Fundraising: “Always Be Raising” vs “Heads Down”

(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.

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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:

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Fundraising: Clarity of Thought

(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.

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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).

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The Cambrian Explosion of SaaS: In Conversation with Sarah Guo, General Partner, Greylock


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.

Here’s the video, and below are some notes.


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Fundraising: Art vs Science

(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”. 

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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.

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Fundraising: Thinking of Your Process As a Product Launch

For founders thinking through fundraising, here’s a simple mental model I like:

“If this was a product launch, instead of a fundraising process, what would you do?”

Almost by definition, founders are very passionate about launching new products, and a lot of it comes instinctively.  That’s often less the case for fundraising, sometimes considered as a counter-intuitive chore. 

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New Blog Mini-Series: Fundraising

I haven’t written much on this blog about fundraising over the years, in part because there’s already so much good content on the topic out there. 

But each time I get a chance to participate in tech community events, which I have done a fair bit in the last 9-12 months, I’m reminded that, for many founders, fundraising is as opaque and ambiguous a process as ever.  

The venture financing landscape keeps shifting:  dislocation of the traditional seed/A/B/C path, lots of new funds, older funds that evolve their strategies, long bull market (for now), increasing bifurcation between the “haves” (startups that can literally raise billions of dollars of venture money) and “have nots” (the many others that can’t get a simple financing done), etc..  New generations of entrepreneurs arrive on the scene all the time, and have to make sense of a complex process in this shifting environment.  

As a result, for all press about quick oversubscribed rounds and mega-financings, most founders experience a good amount of head scratching and frustration.

So I’m going to do my bit to help clarify, and share a few models and ideas I have learned along the way, in the hope that some entrepreneurs may find it helpful.

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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.

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The Significance of the Datadog IPO

The Datadog IPO just happened, and it’s proven to be a resounding success, not surprisingly given the company’s superb metrics – big revenues ($333M ARR), happy customers that keep buying more (146% net revenue retention) and, unlike many others, a history of profitability. To make the story even more epic, it transpired that the company had turned down a last minute big acquisition offer from Cisco shortly before the IPO, which valued the company higher than its proposed IPO range.

While I’m a small personal shareholder in the company and friendly with its founders, this is not going to be a VC victory lap kind of a post, for the simple reason that I did not invest in the company as a VC (as the early rounds of financing took place before my current tenure, in my defense!).

Regardless, I wanted to write a few quick thoughts, as I believe this particular IPO should be loudly celebrated.

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New Investment: Crossbeam

I’m excited to announce that FirstMark has led a $12.5M Series A investment in Crossbeam, alongside Salesforce Ventures and Hubspot Ventures, with existing investors Uncork, First Round and Slack Fund participating.

At its core Crossbeam is a data escrow service.  It allows companies that are partnered with each other (or looking to be) to combine their data sets (mostly customers and prospects) in a secure, trusted, compliant way, into a third party data warehouse (Crossbeam).  They can then run analytics, without exposing the raw data behind the scenes, to identify opportunities to partner together.

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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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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.

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