Finding Genius

by Kunal Mehta

  Prior to Fushman ‘making it’ in the United States, his family emigrated from the Soviet Union, to Israel, to Germany, and finally to New York in a matter of a few years. When they arrived in New York, his family struggled to create a life for themselves and crammed into a one bedroom apartment where Fushman spent five years sleeping on a futon. As he recalls, it is an understanding of these ‘lows’ that eventually gave him a willingness to ‘lose it all’ and to take uncharacteristic risks to succeed. The qualities that set apart founders are only developed by growing and learning in unstructured environments where a person’s character and mettle is tested. Reflecting on his investments in UiPath, KeepTruckin, and Slack, Fushman says:

  “When Kleiner Perkins was raising this fund, its 17th fund, we did a study and found that out of our last fund, 70% of our founders were immigrants. This was not by design. Between Stewart [Slack], Shoaib [KeepTruckin], Eliot [Plastiq], Daniel or Marius [UiPath], there is a commonality between all of them that drew me to their individual genius. There is a commonality where in order to be a great founder, you need to take the right risk at the right time, but more importantly be willing to take that risk and make that leap, where you know you may lose everything. I believe that a ‘nothing to lose’ background or mentality, that is shaped over several years, prepares you for that. With my own background, and the situations I have been through, I know that what I’m doing now is so infinitely better than all of that. My background isn’t the lowest of lows, but if I lose it all and if I go back to that situation, I’ll be fine. I’m willing to set it all on fire to some extent to go the bigger distance. That’s the mentality you see in entrepreneurs that are doing really well. There is nothing holding them back and they know that if they fail, they’ll be okay. Immigrants exhibit that behavior because they’ve had to do that. They have started from nothing and know they can go back to that. You need that level of conviction to become a great founder. If you’re in the middle, have too much opportunity cost, you may not go all the way in. The risks will hold you back.”

  The universal theme in every conversation I had with every venture capitalist was that entrepreneurial genius is determined, in part, by the risk tolerance of a founder. Some claim that this is a trait that great entrepreneurs are born with; but the majority, including Fushman, believe it is a trait developed over time. While Fushman sees this in immigrant founders, other VCs search for this characteristic in alternate settings: repeat founders, founders with atypical career paths, or minorities that have historically been marginalized yet still found ways to succeed. I had this same finding when writing Disruptors: the successful founders had a willingness to lose it all. They put themselves in unstructured situations to see how they would react and thrive. They tested and strengthened their instincts, their creativity, and their ability to learn about new concepts and areas without instruction. In writing Finding Genius, the theme reemerged, as I learned that venture capitalists search for a tolerance for the unstructured and for experience operating in high-risk environments. When looking for genius, VCs search for the ‘antifragile’ entrepreneur.

  When the German national team won the FIFA World Cup in 2012, a study was commissioned to understand what set the players on this team apart. The purpose was to use these findings to train the next generation of athletes. It turns out, according to the research, that the players who went on to make the national team had spent more time learning through unstructured play, rather than structured play dictated by drills, regiments, strict scheduling, or disciplinarian coaches. The unstructured play in small back alleys prepared players for faster reaction times. Shooting on smaller-than-regulation size goals improved accuracy. This is evidence of the ‘implicit learning’ that happens when individuals are not being taught in obvious ways. With languages or musical instruments, children learn by implicitly observing others and developing an ear for the right pronunciation or tune. While exceptional athletes or musicians have a certain disposition that sets them apart from others, their true genius evolves through unstructured paths or learning, because they willingly seek out new and innovative ways of learning.

  In the documentary In Search of Greatness, Gretzky says that his performance on the ice was less about his size, speed, or strength — all factors tested by the Combines for athletic genius — but instead his obsession with the sport from an early age that led to a creativity and vision about the sport that had not been held before. Gretzky not only chose to pick up a hockey stick but, from the age of four, he also obsessively watched every hockey game and memorized where the puck would go at all points during the game. In doing so, Gretzky formed his own thesis on the sport. This kind of unstructured, implicit learning allows individuals to find their own path to success, as opposed to conforming to a structure put in place by others before them. Similarly, there is no clear or defined path to entrepreneurial genius; it is in allowing founders to take those risks and to fail that genius begins to develop. Through failure, genius founders can develop the creativity to attack problems that seem impossible to solve. We see this in examples of entrepreneurial geniuses like Elon Musk focusing on commercial transport from the earth to the moon, or building hyperspeed tunnels underground for faster transportation. Venture capitalists seek to fund repeat founders because they know that these individuals are comfortable with non-conformity and a lack of structure. In fact, they thrive when they are not confined to a set of rules, roles or responsibilities.

