Investing in Frontier Tech


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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Playing “fake VC” (or the portfolio approach to getting a job in venture capital)

How does one get a VC job?

Method 1:  Start a tech company, drive it a multi-billion dollar success. Drop a few bon mots on Twitter to your robust group of followers, make visionary statements during your TechCrunch Disrupt fireside chat, and build a reputation as a helpful mentor to entrepreneurs.  Then wait by your phone as major firms call you with General Partner offers.  Or start your own firm.

Method 2: Welcome to the long hard slog.  And read on.

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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   Many thanks to my colleague Jim Hao, who worked with me on this presentation.

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.

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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.

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Joining FirstMark Capital

Today I’m very excited to announce that I’m joining FirstMark Capital as Managing Director.  My main investment focus will be on areas that correspond to my professional background (B2B, enterprise, Big Data, fintech, education, etc.), but I will also happily be open to any big idea involving technology.

As anyone who follows the venture capital industry knows, opportunities of this nature and quality don’t come by very often, and I’m incredibly grateful and honored by the trust that the FirstMark team has placed in me.

At a time when venture capital has been facing substantial challenges and transformation, FirstMark is in my opinion a perfect example of “VC done right”, resulting in much deserved early success:

  • Results:  Probably in large part because FirstMark’s philosophy has been to focus the light entirely on their entrepreneurs, I don’t think people have quite caught on to just how impressive a firm FirstMark has become in the short span of five years since its creation.  In many ways, FirstMark is one of the industry’s best-kept secrets: their first fund ($200 million) is one of the very best of its vintage, and the follow up fund ($225 million) has already had some real breakouts.
  • Disruption & Innovation: A quick perusal through the FirstMark’s portfolio immediately tells a story of thoughtful but gutsy bets in a number of highly disruptive plays.  Beyond the more visible runaway hits (first VC money in Pinterest), FirstMark has invested in companies reinventing education (Knewton), finance (SecondMarket), television (Aereo), news distribution (NewsCred), gaming (Riot Games) and… your brain (Lumosity).
  • Founder/CEO Support: Perhaps the ultimate testament to FirstMark’s approach in my opinion is that the CEOs of its portfolio companies simply rave about the firm.  The FirstMark team brings a tremendous amount of intelligence, hard work, experience and connections to the table, as well as a fair amount of New York-style hustle.
  • Community and Portfolio Services: The venture capital model has been gradually evolving over the last few years from a capital-centric model (where VCs provide mostly funding and oversight) to a service-centric model (where money is increasingly commoditized and VCs add value by providing a suite of operational services that enable entrepreneurs and their startups to fully realize their success potential).   FirstMark is one of the few funds, typically part of a new generation of VCs, that have made a true commitment to providing operational services to their portfolio companies, whether in terms of recruiting, research or knowledge sharing. In my own community-building endeavor (the monthly Big Data event I run, see below), I have experienced firsthand both the level of effort required to pull this off and the tremendous benefits one gets in return, and I was extremely impressed with the current programming and roadmap for community that FirstMark has put in place, with apparently a lot more coming.

Beyond the intrinsic qualities of the firm, perhaps the most important factor that drew me to FirstMark is the effortless personal fit I have with its partners and team.  Venture capital can be a tough business, with its fair share of ego and difficult types, so it’s an extraordinary privilege to get to work with a team of highly intelligent, humble, focused, talented and overall great group of individuals.

I will certainly miss my friends at Bloomberg, a company for which I have developed a profound admiration over the years, but I couldn’t be more happy and excited about the future. I look forward to being even more active in the startup community and interacting with many of you.

For those of you in NYC who are wondering, I will still very much continue to run my Big Data meetup (the NYC Data Business Meetup) and in fact I will use the opportunity to take it to the next level over the next few months – stay tuned.


Enterprise Tech Panel in NYC

Mark Birch has a good summary of a recent panel organized by the NYC Enterprise Tech Meetup (which also has a video of the panel on its site, unfortunately with poor audio quality).  In addition to Mark, the panel featured David Aronoff (General Partner, Flybridge Capital Partners), Jeanne Sullivan (General Partner, StarVest Partners), Raju Rishi (Venture Partner, Sigma Partners) and myself.  Many thanks to Jonathan Lehr, the organizer of the event, for putting it together. Couple of pics below and also one here (I know! Panel pics are just so exciting!).

One key takeaway for me is that the NYC area used to have a pretty vibrant enterprise tech scene (with Computer Associates, etc.) in the eighties and up until the mid-nineties (before my time), which makes the relative dearth of enterprise tech startups in NYC over the last dozen years somewhat odd.  I’m excited to see a whole new wave of NYC startups rising to prominence, including 10Gen, Opera Solutions, Enterproid, Nodejitsu, AppFirst, Datadog, Mortar, etc.

Data-driven venture capital

I have been very intrigued by the recent emergence of “data driven” firms, aiming to use data to reinvent venture capital.

