We believe that there is a massive opportunity in the intersection of software and biology, which we broadly define as “Health Informatics.” This term has a formal definition but we tweaked it to make our own. It is a software-first approach to solving problems in human biology, medical research and ultimately, patient care. We think the timing is right for software developers to make an impact in these areas. The ultimate goal is to use software, IT and data science to help diagnose, treat, reduce and cure disease - at the physical, mental and emotional levels. If we see a bright founder working in this area, the opportunity will move to the “top of the pile” as if it's in one of our other preferred trends.
The catalyst of this trend is the cheap, abundant data of two types - medical and molecular data. Cheap, abundant data combined with new ways of measurement and analysis leads to technological breakthroughs. There will be a flood of medical data driven by electronic medical records and the like. For example, the recent Affordable Care Act (i.e., “Obamacare”) basically pays doctors for complying with a federal mandate to move from paper to software-based solutions. By 2024 or so, every hospital will move to electronic records. Right now, it's estimated that only 2% of hospitals comply with this law. Thus in ten years, the amount of data will increase 50X.
On the molecular front, the costs of sequencing technology is falling almost 5x faster than Moore's Law. (The interpretation and analysis of the data lags this trend but is improving as well.) President Obama also recently announced a major national initiative to map the human brain in the same way that human genome was mapped back in the early 1990's. This is just another example of “offline” or analog data being digitized and accessible to coders.
Google motivated a generation of bright computer scientists to learn the ins-and-outs of the advertising industry and turn it into a software problem. The original PayPal founding team went through the schlep work of learning the norms, regulations and other vagaries of the payment industry in the early 2000's. And they used software to help re-invent it. And most recently, Palantir went through the hard, unsexy work of understanding the intelligence and defense industries and used software to attack hard, important problems. We want to back founders who want to do the same when it comes to health informatics.
We are already working with and investors in founders and companies in this area. Those include Counsyl, Benchling, Practice Fusion, ElationEMR, DNA Nexus, Medisas, Comprehend Systems and Flatiron. Counsyl in particular is a flagship company. They were featured yesterday in TechCrunch. The founding team has a traditional computer science background. In fact, they even started the company thinking they were going to be a pure software shop. Ultimately, they built a lab using cheap, commodotized hardware - a trend that wasn't in vogue then but is now. Ramji Srinivasan, the CEO of Counsyl, is the archetype of what we will look for. He will be an advisor to us on this effort.
Another advisor is Professor Atul Butte, a Stanford University School of Medicine professor, researcher and entrepreneur in medical bionformatics. He has been a thought leader and pioneer in the area of applying computer and data science to biomedical research. He is a sounding board and inspiration for us to pursue this trend and we're thrilled to have him with us as we learn more about this fascinating area.
But perhaps the most critical advisors to us will be Jeremy Richman and Jennifer Hensel. They are scientists and lost their only child, Avielle, in the Newtown shooting. They courageously set up the Avielle Foundation to use science and technology to help understand why these tragedies happen. They encouraged us to think more holistically about how to reduce gun violence and how to use technology to identify and treat the root causes of these tragedies. Biomedical research, bioinformatics and brain health are all areas that need further investigation to understand the “why” as well as the “how.” Great software companies and entrepreneurs can play a fundamental role here.
On a personal front, I am a cancer survivor so I have a selfish reason to accelerate this vision. Scientists are more confident than ever that genetic mutations play a huge role in why cancer happens. I believe that great software companies in the mold of Google, PayPal and Palantir will help make cancer a chronic condition and quite possibly, cured.
To be clear, while this megatrend has philanthropic and personal benefits, this is not a philanthropic or personal venture. We believe this is a massive market opportunity for young hackers and founders. And we want to be crystal clear that this effort is consistent with our historical focus - great founders using software to address new and large markets.
Kudos to Gautam Sivakumar (Medisas), Ramji Srinivasan (Counsyl), Sajith Wickramasekara (Benchling), Jeremy Richman and Jennifer Hensel for helping me with this.
I am thrilled to welcome (back) Brian Pokorny to SV Angel! He will join as a General Partner starting today.
Many people in the tech community already know Brian, a.k.a. “Coach”. He was most recently the CEO of DailyBooth/Batch, a startup that was acquired by AirBnB.
Prior to DailyBooth, Brian and I worked with Ron as partners for SV Angel on investment decisions and adding value to portfolio companies. Thus he is stepping back into a familiar role. However, he returns with a wealth of operational and startup knowledge from his 3 years at a startup. This experience will be invaluable insight for our portfolio founders. Many of the best young founders will already attest that he's become a true “Coach” and trusted advisor to many entrepreneurs.
Brian has been angel investing since 2006 and reviewing investment opportunities since 2004. Having seen so many opportunities, he has unique insight into the trends and cycles related to the Internet. Some of his personal angel investments include Twitter, Square, OMGPOP, Milo, Chomp, Tweetdeck, Circle, Bump, Posterous, Milk, Couple, Elepath, and MessageMe.
On a personal note, I've known Brian since 2005 and we became friends through working together at Google. And so it is great for me to do business with a friend. Welcome back, “Coach”!
If I could do it all over again, I'd be a sportswriter. Growing up in the mid-80's, my favorite thing was to wake up and get the paper-delivered Boston Globe and open the sports pages (an anachronism for anyone born after 1975, probably). I wrote for the town newspaper and it's the one thing (other than playing sports) that I truly enjoyed as a kid.
I still read some columns today. One column in particular is Peter King of Sports Illustrated. He covers the NFL and mixes reporting, insight and locker-room gossip. He has the best access to teams, key personnel and leaders like an old school Peter Gammons, Will McDonough or Bob Ryan.
One of his features that I love is his weekly “Ten Things I Think I Think” which are quick hit snippets where he humbly pontificates about football and other stuff. With a hat tip to him, here goes my first installment on tech, startups and other stuff:
I think if you wanted to read one thing only to understand the history, philosophy and tactics related to startups, my vote is Blake Master's notes on Peter Thiel's lectures for his CS183 class at Stanford. I'd argue that that's all you really need and will be as relevant in 20 years as it is today. (We are investors in Judicata where Blake is co-founder.)
I think LinkedIn's product development in the past year is very impressive for a company of its size. Adding and accepting connections is very slick and easy, for example. It's become my own little “Minesweeper.” I've done it so much that people probably think I'm moving jobs (I'm not). Some of their “thought leader” pieces are must-reads as well.
I think sanebox was on that list but trying to figure out how to use it with mailbox. (Mailbox and Dropbox were/are SV Angel portfolio companies.)
I think Google is the LeBron James of technology. Their greatness on so many dimensions is so consistent and persistent that we just take that for granted. They are not perfect but criticizing them for various tech shortcomings is like criticizing LeBron because he's less effective driving left than right. ( used to work at Google so am a Google apologist.)
I think it feels like startup ideas in the past 12 months or so are on average more diverse, bolder and harder. I don't mean to diss the earlier startups because we also believe that many if not all of the biggest things look trivial at first.
I think I don't know enough about 3D Printers to say with any data or conviction but articles like this make me think that they may be overhyped in the short-term for many use cases but underhyped in the long-term in others. The 3D printed gun seems inevitable…
I think we at SV Angel are seeing some of our “top” investment opportunities appear on AngelList and the like. So I think the “adverse selection” question is being answered.
I think the food at Blue Bottle Coffee is better than the coffee. And that's not a back-handed slap because the coffee is very good.
I think that I am forcing this post so I will stop here.
You need to prepare, listen and engage.
-Charlie Rose
I've met a lot of incredibly successful, accomplished and well-known people. In some cases, I've even worked with them. This is one of the huge perks of working with Ron Conway, who I put in this bucket. (Incidentally, this sounds like a nested humblebrag but just because I met these folks obviously doesn't put me in that bucket.)
For me, one skill that really stands out among some of these folks is their ability and willingness to listen. I'm not just talking about the level of listening where you remember what the other person said. And not the pollyannish notion of “I feel your pain.” I'm talking about the deeper level of listening that requires a mastery of body language, a genuine level of empathy and most important, a genuine interest and curiousity in the speaker or subject matter, or both. They are curious in either the person or the material, regardless of the person or material.
I've tried to become a better listener. About fifteen years ago, I bought this book and even highlighted portions of it. I got through about a quarter of it and threw it into my “Books Aborted” pile, which is a very large pile by the way.
I don't want to sound too obsequious, but Ron is one of the best listeners I have ever seen. People like him lock into the other person a la Bill Clinton, who is probably the Mozart when it comes to this. For every meeting that Ron does, he brings a huge pile of notes related to the other person (i.e., he prepares) and completely locks into that person. His signature move is to furiously jot notes to record and remember what the other person is saying. I've asked him why he does this and he said, “Because I forget shit.” But the signal it sends to the speaker is immeasurable. It's charisma in a nutshell.
When I first met Ron in 2005, we would talk about online video, his then-current interest. He would scribble notes furiously, sometimes on cocktail napkins. I thought I was the smartest person in the world on video. When I realized a few years later that he did that with virtually everyone on every topic, I was a bit crestfallen to say the least.
I wish I were a better listener. I'm subpar. Ask my wife. And to her chagrin, I want to improve not necessarily because I want to be a better and more empathic person. it's because I think it's an incredibly valuable skill to have as an investor, particularly in the startup world.
Regardless of your approach or business model, a huge part of startup investing (and in some cases, the only part) is human talent evaluation. “We invest in people” is probably the most common refrain for startup investors. And if you're investing in people, you need to understand not only their skill, aptitude and potential but also their insight, drive, motivation and toughness - the immeasurables.
And that's where listening comes in. One difference between a good investor and a great investor is that the great investor asks the right questions. And just as importantly, they're able to process and synthesize the answers. They ask the incisive ones that cut through the noise. And identifying the right question is a function of great listening. Listening increases the throughput of data and also filters out the noise. If you watch Charlie Rose or watch great interviewers, they ask the great follow-up, unscripted questions that reveal the most important and telling details. This can unearth the people who have the missionary and authentic drive to do something great - and just as importantly, weed out the people who say the right things but don't actually mean it.
The other person who comes to mind for me ont his topic is Michael Moritz, the legendary investor at Sequoia Capital. We've co-invested a lot with Sequoia, but I've never really worked with him personally. But his path to investing is not necessarilyt the conventional one. Before his investing career, he was a journalist and writer. And without speaking to him about this or knowing if this is bullshit, it kind of makes sense. Without knowing anything about the profession, a great journalist has to ask the right questions and synthesize the answers (among other things). And that's a dynamic exercise that's a direct function of getting the right information and inputs - i.e., being a great listener.
Clayton Christensen said that questions are places in your mind where answers fit. The relevant facts are there for everyone but you need to be able to ask the right questions to unearth them. The great investors can get to the most insightful answers and meaningful insights faster than the rest of us. In the startup world, where data is scarce but potential is abundant, that is an elite skill.
The following is above my pay grade to critique but here goes. There's a strain of thought in computer science that “data beats algorithms.” That is, having more data is better than having a superior algorithm. For example, some contend that the winners of the Netflix Prize had a relatively simple algorithm but won because they had more data.
Imran Haque, Counsyl's Director of Research, tweaked this claim. He said that cheap, abundant data leads algorithms. And by doing that, they can lead to technological (mainly software) breakthroughs.
In the mid 1990's, cheap text created by the Web led to better machine learning, machine translation technologies, AI technologies, PageRank and other breakthroughs. In the mid 2000's, cheap image data created by cell phones, digital cameras and the like helped actualize arithmetic coding, effective computer vision and quite possibly to break throughs like self-driving cars.
Imran said that the next breakthrough is cheap molecular data. The cost of sequencing one human genome (i.e., 6 billion bits!) cost $9MM in 2007 and $7K in 2013. These costs are falling almost 5x faster than Moore's Law since 2007. Because of this and advances in other fields like robotics, Imran's claim is that this will be the most interesting area of computer science for the next 10 years.
Yesterday was a depressing day for me. I've been a fan of the New England Patriots (and other Boston sports teams) for over 30 years. And I just can't root for them anymore. [Endnote]
Putting aside the fact that no grown-ass man should be this depressed about a sporting event, it's a cold, hard reminder that professional sports is a business. It's run by billionaires who happen to be (in most cases) die-hard sports fans. I'm pissed off at him and the ownership now, but Robert Kraft, the owner of the Patriots is not only the best owner in all of sports but also a die-hard Patriots fan. His decision to buy the Patriots was a dumb business decision (at the time) driven by his inner sports fan. Every Patriots fan is grateful for what he did.
I've written and talked before about the parallels between engineers and athletes. Others have made the same points so I won't rehash them here. I think it all starts from the basic idea that professional athletes and top engineers/founders all have a talent that can be learned by many but mastered by few. There are some parts God-given talent, hard work and circumstances. But ultimately only very few the first part. The rest of us can only be fans.
Vinod Khosla was quoted recently as saying (and I paraphrase) that investors aren't your friends. And I cannot agree more. It sounds overly harsh and this wasn't his point but they're called “investors” for a reason - they have a job to make money. If they didn't have that job, then they'd be “donors” or “patrons” or “sponsors.” They are not unlike Robert Kraft - a sports fan who is in the sports business. As an aside, I'm a huge believer in the saying that it's better to make friends through business than doing business with friends.
I was a physics major but I'm no physicist. I have a graduate degree in engineering but I'm no engineer. I realized that in my mid-20's and made the career switch. I wanted to be an advisor to people who were building companies using awe-inspiring technology. I would be a fan. And the best part of my job (and most investors would agree 100%) is being a fan - a spectator to unbelievably talented people create great things.
After a few glasses of wine and sitting alone with Ron Conway one night, I asked him why he continues to work at his current pace. “You're looking into the future!” he bellowed.
But legally I am a fiduciary first. I signed a legal contract to be basically a legal custodian of other people's money. I can be a fan and irrationally support our portfolio companies - but only to a limit. We at SV Angel say that we will always put the interests of the founders first. Ahead of investors', corporate partners' and our limited partners'. One part of this is based in the idea that they are the athletes and we are the fans. Without them, all the other stuff doesn't exist. That's the “feel-good” part. The reality is that we also do it because it's our business model - not because we're good guys. We do it in the same way that “Don't be evil” is Google's business model and “Putting customers first” is Amazon's.
I'm not a finance guy. Which is pretty scary given that I'm theoretically in the “finance profession.” I didn't even take a single economics class in college. I faked-it-until-I-maked-it and just tried to learn on the job.
Probably the useful concept for me in the context of tech was the notion of “operating leverage.” It basically measures your marginal profit - is your revenue leading to growth or are you just running standing still? One way of looking at this is your fixed costs relative to your variable costs. A business that has high fixed costs relative to lower variable costs has high operating leverage. They make more profit (and not just money) as they get more customers. The classic examples are hotels, restaurants and airlines. The beauty of a business with high operating leverage is that you get a lot of leverage from your customers or users (sorry for circular statement).
As an aside, I think “leverage” is one of the most misused and misunderstood concepts - I didn't think about the precise definition until I heard this Larry Page quote (one of my favorites): “Use the leverage in the world so you can be truly lazy.” I think Paul Graham does one of the best jobs in describing it in his 2004 essay, “How to Make Wealth.” .
