growth equity firms excel at using data to price assets and secure deals.

"It's really in that stage, in between that sort of series A, series B, where it's not quite obvious where there is some data, but it's not the only thing at that point, you are still buying the team, but you're also buying the business, right?"

This quote highlights the transitional phase between Series A and B where both the team and the business are evaluated, with data playing an important but not exclusive role.

Data's Influence on Winning Deals

  • Data is considered a form of truth and is used to articulate the value and potential of a company objectively.
  • Using data to provide feedback helps founders understand their company's strengths and weaknesses.
  • Developing a relationship with founders through data-driven insights can be a decisive factor in winning investment deals.

"So for us, winning is sort of that last step. It's where we've developed that context, which data is such an important piece of. Right. We want them to understand that it's not just us investing in them. We're buying a piece of this company. We believe in the thing that they've built, and that belief doesn't come from nowhere."

The quote explains how data is used to build a context for investment, helping founders understand the investor's belief in their company and its foundation on objective data analysis.

Data-Driven Portfolio Management

  • Data-driven product development and growth strategies from experiences at Facebook are applied to portfolio management.
  • The focus is on driving product market fit and leveraging data to enhance it.
  • Different venture firms like Andreessen Horowitz have their own strengths, with some focusing on market development and others on data-driven product fit.

"Our focus was really intending to be focused on this product market fit, using data to drive product market fit, to amplify product market fit and to drive that flywheel."

This quote outlines the strategic focus on using data to achieve and amplify product market fit within portfolio companies, drawing from past experiences and industry practices.

Common Challenges in Growth and Product Market Fit

  • Early-stage companies often struggle with execution compared to established companies like Facebook.
  • The goal is to help companies not just increase activity but to ensure they are doing the right things and recognizing it.

"It's that aspect of knowing that you did the right thing, which is really important."

The quote stresses the importance of not only executing but also having the knowledge and confirmation that the actions taken were correct in driving growth and finding product market fit.## Anecdotal Evidence vs. Data in Early Stage Companies

  • Anecdotal evidence is useful for very early stage companies with few customers or users.
  • As the number of customers and users grows, data becomes necessary to validate anecdotal insights.
  • Data checks the reality of customer behavior against company assumptions.

"But once things start pushing into dozens of customers, possibly hundreds of users, maybe several hundreds of users, that's where data is there to really check the anecdote, to make sure that people are really doing what you think they're doing."

This quote emphasizes the transition point at which companies need to rely on data over anecdotes to accurately understand customer behavior and scale effectively.

Product-Market Fit and Data Application

  • Data usage and application are tailored to the specific needs of companies at different stages.
  • Expertise in data analysis allows for quick, efficient insights without distracting from product development.
  • Slack and Carta are examples of companies that began investing in data as a discipline around Series C.

"And oftentimes we will go and just do the measurements to go in, do some of the analysis with the raw data ourselves, because the folks on our team, we have massive expertise in this area, so we can do it very quickly..."

This quote highlights the practice of providing specialized data analysis to companies that may not have the capacity to do it themselves during early growth stages.

Pricing Strategy and Data in Early Stages

  • In early stages like Series A and B, precise pricing is less critical due to the nature of outcome distributions.
  • Venture funds at early stages do not focus on small margins but rather on the binary outcomes of investments.

"The probability distributions that we're dealing with at the very earliest stage are such that if you're plus or, -20 30, even 40, 50%, it's not going to make or break the result."

The quote explains that due to the high variability of early-stage investment outcomes, precise pricing is not as impactful on the overall success of the investment.

Portfolio Construction in Venture Capital

  • There is no one-size-fits-all approach to portfolio size in venture capital.
  • Data can help reduce historical loss ratios in venture investments.
  • Portfolio construction varies based on the outcome distribution of selected assets.
  • The goal is to have a portfolio where a single great outcome can return the fund.

"And so this is an example where you're actually manipulating part of the outcome distribution. Maybe not manipulating, but you're selecting for certain characteristics of the outcome distribution of the assets that you're buying."

This quote discusses how data can influence the selection of assets to create a more favorable outcome distribution, affecting the strategy for portfolio construction.

Reserve Allocation and Data Usage

  • Data analysis becomes increasingly important and central in later stages of investment.
  • Data informs reserve allocation and collaboration with co-investors.
  • The focus is on the long-term success of the company, which may involve sacrificing some ownership for better growth investment partners.

"And so we continue to do that work, and we do that work both for our own reserve allocation, but also for our work with other co investors."

The quote indicates the ongoing role of data in making informed decisions about reserve allocation and partnerships with growth investors.

The Concept of "N of One" Markets

  • "N of one" refers to a unique market position where a company stands out distinctly from competitors.
  • It contrasts with "one of n" markets, where companies are indistinguishable from one another.
  • "N of one" is akin to a monopoly but focuses on customer choice and superior product offering rather than anti-competitive behavior.

"We usually think of not n of one as one of n a situation when there are sort of many companies that are doing roughly the same thing and none of them is able to really get a strong upper hand in the market."

