The New Gold Rush? Wall Street Wants your Data

 

trading-data

 
A few months ago, Foursquare achieved an impressive feat by predicting, ahead of official company results, that Chipotle’s Q1 2016 sales would be down nearly 30%. Because it captures geo-location data from both check-ins and visits through its apps, Foursquare was able to extrapolate foot-traffic stats that turned out to be very accurate predictors of financial performance.
 
That a social media company could be building a data asset of immense value to Wall Street is part of an accelerating trend known as “alternative data”. As just about everything in our lives is getting sensed and captured by technology, financial services firms have been turning their attention to startups, with the hope of mining their data to extract the type of gold nuggets that will enable them to beat the market.
 
Could working with Wall Street be a business model for you?
 
The opportunity is open to a wide range of startups.  Many tech companies these days generate an interesting “data exhaust” as a by-product of their core activity.  If your company offers a payment solution, you may have interesting data on what people buy. A mobile app may accumulate geo-location data on where people shop or how often they go to the movies.  A connected health device may know who gets sick when and where.  A commerce company may have data on trends and consumer preferences. A SaaS provider may know what corporations purchase, or how many employees they hire, in which region. And so on and so forth.
 
At the same time, this is a tricky topic, with a lot of misunderstandings. The hedge fund world is very different from the startup world, and a lot gets lost in translation.  Rumors about hedge funds paying “millions” for data sets abound, which has created a distorted perception of the size of the financial opportunity.  A fair number of startups I speak with do incorporate idea of selling data to Wall Street into their business plan and VC pitches, but how that would work exactly remains generally very fuzzy.
 
If you’re one of the many startups sitting on a growing data asset and trying to figure out whether you can make money selling it to Wall Street, this post is for you: a deep dive to provide context, clarify concepts and offer some practical tips.
 

Continue reading “The New Gold Rush? Wall Street Wants your Data”

Investing in Frontier Tech

drone

Over the last few months, the usual debate around unicorns and bubbles seems to have been put on hold a bit, as fears of a major crash have thankfully not materialized, at least for now.

Instead another discussion has emerged, one that’s actually probably more fundamental. What’s next in tech? Which areas will produce the Googles and Facebooks of the next decade?

What’s prompting the discussion is a general feeling that we’re on the tail end of the most recent big wave of innovation, one that was propelled by social, mobile and cloud.  A lot of great companies emerged from that wave, and the concern is whether there’s room for a lot more “category-defining” startups to appear.  Does the world need another Snapchat? (see Josh Elman’s great thoughts here).  Or another marketplace, on-demand company, food startup, peer to peer lending platform? Isn’t there a SaaS company in just about every segment now? And so on and so forth.

One alternative seems to be “frontier tech”: a seemingly heterogeneous group that includes artificial intelligence, the Internet of Things, augmented reality, virtual reality, drones, robotics, autonomous vehicles, space, genomics, neuroscience, and perhaps the blockchain, depending on who you ask.

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Phosphorus and the Rise of the New Genomics Startup

 

As we are perhaps reaching the end of a cycle of innovation in tech – the one that resulted from the simultaneous emergence of social, mobile and cloud – and collectively pondering what’s next, one of the areas I’ve found particularly exciting recently is the intersection of Big Data and life sciences.

A little over two years ago, in connection with my investment in Recombine, a genomics startup, I wrote (here) about another powerful combination of trends: the sharp drop in the cost of sequencing the human genome, the maturation of Big Data technologies, and the increasing commoditization of wet lab work.

The fundamental premise was, and still very much is, as follows:

Continue reading “Phosphorus and the Rise of the New Genomics Startup”

The Power of Data Network Effects

In the furiously competitive world of tech startups, where good entrepreneurs tend to think of comparable ideas around the same time and “hot spaces” get crowded quickly with well-funded hopefuls, competitive moats matter more than ever.  Ideally, as your startup scales, you want to not only be able to defend yourself against competitors, but actually find it increasingly easier to break away from them, making your business more and more unassailable and leading to a “winner take all” dynamic.  This sounds simple enough, but in reality many growing startups, including some well-known ones, experience exactly the reverse (higher customer acquisition costs resulting from increased competition, core technology that gets replicated and improved upon by competitors that started later and learned from your early mistakes, etc.).

