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. 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.

Continue reading “ and the emergence of the AI-powered application”

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:

Continue reading “Lending Club IPO: Nice Guys Don’t Finish Last, and Other Lessons”

The Internet of Things: Reaching Escape Velocity

An edited version of this post appeared on TechCrunch here.  A downloadable version of the chart is available on SlideShare here.

It’s been about 18 months since my original attempt at charting the Internet of Things (IoT) space. To say the least, it’s been a period of extraordinary activity in the ecosystem.

While the Internet of Things will inevitably ride the ups and downs of inflated hype and unmet expectations, at this stage there’s no putting the genie back in the bottle. The Internet of Things is propelled by an exceptional convergence of trends (mobile phone ubiquity, open hardware, Big Data, the resurrection of AI, cloud computing, 3D printing, crowdfunding). In addition, there’s an element of self-fulfilling prophecy at play with enterprises, consumers, retailers and the press all equally excited about the possibilities. As a result, the IoT space is now reaching escape velocity. Whether we’re ready for it or not, we’re rapidly evolving towards a world where just about everything will be connected. This has profound implications for society and how we collectively interact with the world around us. Key concerns around privacy and security will need to be addressed.

For entrepreneurs, the opportunity is massive. Where Web 1.0 connected computers to the Internet and Web 2.0 connected people, Web 3.0 is shaping up to be connecting just about everything else – things, plants, livestock, babies… Each new wave has spun out giant companies (Google and Amazon for Web 1.0, Facebook and Twitter for Web 2.0). Will Web 3.0 create a comparable group of behemoths?

Continue reading “The Internet of Things: Reaching Escape Velocity”

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.

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 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”

The State Of Big Data in 2014: a Chart

Note: This post appeared on VentureBeat, here.

It’s been almost two years since I took a first stab at charting the booming Big Data ecosystem, and it’s been a period of incredible activity in the space. An updated chart was long overdue, and here it is:

(click on the arrows at the bottom right of the screen to expand)

A few thoughts on this revised chart, and the Big Data market in general, largely from a VC perspective:

Getting crowded: Entrepreneurs have flocked to the space, VCs have poured money into promising startups, and as a result, the market is starting to get crowded. Certain categories like databases (whether NoSQL or NewSQL) or social media analytics feel ripe for consolidation or some sort of shakeout (which may have already started in social analytics with Twitter’s acquisitions of BlueFin and GNIP). While there will be always room for great new startups, it seems that a lot of the early bets in the broader infrastructure and analytics segments have been made at this stage, and the bar for success is getting higher – which doesn’t mean that VC money will stop pouring in. In terms of this specific industry chart, we’ve clearly reached the limit of how many companies we can fit one page. I’m sure there are a number of great companies we either missed or didn’t have enough space to include – apologies in advance to those, and I’d love to hear people’s thoughts and suggestions in the comments section about who else should be included.

Still early: Overall, we’re still in the early innings of this market. Over the last couple of years, some promising companies failed (for example: Drawn to Scale), a number saw early exits (for example: Precog, Prior Knowledge, Lucky Sort, Rapleaf, Nodeable, Karmasphere, etc.), and a handful saw more meaningful outcomes (for example: Infochimps, Causata, Streambase, ParAccel, Aspera, GNIP, BlueFin labs, BlueKai). Meanwhile, some companies seem to be reaching significant scale, and have raised spectacular amounts of money (for example, MongoDB has now raised over $230M, Palantir almost $900M and Cloudera $1B). But overall, we’re still early in the curve in terms of successful IPOs (Splunk or Tableau notwithstanding) and large exits, although the big companies are getting more acquisitive in the space (Oracle with BlueKai, IBM with Cloudant). In many segments, startups and large companies are jockeying for position and no obvious leader has emerged.

Hype, meet reality: A few years into a period of incredible hype, is Big Data still a thing? While less press worthy, the next couple of years are going to be hugely important for this market, as corporations start moving Big Data projects from experimentation to full production. While they will lead to rapidly increasing revenues for some Big Data vendors, those deployments will also test whether Big Data can truly deliver on its promise. Meanwhile, the fundamental need for Big Data technology keeps increasing, as the deluge of data keeps accelerating, powered in part by the rapidly emerging Internet of Things industry.

