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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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.

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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.

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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:

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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.

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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”