While there are various types of competitive moats, such as a powerful brand (Apple) or economies of scale (Oracle), network effects are particularly effective at creating this winner takes all dynamic, and have been associated with some of the biggest success stories in the history of the Internet industry.

Network effects come in different flavors, and today I want to talk about a specific type that has been very much at the core of my personal investment thesis as a VC, resulting from my profound interest in the world of data and machine learning: data network effects.

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Sketchfab and the democratization of 3D content

We’re about to see a lot more 3D content in our digital lives.  Various trends, some years in the making, are now intersecting to make this a near-term reality.

On the production side, 3D has of course existed for many years – this has been, in particular, the world of Computer Aided Design (CAD), which originated in part from MIT’s Sketchpad project in the early sixties.  In one form or another, 3D has been used as a professional format across many industries, such as architecture, engineering, construction, and entertainment. Creation of 3D content (even for consumer-facing products like gaming) has remained largely the province of a comparatively small group of specialized professionals. Continue reading “Sketchfab and the democratization of 3D content”

Hardware Startups: The VC Perspective

Among all the excitement for the Internet of Things and the resurgence of hardware as an investable category, venture capitalists, many of whom new to the space, have been re-discovering the opportunities and challenges of working alongside entrepreneurs to build hardware companies.  Below are the slides that David Rogg and I prepared for the recent Connected Conference, a great global event held in Paris.  They’re a good snapshot of how someone like me thinks about the hardware space, mid-2015.

 

 

The “Straight to A” Round

The venture financing path has evolved incredibly fast over the last 18 months. In this very busy financing market, what used to be a reasonably well understood progression from a seed round to a Series A to a Series B, etc. has now morphed into a more complex nomenclature of pre-seeds ($500k or less), crowdfunding rounds (especially for hardware), seeds ($1M-$2M, 6-9 months after the pre-seed), seed primes (an extra $1M or so, 12-18 months after the seed), Series A (now routinely $10-$12M in size, occasionally up to $15M), Series A-1, Series B, C, D, E, F etc. (as companies remain private longer).

The latest entrant in this rapidly evolving nomenclature seems to be what I’d call the “Straight to A” round, where the founders skip the seed stage altogether and raise directly a $5M-$10M Series A, often before building anything, sometimes even before incorporating a company. I had seen it here and there in the past, but it now seems to have become an accelerating trend. Continue reading “The “Straight to A” Round”

The Astounding Resurrection of AI [Slides]

A few days ago, I was invited to speak at a Yale Entrepreneurship Breakfast about about one of my favorite areas of interest, Artificial Intelligence.  Here are the slides from the talk — a primer on how AI rose from of the ashes to become a fascinating category for startup founders and venture capitalists.  Very much a companion to my earliest post about our investment in x.ai.   Many thanks to my colleague Jim Hao, who worked with me on this presentation.

x.ai and the emergence of the AI-powered application

AI is experiencing an astounding resurrection.  After so many broken promises, the term “artificial intelligence” had become almost a dirty word in technology circles.  The field is now rising from the ashes.  Researchers who had been toiling away in semi-obscurity over the last few decades have suddenly become superstars and have been aggressively recruited by the largest Internet companies:  Yann LeCun (see his recent talk at our Data Driven NYC event here) by Facebook; Geoff Hinton by Google; Andrew Ng by Baidu.  Google spent over $400 million to acquire DeepMind, a 2 year old secretive UK AI startup. The press and social media are awash with thoughts on AI.  Elon Musk cautions us against its perils.
 
What’s different this time? As Irving Wladawsky-Berger pointed out in a Wall Street Journal article, “a different AI paradigm emerged. Instead of trying to program computers to act intelligently–an approach that hadn’t worked because we don’t really know what intelligence is– AI now embraced a statistical, brute force approach based on analyzing vast amounts of information with powerful computers and sophisticated algorithms.”  In other words, the resurgence of AI is partly a child of Big Data, as better algorithms (in particular, what’s known as “deep learning”, pioneered by LeCun and others) have been enabled by larger than ever datasets and the ability to process those datasets at scale at reasonable cost.