Infrastructure: Hadoop seems to have solidified its position as the cornerstone of the entire ecosystem, but there are still a number of competing distributions – this will probably need to evolve. Spark, another open source framework that builds on top of the Hadoop Distributed File System, is getting a lot of buzz right now because it promises to fill in the places where Hadoop has been weak, namely interactive speeds and good programming interfaces (and early signs seem to point to fulfilling that promise). Some themes (for example, in memory or real time) continue to be top of mind; others are appearing (for example, there’s a whole new generation of data transformation/munging/wrangling tools, including Trifacta, Paxata and DataTamer). Another key discussion is whether enterprise data will truly move to the cloud (public or private), and if so, how quickly. Many will argue that Fortune 500 companies will keep their data (and the software to process it) on premise for years to come; a generation of Hadoop-in-the-cloud startups (Qubole, Mortar, etc.) will argue that all data is moving to the cloud long term.

Analytics: This has been a particularly active segment of the Big Data ecosystem in terms of startup and VC activity. From spreadsheet type interfaces to timeline animations and 3D visualizations, startups offer all sorts of different analytical tools and interfaces, and the reality is that different customers will have different type of preferences, so there’s probably room for a number of vendors. Go to market strategies differ as well – some startups focus on selling tools to data scientists, a group that is still small but growing in numbers and budget. Others adopt the opposite approach and sell automated solutions targeting business users, bypassing data scientists altogether.

Applications: As predicted, the action has been slowly but surely moving to the application layer of Big Data. The chart highlights a number of exciting startups that are fundamentally powered by Big Data tools and techniques (certainly not an exhaustive list). Some offer horizontal applications – for example, Big Data powered marketing, CRM tools or fraud detection solutions. Others use Big Data in vertical specific applications. Finance and ad tech were always early leaders in adopting Big Data, years before it was even called Big Data. Gradually, the use of Big Data is spreading to more industries, such as healthcare and biotech (particularly in genomics) or education. This is only the beginning.

Many thanks for my FirstMark colleague Sutian Dong for doing a lot of the heavy lifting on this chart. My former colleague Shivon Zilis of Bloomberg Beta contributed immensely to prior versions of this chart.


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.


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.

10 Quick Takeaways from CES 2014

1.  Big brand curved TVs and mega booths are cool, but to me this year’s show was all about the rise of the crowdfunded hardware startup.


 2.  It’s official, there are now more wearable wristband vendors than there are human wrists on the planet.


3.  The wearables category is still waiting for its disruptive “iPhone moment”.  New releases show nice progress, but mostly incremental.  Smart watches have a long way to go.


4.  Accelerating trends on display, still early: family tech and senior tech.


5.  The lines between the tech and non-tech worlds keep blurring.  Pizza Hut and Ford both had a very noticeable presence and were pitching their tech innovation.


6.  Hardware innovation is truly global.  Some of the most interesting startups I met were from Manchester (UK), Ukraine and Lebanon.  France continues to be very active in the space (Parrot, Withings, Netatmo,, etc.). [UPDATE: See below some great 3D visualizations of the latest Withings and products, produced by SketchFab]


7.  China was left, front and center.  Not just as the “workshop of the world” but, more strikingly, as as a producer/innovator in their own right. The rise of the juggernaut only seems to be accelerating.


8.  In home automation, entrepreneurs were talking a lot about AllJoyn, Qualcomm’s open source platform and language, and the AllSeen alliance that is going to promote an open standard for the Internet of Things.


9.  In 3D printing, Makerbot is killing it, with its three gorgeous new printers.  Toys still seem to be the killer app for consumer 3D printing, although the new Chefjet chocolate 3D printer by 3D systems was pretty awesome. Consolidation in the consumer 3D printer space seems likely, in the not-too-distant future.


10.  Yves Behar and Bre Pettis are incredible creative and entrepreneurial minds, who deserve all the hype they get.  I got to witness this firsthand as a judge on the finals of the first TechCrunch Hardware Battlefield (with Jen McCabe, also very sharp), as they turned the judging into a real time mentoring session, providing  insights that were worth way more than the top $50,000 prize.  Exciting and inspiring.

