Matt Turck

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.

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.

New Investment: Text IQ

Great power, great responsibility: as the data revolution continues, and companies big and small are increasingly able to collect and analyze massive amounts of data, they are facing rapidly mounting pressure to become much better data stewards. Security, privacy and compliance were largely an afterthought, but there are becoming crucial topics. Regulation is rapidly spreading around the world – witness for example GDPR, CCPA and the even more ambitious New York privacy bill.

As abundantly evidenced in the news, corporations are woefully unprepared to handle the challenge of identifying, managing and securing sensitive data in its various forms — PII, PHI, attorney privileged information, etc. The problem keeps getting worse as the amount of unstructured data keeps increasing in the enterprise. Too often, corporations tend to deal with it after the fact, once a crisis has occurred, in the context of litigation. Only a very small minority is able to address those issues proactively.

Data Science at Massive Scale: In Conversation with Solmaz Shahalizadeh, Shopify

Shopify (NYSE:SHOP) is one of those unlikely success stories that entrepreneurial dreams are made of.

In 2006, co-founder and CEO Tobias Lutke was a 24 year old German autodidact programmer who had followed his girlfriend to Ottawa, Canada. He partnered with an older entrepreneur, Scott Lake, to start an eCommerce business selling snowboards, Snowdevil. As Tobi realized there was no decent out of the box framework to build an e-commerce store at the time, he started building the Daredevil snowboard store from scratch, using the then nascent Ruby on Rails. Word spread out within the community about the quality of his work, and the duo decided to focus on the software platform, rather than the snowboard store. A world away from Silicon Valley, Shopify was born.

Fast forward to today , with many steps along the way, including a Series A round of financing in which our firm FirstMark invested: Shopify is a ~$34B public company that’s grown extremely fast in recent years and helps SMBs outfit their stores with a variety of essential tools. Shopify powers the online stores of more than 800,000 merchants in over 175 countries.

As tends to be the case for all major Internet franchises, Shopify recognized early the transformational power of harnessing and using data. Data science and machine learning were used in one product, then the next and over the years have become a cornerstone of the company.

The Path to a $2.7B Unicorn: In Conversation with Zach Perret, CEO, Plaid

“We are, by far, the earliest company here”. This how Zach Perret, CEO of Plaid, started his talk at his first appearance at Data Driven NYC, back in February 2013. “We are basically three guys, coding 24 hours a day, and building developer tools…”.

Fast forward to today: the company was valued at $2.7B (“allegedly”, says Zach) in its most recent $250M round; Plaid has integrated with 15,000 banks in the U.S. and Canada and 4,000 fintech applications. One in four people in the U.S. have linked an account using Plaid. And they have just acquired New York based competitor Quovo for $200M (“reportedly” as well).

Not bad for a self-described “data plumbing” company. As today’s consumers expect to live fully digital financial lives, with their phone at the core, Plaid provides the financial infrastructure that enables developers in fintech companies to build great applications, and have consumers connect those to their bank accounts – basically Plaid is the connective tissue between the app and the bank, and takes care of moving all the data back and forth in the background.

It was a lot of fun having Zach back at the event 6 years later. Here’s the video of our fireside chat, and my notes are below the fold.

Bootstrapping

Perhaps this is slightly strange for an early stage venture VC, but I’m fascinated by entrepreneurs who bootstrap their tech startup and build them into very large, industry-leading companies.

The odds of building a massive company are low enough for the lucky few that manage to raise tens (or hundreds) of millions of venture capital money but, now, doing it with no outside investment? That is a really hard way to do it.

It can be a really long journey, as well. In fact, for all the obvious advantages of bootstrapping (less/no dilution, more control, etc.), the main trade-off involved in bootstrapping seems to be… time. It just takes longer to build a product and get to early scale simply based on cash-flow (or a small amount of debt or founder money).

I tweeted this a couple of days ago, and it led to an interesting thread:

Go to Market Strategies for Enterprise Startups: In Conversation with Martin Casado, GP, Andreessen Horowitz

What is the number one mistake technical founders make? Why is pricing so important? Should entrepreneurs avoid at all costs having a service component to their business? What is fundamentally new and different in go to market strategies for modern enterprise software startups?

A self-avowed “failed physicist”, Martin Casado is a General Partner at Andreessen Horowitz, and previously was the co-founder and CTO of Nicira, a pioneer in software-defined networking and network virtualization that was acquired by VMware for $1.26 billion.

I have had the pleasure of getting to know Martin through the board of ActionIQ, a great NYC startup in which we are both investors.

Martin joined us for a fireside chat at the most recent edition of Data Driven NYC. The conversation centered largely around one of Martin’s favorite topics, go to market strategies for enterprise startups. There’s plenty of interesting thoughts and directly applicable advice for entrepreneurs in there, as Martin spoke as much from his previous founder experience as he did as a VC.

Here’s the video, and my notes from the chat are below the fold.

From 0 to $200M in Revenues in 7 years: In Conversation with Ben Uretsky, Co-Founder, Digital Ocean

Who would be crazy enough to compete head-on with AWS?

The question was almost as obvious seven years ago than it is today. Yet in just a few years since its founding, Digital Ocean, a cloud infrastructure startup based in New York with data centers around the world, has managed to build a very impressive and fast-growing business, successfully competing with the giants of cloud computing.

Ben Uretsky, co-founder of the company (with his brother Moisey and 3 others) and its CEO from 2011 to 2018, stopped by for a chat at Data Driven NYC to tell the story of the company and share some lessons learned.

Here’s the video, and below are my notes from our great chat.

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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”

Scaling AI Startups

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.

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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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Beyond IoT: Building Decentralized, Intelligent Infrastructure

As I wrote recently, the Internet of Things (IoT) has been experiencing, at a minimum, some serious growing pains. This is particularly true for consumer IoT where a lot of old issues (interoperability) remain, while others (security) are becoming more concerning. With a few bright exceptions, many consumer IoT products solve first-world problems, often representing a marginal improvement over existing solutions.

But the IoT was always meant to be more ambitious and exciting than just the smart home, the factory or other discreet “single-player mode” use cases. The internet of things was always about networks, where connected objects could be tracked and activated across wide geographic areas, supply chains, health systems and other contexts representing trillions of dollars of economic value.

Rather than IoT, perhaps we should start using the expression “intelligent infrastructure” more frequently to describe those networks. With the parallel progress of machine learning at the edge, intelligent infrastructure will enable software-based intelligence to permeate the physical world, enabling real-time optimization and orchestration of connected “things” (objects, vehicles, machines, buildings), at a system level. Uber, Lyft and others give us perhaps the closest approximation what such networks could look like at scale, except that, in an intelligent infrastructure paradigm, such communications would be machine-to-machine, with no human in the loop.

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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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The Compression of the Hype Cycle

I spend a lot of time thinking about hype cycles, across industries (Big Data/AI, IoT) and ecosystems (New York).

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.

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