The risk of such a business is that it's, uh, highly risky. You put a lot of money up front and you don't know if people will come. You don't know if you'll recoup the high up-front costs before it's too late. You have a low “juice-to-squeeze” ratio, as I like to say.
It's now a fact-of-life that software-based entrepreneurs can benefit from lower fixed costs because of Amazon web services and the like. The corollary is that you can have a business with “high operating leverage” because the fixed costs have basically vanished. Your only job then is to reduce or at least harness your variable costs, i.e., get users in some automated or scalable way. One example as applied to the Internet is self-service systems. Investors love the “self-serve” model (“Once you can get your ad system from manual to self-serve…”)
Jeff Bezos understood this idea as applied to tech earlier than most. If you read his shareholder letters over their 10+ year existence, operating leverage is a common theme. He made a massive up-front investment in computing capacity, recommendation systems and structured data capabilities to create operating leverage in Amazon's business. (There's still an open debate if he's truly achieved operating leverage as classically defined, but if stock price is one proxy, then he's achieving his goals.)
[O]ne of the most important things we’ve done to improve convenience and experience for customers also happens to be a huge driver of variable cost productivity: eliminating mistakes and errors at their root. Every year that’s gone by since Amazon.com’s founding, we’ve done a better and better job of eliminating errors, and this past year was our best ever. Eliminating the root causes of errors saves us money and saves customers time. (2001)
In his 2002 shareholder letter, Bezos lays out how his goal is to create operating leverage through customer experience and customer service. He's trying to automate a highly human-oriented task. It's like a playbook for many of the e-commerce companies today more than ten years later:
Traditional stores face a time-tested tradeoff between offering high-touch customer experience on the one hand and the lowest possible prices on the other. How can Amazon.com be trying to do both? The answer is that we transform much of customer experience—such as unmatched selection, extensive product information, personalized recommendations, and other newsoftware features—into largely a fixed expense. With customer experience costs largely fixed (more like a publishing model than a retailing model), our costs as a percentage of sales can shrink rapidly as we grow our business. Moreover, customer experience costs that remain variable—such as the variable portion of fulfillment costs—improve in our model as we reduce defects. Eliminating defects improves costs and leads to better customer experience. We believe our ability to lower prices and simultaneously drive customer experience is a big deal, and this past year offers evidence that the strategy is working.
I'm not a big believer in post-mortem analysis. It's very hard to do it without infecting analysis with what you know now. For example, when it comes to “misses” in startup investing, one often looks at where “things went wrong.” But maybe nothing went wrong. Maybe you created a systematic plan and process, executed the process based on the facts that you had at the time and made a decision based on the probabilities. Your decision wasn't wrong or a “miss”; the probabilities just played themselves out.
Put another way:
[D]ecisions are about probabilities is that decisions should not be judged by outcomes but by the quality of the decision-making, though outcomes are certainly one useful input in that evaluation. Any individual decisions can be badly thought through, and yet be successful, or exceedingly well thought through, but be unsuccessful, because the recognized possibility of failure in fact occurs. But over time, more thoughtful decision-making will lead to better overall results, and more thoughtful decision-making can be encouraged by evaluating decisions on how well they were made rather than on outcome. In managing trading rooms, I always focused on evaluating and promoting traders not on their results alone, but also and very importantly, on the thinking that underlay their decisions.
This may be a tall tale, but I once heard that a successful hedge fund manager started his career by identifying every investment decision that Warren Buffett ever made. He then analyzed each decision with the information that Buffett had at that time. That process was probably as painstaking as it sounds. Because a lot of Buffett's investment decisions were made before the Internet even existed. So he had to access all the newspaper and publicly available information at the time. It seems like that's what's needed if you're going to do a real post-mortem analysis. When we consider our misses (which are plenty and painful) at SV Angel, we try to do this and think about our process and not the outcome. With each miss, we may tweak our process. Even that could be a mistake - confusing procedure with substance. The challenge is to decide whether the process ever needs to be tweaked in light of the outcomes.
