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.“
[July 14, 2020 IPO update – stock popped + 195% first day of trading, see end of post for details on IPO]
The upcoming nCino IPO is an interesting story, and a good reminder that strong cloud/SaaS companies can be built outside of the usual Silicon Valley or NYC venture path (even with VCs on board)
(As a side note: we often do those S-1 summaries internally to keep tabs on the software IPO market, so my colleague Avery Klemmer and I figured we’d “open source” this one in case it might be interesting to others – thoughts and comments welcome. Our firm FirstMark is not an investor in nCino)
HIGH LEVEL THOUGHTS & LESSONS:
nCino is a refreshing example of a successful software company that’s built a little bit outside of what’s currently in favor in venture capital circles:
Vertical software: Many VCs these days tend to prefer broad horizontal opportunities, with a concern that vertical software ultimately has a limited TAM, even a large one – but nCino is 100% focused on the banking market
Service heavy, no bottoms up GTM: nCino sells its products through AEs , with long sales cycles, and significant implementation services are involved
Platform dependency? Being built on top of someone else’s platform is often a concern for investors. nCino is built on top of Salesforce (like Veeva). They’ve seemingly safeproofed this relationship by reselling Salesforce products in their deals, and raising money from Salesforce, wnich is a large investor.
Not a Silicon Valley or NYC story: Launched in 2012 by executives of North Carolina-based Live Oak Bank as a spin-off venture. Still based in Wilmington, North Carolina
It’s a spinoff from a bank: the company was originally founded as a majority-owned subsidiary of Live Oak Bancshares, a bank holding company. It then raised $9m in seed funding in 2013 from a variety of individuals including John Mack of Morgan Stanley and Chip Mahan, the Chairman of Live Oak Bank. So nCino didn’t have the structure that most VCs like to see, where the founding team has high ownership and the first money in comes from institutional investors. This history as a spin-off is probably the reason why the CEO of the company owned only 1.6% at IPO time, although we don’t know this for sure).
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.
While it’s certainly possible to build a tech giant solely in Europe, the path to building a global, category-dominating company will, for most European tech startups, require building a strong presence in the US.
As a result, sooner rather than later, European startups will start thinking through their US expansion strategy. One deceptively simple question of that strategy is “where should we build our US headquarter”?
Up until a few years ago, there wasn’t much of a debate: Silicon Valley, despite the distance and time difference with Europe, was the obvious choice. There was essentially nowhere else to go, except perhaps Boston for life sciences.
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).
No longer. But, also, kinda, yes… It’s complicated.
When people talk about “fundraising seasons”, they mean that there are certain times of the calendar year when you should be running your fundraising process. And conversely, there are times of the year when you shouldn’t, because venture capitalists won’t be paying attention.
Fundraising can feel like an awful lot of pushing. You push to get an introduction to investors, you push to get them to commit to a meeting, you push to convince them, you push to get them to issue a term sheet. Push, push, push. Historically, and for most people still, “push” has been the default mode.
But as an industry, we live in interesting times, and there’s more of an opportunity for founders to be in “pull” mode, a situation where investors come to you and do the courting and convincing – obviously, a much better position to be in for founders, especially when the investor is senior enough to write the check.
(This is the fourth post in this fundraising mini-series: quick, simple ideas that I’ve used in various fundraising conversations over the years, that I’m sharing here, one by one)
If there’s one tactical topic everyone seems to have a strong opinion on when it comes to fundraising, it’s whether entrepreneurs should be actively talking to new VCs *in between* rounds of financing, for relationship building purposes.
Many founders have had the same experience: something public or semi-public comes out about your company (a funding announcement, a press article, a blog post, a tweet, even a LinkedIn update of some sort…) and, voila, your inbox starts filling up with emails, typically from VC firm associates saying that they “heard good things” about your company and would “love to catch up”. At first, it may be vaguely flattering, but as more emails pile up, it gets tedious, sometimes overwhelming. And perhaps slightly annoying: everyone says you’re supposed to get a warm intro to a VC, but then VCs can just email you cold, and somehow they expect you to drop everything you’re doing to talk to them? Sheesh, the nerve.
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.
(This is the third post in this fundraising mini-series: quick, simple ideas that I’ve used in various fundraising conversations over the years, that I’m sharing here, one by one)
You’ll often hear VCs recall how they knew they wanted to invest in a startup within the first 10 minutes of a one-hour pitch meeting with the entrepreneur.
For this to happen, a lot needs to align, both in terms of fit (right company for the right investor at the right time) and intrinsic merits of the opportunity (quality of the founding team, metrics, etc). But ultimately investors describe the experience less as checking a lot of boxes, and more as something akin to a state of flow: seeing, through the eyes of a founder, a future that is both exciting and inevitable.
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.
(This is the second post in this fundraising mini-series: quick, simple ideas that I’ve used in various fundraising conversations over the years, that I’m sharing here, one by one)
Much of the frustration that startup founders experience about fundraising is due to the lack of clarity around two simple questions: “What do investors want?” and “How do they make investment decisions?”
Part of what makes the exercise such a “black box” is that the answer is often “it depends”. Investment decisions are made by humans, based on imperfect information, in an environment that constantly changes.
In addition, parameters evolve with each round: different expectations, criteria, and processes, and often different venture firms. You may feel you’re building the same company all along, but investors at different stages will be looking at it in very different ways.
As a starting point to understand how investors (angels and VCs) make decisions, one simple framework that I find myself using in conversations about fundraising is “art vs science”.
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.
