New York: The New Center of Gravity of European Tech

While it’s certainly possible to build a tech giant solely in Europe, the path to building a global, category-dominating company will, for most European tech startups, require building a strong presence in the US.

As a result, sooner rather than later, European startups will start thinking through their US expansion strategy.  One deceptively simple question of that strategy is “where should we build our US headquarter”?

Up until a few years ago, there wasn’t much of a debate: Silicon Valley, despite the distance and time difference with Europe, was the obvious choice. There was essentially nowhere else to go, except perhaps Boston for life sciences.

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Building a $12B Public Company: In Conversation with Olivier Pomel, CEO, Datadog

By any measure, Datadog is an incredible entrepreneurial success story. The company went from a tiny startup in 2010 that had trouble raising money, to a public company that, at the time of writing, has a market capitalization of $12.5B. It was a pioneer in the category of DevOps and observability, and it’s now a clear leader. With revenues hovering around $350M, it has 1,300 employees across 31 locations around the world.

Perhaps improbably, the founders built the company out of New York, which many people over the years have thought of as a hub for adtech, media and commerce startups only. Along the way, they faced a lot of skepticism: “Whenever we pitched West Coast investors it was sort of seen as a form of mental deficiency to be based in New York and doing infrastructure“, says Olivier. I wrote a few months ago about the significance of the Datadog IPO for the ecosystem and beyond. Ironically, out of the three top public tech companies in New York today, two are infrastructure software companies (Datadog and MongoDB).

Not one for gratuitous self-aggrandizing, Olivier has given surprisingly few interviews over the years, and it was a real treat to sit down with him for a fireside chat in front of a packed house of 350 attendees at our most recent Data Driven NYC.

We had an in-depth conversations and covered a lot of topics.

The first half of our conversation was focused on Datadog itself, starting with a high level overview of the observability and DevOps space to make the discussion approachable by people who don’t know the space.

The second half of the conversation was focused on all sorts of lessons learned along the way of building a major company- sales, marketing, fundraising, etc.

Below is the video. We have also provided a full written transcript to make the content easy to scan through (many thanks to Karissa Domondon for her help with this).

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The Significance of the Datadog IPO

The Datadog IPO just happened, and it’s proven to be a resounding success, not surprisingly given the company’s superb metrics – big revenues ($333M ARR), happy customers that keep buying more (146% net revenue retention) and, unlike many others, a history of profitability. To make the story even more epic, it transpired that the company had turned down a last minute big acquisition offer from Cisco shortly before the IPO, which valued the company higher than its proposed IPO range.

While I’m a small personal shareholder in the company and friendly with its founders, this is not going to be a VC victory lap kind of a post, for the simple reason that I did not invest in the company as a VC (as the early rounds of financing took place before my current tenure, in my defense!).

Regardless, I wanted to write a few quick thoughts, as I believe this particular IPO should be loudly celebrated.

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From 0 to $200M in Revenues in 7 years: In Conversation with Ben Uretsky, Co-Founder, Digital Ocean


Who would be crazy enough to compete head-on with AWS?

The question was almost as obvious seven years ago than it is today.  Yet in just a few years since its founding,  Digital Ocean, a cloud infrastructure startup based in New York with data centers around the world, has managed to build a very impressive and fast-growing business, successfully competing with the giants of cloud computing.

Ben Uretsky, co-founder of the company (with his brother Moisey and 3 others) and its CEO from 2011 to 2018, stopped by for a chat at Data Driven NYC to tell the story of the company and share some lessons learned.

Here’s the video, and below are my notes from our great chat.

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The NYC Tech Ecosystem: Catching Up to the Hype


I’m fascinated by tech ecosystems, and the network effects behind them.  I wrote about Berlin (here) and about Paris (here)  But of course, as an NYC venture capitalist, I’m particularly interested in New York – I wrote about the strong NYC data community a while back (here), and about NYC as a great home for European entrepreneurs (here).

