Internet of Things: Are We There Yet? (The 2016 IoT Landscape)

 

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)”

Is Big Data Still a Thing? (The 2016 Big Data 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)”

The Power of Data Network Effects

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

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

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

Network Effects vs. Data Network Effects

The concept of network effect (in general) is by now well understood: a flywheel type situation where a good or service becomes more valuable when more people use it.   Many examples out there from the telephone system (the value of a phone increases if everyone has a phone) to Facebook to many marketplaces (with some nuances for the latter).

While they produce many of the same benefits, data network effects are more subtle and generally less well understood.  Data network effects occur when your product, generally powered by machine learning, becomes smarter as it gets more data from your users.  In other words:  the more users use your product, the more data they contribute; the more data they contribute, the smarter your product becomes (which can mean anything from core performance improvements to predictions, recommendations, personalization, etc. ); the smarter your product is, the better it serves your users and the more likely they are to come back often and contribute more data – and so on and so forth.  Over time, your business becomes deeply and increasingly entrenched, as nobody can serve users as well.

Data network effects require at least some level of automated productization of the learning.  Of course, most well-run businesses “learn” in some way from data, but that’s typically done through analytics, with human analysts doing a lot of the work, and a separate process to build insights into the product or service.  The more automation you build into the loop, the more likely you are to get a flywheel effect going.

Continue reading “The Power of Data Network Effects”

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.

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)”

Sketchfab and the democratization of 3D content

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

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

Hardware Startups: The VC Perspective

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

 

 

The “Straight to A” Round

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

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

The Astounding Resurrection of AI [Slides]

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

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

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

Continue reading “x.ai 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.:

1.  Nice guys don’t finish last. According to some, the tech ecosystem has been grappling with a proliferation of jerks with oversized egos at the helm of very successful startups (see Pando’s “asshole rollcall” here). Whether one shares that point of view or not, Renaud is exactly the opposite of that. Kind, loyal, generous and understated, he’s the living proof that world-class entrepreneurial talent, drive and persistence don’t necessarily come associated with arrogance and low EQ.

2.  CEO focus does matter. Renaud has been focusing maniacally on his venture for the last eight years. Up until recently, he had spoken at comparatively few conferences. He doesn’t have a portfolio of cool angel investments on the side. Heck, he doesn’t even have a Twitter account.

3. Great founders can come from all sorts of backgrounds. Renaud defies a lot of current startup CEO stereotypes. He is not a technical founder. He started his career as a (gasp) corporate lawyer.   He’s a sole founder. He is French, with an unmistakable accent. 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.