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Lending Club IPO: Nice Guys Don’t Finish Last, and Other Lessons

The superb Lending Club success story is what the startup world is all about: a software-based reinvention of massive and inefficient industry; a product that puts consumers first and delivers undeniable benefits ; and an entrepreneurial mega-hit that brings incredible riches and returns to its founder and investors.

In some ways, Lending Club is a classic Silicon Valley story; in some other ways, it is pretty atypical. As a friend of Renaud Laplanche’s for over 20 years, I have had a chance to witness from up close some parts of his journey with Lending Club. It is full of interesting lessons for entrepreneurs and the tech industry in general:

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A Few Non-Obvious Things I Learned as a New VC

I joined FirstMark as a partner a little over 18 months ago now, and it’s been a thrilling ride.  It’s also felt like a steep learning curve: lots of nuances, and lots of institutional memory to absorb.  Below is a glimpse into what I’ve seen happening “behind the scenes” on the VC’s side to the table – stuff that was not obvious to me in my former roles as entrepreneur, angel investor or corporate incubator/strategic.

1.  A real commitment.  Like for many new VCs operating at the Series A level,  the biggest shock to the system was the realization that one gets to make very, very few investments – basically two or three a year.  You quickly find yourself having to choose between a number of opportunities you really like. Making a new investment is a big deal, and a decision that one has to live with for years to come. You also get to work with an entrepreneur very closely, and live up to their level of trust and expectations.  In a way, it feels like a marriage, except one where divorce is not really an option.  There’s an occasionally brutal asymmetry between the fundraising process (which can be quick and intense, especially if it is competitive) and what happens afterwards, which is a lot of hard work over a long period of time.  Both the entrepreneur and the VC would be well advised to get to know who they’re about to work with for the next few years of their lives.  You don’t need to be friends with your VC (although friendships develop over years of working together), but you do need a core of mutual respect and commitment to hard work and excellence, as well as a shared vision of the future.

 

2.  Conviction, not data. Early stage VCs (seed and Series A) operate in a daunting scarcity of data points. You get a few numbers, a few meetings with the founders, and also you see a bunch of companies, so you get a sense of how an opportunity compares to others. Other than that, and for all the thinking about data driven VC investing, the reality is that investment decisions are mostly about storytelling and forming personal conviction – painting a vision of the world where a company becomes hugely important. One consequence for entrepreneurs to bear in mind: VCs are really hungry for any data point that can help them.  It’s certainly true about the “big things” (revenue, traction, etc., especially as they compare to other opportunities the VC is seeing), but it’s also true for the “small things”, which can become become disproportionately important  (particularly if they add up), as the VC is trying to piece together a story: whether that’s signs of possible greatness (e.g., your former boss really insisted on putting $50k in your new venture) or trouble (being rude to the receptionist, consistently taking forever to reply to emails, etc).

 

3.  Not a single way to reach conviction:  VCs come in all sorts of flavors – some successful investors are deeply analytical (build roadmaps and investment thesis, get into details) while others are more “social” (relying on networks of trusted experts they’ve built over years to help them identify signal from noise).  What’s been interesting to me is that you find very successful investors on both sides of the spectrum, and also find those different types happily co-existing within the same firm.   Naturally, everyone is also heavily influenced by their professional history (what worked for them in the past as an operator or investor), as well as all sorts of personal criteria that often have nothing to do with the intrinsic merits of an opportunity – for example, the bar for a new investment will be naturally higher if an investor is already on 12 boards and always on the brink of being overwhelmed by the amount of work they face.   For the entrepreneur, it’s always a good idea to understand who they’re pitching to, as in any sales process, as an investor’s personal circumstances and background matter immensely.

The French Startup Ecosystem: At a Tipping Point

I know, when thinking about hotbeds of startup innovation, France doesn’t exactly jump to mind. Sure, there are interesting things happening in European tech – in London, or Berlin (which I covered here). Or Finland. But France? Ask U.S investors and entrepreneurs, and you’ll hear more or less the same thing: high taxes. Impossible to fire people. Government intervention. Language barrier. Fear of failure. Strikes. The country of the the 35 hour law, where people are prohibited by law to answer email past 6pm.

