2016/09/26

How the Rockefellers’ VC Firm Picks Tech Startups

jet-planes-formationVeteran venture capital firm Venrock, an investing arm of the Rockefeller family, has backed several successful tech companies since its inception in 1969: Intel, Apple, DoubleClick, to name a few. A recent win is the sale of Dollar Shave Club to Unilever for $1 billion; it also was one of the backers of Nest Labs, which was bought by Google for $3.2 billion in 2014. Venrock partner David Pakman said his firm looks for digital business models that can disrupt slower-moving incumbents. He noted that traditional firms are losing to tech startups because they don’t know how to talk to the digital customer. In contrast, digital-first firms tend to be ‘conversational’ brands skilled in engaging the always connected customer. Pakman discussed Venrock’s strategy on Wharton Business Radio’s Knowledge@Wharton show, which airs on SiriusXM Channel 111.
An edited transcript of the conversation appears below.
Knowledge@Wharton: Venrock was an investor in Dollar Shave Club, which was recently sold to Unilever for $1 billion. In some respects, that sale had to make you feel good about the faith you had in the idea originally.
David Pakman: It’s always nice to see an entrepreneur get their dream to come true. This was an incredible team. And it’s true, not a lot of investors believed in them early. It was a company that was hard to raise money for in the early days. But I saw a lot of interesting numbers and trends, and I believed in the team. It had a lot of conviction. So we led the Series A and the Series B rounds, and it worked out great for everyone.
Knowledge@Wharton: Dollar Shave Club has started an amazing trend in retail. Even a company like Gillette has added an online component.
Pakman: Every brand today has to become a direct-to-consumer brand. So much of our attention has been splintered away from legacy broadcast media to mobile phones and social networks. The only way to reach consumers now, since so few of us actually watch television commercials and watch legacy TV, is online and through mobile devices. The way to be authentic there is direct to consumer. We expect every brand will have to become a direct-to-consumer brand, and many of the largest categories of products in the world are still dominated by companies that are not direct-to-consumer brands. They don’t know their customers. They don’t sell on the internet. That’s an investment thesis of ours.
Knowledge@Wharton: You mentioned Apple. Music is a love of yours. Not only playing and writing it, but part of your work at Apple was involved with music. … Is it amazing to you to see just how much digital music platforms have grown and how much more it can grow in the years to come?
“Every brand today has to become a direct-to-consumer brand.”
Pakman: The internet itself has been just incredible to me. To understand that there are 3 billion people connected to it now, receiving information communication digitally, is far beyond what I ever expected to happen. But it is that now. And the real question we ask ourselves, and I think a lot of entrepreneurs ask themselves, is how do you capitalize on those macro trends of everyone being connected in real time, all the time, with a device that can record and play back any media type, any time? The world’s your oyster, and massive new businesses have been built because of it.
Knowledge@Wharton: What are the things you look for when you’re looking to help an entrepreneur with an idea?
Pakman: We think of different categories first. I’ve spent a lot of time in consumer services, in consumer media and in consumer products. We also look at enterprise infrastructure and enterprise applications. Each one has some different characteristics about what makes a disruptable idea promising, but it really starts with the fact that Silicon Valley best practices of creating companies and technologies move at an unbelievably rapid pace, compared to many large incumbent organizations in existing markets. Being able to move more quickly, to test, to reach customers directly, to iterate very quickly on products to help them reach perfection, is anathema to the way large companies work. At the core, no matter what segment we’re in, we’re just working with very fast-moving entrepreneurs that are willing to break glass and experiment and find new products that will work. …
Knowledge@Wharton: Your company also invested in Nest. The connected home is something that is talked about more and more these days, and the ability to control your home thermostat via an app is something more people will do over the years. When you started to look at Nest, what intrigued you?
Pakman: A lot of it was the team. This is an extraordinary team, led by Tony Fadell and Matt Rogers out of Apple, who oversaw the creation of a huge number of iPods, iPhones and iPads. They believed that with the Silicon Valley best practices model of applying this supreme element of design and functionality into these unloved product categories of home thermostats, you could create not just a desirable product, but a premium product.
There really wasn’t a $250 thermostat. You couldn’t spend that much money if you wanted to. Yet you spent $30 or $50 for a really disappointing product. So, they created a premium product experience. We fell in love with that idea and moved much more quickly and brought a much higher level of customer knowledge than the incumbents they were fighting against — Honeywell in their case. We love that story. It’s very consistent with our view that you can disrupt many incumbents in very large markets by bringing this customer-centric, design-first software combined with machine learning and data to produce products that are smart and get better over time.
Knowledge@Wharton: Is it easier for companies like Nest to see a level of success now, compared with 30 years ago?
