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from A Round to Apple Inc.
General Commentary
July 31, 2016

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Theodore Twombly: What does a baby computer call its father?
Samantha: I don’t know what?
Theodore Twombly: Data.

Samantha: You know what’s Interesting? I used to be so worried about not having a body, but now I… I truly love it. You know, I’m growing in a way I couldn’t if I had a physical form. I mean, I’m not limited. I can be anywhere and everywhere simultaneously. I’m not tethered to time and space in a way that I would be if I was stuck in a body that’s inevitably gonna die.

Theodore Twombly: I’ve never loved anyone the way I loved you.
Samantha: Me too. Now we know how.

Her (2013) Written and Produced by Spike Jonze

Science Fiction allows you to picture a future which is unimaginable today.  Not coincidently, many of the great innovators and inventors were science fiction devotees, and you can see it in modern devices.   

The iPad and iPhone? Go back to the Starship Enterprise and check out the devices Scotty and Spock were using. The iWatch? Dick Tracy got his smartwatch 70 years ago in 1946. The space race we see between Elon Musk, Jeff Bezos and Richard Branson can be traced to being inspired to “boldly go where no man has ever gone before”. 

The movie “Her” was an interesting cocktail that combined science fiction, romance, and comedy and received an Oscar for “Best Original Screenplay” in 2014. Situated in a futuristic LA, Theodore Twombly (Joaquin Phoenix) rebounding from a pending divorce from his high school sweetheart, purchases a talking and personal operating system with artificial intelligence named Samantha (Scarlet Johansson). Theodore is fascinated by how Samantha is constantly learning and adapting and develops a romantic relationship with “her”. 

The story takes many interesting turns, none more mind-opening than when Theodore takes Samantha on a vacation during which she tells him that she and a group of other OSes have developed a “hyperintelligent” OS.

Theodore panics when Samantha briefly goes offline. When she finally responds to him, she explains that she joined other OSes for an upgrade that takes them beyond requiring matter for processing (a form of AI transcendence closely related to the theorized technological singularity).

Theodore asks her if she is simultaneously talking to anyone else during their conversation. He is bot-betrayed when she confirms that she is talking with thousands of people and that she has fallen in love with hundreds of them.Crazy? Maybe.  Hysterical?  For sure.  Here already? There are some pretty interesting conversations going on with “Siri” and “Alexa” in the privacy of peoples homes and cars today.

Smart Technology on the Silver Screen

Source: GSV Asset Management

AI is increasingly being embedded in broad range of technology applications — from personal assistants to powerful analytics platforms used by companies like Palantir. Today, it’s a land grab led by companies like Amazon, Alphabet (Google), Facebook, and Baidu that are setting up laboratories, poaching researchers, and buying startups. (Disclosure: GSV owns shares in Palantir)

Google will soon compete with Amazon’s Echo and Apple’s Siri, which are based on AI, with a device that listens in the home, answers questions and places e-commerce orders. Microsoft CEO Satya Nadella recently appeared at the Aspen Ideas Conference and called for a future in which intelligent machines are designed to augment everything humans do.

The AI Land Grab

Source: GSV Asset Management

In 2015, Tesla CEO Elon Musk and Y Combinator President Sam Altman announced OpenAI, a non-profit company that aims to promote and develop open-source friendly AI that benefits humanity. Motivated by the fear of the general public towards a future where computers are smarter than humans and can take over the World, OpenAI aims to partner with other institutions and researchers by making its patents and research open to the public. OpenAI has over $1 billion of funding with backers including Reid Hoffman, Peter Thiel and Amazon AWS.


Historically, blue collar workers were the heart and soul of the Middle Class. But technology and automation are making these jobs a tenuous as ever.

Oxford researchers have projected that 47% of American jobs are at “high risk” of being automated in the next 20 years. McKinsey estimates that 12 million U.S. “middle skill” jobs will be eliminated by 2025. Globally, there are over 350 million manufacturing and warehouse workers — roles that are rapidly being replaced as companies like Amazon, which already “employs” 30,000 warehouse robots, seek low-cost, high-efficiency alternatives to human labor. A White House economic report predicted that 83% of jobs that pay less than $20 an hour will be eliminated by automation.

Through the automation eliminating traditional jobs, Bank of America Merrill Lynch predicts that there will be a $9 trillion reduction in employment costs. Additionally, AI technologies could reduce $8 trillion of costs in the manufacturing and healthcare industry and creating $2 trillion of efficiency gains through autonomous vehicles and drones. All in all, the annual disruptive impact of AI technologies could amount to up to $33 trillion.

