The concept of “personal branding” has evolved dramatically over the last few years. What used to be the territory of naturally self-promotional individuals has now become an imperative for many professionals. Whether you want it or not, employers and potential customers will look you up on social media and form judgement. It is essentially no longer possible to “opt out”. Seen more positively, personal branding can be a powerful vehicle for professional empowerment, opening up new opportunities and limiting one’s reliance on any given employer, in a context where more of us than ever will switch occupations, industries and locations several times during our careers.
A few days ago, I sat down Sam DeBrule of Machine Learnings for a broad conversation about AI and startups. We got into a number of topics including creative data acquisition tactics, data network effects, and what makes AI startups different.
The interview is here: Why AI Companies Can’t Be Lean Startups – A Conversation with Matt Turck of FirstMark Capital.
Perhaps more rapidly than ever, our work lives are changing. The concept of a career is dramatically evolving – the idea of lifelong allegiance to a company, occupation or industry has all but disappeared, whether by choice or necessity. How we get our jobs, how long we keep them, whether we are employed full time or a freelancer, what we actually do at work – all of this is morphing at an unprecedented pace.
As everywhere else, software has had a disproportionate impact on this evolution, exposing opportunities and increasing market fluidity (job boards, LinkedIn), changing hiring processes (applicant tracking systems) and hiring criteria (“how are your Salesforce skills?”). It is impacting the daily reality of work (Slack), vacation (always on) or the very nature of the job itself (data scientist, Uber driver). And, as the generation that never knew a world without the Internet hits the job market, we’re in the early innings of an even more profound evolution: the gradual penetration of artificial intelligence in all layers of human activity.
Against this rapidly evolving reality, we’re educating future generations through one dominant model – college – that emerged in Europe in the XI century and in America in the XVII century. From the Ivy League to the GI Bill, college is a deeply ingrained part of the American fabric, a widely accepted norm and a major goal for the vast majority of families.
It feels good to be a data geek in 2017.
Last year, we asked “Is Big Data Still a Thing?”, observing that since Big Data is largely “plumbing”, it has been subject to enterprise adoption cycles that are much slower than the hype cycle. As a result, it took several years for Big Data to evolve from cool new technologies to core enterprise systems actually deployed in production.
In 2017, we’re now well into this deployment phase. The term “Big Data” continues to gradually fade away, but the Big Data space itself is booming. We’re seeing everywhere anecdotal evidence pointing to more mature products, more substantial adoption in Fortune 1000 companies, and rapid revenue growth for many startups.
Meanwhile, the froth has indisputably moved to the machine learning and artificial intelligence side of the ecosystem. AI experienced in the last few months a “Big Bang” in collective consciousness not entirely dissimilar to the excitement around Big Data a few years ago, except with even more velocity.
2017 is also shaping up to be an exciting year from another perspective: long-awaited IPOs. The first few months of this year have seen a burst of activity for Big Data startups on that front, with warm reception from the public markets.
All in all, in 2017 the data ecosystem is firing on all cylinders. As every year, we’ll use the annual revision of our Big Data Landscape to do a long-form, “State of the Union” roundup of the key trends we’re seeing in the industry.
Let’s dig in.
Today our portfolio company HyperScience is coming out of stealth and talking a bit more about what they’ve been working on for the last couple of years. We have been involved for a little while already as lead Series A investors, and we are excited to now be joined today by our friends at Felicis, a great addition to a strong syndicate from both coasts that also includes Shana Fisher (Third Kind) who led the seed, AME Cloud Ventures, Slow Ventures, Acequia, Box Group and Scott Belsky. The company is announcing today a total of $18M in Series A investment.
HyperScience offers AI solutions targeting Global 2000 corporations and government institutions. Their products enable those customers to automate or accelerate a lot of dusty back office processes, particularly those that involve the manipulation and triage of large amounts of documents and images.
Artificial intelligence is, of course, all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants, often early in the life of their startups.
As always, the reality of building a startup is different, especially when one aims to build a self-standing company for the long term. The path to success in AI requires not just technical prowess but also careful thinking and execution through a range of strategic and tactical questions that are specific to this domain and market.
One possible framework to think through these topics is this “5P”list: Positioning (finding blue ocean), Product, Petabytes (data), Process (social engineering) and People.
Over the last few months, the usual debate around unicorns and bubbles seems to have been put on hold a bit, as fears of a major crash have thankfully not materialized, at least for now.
Instead another discussion has emerged, one that’s actually probably more fundamental. What’s next in tech? Which areas will produce the Googles and Facebooks of the next decade?
What’s prompting the discussion is a general feeling that we’re on the tail end of the most recent big wave of innovation, one that was propelled by social, mobile and cloud. A lot of great companies emerged from that wave, and the concern is whether there’s room for a lot more “category-defining” startups to appear. Does the world need another Snapchat? (see Josh Elman’s great thoughts here). Or another marketplace, on-demand company, food startup, peer to peer lending platform? Isn’t there a SaaS company in just about every segment now? And so on and so forth.
One alternative seems to be “frontier tech”: a seemingly heterogeneous group that includes artificial intelligence, the Internet of Things, augmented reality, virtual reality, drones, robotics, autonomous vehicles, space, genomics, neuroscience, and perhaps the blockchain, depending on who you ask.
In September 2001, I was the co-founder of a NYC tech startup, TripleHop Technologies. We were building recommendation engine software, à la Amazon. We had maybe 25 people on the team at the time.
Our office was located on the 53rd floor of the North Tower at the World Trade Center.
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:
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.
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.
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.