Pakman: Foot traffic to physical retail stores is down, so being really good at dominating store shelves is no longer a core requirement for success because it’s not where people are going as much anymore. You can sell direct and reach consumers. Also, people are watching much less TV and don’t see TV ads. So, being good at television commercials is not as important. You can reach consumers on the internet, on Facebook. With distribution and marketing effectively democratized, the advantages of the incumbents in so many product categories are sort of neutralized. Now you stand on your own based on product quality features, benefits, price. I think those facts are what have laid the groundwork for so many new products in hardware categories to come to market.
Knowledge@Wharton: How much do you think something like Nest is going to grow as more connected homes are built?
Pakman: The expectation is that I can talk and interact with most of the devices in my house that use electricity. That means they all have to become connected, so I think that they all will become connected. Washing machines are connected now, refrigerators are connected. I’m not so sure they’re doing useful things with that connection just yet, but some of them are. They’ll certainly turn your lights on and off or identify when there’s someone suspicious walking around my house or open my garage door when I approach in my car. Those are all useful features that connected devices can do, so I fully believe that they either already are connected or will be. The home itself is an entire connected appliance.
“Being really good at dominating store shelves is no longer a core requirement for success because it’s not where people are going as much anymore.”
Knowledge@Wharton: More than 3 million consumers are using Dollar Shave Club, and Unilever is now part of the formula. When that deal was announced, you talked about how beneficial it could be for the Dollar Shave Club to have the backing of Unilever and leverage the reach that it has globally.
Pakman: Dollar Shave Club has had extraordinary success. … They’re at more than 16% U.S. market share of the men’s cartridge market in just four years, and they’re growing incredibly quickly. But to reach their dream of being a globally dominant men’s grooming brand, they need help internationally. Unilever is an incredible company that has a massive global footprint and can really speed their deployment to a number of countries and help them launch there. With the resources and the knowledge of the company, I think it really accelerates co-founder Michael Dubin’s ambition to be a dominant global brand.
Knowledge@Wharton: How much does the timing work for him? Because Dollar Shave Club had the razors, but they were starting to get more into gels and other grooming products for men.
Pakman: Yes, they have 27 other products today, from soaps and hair products to shaving cream, and are launching many more. It’s definitely part of the brand. Michael always said, “Anyone can sell razors on the internet.” The goal was to create a globally dominant men’s lifestyle brand, a men’s grooming brand. That is very complementary with Unilever’s skill set and is definitely part of the playbook.
Knowledge@Wharton: There are successful legacy companies that are still not monopolizing the internet the way they should. Those companies may have been so big over the years that it may not knock them off the books, but it certainly does affect their bottom line. They’re missing even further opportunities for growth.
“We can bring to market competitors that are nimbler and use distribution and marketing methods that are just more suited to the modern times.”
Pakman: Our bet is there are tens of thousands of companies that meet that description. We’re betting against that category, that we can bring to market competitors that are nimbler and use distribution and marketing methods that are just more suited to the modern times.
Knowledge@Wharton: Gillette start its own online club. How much market share are they retaining since they began losing ground to Dollar Shave Club?
Pakman: I read the transcript of their earnings call and their press release, and their sales are still down in the United States. It’s the only market we compete in, and they expressly named U.S. online competition as the culprit. From that, I can only tell you it doesn’t look like it has been very successful.
I think consumers are voting with their attention and their dollars, and we’ve seen a lot of market share lost from the incumbents to the online competitors. The question is, what can they do about it? The hardest part for them is trying to figure out how to talk with and listen to their customers.
I think the biggest difference for brands that sell on the internet is, they tend to be conversational brands. Brands that talk with their customers, rather than shout at them. Broadcast television is really a platform for shouting at your customers and hoping they hear what you say. But online, you can’t get away with that. If you look at Gillette’s Facebook page, when they post videos that mock the online competitors, they get thousands of comments saying, “What are you talking about? Your prices are way too high! We like these other guys!” My guess is they’re pretty surprised by that.
Knowledge@Wharton: Is timing a factor? We were in a recession several years ago and people didn’t have as much money to spend, even on the smallest things like razors. Having this option at a lower price point just made it easier to go that way, and the product being good made it easier for them to stay.
Pakman: It’s just a smart decision to pay less for something that’s just as good. And guys are smart. It’s not much more complex than that. I think the economic model that Gillette lived under was that they spend hundreds of millions of dollars a year on television advertising, paying Roger Federer to endorse the product. You’ve got to keep prices high to pay for all that. If you can make a direct-to-consumer model, have higher product margins for yourself and much lower marketing costs, you can lower the price. That’s what Dollar Shave did, and that’s a really powerful method of innovation.
Knowledge@Wharton: Are there other areas within the retail sector that you watch for growth?
Pakman: Cars. Think about the companies that make cars, particularly U.S. operators. They do not sell direct to consumer. They don’t even know their customers. The customers are dealers. The dealers, I would argue, are neutral at best to the car-buying experience but are probably net negative? Haggling, no transparency, misleading advertising. The [CBS] news show 60 Minutes runs some investigation where they’re telling you they’re changing the oil and not doing it, right? No trust, no transparency. Value destruction in the entire experience of buying a car.
