Not so long ago, AI startups were the new shiny object that everyone was getting excited about. It was a time of seemingly infinite promise: AI was going to not just redefine everything in business, but also offer entrepreneurs opportunities to build category-defining companies.
A few years (and billions of dollars of venture capital) later, AI startups have re-entered reality. Time has come to make good on the original promise, and prove that AI-first startups can become formidable companies, with long term differentiation and defensibility.
In other words, it is time to go from “starting” mode to “scaling” mode.
To be clear: I am as bullish on the AI space as ever. I believe AI is a different and powerful enough technology that entire new industry leaders can be built by leveraging it, as long it is applied to the right business problems.
At the same time, I have learned plenty of lessons in the last three or four years by being on the board of AI startups, and talking to many AI entrepreneurs in the context of Data Driven NYC. I’ll be sharing some notes here.
This post is a sequel to a presentation I made almost three years ago at the O’Reilly Artificial Intelligence conference, entitled “Building an AI Startup: Realities & Tactics“, which covered a lot of of core ideas about starting an AI company: building a team, acquiring data, finding the right market positioning. A lot of those concepts still hold, and this post will focus more on specific lessons around scaling.
Continue reading “Scaling AI Startups”
At the kind invitation of Rob May and the Botchain team, I had the opportunity recently to keynote Brains and Chains, an interesting conference in New York exploring the intersection of artificial intelligence and blockchain.
This is both an exciting and challenging topic, and the goal of my talk was to provide a broad introduction to kick things off, and frame the discussion for the rest of the day: discuss why the topic matters in the first place, and highlight the work of some interesting companies in the space.
Below is the presentation, with some added commentary when relevant. Scroll to the very bottom for a SlideShare widget, if you’d like to flip through the slides.
Continue reading “AI & Blockchain: An Introduction”
It’s been an exciting, but complex year in the data world.
Just as last year, the data tech ecosystem has continued to “fire on all cylinders”. If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed.
On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world. It is no accident that one of the key themes in the corporate world in 2018 so far has been “digital transformation”. The term may feel quaint to some (“isn’t that what’s been happening for the last 25 years?”), but it reflects that many of the more traditional industries and companies are now fully engaged into their journey to become truly data-driven.
On the other hand, a much broader cross-section of the public has become aware of the pitfalls of data. Whether it is through the very public debate over the risks of AI, the Cambridge Analytica scandal, the massive Equifax data breach, GDPR-related privacy discussions or reports of growing government surveillance in China, the data world has started revealing some darker, scarier undertones.
Continue reading “Great Power, Great Responsibility: The 2018 Big Data & AI Landscape”
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.
Continue reading “Frontier AI: How far are we from artificial “general” intelligence, really?”
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.
Continue reading “Growing Pains: The 2018 Internet of Things Landscape”
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.
Continue reading “Firing on All Cylinders: The 2017 Big Data Landscape”
What goes up must go down, and the hype around AI will inevitably deflate sooner or later.
One unfortunate consequence of the hype is that it created the widely-shared perception that AI reached seemingly overnight a stage where it can be fully automated, leading both to endless possibilities, as well as concerns about its impact on jobs and society.
However, this is not the reality just yet and, both in private conversations and on social media, I’m starting to increasingly sense a backlash – the general theme being that “so many humans are involved behind the scenes” in various AI products or companies. This is sometimes ushered in Theranos-Like tones, as if the horrible underbelly of the beast was about to be exposed.
So let’s make it clear: today, scores of humans are involved just about everywhere in AI, whether in tiny startups or massive tech companies. In fact, most AI products are very much NOT fully automated, at least not in an end-to-end, 100% bulletproof way. It is probably ok for the general press to get a bit carried away with AI. However, we in the tech industry should probably better understand this reality, and acknowledge it as a necessary step in the process of building a major new wave of technology products.
Continue reading “Debunking the “No Human” Myth in AI”
A few months ago, Foursquare achieved an impressive feat by predicting, ahead of official company results, that Chipotle’s Q1 2016 sales would be down nearly 30%. Because it captures geo-location data from both check-ins and visits through its apps, Foursquare was able to extrapolate foot-traffic stats that turned out to be very accurate predictors of financial performance.
That a social media company could be building a data asset of immense value to Wall Street is part of an accelerating trend known as “alternative data”. As just about everything in our lives is getting sensed and captured by technology, financial services firms have been turning their attention to startups, with the hope of mining their data to extract the type of gold nuggets that will enable them to beat the market.
Could working with Wall Street be a business model for you?
The opportunity is open to a wide range of startups. Many tech companies these days generate an interesting “data exhaust” as a by-product of their core activity. If your company offers a payment solution, you may have interesting data on what people buy. A mobile app may accumulate geo-location data on where people shop or how often they go to the movies. A connected health device may know who gets sick when and where. A commerce company may have data on trends and consumer preferences. A SaaS provider may know what corporations purchase, or how many employees they hire, in which region. And so on and so forth.
At the same time, this is a tricky topic, with a lot of misunderstandings. The hedge fund world is very different from the startup world, and a lot gets lost in translation. Rumors about hedge funds paying “millions” for data sets abound, which has created a distorted perception of the size of the financial opportunity. A fair number of startups I speak with do incorporate idea of selling data to Wall Street into their business plan and VC pitches, but how that would work exactly remains generally very fuzzy.
If you’re one of the many startups sitting on a growing data asset and trying to figure out whether you can make money selling it to Wall Street, this post is for you: a deep dive to provide context, clarify concepts and offer some practical tips.
Continue reading “The New Gold Rush? Wall Street Wants your Data”
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.
Continue reading “HyperScience and the Enterprise AI Opportunity”
In the early days of Big Data (call it 2009 to 2014), a lot had to do with experimentation and discovery. Early enterprise adopters would play around with Hadoop, the then-new open source framework with a funny name, trying to figure out where the technology fit in the broader landscape of databases and data warehouses. People would also try to figure out what a “data scientist” was – a statistician who can code? An engineer who knows some math? It was a time of hype, immature products and trial and error.
Continue reading “Dataiku or the Early Maturation of Big Data”
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.
Continue reading “Building an AI Startup: Realities & Tactics”
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.
Continue reading “Investing in Frontier Tech”
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)”
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.
Continue reading “The Power of Data Network Effects”