SV Angel invests in and advises startups. We make money when these startups have a “liquidity event,” which is historically an acquisition or public offering. We invest other people's money. We invest our own money too–this is called a “capital contribution”, which is required of most funds. When there's a liquidity event, the other investors get paid 80% of the profit and we get 20%.
This is the structure of a traditional venture capital firm (VC). But we aren't a traditional VC firm. Traditional VCs invest in 10-20 companies per year. We may invest in 100+. Many VCs need to own a certain percentage of each company and invest more money in the companies that are doing well (i.e., “pro rata” in VC jargon). We don't, and we rarely do our pro rata. Many VCs have a 'higher touch' approach with the company post-investment. Because we invest in more companies, we don't have that approach. We try to help only when the companies ask and usually at 'inflection points,' which are financings, M&A, business development deals, etc.
Our approach isn't mutually exclusive or “better” than other firms. In fact, we need to work with firms who add value in different ways. If we don't, then ours doesn't work. Our business model is different, in the same way that Amazon has a different business model from Google, for example. We think that we can make “venture-like” returns consistently over the long term for our investors with our approach.
When we started in 2009, our goal was to institutionalize what Ron Conway has done over his 20+ year career[*]. Ron has been called a “human router” by some–he makes intelligent and helpful human connections in the tech ecosystem. We wanted SV Angel to become the human “routing layer” by making connections among and developing relationships with the best founders, investors and corporate partners. Each of these 3 constituents or sub-networks want to meet and have relationships with the others. The starting point is investing in a relatively larger number of more promising founders and companies at smaller amounts (i.e., $50-100K)[**]. Most of these companies will fail. But some will become successful and a select few will become breakout companies.
In a nutshell, we try to reduce friction in the ecosystem by making the connections that are a “force multiplier” for startups. Google and other tech companies have shown that if you can reduce friction (i.e., transaction costs), then you can generate and capture value. That's what we're trying to do.
[**] We will occasionally invest larger amounts in later-stage companies if we have unique access (i.e., our investors can't buy the shares somewhere else) and some unique thoughts on the company (e.g., through working with them from the earliest days).
Investing is often described as the process of laying out money now in the expectation of receiving more money in the future. At Berkshire we take a more demanding approach, defining investing as the transfer to
others of purchasing power now with the reasoned expectation of receiving more purchasing power – after taxes have been paid on nominal gains – in the future.
More succinctly, investing is forgoing consumption now in order to have the ability to consume more at a later date. From our definition there flows an important corollary: The riskiness of an investment is not measured by beta (a Wall Street term encompassing volatility and often used in measuring risk) but rather by the probability – the reasoned probability – of that investment causing its owner a loss of purchasing-power over his contemplated holding period. Assets can fluctuate greatly in price and not be risky as long as they are reasonably certain to deliver increased purchasing power over their holding period…
…There are three major categories, however, and it’s
important to understand the characteristics of each…
…Investments that are denominated in a given currency include money-market funds, bonds, mortgages, bank deposits, and other instruments. Most of these currency-based investments are thought of as “safe.” In truth they are among the most dangerous of assets. Their beta may be zero, but their risk is huge…
…The second major category of investments involves assets that will never produce anything, but that are purchased in the buyer’s hope that someone else – who also knows that the assets will be forever unproductive – will pay more for them in the future. Tulips, of all things, briefly became a favorite of
such buyers in the 17th century…
…This type of investment requires an expanding pool of buyers, who, in turn, are enticed because they believe the buying pool will expand still further. Owners are not inspired by what the asset itself can produce – it will remain lifeless forever – but rather by the belief that others will desire it even more
avidly in the future…
…My own preference – and you knew this was coming – is our third category: investment in productive assets, whether businesses, farms, or real estate. Ideally, these assets should have the ability in inflationary times to deliver output that will retain its purchasing-power value while requiring a minimum of new capital investment…