“The biggest difference for brands that sell on the internet is, they tend to be conversational brands — brands that talk with their customers, rather than shout at them.”
Now, you take Tesla, which is a direct-to-consumer car company. No dealers. No-haggle pricing. You can buy a Tesla on your phone by looking at a web page. They tell you when it’s delivered. There’s no commissions paid to salespeople. It’s completely rethinking the model, sort of the Apple model if you brought it to cars. The only problem with Tesla has been it’s expensive. You get companies like that to solve that problem and it can fundamentally change the entire expectations of what a car company is. I think that’s a massive disruption point for the automobile industry.
Knowledge@Wharton: Is the medical industry in that realm as well? Even though we’re seeing more telehealth in that venue, the experience for a lot of people still is not great when they have to go see the doctor.
Pakman: Totally. Just as one example, we have a company called Doctor On Demand. You download the app for your phone, you pay $40 and you get an appointment on demand with a licensed, certified physician who does video chat with you and can prescribe medication right there and then. If it wasn’t a good experience, you don’t pay the $40. That’s obviously not perfect for every medical condition, but it is for a whole bunch of stuff. It’s one example of service-level innovation to make healthcare easier, better and nicer.
Knowledge@Wharton: What are some of the other companies that you are involved with that are doing the same type of work to make things easier for the consumer?
Pakman: We’ll stick with cars for a minute. I’ll give you an example. There’s no question that, within some number of years, there are going to be many self-driving cars on the road. There’s no question that the future of all automobiles will be self-driving. It’ll be massively safer and reduce the 39,000 traffic deaths in the U.S. per year. We have 1.2 million a year, globally. The question is, how do you bring that technology to market? Left to its own devices, what will happen is all that tech will appear in brand new cars at the high end. And what a shame, if the only way to get a self-driving car is to buy the top of the line and to buy a new car.
One company we invested in is called Pearl. They make safety and autonomous products that you can buy off the shelf and put on your car and start to get some of the benefit off all this stuff. Their first product is the most amazing rear-vision backup camera system. Self-installed, solar charged, totally wireless, streams to your phone. It’s very similar to the Nest play of creating a premium product in a category but that’s super smart, intelligent, and you can buy it and install it yourself in your existing car. I think we’ll see a lot of interesting stuff coming from a company like that.

The Right Way to Take Risks — in Business and Life

risk
The truism “nothing ventured, nothing gained” is an often heard phrase. But risk-taking must be balanced with prudence. Karen Firestone, chairman, CEO and co-founder of Aureus Asset Management, knows how to strike that balance. She lived it herself — leaving a fund management job at Fidelity Investments after 22 years to co-found a wealth advisory firm in 2005. It worked out: Aureus now has $1.5 billion under management. Firestone wrote about the topic in her book, Even the Odds: Sensible Risk-Taking in Business, Investing and Life. She discussed her book on the Knowledge@Wharton show onWharton Business Radio’s Knowledge@Wharton show, which airs on SiriusXM Channel 111.
An edited version of that interview appears below.
Knowledge@Wharton: Before we dig into the book, I want to start by pointing out part of the title: the concept of sensible risk-taking. How much of a challenge is that today, whether it be in business or in people’s personal lives?
Karen Firestone: Well, risk-taking is a factor in everybody’s life, all through your day, every single day, and we may be aware of it or not. Being sensible about it is another challenge, and I think that what makes it even harder today is that when people are under increased levels of stress — which we all are — we tend to act more impulsively, and we don’t think things through in a logical or sensible way. Given that I spend most of my work day, and a lot of my time, thinking about risk-taking professionally, it’s sort of a natural outgrowth that I think about risk [more generally –] the way that we deal with it in complicated fashions and ways to be more sensible.
Knowledge@Wharton: It seems like, especially since the recession, we are in a period where the concept of understanding risk-taking and actually taking that risk has been heightened.
Firestone: It’s amazing, if we think about how disturbing the news is. Just think about the last few weeks as we were heading into the period of the political conventions. You think, there’s so much upheaval, there’s violence, there’s distress. How can we possibly, for example, make an investment? How can we possibly put money into the stock market when we see all of this happening around us? Yet what we talk about here at our company lately is that the market seems to be one of the few, in a sense, calm locations for thought and reasonable consideration of what to value. The market has gone up 300% since the bottom in 2009, and it has climbed what we think of as the “Wall of Worry” [or overcoming a host of negative factors.] If people hadn’t sold stocks in 2008, just held on, they would have hit bottom, and then they would have risen steadily for all of these years.
“The market has gone up 300% since the bottom in 2009, and it has climbed what we think of as the ‘Wall of Worry’.”
