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:
- Cheap genome sequencing will unleash massive amounts of data (about a hundred gigabytes of data per human genome!);
- Big Data technologies will make it possible to process those massive amounts of data quickly and cheaply enough;
- Machine learning and artificial intelligence will enable deep analysis of the data;
- The hope is that through this analysis we’ll discover new insights in human health, and will be able to provide personalized treatment that will focus on every patient’s specific genetic makeup and needs.
Continue reading “Phosphorus and the Rise of the New Genomics Startup”
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.
Continue reading “The NYC Tech Ecosystem: Catching Up to the Hype”
Is the Internet of Things the world’s most confusing tech trend? On the one hand, we’re told it’s going to be epic, and soon – all predictions are either in tens of billions (of connected devices) and trillions (of dollars of economic value to be created). On the other hand, the dominant feeling expressed by end users (including at this year’s CES show, arguably the bellwether of the industry) is essentially “meh” – right now the IoT feels like an avalanche of new connected products, many of which seem to solve trivial, “first world” problems: expensive gadgets that resolutely fall in the “nice to have” category, rather than “must have”. And, for all the talk about a mega tech trend, things seem to be moving at the speed of molasses, with little discernible progress year on year.
Part of the problem is perhaps one of semantics. While gadgets are indeed part of the category (and quite often very large markets onto themselves), the Internet of Things (which we define as any “connected hardware” other than desktops, laptops and smartphones) is a much broader, and deeper, trend that cuts across both the consumer, enterprise and industrial spaces. Fundamentally, the Internet of Things is about the transformation of any physical object into a digital data product. Once you attach a sensor to it, a physical object (whether a tiny one like a pill that goes through your body, or a very large one like a plane or building) starts functioning a lot like any other digital product – it emits data about its usage, location and state; it can be tracked, controlled, personalized and upgraded remotely; and, when coupled with all the progress in Big Data and artificial intelligence, it can become intelligent, predictive, collaborative and in some cases autonomous. An entirely new way of interacting with our world is emerging. The importance of the IoT perhaps emerges more clearly when you think about it as the final chapter of “software eats the world”, where everything gets connected.
Continue reading “Internet of Things: Are We There Yet? (The 2016 IoT Landscape)”
In a tech startup industry that loves its shiny new objects, the term “Big Data” is in the unenviable position of sounding increasingly “3 years ago”. While Hadoop was created in 2006, interest in the concept of “Big Data” reached fever pitch sometime between 2011 and 2014. This was the period when, at least in the press and on industry panels, Big Data was the new “black”, “gold” or “oil”. However, at least in my conversations with people in the industry, there’s an increasing sense of having reached some kind of plateau. 2015 was probably the year when the cool kids in the data world (to the extent there is such a thing) moved on to obsessing over AI and its many related concepts and flavors: machine intelligence, deep learning, etc.
Beyond semantics and the inevitable hype cycle, our fourth annual “Big Data Landscape” (scroll down) is a great opportunity to take a step back, reflect on what’s happened over the last year or so and ponder the future of this industry.
In 2016, is Big Data still a “thing”? Let’s dig in.
Continue reading “Is Big Data Still a Thing? (The 2016 Big Data Landscape)”
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.
Continue reading “The Power of Data Network Effects”
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.
The good news is that it’s a great time to get into venture capital. While venture capital remains a small industry with comparatively few openings, many VC firms have raised a lot of money lately, so they need more people to help them deploy it. A number of investment analyst/associate positions are advertised (or were recently) – for example, we just posted a new opening at FirstMark, see here. I’m also aware of several General Partner-level searches being conducted right now.
Ok, but how do you get those jobs? Ask any VC, and they will tell you that they get that question all the time, and I’m no exception — in fact, I’m not-so-secretly hoping that this (long) blog post will help cover 80% or so of the content discussed in the frequent conversations I have on the topic, so I can focus on the 20% that’s specific to each person’s circumstances.
It’s not just the odds that make the topic complicated – it’s also that that there’s no single or sure-fire path to the job. Continue reading “Playing “fake VC” (or the portfolio approach to getting a job in venture capital)”
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”
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 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”
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.
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 “x.ai and the emergence of the AI-powered application”
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”