  Genius entrepreneurs are built to withstand risk and ambiguity, are humble enough to recognize their shortcomings, and act as beacons for others to rally around toward a shared vision. They are storytellers who develop a passion for change and possess the persistence to see that change come true. Like the artists, athletes, and inventors mentioned throughout this book, entrepreneurs are wholly committed to their pursuit of genius. The most successful founders are eccentric, focused, and have a disposition far removed from most people. More than a decade ago, Fred Wilson of Union Square Ventures described the qualities of entrepreneurial genius as a desire to accept risk, an ability to construct a vision and sell it to others, a conviction in one’s self, and a magnet for talent. Many of these traits have been reinforced by the dozens of partners behind successful venture funds.

  The Role of Venture Capital

  Both Disruptors and Finding Genius share a common belief: that those exotic, hard-to-quantify, genius traits possessed by the iconic entrepreneurs who VCs have backed are birthed out of chaos and unstructured environments. The best venture capitalists find ways to support this entrepreneurial path by forming structure around an entrepreneur’s vision, but not blocking it or giving overly-prescriptive input when the answers are not immediately obvious. As told through the anecdotes shared by the VCs in this book, starting a company is an irrational endeavor — one that venture capital thrives on. Alignment is hard to find but the intelligent, pattern-recognizing, and experienced venture capitalists can be the trail-wise sidekicks they are meant to be. Fushman elaborates on this with a key takeaway:

  “Founders have to bend their risk curve over time. To start a company, you have to ignore everyone’s feedback. If you think of the big companies and best founders, they don’t listen to anyone. Drew [Houston] started Dropbox at a time when you couldn’t define the market. There were other companies in the space that had been acquired by Microsoft but those weren’t big outcomes. You need to ignore the inputs and feedback, but slowly start paying attention to the market and what people around you give feedback on. That’s important to me — a founder opening their aperture for learning and listening over time. It’s the same about building products. You have to be deeply opinionated about building a product in the beginning but over time as your product user base grows, your users have a better sense of what they need. They will tell you, if you ask them the right way, of how you should build and change and evolve your product. You have to go from being opinionated about a product to taking that as an input to your process and change y
our trajectory.”

  Looking Forward

  An important lesson shared by Walter Isaacson in The Innovators is the concept that innovation and disruption are shaped by ‘expanding the ideas handed down from previous generations.’ He writes: ‘The best innovators were those who understood the trajectory of technological change and took the batons from innovators who preceded them.’ Venture capital continues to do the same in its relatively short span of operating as an asset class built to support entrepreneurs. Even as more capital floods into the ecosystem, venture capitalists as of 2019 seem more committed than ever before to finding more equitable ways of allocating this capital. This shift has begun at the LP level and continues to flow to GPs who demonstrate a commitment of funding founders of diverse gender identities, ethnic backgrounds, and geography. Similarly, while some outsiders from the technology ecosystem believe that the opportunity and the scope of venture-scale businesses has hit its peak, insiders believe this is a pessimistic tone for the potential of genius entrepreneurs determined to change the world. In our interview, Andrew Parker of Spark Capital disagreed with the prevailing sentiment that entrepreneurial genius has been diluted and that the future looks bleak. He responds to this claim with an alternative outlook:

  “That assumes the universe of exciting or interesting ideas or possibilities is finite and that doesn’t sound right or optimistic to me at all. The next really innovative trend or idea is going to come from a totally orthogonal vector that people aren’t thinking about today. It’s not some zero-sum game where there are only 100 ideas in the world and once you have 1000 entrepreneurs chasing those 100 ideas you don’t need the 1001 person. Instead there are unlimited pools of opportunities to come from some combination of future technologies, progress, business model innovation, and international markets, and that future is already here.

  SECTION 4

  THE FUTURE VENTURE CAPITALISTS ARE BETTING ON

  RAYFE GASPAR-ASAOKA

  CANAAN PARTNERS

  I had the pleasure of meeting Rayfe Gaspar-Asaoka, an investor with Canaan Partners, through our shared interests in the future of mobility and industrial automation. At the time we met, Rayfe had just closed an investment in Apex.AI, a company working with automotive developers to implement complex artificial intelligence (AI) software into vehicles to support autonomous driving. Bold initiatives such as these are often established by teams of Ph.Ds., scientists, and subject matter experts; and the founders of these sophisticated technology companies search for investors who can match them at an intellectual level. As entrepreneurs within this industry will attest to, Rayfe is that type of an investor. In collaborating with Rayfe, I have been consistently inspired by his ability to break down complex topics and technologies to reveal the true value they provide to those without technical experience. Case in point: AI.