We’re going on nearly a decade of the market going up, and I would say that economies continue to operate, people continue to need goods and services, and we just calmly think through it. … We’ve added millions of jobs since the bottom of the recession. The U.S. dollar is strong, and this country has become the safe haven for investors all around the world to put their assets in as well. That can be, in a sense, a way to calm people down who are afraid, because it’s very scary when you think about the risks that we are constantly confronted with and bombarded by in the news, which are disturbing and do make us pause. But we have to be more calm and considered about investing and think, “well this might not be so bad.”
… I think it’s a testament to the ability of economies, and underlying commercial endeavors, to keep going through this. We persevere, we continue to go to work, and people have to be aware that risk-taking is part of their experiences. Your experience, mine, everyone around us. We can’t be afraid. We can’t be hiding in a shell, thinking “I don’t know what to do, the market could collapse and I could lose 50% of my money!” This is what keeps people from investing, and so their choice is getting 0.06% on their savings account. That’s the option. Is that good? I mean, I don’t think it’s so good.
Knowledge@Wharton: With the concept of risk-taking, you have to look at it from the business perspective, you have to look at it in your investments, but you also have to look at it in life.
Firestone: What I tried to do in my book is describe these tenets of risk-taking that apply across business and investing in life. There are four tenets: right-sizing the risk; right-timing; relying on knowledge and experience; and remaining skeptical of promises and projections. Those apply to investing tremendously. I think all the time about the right size of the positions that we take in companies; whether it’s the right time, because timing of when you buy or sell is critically important. How much we know about the investment, and then being skeptical about what we hear from Wall Street or what we might hear from the companies.
But if you think about how we apply those in our life, take just some simple examples.
Right-sizing: If you’re going to buy a house, you want to make sure that you’ve got a house that’s the right size, not too big, not too small. Otherwise, it will be a mistake. It’s getting a mortgage that’s of a size you can handle; the size of your investment in that property is really important. So that’s something that everybody deals with involving size, whether you’re buying a house or you’re renting a place.
Right-timing: You definitely don’t want to open an ice cream shop in November, if you have a choice. If you’re living in the Northeast, or you’re living in Philadelphia, you would rather open it in April or May. Timing affects many decisions, no matter what they are. When you’re getting married, when you’re getting engaged, when you’re having children, timing is just a factor. Sometimes it’s more important or less, but it’s definitely a risk factor.
“There are four tenets: right-sizing the risk; right-timing; relying on knowledge and experience; and remaining skeptical of promises and projections.”
Relying on knowledge and experience: You don’t want to, for example, take on an intern at your show if that person has only worked in an art gallery, and they say, “Oh, but I’m really, really interested in broadcasting and radio” but they’ve shown no interest before. Is that something you should know about or not? I would say yes, because it’s a risk that you hire somebody who isn’t capable and doesn’t show any aptitude. So you’re relying on knowledge and experience.
Remaining skeptical: If you’ve got a buddy and you’re out to dinner with him, and he says, “I’ve got a great new idea for a restaurant and bar that I would like to open downtown, and I would like you to invest this many thousands of dollars.” If you don’t ask a few questions about that, it would be pretty illogical. You better not be too credulous and say, “Hey, Jason’s a great guy, I really think that he’ll be a great bar owner.” That would be silly — and it would be a lot of risk. That’s exposure to danger and uncertainty; that’s what risk is. You do need to be exposed to uncertainty to make any money or to make the right decision, but if you don’t think through why it might go wrong, you would be pretty gullible.
Knowledge@Wharton: One of the areas of risk that is brought up a lot now, especially in the last decade, is the risk of leaving a job and either going to another or starting your own business. And you had that decision to make. You were working at Fidelity Investments, and then you went out on your own. What was that process like for you, in terms of factoring in all of the risks that potentially could be there in terms of starting your own firm?
Firestone: There’s definitely risk every time you start a business or take a new job. The first thing that I had to decide – as everybody does – was, can I handle it financially? If you leave a job and you have a better paying job, well, you’ve solved that problem. If you leave a really good job — I had a great job at Fidelity, I managed the Destiny Fund and the Large Cap Fund, and I had been there for 22 years, I was a shareholder, it was really a wonderful, wonderful position, and I loved Fidelity and still do. But I was starting a business with a partner, and we weren’t going to take any salary for at least a year, he and I. But I saved money, and Fidelity was going to buy back my stock. I was in a pretty good position financially to do it. But sometimes people aren’t, and you have to consider that.
For me, that wasn’t the biggest problem. I think in the case of somebody who has an established career, reputational risk is a big factor. That was something we had to think through: Could we handle it if our new company just bombed, if we had a terrible performance? What if we were able to attract some clients, and then we did a very bad job for them? And that was where we had to have some confidence in our abilities in the past. My partner, David, had been a partner at Wellington, he had managed the MIT Endowment. I had managed billions of dollars. The two of us had many years of experience, and we hired a couple of younger partners to help us who had experience as well, and we were pretty confident. We had to give it a chance. But we were confident and it did work.