This quote defines the "n of one" concept by contrasting it with saturated markets where no company has a significant advantage, highlighting the strategic importance of achieving a unique market position.## End of One Concept

  • The "end of one" concept refers to achieving the positive aspects of a monopoly without the negative implications.
  • It implies a unique market position where a company has significant control or influence.

Monopoly, the good parts of monopoly without the bad parts.

This quote explains the essence of the "end of one" concept, highlighting the aim to harness monopoly's benefits while avoiding its drawbacks.

Defensibility in Tech

  • In the long term, no company is truly defensible against competition, especially not against tech giants like Facebook, Amazon, Google, and Apple.
  • Defensibility is about maintaining a competitive edge for a sufficient period to innovate and grow.
  • Network effects and strong brands are two key factors that can provide medium-term defensibility.
  • Network effects, particularly in marketplaces, are challenging to assail and can propel a business forward.
  • Building a strong brand is less methodical and can take longer than creating network effects.
  • Venture capitalists must stay alert for new models that can offer medium-term defensibility.

Nothing is defensible in the long term, right? The question is like, are you in an era where you can defend it for a while and give yourself enough breathing room to possibly innovate something else?

Jonathan Sue acknowledges the temporary nature of defensibility and emphasizes the importance of using that time to innovate and find new competitive advantages.

Right now, the most obvious pattern that we've seen in the last 15 years now is really the concept of a network effect, right?

Jonathan Sue points out that network effects have been the most evident form of defensibility in recent years, particularly in marketplaces.

Quickfire Round

  • Quickfire rounds are a rapid series of questions to gain quick insights into the guest's thoughts and preferences.

Well, speaking of eyes wide open, Jonathan, it's now the quickfire round.

Harry Stebbings introduces the quickfire round, indicating a shift to a faster-paced, more spontaneous segment of the interview.

Personal Favorites and Beliefs

  • Jonathan Sue's favorite book is "The Origin of Political Order" by Francis Fukuyama, which explores the history of political organization.
  • He believes data can be useful in a broader range of contexts than most people recognize, including early-stage investing.

My favorite book was actually the origin of political order by Francis Fukuyama.

Jonathan Sue shares his favorite book and its subject matter, emphasizing its relevance to understanding political organization.

I believe that data can be useful in a wide variety of contexts, much wider than most people give it credit for.

Jonathan Sue expresses his belief in the broad applicability of data, suggesting it is undervalued in many areas, including early-stage investing.

Challenges and Experiences

  • Starting a new company involves early growing pains and the challenge of establishing it as a significant entity.
  • Memorable LP (Limited Partner) meetings can occur when there is a shared background, such as physics, leading to engaging discussions beyond the primary topic.

Right now we're, I guess, eight months old right now, and so we're going through those really early growing pains of just making ourselves a big real thing.

Jonathan Sue describes the challenges faced by Tribe, a new company, as it works through the initial stages of growth and development.

That has happened a couple of times where the LP actually has some background in physics and we actually end up having a conversation about, okay, what did you study in string theory?

Jonathan Sue recounts memorable LP meetings where he could connect with LPs over a common interest in physics, highlighting the uniqueness of such encounters.

Misconceptions About Data

  • There is a misconception that data usage is limited to machine learning or AI, overlooking traditional data applications like accounting and college admissions.
  • Accounting is seen as the first form of data science, and college admissions involve significant data use.

I think the biggest misconception here is that the only way to use data is like the way that machine learning or AI works.

Jonathan Sue addresses the narrow perception of data usage and advocates for recognition of its broader applications, such as in accounting and college admissions.

Inflection Points

  • Learning about private equity and the history of accounting served as an inflection point for Jonathan Sue, broadening his understanding of data's role in making unbiased decisions.

Learning good old fashioned accounting, I really mean, like, old fashioned style accounting.

Jonathan Sue reflects on how understanding traditional accounting practices was pivotal in shaping his perspective on data's broader context.

Investment Excitement

  • Tribe's co-led investment in the series D of Carda was exciting because Carda is seen as a unique, potentially "n of one" company.

The most recently announced one was when we co led the series D of Carda back in, I think it was November with co led that round with Meritech.

Jonathan Sue shares his enthusiasm for Tribe's recent investment in Carda, indicating the company's unique position and potential for success.

Customer-Centricity and Self-Care

  • Companies must prioritize customer-centricity, and tools like mParticle can help maintain a holistic understanding of customers.
  • Personal well-being, such as getting enough sleep, is essential, and apps like Calm can aid in improving sleep quality.
  • Bookkeeping challenges can be mitigated with solutions like Botkeeper, which combines human expertise with AI for efficient accounting.

At the end of the day, your customers have to be at the center of everything you do.

Harry Stebbings emphasizes the importance of placing customers at the core of business operations, highlighting the role of customer data platforms in achieving this.

One in three adults in the US do not get enough sleep.

Harry Stebbings discusses the common issue of sleep deprivation and suggests the Calm app as a solution for better sleep.

Botkeeper provides automated bookkeeping support to businesses by using a powerful combination of skilled accountants alongside machine learning and artificial intelligence.

Harry Stebbings introduces Botkeeper as a solution to the common issue of bookkeeping, showcasing the integration of human expertise with AI.

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