  The study of AI has a rich history that dates well before it became a topic of conversation in popular media or culture. This field has attracted top researchers, scientists, entrepreneurs, academics, and programmers from all over the world. The widespread potential applicability of AI across all industries and its impact on the global economy are not overstated. When I approached Rayfe about contributing a chapter to Finding Genius, he immediately saw the value in sharing the framework he uses to help ask the right questions to determine if an AI company is built for long-term success. As you will learn through this chapter — a primer on AI, as well as a forward-looking perspective on industrial automation — Rayfe is a systems thinker and a technologist who also has an ability to tell a story. That’s a rare and valuable combination.

  In earlier chapters, I discussed the importance of developing an ‘information edge.’ Rayfe is able to set himself apart from other investors who cannot evaluate a technical product or application. As Rayfe reveals in this chapter, venture capitalists are ‘often investing in AI companies before any commercial maturity. This means that understanding the AI technology at a fundamental level is critical to the investment decision, especially given all of the hype and promise around AI.’ This specific market segment requires a deep technical know-how if investors want to succeed by identifying winners early on. Rayfe shares a framework for investing that cuts through the noise to determine what is truly an AI-first company with the potential to create long-term value. These frameworks and questions are not only relevant to AI companies but also provide a relevant foundation from which entrepreneurs and venture capitalists can think about other industries or nascent technologies.

  BUILDING AN AI TOOLKIT AND INVESTING THROUGH THE HYPE

  Rayfe Gaspar-Asaoka, Canaan Partners

  In 2017, Andrew Ng, Stanford professor and one of today’s giants within the field of artificial intelligence (AI) famously said “Artificial Intelligence is the new electricity. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” On the surface, that seems like a very bold claim. But taking a step back, what is artificial intelligence and why does it have the potential to enable change in every facet of the way we live, work, and interact with the world?

  AI is a technology that allows machines to perform tasks at a level comparable to, and in some cases, superior to that of a human. While AI has been dramatized over the decades through futuristic science fiction stories, AI is already here, and in fact, powers a lot of today’s world without us even realizing it. Every time we open up our Netflix app, there is an AI algorithm running in the background that personalizes our recommendations based on our past history and preferences. Or whenever we make a command to Siri, Alexa, or Google Assistant, there is an AI algorithm that processes our voice into a machine-readable command and action. And just like a human, the AI technology powering these actions is constantly ingesting data (more shows watched, more commands heard) and continuously improving.

  But as an early stage investor in startups, it is important to understand not only the differences between the various types of AI, but how to sift through the current AI hype. The challenge is finding the companies that leverage AI as an essential pillar of their long-term success versus those that are using AI as a marketing buzzword. In this chapter, I’d like to share a couple of the frameworks that I use to help me understand a company’s AI technology (their “AI toolkit”), long-term potential for success, and a particular application of AI that I am excited about today.

  What’s in Your AI Toolkit?

  Given the near-infinite combinatorics of tasks humans can perform, the field of study of AI is a broad one that can be broken down into various subspecialties, each with its own set of algorithms and models that are optimized for a particular task. The major fields of AI today are machine learning, deep learning, and reinforcement learning, although up and coming areas such as transfer learning and Generative Adversarial Networks (GANs) are quickly becoming table stakes in today’s AI applications. I think of each of the different subfields and algorithms as tools for an engineer to use as part of their AI toolkit. Each tool in the AI toolkit has its own strengths and weaknesses based on the setup of the problem and the data you are given. Below is a short definition of each, with a few examples to help you better understand the use cases of each.

  Machine Learning (ML)

  Machine learning is one of the foundational domains within AI. Many of the building blocks that were developed in the field of machine learning have served as a framework for other domains as well. In our toolkit analogy, machine learning is the hammer, a versatile, must-have tool that everyone must have.

  There are many different types of machine learning models, but they all share the same workflow: 1) ingest data (known as “training data”), 2) make predictions based on that data, and 3) optimize the predictions over time. The recommendation algorithm for Netflix is a real-world example of a machine learning algorithm. Every time you start up the Netflix app, it ingests data (your watch history), makes predictions
on that data (shows relevant to your historical preferences), and then optimizes that over time (based on how close your selections match the recommendations).

  While there are many ML models (and more coming out of research every day), each problem can be broken down along two axes that help to structure the type of algorithm to use. On the first axis is classification versus regression; this is simply defining whether the answer to the problem should be discrete (classification) or continuous (regression). For instance, if you are trying to build a model to predict the price of Bitcoin in 10 years, the answer is a continuous one, with it ranging from $0 to priceless, and every number in between. This would fall into the camp of regression machine learning algorithms. On the other hand, a model that predicts whether or not an email you receive should be marked as spam or pass through into your inbox is discrete (spam or inbox), and best defined as a classification problem. ML algorithms can be built to work with both linear data (linear regression) and non-linear data (logistic regression, neural networks, etc.).