“We can’t be afraid … thinking ‘I don’t know what to do, the market could collapse and I could lose 50% of my money!’
You have to rely on the skills and experiences that you’ve had to see: Have I been able to achieve this in the past? And is anybody going to pay me to do it? They pay other people to do it; why are they going to pay me? And we decided: We have a track record, people are going to give us a chance, and they might like us and not be so happy with their investment advisors that they had. That’s true in many businesses. People are willing to say, “I’ve had an experience that isn’t so great — with a lawyer, an accountant, an investment professional — and I think now is the time I’m going to seek somebody else.”
What turned out to be an amazing but hidden benefit to us was that 2008 and 2009, when people lost a lot of money, shook up the industry, so there were many people who had been very happy in 2005, 2006, and 2007 looking for new investment managers. That actually helped us in a strange way. We got a lot of business as a result, because we had a very good performance our first few years, and people would say, “these people have been in business for a few years, done well, and they have strong backgrounds.” So that helped.
We’ve been very lucky and fortunate for having a core group of people who have stuck with Aureus, and we’ve added partners and grown. We started with our money. I couldn’t take my clients from Fidelity; I had a non-compete, I wasn’t going to do it. You just grow, and we are up to $1.5 billion, so I’m knocking on my desk here that it’s worked out OK.
Knowledge@Wharton: One of the things that you advise in the book is to always have a healthy skepticism. You just can’t buy in to everything 100% of the time.
Firestone: You can’t buy into everything [even] 10% of the time. I mean, if someone says to you, “Isn’t it a beautiful day?” Well, you look outside and you see a blue sky, I guess the answer is yes, I can agree with that. But there aren’t too many situations like that, and I happen to be a pretty cynical person. I’m cynical and happy. A lot of cynical people are unhappy, but I find it interesting — I guess it is part of my nature to say, “Really? Is that true?”
“You do need to be exposed to uncertainty to make any money or to make the right decision, but if you don’t think through why it might go wrong, you would be pretty gullible.”
I try not to be too negative about it, but having witnessed cases, both with myself and my family and friends, where people have gone into investments, relationships, exposures in business – gone into those not being skeptical, just being gullible, and thinking for sure that it’s going to work, and it has been a terrible mistake. And the repercussions can be just so much worse than anticipated. It’s just not so hard to spend a couple of hours asking questions and doing some research on your own. Everybody does it when they buy a car. Think about it: Nobody buys a car without looking at Consumer Reports, or going through the Internet, and asking a lot of their friends about a car they just bought, or what they think about this or that car. People spend the amount of time and effort on buying a car that they should for sure spend if they are taking on a partner in a business, buying a business, starting a new job — but they just don’t do it.
Knowledge@Wharton: You talk about a variety of companies in the book that you’ve dealt with. Halliburton is one, and it’s interesting because of how Halliburton gained a reputation within the oil industry, and then that reputation got quite sullied. We’re talking about an industry that has gone through an amazing downturn because of the price of oil falling so quickly; it’s built up back up a little bit, but it’s not even close to the levels it was. Talk a little bit about that relationship you have with Halliburton, and the process of assessing the risk that you had to deal with in terms of Halliburton as an investment possibility.
Firestone: Halliburton has been a very interesting case in volatility. Volatility as it relates to the price of oil, since it is one of the largest oil service companies in the world. So its clients are oil companies, oil and gas companies, drilling companies, integrated oil companies. And also, volatility as it relates to the risks about its environmental treatment, and concerns that have manifested through lawsuits and congressional hearings. And [former vice president] Dick Cheney as the leader of Halliburton was controversial. So it has faced volatility related to investigations and injunctions, etc.
Halliburton’s story was one in which we had bought the stock at a very good price. The price of oil was going up, Halliburton’s stock was going up, we were making money with it, and then we had a client who was very environmentally conservative. It was a nonprofit that owned conservation land. Because of that mission — preserving land and the environment — they didn’t want to own any oil and gas stocks. We had bought Halliburton before they told us, “Oh, sorry we don’t want to own any of these,” and we discussed internally whether we should sell it or not. The stock had been going up, and my partners and I decided, well, they are giving us a year. They said, “You can own it until the end of the year,” and it was the spring. So we had plenty of time.
But here’s the way we analyzed it: We would get no credit from this group for owning Halliburton if the stock went up and then we sold it at the end of the year because they just didn’t want it. They would have hated it if we didn’t sell it and the stock went down. We would have lost that account, because that was more important to them than the price of the stock. What mattered was the environment, and that Halliburton had been accused of polluting the environment, had to pay a big fine. That was something that could not exist within the framework of their mission as a conservation-land owner. We had to sell it.
