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.
Whether you use the Carlota Perez surge cycle (see this great Fred Wilson post) or the Gartner version, hype cycles convey the fundamental idea that technology markets don’t develop linearly, but instead go through phases of boom and bust before they reach wide adoption.
Hype cycles are a great framework for investors (and founders), because entering the market at the right time is both crucial and very hard.
2017 was an extraordinary and crazy year in the world of cryptocurrencies. Prices skyrocketed (Bitcoin: +1,400%; Litecoin: +5,400%, Ethereum: +8,700%; Ripple +35,000%). ICOs raised over $3 billion. Crypto hedge funds emerged all over the map and a handful of blockchain startups reached unicorn-level valuations.
Almost inevitably, the price of individual cryptocurrencies will experience substantial volatility in 2018, and the first few days of January already look like a rollercoaster. Prices may very well crash altogether. In more ways than one, the space feels reminiscent of the dot-com days of the late 1990s, whether it is stories of newly minted bitcoin millionaires, the undeniable speculation rampant throughout the market, or the emergence of many weird things. While growing and expanding, the actual use cases of the blockchain still trail behind.
Taking a step back from the immediate frothiness, however, it seems that the crypto world has hit the point of no return, vaulting from a fringe movement into the mainstream collective consciousness, with strong interest both from the public and Wall Street. The blockchain has cemented its position as a new paradigm, which will only grow in importance, offering new solutions to the world, and new opportunities to entrepreneurs.
For proponents of the Internet of Things, the last 12-18 months have been often frustrating. The Internet of Things (IoT) was supposed to be huge by now. Instead, the industry news has been dominated by a string of startup failures, as well as alarming security issues. Cisco estimated in a (controversial) study that almost 75% of IoT projects fail. And the Internet of Things certainly lost a part of its luster as a buzzword, easily supplanted in 2017 by AI and bitcoin.
Interestingly, however, the Internet of Things continues its inexorable march towards massive scale. 2017 was most likely the year when the total number of IoT devices (wearables, connected cars, machines, etc.) surpassed mobile phones. Global spending in the space continues to accelerate – IDC was forecasting it to hit $800 billion in 2017, a 16.7% increase over the previous year’s number.
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.
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.