Interview with Machine Learnings

A few days ago, I sat down Sam DeBrule of Machine Learnings for a broad conversation about AI and startups.  We got into a number of topics including creative data acquisition tactics, data network effects, and what makes AI startups different.

The interview is here:  Why AI Companies Can’t Be Lean Startups – A Conversation with Matt Turck of FirstMark Capital.

 

Firing on All Cylinders: The 2017 Big Data Landscape

 

It feels good to be a data geek in 2017.

Last year, we asked “Is Big Data Still a Thing?”, observing that since Big Data is largely “plumbing”, it has been subject to enterprise adoption cycles that are much slower than the hype cycle.  As a result, it took several years for Big Data to evolve from cool new technologies to core enterprise systems actually deployed in production.

In 2017, we’re now well into this deployment phase.  The term “Big Data” continues to gradually fade away, but the Big Data space itself is booming.  We’re seeing everywhere anecdotal evidence pointing to more mature products, more substantial adoption in Fortune 1000 companies, and rapid revenue growth for many startups.

Meanwhile, the froth has indisputably moved to the machine learning and artificial intelligence side of the ecosystem. AI experienced in the last few months a “Big Bang” in collective consciousness not entirely dissimilar to the excitement around Big Data a few years ago, except with even more velocity.

2017 is also shaping up to be an exciting year from another perspective: long-awaited IPOs.  The first few months of this year have seen a burst of activity for Big Data startups on that front, with warm reception from the public markets.

All in all, in 2017 the data ecosystem is firing on all cylinders.  As every year, we’ll use the annual revision of our Big Data Landscape to do a long-form, “State of the Union” roundup of the key trends we’re seeing in the industry.

Let’s dig in.

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Debunking the “No Human” Myth in AI

 

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What goes up must go down, and the hype around AI will inevitably deflate sooner or later.
 
One unfortunate consequence of the hype is that it created the widely-shared perception that AI reached seemingly overnight a stage where it can be fully automated, leading both to endless possibilities, as well as concerns about its impact on jobs and society.
 
However, this is not the reality just yet and, both in private conversations and on social media, I’m starting to increasingly sense a backlash – the general theme being that “so many humans are involved behind the scenes” in various AI products or companies.  This is sometimes ushered in Theranos-Like tones, as if the horrible underbelly of the beast was about to be exposed.
 
So let’s make it clear: today, scores of humans are involved just about everywhere in AI, whether in tiny startups or massive tech companies.  In fact, most AI products are very much NOT fully automated, at least not in an end-to-end, 100% bulletproof way.   It is probably ok for the general press to get a bit carried away with AI.  However, we in the tech industry should probably better understand this reality, and acknowledge it as a necessary step in the process of building a major new wave of technology products.
 

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The New Gold Rush? Wall Street Wants your Data

 

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A few months ago, Foursquare achieved an impressive feat by predicting, ahead of official company results, that Chipotle’s Q1 2016 sales would be down nearly 30%. Because it captures geo-location data from both check-ins and visits through its apps, Foursquare was able to extrapolate foot-traffic stats that turned out to be very accurate predictors of financial performance.
 
That a social media company could be building a data asset of immense value to Wall Street is part of an accelerating trend known as “alternative data”. As just about everything in our lives is getting sensed and captured by technology, financial services firms have been turning their attention to startups, with the hope of mining their data to extract the type of gold nuggets that will enable them to beat the market.
 
Could working with Wall Street be a business model for you?
 
The opportunity is open to a wide range of startups.  Many tech companies these days generate an interesting “data exhaust” as a by-product of their core activity.  If your company offers a payment solution, you may have interesting data on what people buy. A mobile app may accumulate geo-location data on where people shop or how often they go to the movies.  A connected health device may know who gets sick when and where.  A commerce company may have data on trends and consumer preferences. A SaaS provider may know what corporations purchase, or how many employees they hire, in which region. And so on and so forth.
 
At the same time, this is a tricky topic, with a lot of misunderstandings. The hedge fund world is very different from the startup world, and a lot gets lost in translation.  Rumors about hedge funds paying “millions” for data sets abound, which has created a distorted perception of the size of the financial opportunity.  A fair number of startups I speak with do incorporate idea of selling data to Wall Street into their business plan and VC pitches, but how that would work exactly remains generally very fuzzy.
 
If you’re one of the many startups sitting on a growing data asset and trying to figure out whether you can make money selling it to Wall Street, this post is for you: a deep dive to provide context, clarify concepts and offer some practical tips.
 

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HyperScience and the Enterprise AI Opportunity

 

Today our portfolio company HyperScience is coming out of stealth and talking a bit more about what they’ve been working on for the last couple of years. We have been involved for a little while already as lead Series A investors, and we are excited to now be joined today by our friends at Felicis, a great addition to a strong syndicate from both coasts that also includes Shana Fisher (Third Kind) who led the seed, AME Cloud Ventures, Slow Ventures, Acequia, Box Group and Scott Belsky.  The company is announcing today a total of $18M in Series A investment.

HyperScience offers AI solutions targeting Global 2000 corporations and government institutions. Their products enable those customers to automate or accelerate a lot of dusty back office processes, particularly those that involve the manipulation and triage of large amounts of documents and images.

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Building an AI Startup: Realities & Tactics

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Artificial intelligence is, of course, all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants, often early in the life of their startups.

As always, the reality of building a startup is different, especially when one aims to build a self-standing company for the long term.  The path to success in AI requires not just technical prowess but also careful thinking and execution through a range of strategic and tactical questions that are specific to this domain and market.

One possible framework to think through these topics is this “5P”list: Positioning (finding blue ocean), Product, Petabytes (data), Process (social engineering) and People.

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