Yet things have started to accelerate meaningfully in French early stage tech, particularly in the last two or three years. I was fortunate to be recently invited as part of a delegation of US VCs and media guests to spend a few days in Paris to meet with local entrepreneurs and VCs, as well as President Hollande and other senior members of the French government. As a Frenchman who has spent his entire professional career in the US, I’m perhaps more cynical than most about those matters, but I came back from my trip genuinely intrigued by the potential of the French tech scene.

For anyone who cares to look, the fairly obvious conclusion is that there’s a huge gap between perception and reality, when it comes to the French startup ecosystem. Very significant progress has been made on all fronts – more interesting startups, more funding, lots more talent rushing into the sector, improved legistation, etc. – yet the word has not caught on.

Continue reading “The French Startup Ecosystem: At a Tipping Point”

Can the Bloomberg Terminal be “Toppled”?

In the eye of some entrepreneurs and venture capitalists, the Bloomberg terminal is a bit of an anomaly, perhaps even an anachronism.  In the era of free information on the Internet and open source Big Data tools, here’s a business that makes billions every year charging its users to access data that it generally obtains from third parties, as well as the tools to analyze it.  You’ll hear the occasional jab at its interface as reminiscent of the 1980s.  And at a time of accelerating “unbundling” across many industries, including financial services, the Bloomberg terminal is the ultimate “bundling” play: one product, one price, which means that that the average user uses only a small percentage of the terminal’s 30,000+ functions.  Yet, 320,000 people around the world pay about $20,000 a year to use it.

If you think that this sounds like a perfect opportunity for disruption or “unbundling” at the hand of nimble, aggressive startups, you’re not alone.  I spent four years at Bloomberg Ventures, and this was a topic that I heard debated countless times before, during and after my tenure there. Most recent example: a well written article in Institutional Investor a few weeks ago declared the start of “The Race to Topple Bloomberg“, with a separate article highlighting my friends at Estimize and Kensho as startups that “Take Aim at Bloomberg“.

Yet, over the years, the terminal has seen its fair share of would be disruptors come and go. Every now and then, a new wave of financial data startups seems to be appearing, attempting to build businesses that, overtly or not, compete with some parts of the Bloomberg terminal.  Soon enough, however, those companies seem to disappear, through failure, pivot or acquisition.

What gives? And where are the opportunities for financial data startups?

Frontal assault: good luck

To start, Bloomberg is not exactly your run-of-the-mill, lazy incumbent. Perhaps I drank too much of the Kool-Aid while I was there, but I left the company very impressed.  Bloomberg, which was itself a startup not that long ago, comes armed with a powerful brand, deep pockets, a fiercely competitive culture, a product that results from billions of dollars of R&D investment over the years, and a technology platform that basically never goes down or even slows down, supported by generally excellent customer service.

But great incumbents have been disrupted before.  So there is perhaps another set of less immediately apparent reasons why the terminal has so far been very resilient to disruption by startups:

1.  It is protected by strong network effects.  One surprisingly misunderstood reason to the long term success of the Bloomberg terminal is that, beyond the data and analytics, it is fundamentally a network.  In fact, it was probably the first ever social network, long before the term was coined. Although some believe that its cachet as a status symbol is starting to erode, “the Bloomberg” (as it is often called) has been for decades the way you communicate with other finance professionals (for legitimate or not so legitimate reasons).  In its relevant target market, everyone is on it and uses it all day to communicate with colleagues, clients and partners. Web based services (Facebook, Dropbox, Gmail), often banned in financial services companies, haven’t made much of a dent in that, at least for desktop communication.

