AI – Page 2 – Matt Turck

Quick S-1 Teardown: C3.ai

For anyone following the software industry, there’s been a little bit of snark about C3.ai (“C3”) over the years. Here’s a company that was founded by Silicon Valley royalty (Tom Siebel, who sold Siebel Systems to Oracle in 2006 for just shy of $6B), with seemingly limitless access to capital, that somehow seemed to be pivoting every few years to something new – from energy at first, to the Internet of Things, to Artificial Intelligence.

C3 also largely eschewed the startup echochamber – funded personally by its founder at first, it didn’t raise money from the usual VC suspects, target well-know startups as its first customers, or open source any AI frameworks, working instead with a small group of Fortune 1000 and government customers. As a result, it didn’t build the kind of buzz that often precedes the most notable startups on their way to becoming public.

Lo and behold, what emerges in this IPO is a solid company by enterprise software IPO standards, with $157m in revenue, growing 71% yoy, a 75% gross margin and a $69m loss.

It will be interesting to see how the market reacts to this IPO.

On the one hand, C3 is not growing anywhere as explosively as a Snowflake, and in fact seems to have just had a bad quarter of decelerating growth. There are also other concerns, including account concentration and a substantial loss (not as pronounced as a Snowflake or Palantir, but still on the higher range of the software market).

On the other hand, the tailwinds around the deployment of ML/AI in the enterprise are very strong, and C3 is clearly positioning itself as one of the very first enterprise AI companies to go public: its ticker symbol on the NYSE will be “AI”, and the term “machine learning” is mentioned 56 times in the S-1.

This IPO will be an interesting test for the continued appetite of financial markets for all things AI.

Here’s a quick analysis of the S-1 and main characteristics of the business, put together by my FirstMark colleague John Wu and I.

Resilience and Vibrancy: The 2020 Data & AI Landscape

In a year like no other in recent memory, the data ecosystem is showing not just remarkable resilience but exciting vibrancy.

When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Yet, as per Satya Nadella, “two years of digital transformation [occurred] in two months”. Cloud and data technologies (data infrastructure, machine learning / artificial intelligence, data driven applications) are at the heart of digital transformation. As a result, many companies in the data ecosystem have not just survived, but in fact thrived, in an otherwise overall challenging political and economic context.

Perhaps most emblematic of this is the blockbuster IPO of Snowflake, a data warehouse provider, which took place a couple of weeks ago and catapulted Snowflake to a $69B market cap company, at the time of writing – the biggest software IPO ever (see our S-1 teardown). And Palantir, an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22B, at the time of writing (see our S-1 teardown).

When is AI not AI?

Earlier this week, Forbes published a piece on ScaleFactor, a startup using AI to automate accounting, which shut down after raising $100m.

Here’s the heart of the issue covered in the story: “Instead of [AI] producing financial statements, dozens of accountants did most of it manually from ScaleFactor’s Austin headquarters or from an outsourcing office in the Philippines, according to former employees. Some customers say they received books filled with errors, and were forced to re-hire accountants, or clean up the mess themselves.“

Facebook as an AI company: In conversation with Jerome Pesenti, VP of AI, Facebook

While AI may seem like a futuristic goal for most companies around the world, Facebook has already been there for a while. “There’s pretty much a deep learning system in every single Facebook product and they are very much at the core of them” says our guest Jerome Pesenti, VP of AI at Facebook.

Jerome leads the development of artificial intelligence at Facebook, and oversees hundreds of scientists and engineers whose work shapes the company’s direction and impacts our world.

We had had the pleasure of welcoming Jerome at Data Driven NYC in October 2017, in his prior role as CEO, BenevolentAI, and we had chatted about using AI for drug discovery.

It was wonderful to welcome him back in his new capacity at our first **online** Data Driven NYC, courtesy of the coronavirus. It was a fascinating, in-depth conversation.

Below are: a) the video, b) some highlights and c) the full transcript.

Pages: 12

Building a $12B Public Company: In Conversation with Olivier Pomel, CEO, Datadog

By any measure, Datadog is an incredible entrepreneurial success story. The company went from a tiny startup in 2010 that had trouble raising money, to a public company that, at the time of writing, has a market capitalization of $12.5B. It was a pioneer in the category of DevOps and observability, and it’s now a clear leader. With revenues hovering around $350M, it has 1,300 employees across 31 locations around the world.

Perhaps improbably, the founders built the company out of New York, which many people over the years have thought of as a hub for adtech, media and commerce startups only. Along the way, they faced a lot of skepticism: “Whenever we pitched West Coast investors it was sort of seen as a form of mental deficiency to be based in New York and doing infrastructure“, says Olivier. I wrote a few months ago about the significance of the Datadog IPO for the ecosystem and beyond. Ironically, out of the three top public tech companies in New York today, two are infrastructure software companies (Datadog and MongoDB).