Now, we got very lucky. We sold it, and for a while the stock went up, and then it started to go down, and, as you know the price of oil collapsed. So when we sold Halliburton, we had a very big gain on the stock, and then it, as I said, went up a little higher to $73 a share. I’m going to just give you the price, it’s interesting. In July 2014, the stock was $73, and it hit a bottom of $29 in February of this year. So it was really lucky we sold it.

‘Rogue Algorithms’ and the Dark Side of Big Data

alorithmMost of us, unless we’re insurance actuaries or Wall Street quantitative analysts, have only a vague notion of algorithms and how they work. But they actually affect our daily lives by a considerable amount. Algorithms are a set of instructions followed by computers to solve problems. The hidden algorithms of Big Data might connect you with a great music suggestion on Pandora, a job lead on LinkedIn or the love of your life on Match.com.

These mathematical models are supposed to be neutral. But former Wall Street quant Cathy O’Neil, who had an insider’s view of algorithms for years, believes that they are quite the opposite. In her book, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, O’Neil says these WMDs are ticking time-bombs that are well-intended but ultimately reinforce harmful stereotypes, especially of the poor and minorities, and become “secret models wielding arbitrary punishments.”
Models and Hunches
Algorithms are not the exclusive focus of Weapons of Math Destruction. The focus is more broadly on mathematical models of the world — and on why some are healthy and useful while others grow toxic. Any model of the world, mathematical or otherwise, begins with a hunch, an instinct about a deeper logic beneath the surface of things. Here is where the human element, and our potential for bias and faulty assumptions, creeps in. To be sure, a hunch or working thesis is part of the scientific method. In this phase of inquiry, human intuition can be fruitful, provided there is a mechanism by which those initial hunches can be tested and, if necessary, corrected.
O’Neil cites the new generation of baseball metrics (a story told in Michael Lewis’s Moneyball) as a healthy example of this process. Moneyball began with Oakland A’s General Manager Billy Beane’s hunch that using performance metrics such as runs batted in (RBIs) were overrated, while other more obscure measures (like on base percentage) were better predictors of overall success. Statistician Bill James began crunching the numbers and putting together models that Beane could use in his decisions about which players to acquire and hold onto, and which to let go.
While sports enthusiasts love to debate the issue, this method of evaluating talent is now widely embraced across baseball, and gaining traction in other sports as well. The Moneyball model works, O’Neil says, for a few simple reasons. First, it is relatively transparent: Anyone with basic math skills can grasp the inputs and outputs. Second, its objectives (more wins) are clear, and appropriately quantifiable. Third, there is a self-correcting feedback mechanism: a constant stream of new inputs and outputs by which the model can be honed and refined.
These WMDs are ticking time-bombs that are well-intended but ultimately reinforce harmful stereotypes, especially of the poor and minorities.
Where models go wrong, the author argues, all three healthy attributes are often lacking. The calculations are opaque; the objectives attempt to quantify that which perhaps should not be; and feedback loops, far from being self-correcting, serve only to reinforce faulty assumptions.
WMDs on Wall Street
After earning a doctorate in mathematics at Harvard and then teaching at Barnard College, O’Neil got a job at the hedge fund D.E. Shaw. At first, she welcomed the change of pace from academia and viewed hedge funds as “morally neutral — scavengers in the financial system, at worst.” Hedge funds didn’t create markets like those for mortgage-backed securities, in which complicated derivatives played a key part in the financial crisis — they just “played in them.”
But as the subprime mortgage crisis spread, and eventually engulfed Lehman Bros., which owned a 20% stake in D.E. Shaw, the internal mood at the hedge fund “turned fretful.” Concern grew that the scope of the looming crisis might be unprecedented — and something that couldn’t be accounted for by their mathematical models. She eventually realized, as did others, that math was at the center of the problem.
The cutting-edge algorithms used to assess the risk of mortgage-backed securities became a smoke screen. Their “mathematically intimidating” design camouflaged the true level of risk. Not only were these models opaque; they lacked a healthy feedback mechanism. Importantly, the risk assessments were verified by credit-rating agencies that collected fees from the same companies that were peddling those financial products. This was a mathematical model that checked all the boxes of a toxic WMD.
Disenchanted, O’Neil left Shaw in 2009 for RiskMetrics Group, which provides risk analysis for banks and other financial services firms. But she felt that people like her who warned about risk were viewed as a threat to the bottom line. A few years later, she became a data scientist for a startup called Intent Media, analyzing web traffic and designing algorithms to help online companies maximize e-commerce. O’Neil saw disturbing similarities in the use of algorithms in finance and Big Data.
In both worlds, sophisticated mathematical models lacked truly self-correcting feedback. They were driven primarily by the market. So if a model led to maximum profits, it was on the right track. “Otherwise, why would the market reward it?” Yet that reliance on the market had produced disastrous results on Wall Street in 2008. Without countervailing analysis to ensure that efficiency was balanced with concern for fairness and truth, the “misuse of mathematics” would only accelerate in hidden but devastating ways. O’Neil left the company to devote herself to providing that analysis.