2.  It is an aggregation of niche products.  In the world of financial data, there is enough specificity to each asset class (and subsegment thereof) that you need to build a substantially different product for each, which requires deep expertise, as well as a huge amount of effort and money, to address a comparatively small user base (sometimes just a few tens of thousands of people around the world).  Bloomberg started with fixed income data and over many years, used its considerable cash flow to gradually conquer other classes (still a work in progress, to this day).  So disrupting the Bloomberg is not as “easy” as coming up with a great one-size-fits-all product.  It would take immense amounts of venture capital money to build a direct competitor across all those niches.

3.  It’s not “just” a technology play.  Providing financial data at scale is not a pure technology play, so it is not a matter of coming up with radically better technology to aggregate and display data, either.  At this stage at least, there is a whole web of human processes, relationships and contracts with underlying data providers that has been put on place over many years.

4.  It’s a mission critical product. This is a key point.  In the financial world, data is used to make gigantic bets, so total accuracy and reliability is an absolute must – which makes people cautious when experimenting with new products, particularly built by a startup.

The Bloomberg terminal business may face macro headwinds, as described in the Institutional Investor piece (dwindling of the number of relevant jobs on Wall Street and a global shift from desktop data to data feeds).  However, as a result of the above, I don’t see the Bloomberg terminal being entirely “toppled” by any one given startup anytime soon, and I think even competing directly with any of its key functionalities (unbundling) is a tall order for startups, even with access to large amount of VC money.  Not that it can’t be done – I just think there are lower hanging fruits out there and some real benefit to position away from the Bloomberg.

Where are the opportunities in financial data?

While I don’t see much opportunity for startups to build a Bloomberg terminal replacement (or a a replacement to Thomson Reuters or Factset, to be clear), I think there are fertile grounds “around” and “below” the terminal – meaning in areas where the company is unlikely to want to go.

Specifically, I believe there are going to be ongoing opportunities to apply some of the quintessential internet concepts and processes (networks, crowdsourcing, etc) as well as new-ish technology (Big Data)  to the world of financial data, including:

1.  Finance networks/communities.  Like the Bloomberg terminal did, some of the more interesting “adjacent” plays opportunities will marry data, tools and community.  Historically, capital markets haven’t seen much of a sharing culture (lots of nuances here, I know), which is in part due to the nature of finance investing itself – however, it’s going to be interesting to see how, at least in certain areas, that culture will evolve as digital natives rise in the ranks of their organizations.  Beyond early entrants Stocktwits and Covestor (which generally target a more casual audience), examples of such professional communities include SumZero, initially for Buy Side analysts but now wider, and more recently Quantopian, an algorithmic trading community where scientifically educated people and other quant types share strategies and algorithms.  Early stage startup ThinkNum thinks financial models should be shared and wants to the “Github” for financial models.  What else can be shared?

2.  App stores. The app store model is an interesting way of leveraging the expertise of a “crowd” of specialized third party developers (Bloomberg launched its own a couple of years ago). OpenFin, for example, provides infrastructure to enable the deployment of in-house app stores, addressing the necessary compliance, security and inter-operability requirements (having data flow from one tool to the other). A combination of an in-house app store infrastructure with some best of breed applications (say, a ChartIQ, which provides HTML5 financial charts, including technical analysis tools) is an interesting approach to target the portion of the market “below” the terminal, as  companies that cannot afford a full on terminal infrastructure could pick and choose the apps they need and have them work in their environment.

3.  Crowdsourced data.  From Estimize (which crowdsources analyst estimates) to Premise (which crowdsources macroeconomic data through an army of people around the world equipped with mobile phones), a whole new way of capturing financial data has emerged. Quandl, a financial data search engine, has aggregated over 8 million financial and economic datasets through both web crawling and crowdsourced, community contributions.  Once such a data platform has been built, could third party developers add analytic and visualization tools on top, essentially resulting in a crowdsourced “terminal” of sorts that would be reliable enough, at least for non mission critical, non real time use cases?

4.  Big Data “insights”: Extracting signal from data is obviously the end game here, and interesting startups are heavily focused on those opportunities, from Dataminr (social data analytics for Wall Street) to Kensho (which is working on “bringing the intelligent assistant revolution to finance”). In terms of market positioning, it is unclear to which extent those technologies compete with the Bloomberg terminal (which, for example, has been very active on the social data front), or potentially complete it.