Not one for gratuitous self-aggrandizing, Olivier has given surprisingly few interviews over the years, and it was a real treat to sit down with him for a fireside chat in front of a packed house of 350 attendees at our most recent Data Driven NYC.

We had an in-depth conversations and covered a lot of topics.

The first half of our conversation was focused on Datadog itself, starting with a high level overview of the observability and DevOps space to make the discussion approachable by people who don’t know the space.

The second half of the conversation was focused on all sorts of lessons learned along the way of building a major company- sales, marketing, fundraising, etc.

Below is the video. We have also provided a full written transcript to make the content easy to scan through (many thanks to Karissa Domondon for her help with this).

The Power of Open Source: In conversation with Mike Volpi, General Partner, Index Ventures

Our most recent VC guest at Data Driven NYC, Mike Volpi of Index, has had a pretty amazing last couple of years, with three of his venture investments going public: Zuora, Sonos and Elastic.

Before becoming a VC, Mike ran Cisco’s routing business where he managed a P&L in excess of $10 billion in revenues, and acquired over 70 companies (note: probably a pretty good way to make a lot of friends in Silicon Valley).

A partner at Index Ventures in San Francisco, Mike invests primarily in infrastructure, open-source and artificial intelligence companies, so he was a perfect guest to have at the event. In particular, he invested in two prior presenting companies: Confluent and Cockroach Labs (in which FirstMark is also an investor).

We had a really interesting conversation about open source, AI and venture capital. Here’s the video below, and l have jotted down a few notes as well, below the fold.

Notes from the chat:

The Killer App for Machine Learning: In Conversation with Pedro Domingos, Head of Machine Learning, D.E. Shaw

Best-selling author, Professor of Computer Science at the University of Washington, recent recipient of the prestigious IJCAI John McCarthy Award for excellence in artificial intelligence research (among other awards) and Head of the Machine Learning Research group at D.E. Shaw: Pedro Domingos has one of the most incredible resumes in the world of AI, and we were thrilled to host him for a fireside chat at our most recent Data Driven NYC.

We covered a bunch of things, including why finance is a killer app for machine learning, his much-lauded book, ‘The Master Algorithm’ and what’s truly scary about AI (hint: not the Terminator).

AI’s Trust Problem: In Conversation with Gary Marcus (Video + Book Notes)

Should we be worried about the prospect of AI superintelligence taking over the world?

“In the real world, current-day robots struggle to turn doorknobs, and Teslas driven in ‘Autopilot’ mode keep rear-ending parked emergency vehicles […]. It’s as if people in the fourteenth century were worrying about traffic accidents, where good hygiene might have been a whole lot more helpful”.

This is one of my favorite quotes from “Rebooting AI: Building Artificial Intelligence We Can Trust,” a new book by Gary Marcus – scientist, NYU professor, New York Times bestselling author, entrepreneur – and his co-author Ernest Davis, Professor of Computer Science at the Courant Institute, NYU.

Gary did us a big honor recently: he chose to speak at Data Driven NYC on the evening of the publication of the book. He also signed a few copies. Our first book launch party!

Particularly if you’re trying to make sense of the still-ongoing hype around AI, including predictions of global gloom, Gary’s book is a fantastic read: a lucid, no-nonsense and occasionally provocative take on the current state of AI, that distills complex concepts into simple ideas, and includes plenty of interesting and often funny anecdotes.

The book builds on Gary’s earlier assessment of deep learning (see Deep Learning: A Critical Appraisal), and advocates for a hybrid approach to AI.

Below is the video of his talk at the event, plus a notes I derived from both the talk and the book. I’ll keep those brief as the book is worth reading in its entirety.

Decoding the Human Nervous System: In conversation with Thomas Reardon, CEO, CTRL-labs

In its largest acquisition since Oculus in 2014, Facebook just announced last night it acquired CTRL-labs, a 4 year old startup based in New York, for a reported $500M-$1B.

Coincidentally, CTRL-labs CEO, Thomas Reardon (who goes by Reardon) was our guest at Data Driven NYC just a couple of weeks ago. Reardon is a particularly compelling entrepreneur, and this was a fascinating fireside chat, where we dove into machine learning, neuroscience, VR and all sorts of cool topics.

CTRL-labs builds what it calls “neural interface technology”: algorithms that decode the activity of individual motor neurons and turns that into control over machines, thereby completely redefining the interaction between humans and machines. Because the technology captures your intentions without requiring any physical movement, you can do things that you could never do by moving, and you can start “imaging experiences where you would have 20 fingers… or 8 arms or legs”.

The video (below) is well worth a watch in its entirety, including the audience Q&A at the end, and I’ve jotted down a few notes as well, for a quick review.

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.

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.

Continue reading “Scaling AI Startups”

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.

Continue reading “AI & Blockchain: An Introduction”

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.

Continue reading “Great Power, Great Responsibility: The 2018 Big Data & AI Landscape”