Misadventures in Education
Ever since the passage of the No Child Left Behind Act in 2002 mandating expanded use of standardized tests, there has been a market for analytical systems to crunch all the data generated by those tests. More often than not, that data has been used to try to identify “underperforming” teachers. However well-intentioned, O’Neil finds these models promise a scientific precision they can’t deliver, victimizing good teachers and creating incentives for behavior that does nothing to advance the cause of education.
In 2009, the Washington D.C. school system implemented a teacher assessment tool called IMPACT. Using a complicated algorithm, IMPACT measured the progress of students and attempted to isolate the extent to which their advance (or decline) could be attributed to individual teachers. The lowest-scoring teachers each year were fired — even when the targeted teachers had received excellent evaluations from parents and the principal.
O’Neil examines a similar effort to evaluate teacher performance in New York City. She profiles a veteran teacher who scored a dismal 6 out of 100 on the new test one year, only to rebound the next year to 96. One critic of the evaluations found that, of teachers who had taught the same subject in consecutive years, 1 in 4 registered a 40-point difference from year to year.
The cutting-edge algorithms used to assess the risk of mortgage-backed securities became a smoke screen.
There is little transparency in these evaluation models, O’Neil writes, making them “arbitrary, unfair, and deaf to appeals.” Whereas a company like Google has the benefit of large sample sizes and constant statistical feedback allowing them to immediately identify and correct errors, teacher evaluation systems attempt to render judgments based on annual tests of just a few dozen students. Moreover, there is no way to assess mistakes. If a good teacher is wrongly fired and goes on to be a great teacher at another school, that “data” is never accounted for.
In the Workplace
Teachers are hardly alone. In the face of slow growth, companies are looking everywhere for an edge. Because personnel decisions are among the most significant for a firm, “workforce management” has become big business – in particular, programs that screen potential employees and promise to take “the guesswork” out of hiring. Increasingly, these programs utilize personality tests in an effort to automate the hiring process. Consulting firm Deloitte estimates that such tests are used on 60% to 70% of prospective employees in the U.S., nearly double the figure from five years ago.
The prevalence of personality tests runs counter to research that consistently ranks them as poor predictors of future job performance. Yet they generate raw data that can be plugged into algorithms that provide an illusion of scientific precision, all in the service of an efficient hiring process. But as O’Neil writes, these programs lack transparency and rejected employees rarely know why they’ve been flagged, or even that they’ve been flagged at all. They also lack a healthy feedback mechanism — a means of identifying errors and using those mistakes to refine the system.
Once on the job, a growing number of workers are subject to another iteration of Big Data, in the form of scheduling software. Constant streams of data — everything from the weather to pedestrian patterns — can be used, for example, to optimize staffing at a Starbucks café. A New York Times profile of a single mother working her way through college as a barista explored how the new technology can create chaos, especially in the lives of low-income workers. According to U.S. government data, two-thirds of food service workers consistently get short-term notice of scheduling changes.
This instability can have far-reaching and insidious effects, O’Neil says. Haphazard scheduling can make it difficult to stay in school, keeping vulnerable workers in the oversupplied low-wage labor pool. “It’s almost as if the software were designed expressly to punish low-wage workers and keep them down,” she writes. And chaotic schedules have ripple effects on the next generation as well. “Young children and adolescents of parents working unpredictable schedules,” the Economic Policy Institute finds, “are more likely to have inferior cognition and behavioral outcomes.”
Following the exposé in the Times, legislation was introduced in Congress to rein in scheduling software, but didn’t go anywhere.
Crime and Punishment
Often, as with both educational reform and new hiring practices, the use of Big Data initially comes with the best of intentions. Recognizing the role of unconscious bias in the criminal justice system, courts in 24 states are using computerized models to help judges assess the risk of recidivism during the sentencing process. By some measures, according to O’Neil, this system represents an improvement. But by attempting to quantify and nail down with precision what are at root messy human realities, she argues, they create new problems.
A new, pseudoscientific generation of scoring has proliferated wildly. … Yet unlike FICO scores, they are “arbitrary, unaccountable, unregulated, and often unfair.”
One popular model includes a lengthy questionnaire designed to pinpoint factors related to the risk of recidivism. Questions might inquire about previous police incidents; and, given how much more frequently young black males are stopped by police, such a question can come to be a proxy for race, even while the intention is to reduce prejudice. Additional questions, such as whether the respondent’s friends or relatives have criminal records, would elicit an objection from a defense attorney if raised during a trial, O’Neil points out. But the opaqueness of these complicated risk models shields them from proper scrutiny.
