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 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: