Some call it “strong” AI, others “real” AI, “true” AI or artificial “general” intelligence (AGI)… whatever the term (and important nuances), there are few questions of greater importance than whether we are collectively in the process of developing generalized AI that can truly think like a human — possibly even at a superhuman intelligence level, with unpredictable, uncontrollable consequences.
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.
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.
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:
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.
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.
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.
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:
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:
Thomson Reuters CTO James Powell runs a great series of podcasts where he interviews people in the technology world about topics of relevance to his organization. I was fortunate to be invited to speak with James about the Internet of Things and Big Data, and it was a lot of fun. Below is the podcast, uploaded on SoundCloud. Thanks to James Powell and Dan Cost for the opportunity.