While they certainly review various data points and metrics before deciding to invest in a startup, as of today venture capital investors largely operate based on “pattern recognition” – the general idea being that, once you’ve heard thousands of pitches, sat on many boards and carefully studied industries for years, you become better than most at predicting who will make a strong founder/CEO, what business model will work and eventually, which startup will end up being a home run.  The trouble is, the model doesn’t always work, far from it, and many VCs end up making the wrong bets, resulting in disappointing overall industry results.  Could VCs be just like the baseball scouts described in Moneyball, who think they can spot future superstars because they’ve seen so many of them before, but end up being beaten by a cold, objective, statistics-based approach?

Enter several firms trying to do things differently:

  • Google Ventures has created various data-driven algorithms that inform their investment decisions – see the team discussing the concept at last year’s Web 2.0 Summit here.
  • Correlation Ventures raised $165M earlier this year for its first fund, which was reportedly oversubscribed (a rarity for a new fund).  Correlation says it has built the “world’s largest, most comprehensive database of U.S. venture capital financings”, which covers “the vast majority of venture financings that took place over the past two decades, tracking everything from key financing terms, investors, boards of directors, management backgrounds, industry sector dynamics and outcomes”.  Based on this data, Correlation has developed predictive analytics models which it uses to guide its investment decisions – as a result, it can make decisions very quickly (less than two weeks) and doesn’t require additional due diligence.
  • Just earlier this week, (which results from the relaunch of BV Capital) also emphasized its own data-driven approach to investment decisions

Since I’m a big fan of anything data-driven (decisions, product, companies), the concept resonates strongly with me.  Predictive analytics have been successfully used in various industries, from retail to insurance to consumer finance.  Other asset classes are highly data driven – fundamental and technical analysis drive billions of dollars of trade; hedge fund quants spend their lives building complex models to price and trade securities; high-frequency trading bypasses human decision making altogether and invests gigantic amounts of money based solely on data.  In this world where everything gets quantified, why should venture capital be an exception?

However, as much as I like the idea, I believe venture capital doesn’t lend itself very well to a model-heavy, quasi “black box” approach.  The creation of a reliable, systematic predictive model is a particularly challenging task when you consider the following obstacles:

  • A relatively sparse data set: while by definition there’s not much data about early stage startups, you could argue that that amount is constantly increasing, as everything is moving online, and everything online can be measured.  You could also argue that, if you could have access to all historical data from all VC firms in the country, and efficiently normalize it, you would end up with a lot of data.  But still that amount of data would pale in comparison to what’s available to public market investors – Bloomberg processes up to 45 billion “ticks” (change in the price of a security)… daily.
  • Limited intermediary feedback points: Before getting to a final outcome (game lost or won), baseball is full of small binary outcomes (a player hits the ball or he doesn’t).  Similarly, in market finance, the eventual success of strategy can typically be broken down in many different points with binary outcomes (you make money or you don’t).  In venture capital, before getting to a final outcome (a startup has a liquidity event), it’s unclear how many of those intermediary, measurable points you get, that can enable you to build models – perhaps a few (the startup’s next round is an “up round” or a “down/flat round”) but certainly nothing compared to the above examples.
  • Extended time horizon: in baseball, the rules of the game do not change from game to game, or season to season.  In venture capital, the “game” can last for years, because investments are highly illiquid.  During that time, pretty much anything can change – regulatory framework, unforeseen disruptive forces in the industry, etc.

In addition, it would be interesting to see how startups react in the long run to investors who are interested in them mostly because they scored well on a model, as opposed to spending extended time getting to know them.  Unlike public stock markets, venture capital fundraising is a two-way dance, and startups often pick their investors as much as their investors pick them.

However, while I have my doubts about using data models as valid predictors of the overall success of an early stage startup, my guess is that there are still plenty of interesting insights to be gleaned from the data, and that forward-thinking VC firms could gain a competitive advantage by actively crunching it  – my sense is that very few firms have done so at this stage.

Interestingly, there are some good data sources and emerging technologies out there that could be leveraged as a first step, without engaging into a massive data gathering or technology development effort:

  • Public (and/or free) sources:  Crunchbase is a great source of data.  There are many directions you could go with mining it – as an example, see what Opani (an early stage NYC big data company) came up with here. I bumped into Semgel, a web app that has taken a stab at instantly gathering and analyzing Crunchbase data.  The Crunchbase data could be augmented with data from marketplaces such as Factual.  See also this intriguing article about pre-money valuations of startups (typically not information that’s disclosed) could possibly be mined from publicly available Delaware certificates of incorporation and similar documents in other states.
  • Private Databases: There a few interesting databases that collect and organize more complex information flows around private companies such as CB Insights (which also offers a data-driven tracking tool called Mosaic)
  • Technologies: In addition to the various open-source big data tools, there are some technologies/companies that could be leveraged to mine VC industry data, including for example Quid, co-founded by the talented Sean Gourley – “understanding co-investment relationships and deriving investment strategies” is one the challenges they address.

If anyone is aware of other efforts around crunching data relevant to VCs, or other ways VCs have been used a heavily data-driven approach, I’d love to hear about it in the comments.