Ben Horowitz resoundingly falls in the category of “needing no introduction”: a highly successful entrepreneur who navigated a perilous situation with his business (Loudcloud, which became Opsware) to a $1.65B acquisition by HP, he’s also the founder of premier Silicon Valley venture capital firm Andreessen Horowitz (aka “a16z”), and the best selling author of two books: “The Hard Thing About Hard Things” and the newly-released “What You Do Is Who You Are”.
It was a special treat to host Ben for a fireside chat at the most recent most recent edition of Data Driven NYC – a great evening that included two other terrific speakers: Amr Adwallah, now VP of Developer Relations at Google Cloud, and previously co-founder and CTO at Cloudera (NYSE: CLDR) and Michael James, co-founder of AI chip Cerebras.
We spent a good hour with Ben and covered a bunch of topics, loosely organized in two parts, first AI and data, and then culture an his new book.
Below are two videos covering each part, as well as a FULL TRANSCRIPT for anyone who prefers to read.
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).
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.
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).
Last year, Sarah Guo made news by becoming the youngest General Partner at Menlo Park firm Greylock Partners, and we were delighted to host her at our most recent Data Driven NYC.
Greylock is one of the oldest firms in venture capital, notable in particular for its investments in Facebook, LinkedIn and AirBnB. Greylock has also actively invested in the data ecosystem, including in a number of companies that presented at Data Driven NYC over the years: Cloudera, Sumo Logic, Trifacta, Instabase, etc.
Sarah is mostly focused on enterprise, SaaS and security investments, and we got into a bunch of interesting topics during this conversation.
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.
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.
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.
“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.
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.
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.
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.