Another trend is the use of crime prediction software to anticipate crime patterns, and adjust police deployment accordingly. But one underlying problem with WMDs, the author argues, is that they essentially become data hungry, confusing more data with better data. And in the case of crime prediction software, even though the stated priority is to prevent violent and serious crime, the data generated by petty “nuisance” crimes can overwhelm and essentially prejudice the system. “Once the nuisance data flows into a predictive model, more police are drawn into those neighborhoods, where they’re more likely to arrest more people.” These increased arrests seem to justify the policy in the first place, and in turn feed back into the recidivism models used in sentencing: a destructive and “pernicious feedback loop,” as O’Neil characterizes it.
The Cancer of Credit Scores
In the wake of a financial crisis that was at the very least exacerbated by loose credit, banks are understandably trying to be more rigorous in their assessment of risk. An early risk assessment algorithm, the well-known FICO score, is not without its problems; but for the most part, O’Neil writes, it is an example of a healthy mathematical model. It is relatively transparent; it is regulated; and it has a clear feedback loop. If default rates don’t jibe with what the model predicts, credit agencies can tweak them.
In recent years, however, a new, pseudoscientific generation of scoring has proliferated wildly. “Today we’re added up in every conceivable way as statisticians and mathematicians patch together a mishmash of data, from our zip codes and internet surfing patterns to our recent purchases.” Crunching this data generates so-called “e-scores” used by countless companies to determine our creditworthiness, among other qualities. Yet unlike FICO scores, they are “arbitrary, unaccountable, unregulated, and often unfair.”
A huge “data marketplace” has emerged in which credit scores and e-scores are used in a variety of applications, from predatory advertising to hiring screening. In this sea of endless data, the author contends, the line between legitimate and specious measures has become hopelessly blurred. As one startup proclaims on its website, “All data is credit data.”
It’s all part of a larger process in which “we’re batched and bucketed according to secret formulas, some of them fed by portfolios loaded with errors.” According to the Consumer Federation of America, e-scores and other data are used to slice and dice consumers into “microsegments” and to target vulnerable groups with predatory pricing for insurance and other financial products.
And as companies gain access to GPS and other mobile data, the possibilities for this kind of micro-targeting will only grow exponentially. As insurance companies and others “scrutinize the patterns of our lives and our bodies, they will sort us into new types of tribes. But these won’t be based on traditional metrics, such as age, gender, net worth, or zip code. Instead, they’ll be behavioral tribes, generated almost entirely by machines.”
Reforming Big Data
In her conclusion, O’Neil argues we need to “disarm” the Weapons of Math Destruction, and that the first step for doing so is to conduct “algorithmic audits” to unpack the black boxes of these mathematical models. They are, again, opaque and impenetrable by design, and often protected as proprietary intellectual property.
Toward this end, Princeton University has launched WebTAP, the Web Transparency and Accountability Project. Carnegie Mellon and MIT are home to similar initiatives. In the end, O’Neil writes, we must realize that the mathematical models which have penetrated almost every aspect of our lives “are constructed not just from data but from the choices we make about which data to pay attention to… These choices are not just about logistics, profits, and efficiency. They are fundamentally moral.”

FEMSA, la mayor embotelladora de Coca-Cola, se expande en Brasil

La empresa mexicana compra a su rival Vonpar para dominar el 49% del sistema Coca-Cola brasileño


A la mexicana FEMSA no le basta con ser el mayor embotellador de productos Coca-Cola en el mundo, quiere más. La empresa ha anunciado la compra de la brasileñaVonpar, con lo que aumenta su volumen de operación en el país en un 25% hasta llegar a dominar casi la mitad del mercado. La adquisición, realizada a través de la subsidaria Spal, alcanzó un valor de 1.120 millones de dólares (3.578 millones de reales). Actualmente Vonpar opera tres plantas embotelladoras, cinco centros de distribución, cuenta con 4.000 empleados y atiende a más de 15 millones de consumidores.
“Creemos firmemente que hemos construido una plataforma incomparable para continuar capitalizando las oportunidades de crecimiento a mayor escala”,afirmó el director general de FEMSA, John Santa María. La empresa realizará pagos en efectivo, un intercambio de acciones y la firma de un pagaré para completar la compra. Esta compleja operación fue destacada por la compañía como una muestra de su flexibilidad para llevar a cabo fusiones y asegura que tienen planes para seguir creciendo.

Aunque la compra fue aprobada por el consejo de administración de FEMSA, todavía está sujeta al visto bueno del Gobierno de Brasil y de la empresa matriz de Coca-Cola. En los últimos 12 meses, terminados en junio, Vonpar vendió 190 millones de cajas de bebidas y consiguió alrededor de 627 millones de dólares en ingresos netos.
FEMSA opera principalmente en MéxicoVenezuela y Brasil, los tres países que más ganancias le generan. Sin embargo, también tiene presencia importante enCentroaméricaColombiaArgentina y FIlipinas entre otros. En los últimos años la empresa ha dejado claro sus deseos de expansión a través de varias adquisiciones e incursiones en otros tipos de giros comerciales.