The New York tech ecosystem is in an interesting place right now.  The emergence of NYC was a big story at tech conferences and in the press maybe four or five years ago.   Fast forward to today: on the one hand, NYC has become the clear Number 2 to the Bay Area; on the other hand, it’s hard not to notice that things have gone a bit quiet – at a minimum,  we seem to be past the stage of unbridled enthusiasm.

The bull case is that New York is now firmly established as a startup hub, and therefore it is less press-worthy than when it was first emerging; to wit, entrepreneurial activity and VC investment levels have never been higher (for context, with $1.9B invested, Q1 2016 saw almost 7x more VC investment in NYC than Q1 2012)

The bear case is that, for all the progress, NYC still suffers from many of the same issues that have plagued it for years: a relative dearth of $1BN+ exits, a lack of local anchor companies that can serve as acquirers, and a comparatively lower concentration of talent, particularly when it comes to not just starting, but actually scaling, startups.

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NYC: A Natural Home for European Entrepreneurs

Last night I was invited to speak at the inaugural NYC European Tech Meetup.  There are tons of obvious reasons why the NYC and European tech ecosystems should work closely with one another, so a meetup on the topic was long overdue.  Congrats to Alban Denoyel and Anthony Marnell for starting it, and thanks for inviting me to speak, was a lot of fun.  Below are the slides I used – the presentation was meant to be a “State of the Union” of European tech in NYC, a high level overview fit for an inaugural meetup and get the conversation started.


Many thanks to David Rogg, our newest associate at FirstMark, for helping me with this.  I’m sure we missed some companies and people – if so, let us know in the comments, and we’ll update the presentation.


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:

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Joseph Turian, Sqrrl, Infochimps and MemSQL

The December NYC Data Business Meetup was focused on big data infrastructure companies, with the co-founders of Sqrrl, Infochimps and MemSQL presenting to a full house.  We started the evening with a presentation by prominent data scientist Joseph Turian.

The slides are here: Joseph TurianSqrrlInfochimps and MemSQL.

Here are the videos:



Joseph Turian, “How to do AI in 2013”


Oren A. Falkowitz, Co-Founder & CEO, Sqrrl


Dhruv Bansal, Co-Founder & Chief Science Officer, Infochimps


Eric Frenkiel, Co-Founder & CEO, MemSQL


And here are a few pics (photo credit: Shivon Zilis):


10Gen, Mortar, Datadog & Rick Smolan at the NYC Data Meetup

Here are the videos and some pictures (scroll down) of the NYC Data Business Meetup held on September 25, 2012

In order of appearance:

1) Rick Smolan told us about his fascinating new project, the “Human Face of Big Data” – see the NY Times coverage here:


2) Mortar (presenter: K Young, CEO). Mortar ( provides a platform-as-a-service for Hadoop.  They take care of all of the necessary infrastructure (via AWS) and allow any software engineer to run jobs on Hadoop using Apache Pig and Python without special training.


3)  Datadog (presenter: Alexis Le Quoc, co-founder). Datadog ( is a service for IT, Operations and Development teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.  Datadog helps software developers and web ops understand their IT Data by putting it all in context.


4) We finished with a fireside chat with Dwight Merriman, CEO and co-founder, 10Gen. 10Gen ( develops MongoDB, and offers production support, training, and consulting for the open source database. Dwight is one of the original authors of MongoDB. In 1995, Dwight co-founded DoubleClick (acquired by Google for $3.1 billion) and served as its CTO for ten years. Dwight was the architect of the DoubleClick ad serving infrastructure, DART, which serves tens of billions of ads per day. Dwight is co-founder, Chairman, and the original architect of Panther Express (now part of CDNetworks), a content distribution network (CDN) technology that serves hundreds of thousands of objects per second. Dwight is also a co-founder and investor in and Gilt Groupe.


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.

Some thoughts on Brewster

Three days in, Brewster, the new personalized address book, has become an instant classic for me.  Perhaps I lucked out, but I didn’t experience much of the delay in processing my contacts that many others reported – I had to wait about 90 minutes which, while not ideal, was fine.  Everything since then seems to have been working like a charm  – the de-duplication and reconciliation of contacts across social networks, in particular, was beautifully done, and that’s not a trivial data problem.

