Part II: Major Trends in the 2019 Data & AI Landscape

Part I of the 2019 Data & AI Landscape covered issues around the societal impact of data and AI, and included the landscape chart itself. In this Part II, we’re going to dive into some of the main industry trends in data and AI. 

The data and AI ecosystem continues to be one of the most exciting areas of technology. Not only does it have its own explosive momentum, but it also powers and accelerates innovation in many other areas (consumer applications, gaming, transportation, etc).  As such, its overall impact is immense, and goes much beyond the technical discussions below.

Of course, no meaningful trend unfolds over the course of just one year, and many of the following has been years in the making. We’ll focus the discussion on trends that we have seen particularly accelerating in 2019, or gaining rapid prominence in industry conversations.

We will loosely follow the order of the landscape, from left to right: infrastructure, analytics and applications.

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A Turbulent Year: The 2019 Data & AI Landscape

It has been another intense year in the world of data, full of excitement but also complexity. 

As more of the world gets online, the “datafication” of everything continues to accelerate.  This mega-trend keeps gathering steam, powered by the intersection of separate advances in infrastructure, cloud computing, artificial intelligence, open source and the overall digitalization of our economies and lives. 

A few years ago, the discussion around “Big Data” was mostly a technical one, centered around the emergence of a new generation of tools to collect, process and analyze massive amounts of data. Many of those technologies are now well understood, and deployed at scale. In addition, over the last couple of years in particular, we’ve started adding layers of intelligence through data science, machine learning and AI into many applications, which are now increasingly running in production in all sorts of consumer and B2B products.  

As those technologies continue to both improve and spread beyond the initial group of early adopters (FAANG and startups) into the broader economy and world, the discussion is shifting from the purely technical into a necessary conversation around impact on our economies, societies and lives.

We’re just starting to truly get a sense of the nature of the disruption ahead. In a world where data-driven automation becomes the rule (automated products, automated cars, automated enterprises), what is the new nature of work? How do we handle the social impact? How do we think about privacy, security, freedom? 

Meanwhile, the underlying technologies continue to evolve at a rapid pace, with an ever vibrant ecosystem of startups, products and projects, heralding perhaps even more profound changes ahead. In that ecosystem, the year was characterized by the early innings of a long expected consolidation, and perhaps a passing of the guard from one era to another as early technologies are starting to give way to the next generation.

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Data, AI & Hedge Funds: In Conversation with Matt Ober, Chief Data Scientist at Third Point

 

The hedge fund world has been evolving dramatically over the last few years.

Just like in other industries, software, data and AI/ML have been playing an increasingly important, and disruptive, role.  Many hedge funds have been scrambling to embrace this evolution – not just to gain an edge, but also to avoid becoming extinct.

Certainly, quantitative hedge funds have been making heavy use of software and data for a while now.  The “quant” funds rely upon algorithmic or systematic strategies for their trades – meaning that they generally employ  automated trading rules rather than discretionary (human) ones, and they will trade tens or hundreds of assets simultaneously.

But another big part of the industry, the “fundamental” hedge funds, had been operating very differently.  Those funds will perform a bottoms up analysis on individual securities to  value them in the marketplace and assess whether they are  “undervalued” and “overvalued” assets.  They’ll often have a much more concentrated portfolio.

In part because the entire hedge fund industry has been performing generally poorly recently (years of performance trailing the stock market), there’s been mounting pressure on hedge funds to evolve rapidly, particularly fundamental ones.

A couple of years ago, Third Point made a big splash when they hired Matt Ober, who was 32 at the time, to become their Chief Data Scientist.  Dan Loeb, the billionaire founder of Third Point, was a prime example of a fund manager who had reached tremendous success through a fundamental approach.  His efforts to hire Matt away from his previous employer and make him Third Point’s head quant was widely viewed as a sign of the times. Continue reading “Data, AI & Hedge Funds: In Conversation with Matt Ober, Chief Data Scientist at Third Point”

AI & Blockchain: An Introduction

 

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.

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Great Power, Great Responsibility: The 2018 Big Data & AI Landscape

 

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.

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Frontier AI: How far are we from artificial “general” intelligence, really?

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.

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Firing on All Cylinders: The 2017 Big Data Landscape

 

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.

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The New Gold Rush? Wall Street Wants your Data

 

trading-data

 
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.
 

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HyperScience and the Enterprise AI Opportunity

 

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.

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Dataiku or the Early Maturation of Big Data

dataiku_logo_color
 
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.

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Investing in Frontier Tech

drone

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.

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Phosphorus and the Rise of the New Genomics Startup

 

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:

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Is Big Data Still a Thing? (The 2016 Big Data Landscape)

 

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.

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The Power of Data Network Effects

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.

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The Astounding Resurrection of AI [Slides]

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.