Mother (click to view in 3D)



Withings Aura (click to view in 3D)


Withings Aura

The Rise of the Female Hardware Entrepreneur

As the fundamentally important debate over women in technology and entrepreneurship rages on (most recently sparked by what Paul Graham said, or perhaps didn’t say), I’ve been intrigued by the comparatively higher proportion of women who seem to be starting companies in one of my areas of predilection: hardware (broadly defined: open hardware, Internet of Things, wearable computing, 3D printing, etc.).

I don’t have much data here, other than my anecdotal personal experience, both as a VC and as the organizer of Hardwired NYC. But, without having to rack my brain for more than a minute or two, a bunch of names of great female founders and/or CEOs in the general hardware space comes up, including, in no particular order:

  • Limor Fried, founder, Adafruit
  • Ayah Bdeir, founder and CEO, littlebits (who spoke at Hardwired NYC last November)
  • Amanda Peyton, co-founder and CEO, Grand St (see her talk at Hardwired NYC here)
  • Jenny Lawton, President, Makerbot (see her talk at Hardwired NYC here)
  • Kegan Schowenburg, co-founder and CEO, Sols (speaking at Hardwired NYC next week)
  • Helen Zelman, co-founder, Lemnos Labs
  • Cheryl Kellond, co-founder and CEO, Bia
  • Monisha Perkash, co-founder and CEO, Lumo BodyTech
  • Daniela Perdomo, co-founder and CEO, GoTenna
  • Mary Huang, co-founder, Continuum Fashion
  • Meredith Perry, founder and CEO, uBeam
  • Julia Hu, founder and CEO, Lark
  • Debra Sterling, founder and CEO, GoldieBlox

And there are many more (both in the U.S and globally), which is exciting.

The question, of course, is why hardware would be an area of particular focus for female entrepreneurs. As a category, hardware is broad, lends itself to all sorts of products, and as a result feels pretty gender-neutral.

Could it be that there are more female role models in hardware, since it is often said that role models are particularly important to female entrepreneurs ? It doesn’t appear that way. Sure, women have run some of the biggest hardware companies in the world (Carly Fiorina and Meg Whitman at HP; Ursual Burns at Xerox) but it’s unclear how much of an inspiration they would be to early stage tech entrepreneurs, and more importantly, a number of software or internet companies have been run by women as well. Perhaps more relevant are female entrepreneurs like Limor Fried, who under her “Lady Ada” moniker has become the closest equivalent to a celebrity in the hardware alpha geek world (and beyond, through her appearance on the cover of Wired in 2011).

What’s interesting is that hardware lends itself particularly well to new entrants – there’s been a big gap in innovation in hardware in the last 10 or 15 years (with some notable exceptions like Apple), and as a result there’s a “missing generation”, and plenty of opportunities for new entrepreneurs to become leaders in what, in some ways, feels like a brand new field.

Curious if anyone can think of an explanation?

Regardless, and to the extent this is indeed a trend, it is particularly exciting and promising, and we should collectively think about how to accelerate it and extend it to other areas of tech entrepreneurship.



Got some great feedback on Twitter, and while my initial goal was not to be comprehensive here, thought it could actually be helpful to start a running list of female hardware founders  – perhaps it can become a good resource.   Here are the people that were recommended to me, who else should I add? (please add in comments)

First Name Last Name Company Location
Jeri Ellsworth Technical Illusions Bellevue, WA
Kati Bicknell Kindara Boulder, CO
Mary Turner AlertMe Cambridge, UK
Liz Salcedo Everpurse Chicago, IL
Anastasia Leng Hatch New York, NY
Christina Mercando Ringly New York, NY
Ezster Ozsvald Notch New York, NY
Gauri Nanda Toymail New York, NY
Lisa Fetterman Nomiku San Francisco, CA
Laura Berman Melon Santa Monica, CA
Amanda Williams Fabule Fabrications Montreal, Canada
Alexandra Deschamps-Sonsino Good Night Lamp London, UK
Alice Taylor MakieLab London, UK
Natasha Carolan MakieLab London, UK
Becky Pilditch Bare Conductive London, UK
Becky Stewart Codasign London, UK
Bethany Koby TechnologyWillSaveUs London, UK
Emily Brooke Blaze London, UK
Jane ni Dhulchaoinfi Sugru London, UK
Jessi Baker Provenance London, UK
Ana Burica Teddy The Guardian Zagreb, Croatia
Mila Burger Loccie Zagreb, Croatia
Alicia Asin Libelium Zaragoza, Spain