I have always liked the concept of a personalized, always current address book.  In a way, it is sort of like the old Plaxo idea, which was probably before its time.  There were various startups that tried to fix the address book, including Sensobi (that eventually was acquired by GroupMe).  The next iteration of the social concept that I’m aware of is Everyme – at least in the initial vision the founders had for it when they were at Y Combinator in the Summer of 2011.  I was a bit bummed when it pivoted (or evolved) to become a private social network.

I really like that Brewster came out of the gate very “feature-rich”.  While I’m all for MVPs and generally agree that “if you are not embarrassed by the first version of your product, you’ve launched too late”, for something like this, I think the founder(s) made the right call to wait until the product was ready before launching.  At this stage of the game, anything that sounds like yet another hyped up app, and asks me to connect all my social networks when I first log in, etc. had better deliver some real value quickly for me to give it a real chance, and that was the case here.  As the founder Steve Greenwood  has apparently been mulling over this concept for many years, the temptation to release early must have been strong, particularly as it sounds like several startups are working on related concepts, including for example FullContact, but from my user’s standpoint, it was well worth it.

A few other aspects of the product (and its launch) that I like:

– I like that Brewster was clearly thought through as a data product – while the “Favorites” tab has an emotional and aesthetically pleasing aspect to it (depending on how attractive one’s friends are, at least…), the rest of the app is very data-centric: the “Lists” tabs has some interesting automatic categorization (I have 171 friends who are ‘Managing Director”, apparently, does that mean I’m old?), while the “Search” tab is awesome, with good suggested searches and the ability to uncover all sorts of interesting common interests across my contact list.

– While everything is automated, I like the fact that the product made me work manually to create my list of “favorites”.  That actually increased my personal investment into the product, and makes me less likely to discard it.

–  I really like that Brewster did not use any of the tired “virality” tricks that have become so common place.  No automatic posting on my Facebook newsfeed; no “Sent using my Brewster address book” tag line in emails, etc.

– I was impressed with the email I got to announce that my account was ready, personalized with pictures of some of my key contacts – great way of delivering a unique experience before I even started using the product in earnest.

The data privacy issue (and the fairly dramatic reactions to it) are of course a concern. I’m actually surprised that I don’t care more about it, personally — I guess I have gone pretty far down the path of accepting some privacy risk (as long as it’s not banking information), in return for getting a lot of value from the product, which I feel is the case here.  But obviously many people will feel differently, and this could sink the company entirely, if not properly addressed.

One functionality that I don’t find as impressive, at least as of now, is the “Updates” section — what it has surfaced so far (birthdays essentially) is not particularly interesting.  What would be really cool, eventually, would be an integration with Newsle, to get news about your friends.  Oh wait, add to this an integration with Cue, as well.  All built in natively into my iPhone address book and calendar.  Ok, so, maybe that’s a bit much to ask.  In the meantime, Brewster is already one of the most interesting apps I have seen in a long time.

There are many roads to success: The Buddy Media example

It’s been a few days now since their acquisition was formally announced, and I continue to be fascinated by the Buddy Media story.  But what fascinates me is less the company itself and all the things that make it great –  and instead the fact that its success tests the conventional wisdom of what makes a venture successful.  Rightly or wrongly, investors, prospective employees, the press, and anyone who tries to predict the highly unpredictable fate of startups, tend to default to some common assumptions about what’s going to work and what isn’t.  The Buddy Media story challenges that conventional wisdom in some interesting ways:

1.  NYC is not a good place to start an enterprise software company

It’s a bit ironic that, for all the talk about NYC being a media and eCommerce hub, the largest acquisition in five years would be an enterprise software company.

2.  It takes forever to build a successful enterprise software company.

It took Buddy Media less than 5 years from start to success, including an initial pivot.  

3.  To build a successful enterprise software company, you need technical co-founders, or at least a technical CEO

Buddy Media’s CEO is a serial entrepreneur with two degrees in journalism.  Buddy Media’s COO is a serial entrepreneur with a background in business development and marketing and a degree in economics.  The other co-founder and Chief Strategy Officer is a digital branding and marketing expert with a degree in Broadcasting and Mass Media.  

4.  Selling to marketers and advertisers is a really tough business.

Fortune 500 marketers and advertising agencies are indeed a tough audience – long sales cycles, often low budgets, a preference for homegrown solutions, a reluctance to buy what others in the industry purchase: not easy.   But the Buddy Media success shows that it can be done, with the right execution: build the best product in your category, focus on sales, make friends in the right places, hire some key people from agencies, and work really hard.

5.  Be really careful with strategic money

Buddy Media took a strategic investment from advertising leader WPP, which ended up substantially accelerating their business.

6. Service companies can’t become product companies

After an initial pivot, Buddy Media had to turn themselves into a service company to survive the 2008 economic recession.  James Altucher has a really interesting post on Techcrunch that describes this phase.  Somehow, they were able to gradually build a product offering.  

7.  The best founders are young and single

Two of the co-founders of Buddy Media are married.  On top of that, they have three children.  While there are famous examples of homeruns started by married founders (Cisco, VMware, etc.), in my experience, behind closed doors most investors think it’s a terribly risky idea.   The Buddy Media story shows that where there is will there is a way: founders with family obligations can still endure the rollercoaster lifestyle of the startup world.


The thriving data ecosystem in NYC

There’s a lot of interest in data-related businesses and products everywhere these days, but it’s been particularly fun to see things accelerating in New York (where I’m based).  Some purely anecdotal evidence: We had 50 very qualified data scientists show up at the recent hackathon we organized (as part of Big Data Week), despite the ungodly start time of 8am on a Saturday.   The Data Meetup I host monthly went from 0 to almost 1,300 members in barely 5 months.  General Assembly is starting a 10 week intensive program in data science.  Microsoft just announced it chose to locate in NYC its new research lab, which includes plenty of data science brainpower (including machine learning specialist John Langford and Jake Hofman, formerly of Yahoo Research).

NYC is becoming a real “hub” for data startups.  In fact, in my opinion data startups are becoming the next “layer” of the NYC tech scene — the way content and advertising startups (24/7, Doubleclick, Silicon Alley Reporter, etc.) were the foundational layer of “Silicon Alley” from 1995 to 2005, and the way social and e-commerce startups (Tumblr, Gilt, Foursquare, Etsy, Warby Parker, Rent the Runway, etc.) became the next building block that led to where we are today.

Due to their often intensely technical nature, data startups represent an interesting opportunity for NYC to develop more of a scientific and engineering-focused startup culture.

NYC has the key components of a thriving data startup ecosystem, including:

1) Customer demand: For those startups that sell to enterprises rather than consumers, NYC is where many of the key buyers are located – specifically, Wall Street and Madison Avenue, which have been among the most voracious and sophisticated users of data.  It’s no accident that some of the key conferences in the space, such as GigaOm’s Structure:Data or Strata, take place in NYC (or have an NYC event in addition to their CA event) – there’s no better place for emerging vendors to show off what they’ve built to potential purchasers.

2) A relevant talent pool: in addition to solid engineering talent, data-driven startups need data scientists, who come in various flavors: statisticians, mathematicians, machine learning experts, programmers, etc.  In part because there has been demand for this type of profiles for a while in financial services, there’s a fair concentration of them in NYC, and I’m seeing an increasing number of them making the jump to startup land.  NYC has a number of prominent data scientists, including (but certainly not limited to), Drew Conway and Jake Porway (both of whom are co-founders of Datakind, f/k/a Data without Borders), Max Shron, Cathy O’Neil (who left D.E. Shaw for a startup, Intent Media), Gilad Lotan, etc.  And of course, we have our very own emerging media star (deservedly so) in the person of Hilary Mason, most recently profiled here.

3) A data community:  Whether it’s Data Drinks or meetups, there’s clearly appetite for data nerds to get together and geek out. Both the NYC Predictive Analytics meetup (organized by Alex Lin) and the NYC Machine Learning meetup (organized by Paul Dix and Max Khesin) have over 2,000 members, while the New York Open Statistical Programming Meetup has 1,700 members.