The big question facing entrepreneurs and VCs alike is how to scale those businesses and turn them into billion dollar companies in a context where solidly entrenched platforms have a stronghold on arguably the juiciest part of the market. But overall I believe that we’re only going to see more startups going after financial data opportunities, with potential for some serious wins – I’m excited to see how it all evolves.

Recombine

The field of bioinformatics is having its “big bang” moment.   Of course, bioinformatics is not a new discipline and it has seen various waves of innovations since the 1970s and 1980s, with its fair share of both exciting moments and disappointments (particularly in terms of linking DNA analysis to clinical outcomes).  But there is something special happening to the industry right now, accelerated by several factors:

•      The cost of full genome sequencing has been dropping precipitously, in fact a lot faster than Moore’s law would have suggested.  Illumina just released brand new machines that make the $1,000 full genome sequencing a realistic possibility.  As a result, an extraordinary amount of data is going to become available at reasonable cost (5.5TB or 6.3 Billion bases… per patient).

•      Big Data technology has had its own, separate evolution, and there is now an arsenal of tools to process and analyze massive amounts of data, at a comparatively cheap cost.

•      Wet lab work has become a more standardized and increasingly automated process, considerably reducing the “friction” involved in collecting and processing physical samples. The cost of setting up biology labs, while still high, is starting to decrease, and molecular techniques are no longer the limiting step in genomic analysis.

As a result of the above, biology is rapidly evolving from being predominantly driven by traditional life sciences research to being largely driven by software and Big Data.  This evolution considerably reduces the capital required to build a successful venture in the space.  It also opens up the field to a new generation of startups run by inter-disciplinarian teams that have at least as much of a software and data science background as a biology background.  A whole new world of bio-hackers is also emerging, from synthetic biology to personalized medicine, the possibilities are immense and the impact on our lives potentially unparalleled.  It is entirely possible that the next generation of great entrepreneurs will be building “biology 2.0” companies, rather than mobile apps.

This opportunity has not been lost on entrepreneurs and the last 3 years or so have seen a rapid acceleration of startup creation, in a wide range of area from diagnostics (Counsyl) to cloud platforms (DNANexus) to lab automation (Benchling, Transcriptic).  Interestingly but not surprisingly considering the above, most of those startups are funded by technology, rather than life sciences, venture capital firms.

Today I’m excited to announce that FirstMark is partnering with Recombine, a New York based startup that very much operates at this intersection between software, Big Data and biology, as its lead Series A investor. Recombine’s CEO, Alex Bisignano, symbolizes this new generation of entrepreneurs who have deep knowledge in multiple technical fields.  He has built around him a great, multi-disciplinarian team, and benefits from the deep industry knowledge and expertise of co-founder Dr. Santiago Munne, the owner of Reprogenetics and pioneer in pre-implantation genetic diagnosis.

Recombine’s core focus is the field of fertility and reproductive genetics, and it has had a spectacular early start with CarrierMap, its first product, generating a profitable multi-million dollar business with a comparatively small seed investment. The CarrierMap test is the most comprehensive, cost-effective, carrier screen on the market, and has already helped thousands of couples to identify and mitigate the risk of passing on serious illnesses to their children.  CarrierMap is sold exclusively through doctors and clinics, it is not a Direct to Consumer product (and therefore falls in a different category than 23andMe).

Beyond this initial focus, Recombine has ambitious plans to fully leverage Big Data technology to help decode the myriad aspects of our genome that are still not well understood. They have already obtained Institutional Review Board (IRB) approval for their first large-scale study, and the company is currently assembling a crack team of data scientists in New York City.  If you have deep expertise in data science field, this is an opportunity to help bring about a revolution in personalized medicine. Come join us!

 

Introduction to the Internet of Things (Slides)

I’m doing a talk on the Internet of Things tomorrow at the SIIA’s “IIS: Breakthrough” conference tomorrow, and here are the slides I’ll use.  It’s meant to be a high level introduction to the topic, for a broad audience of “information industry” professionals.  Also used an earlier version of those slides at the WIN Global Innovator last week, which was fun. Feedback welcome.