4) Investors with a deep interest in the space:  As far as I know, IA Ventures is the only VC firm in the country that has an exclusive focus on data as an investment thesis (Accel’s big data fund is a little different, in that it’s a dedicated pool out of a much larger fund).  Roger Ehrenberg and his talented team (Brad Gillespie, Ben Siscovick, Jesse Beyroutey) are having a tremendous impact on the data world in general, and in NYC in particular (about half of their portfolio is NYC-based). RTP Ventures is a new but very promising NYC investor in the space, with a focus on the infrastructure part of the big data world.  Many of the main NYC investors are also “data friendly”, and have interesting data plays in their portfolio, as part of a broader focus: Union Square Ventures, Betaworks (see John Borthwick’s “data is the new plastic“), RRE, Lerer Ventures, Thrive Capital, kbs+ Ventures, but I’m sure I’m forgetting a number of others.

5) Universities that are willing to get involved:  The key machine learning centers in the country may be Carnegie Mellon, MIT and Stanford, but Columbia is strong as well, and most importantly, there are some terrific professors who are both academically prominent and deeply involved in the NYC tech scene – in particular Chris Wiggins (in addition to being a prominent machine-learning expert, Chris is also the co-founder of HackNY and has mentored many of the data scientists currently employed in NYC startups) and Tony Jebara (who runs the Columbia Machine Learning Laboratory and has also founded and advised several startups including Sense Networks and Bookt).  NYU has some leading authorities the data-intensive field of physical computing and Internet of Things: Tom Igoe and Dan O’Sullivan. Medium term, Cornell may be able to bring some additional academic expertise to NYC (for example, it is home to Joachims Thorsten who is arguably one of the top SVM researchers).

6) A crop of promising data startups:

  • A growing number of NYC based startups offer data and predictive analytics solutions – starting perhaps with Opera Solutions, which very people in the NYC tech scene had heard about until it raised a whopping $84 million in September 2011 from Silver Lake and Accel KKR (Opera Solutions employs some 150 data scientists, out of 400 employees).  In addition, NYC startups have been building all sorts of interesting data and analytics products for social media (Bitly, SocialFlow, Kno.des), news (Visual Revenue), finance (Dataminr), music (NextBigSound, which is moving to NYC), sports (Numberfire, and our own Bloomberg Sports) and of course advertising and marketing (Sailthru, Collective[i], Custora, PlaceIQ, YieldBot, Mediamath, m6d, 33across, Clickable, Buddy Media, etc.).
  • While we’re nowhere near the Silicon Valley on this front,  it’s great to see more big data infrastructure companies in NYC – some like 1010Data largely predate the whole big data craze; others have been appearing more recently, including FluidInfo, CrowdControl, Mortar Data (which is moving to NYC), Datadog, and of course 10Gen, whose MongoDB noSQL database is quickly becoming a must-have for a number of data-driven companies.
  • Finally, several exciting NYC startups are focused on the application of data to create disruptive products in various industries, such as education (Knewton) or consumer finance (Billguard, Bundle).
  • The fact that NYC recently saw a couple of acquisitions of data startups – Chris Dixon’s Hunch and Jordan Cooper’s Hyperpublic – doesn’t hurt either.

7) A data-centric business culture: perhaps it is because some of the key historical entrepreneurial successes in NYC were data companies (Bloomberg LP, Nielsen); or perhaps it is a reflection of the demand of East Coast investors who arguably tend to be very focused on metrics and business models (as opposed to pure vision)… but somehow, as far as I can tell, there’s always been a real culture around data and analytics in NYC.  Now increasingly, I hear CEOs of NYC startups present their companies as data companies, even those you wouldn’t necessarily suspect (recent examples include Dennis Crowley of Foursquare and Yaron Galai of Outbrain).  In addition, NYC startups have been quick to build data science teams, including many that don’t explicitly position “data” as a key part of their value proposition: Etsy, Gilt, The Ladders, GetGlue, Foursquare, Tumblr all have data scientists on board.

All of this is just a start, and I’m excited to see how it all progresses in the next few months and years.