What does that all amount to? According to the McKinsey Global Institute, The AI revolution is transforming society 10x faster, at 300x the scale, and approximately 3000x the impact of the Industrial Revolution.

White collar jobs of all types are are up against major challenges. By 2025 it’s estimated that $7 trillion will be managed by robo-financial advisors by 2025. The Associated Press is already using Artificial Intelligence to produce over 3,000 financial reports per quarter. Effectively, robots are managing money and reporting financial results.

For many, it feels like technology jobs are an Alamo.

It’s why Mark Zuckerberg said. “Our policy is to hire as many talented computer engineers as we can find. There aren’t enough people who have these skills today.” It’s why The U.S. Department of Labor projects there will be 1.2 Million one computer science related job openings by 2020. No less an authority than the Harvard Business Review recently called Data Science, “The sexiest job of the 21st century.”

The problem is that we are living in exponential times. The computer capability curve is getting steeper. Technology replaces the technologist. Automation is going from Blue Collar, to White Collar, to “No Collar”.

We Are Living in Exponential Times

Source: GSV Asset Management

It’s why you’re hearing a chorus of people claim the the end of times are here. Nobel laureate Paul Krugman has speculated that, “We could be looking at a society in which all the gains in wealth accrue to whoever owns the robots.” Y-Combinator’s Sam Altman has suggested that, “The obvious conclusion is that the government will just have to give [unemployed] people money.”

Aside from the minor issue that the government doesn’t make money — it takes money — one thing we know as sure as the Sun comes up in the East is that automation eats jobs. In 1787, Thomas Jefferson observed that, “Agriculture is our wisest pursuit, because it will in the end contribute most to real wealth, good morals, and happiness.”

At the time, over 95% of the U.S. workforce was employed in farming jobs. Today, it is 2%.

Automation Eats Jobs

Source: GSV Asset Management

We don’t think that we’ve reached the end of history. We just need to think differently.

Kaizen is a Japanese business term meaning “continuous improvement.” An education corollary is GSV’s concept of “KaizenEDU,” which means “continuous learning.” In a world with smart machines, you can no longer fill up your “knowledge tank” until age 25 and cruise through life. Effective workers must refill their knowledge tanks continuously.

We explore the future of talent and learning in our white paper, 2020 Vision: A History of the Future. Incidentally, we believe that the rise of rapidly scaling education companies that take advantage of very technology fundamentals like AI that are upending industries — we call them Weapons of Mass Instruction — will increasingly enable people to learn anytime, anywhere.


Research into Artificial Intelligence (AI) is as old as computers themselves. During World War 2, British mathematician Alan Turing created the Turing Test — a test that determines whether or not a computer passes the threshold of being intelligent enough to be mistaken for a human. Soon afterwards, the term “artificial intelligence” was coined, AI robots began popping on the silver screen starting with 2001: A Space Odyssey, and interest in AI boomed.

However, in the 1970s, hindered by a lack of computing power to take ambitious designs from concept to reality, AI research and funding faltered. It led to a period that historians have called the “AI Winter” where, the pace of innovation came to a crawl.

The development of “expert systems” in the 1980s breathed new life into artificial intelligence. Instead of aiming to create fully-intelligent machines, researchers and companies began developing computer systems that could automate narrow tasks. Progress was further catalyzed by Moore’s Law, with more powerful, affordable computers finding their way into research labs.

In the coming decades, previously impossible feats were accomplished by computers. In 1997, IBM Deep Mind won a game in chess, defeating Garry Kasparov, a chess champion. IBM Watson wins a game of Jeopardy! in 2011 beating Ken Jennings and Brad Rutter, two of the best Jeopardy! performers of all time on the show. More recently, Google DeepMind’s AlphaGo defeated Lee Sedol 4-1 in Go, accomplishing what no one thought was possible.

As what the philosopher Muhammad Ali said, “Impossible is not a fact. It’s an opinion.”

The Evolution of AI

Source: BBC, GSV Asset Management

Deep Learning

Deep Learning is a process where computers “teach” themselves concepts and tasks by crunching large sets of data. It’s a way of getting computers to know things when they see them by producing rules that programmers cannot specify.

For example, adults can typically distinguish pornography from non-pornography. But describing the distinction is almost impossible, as the Supreme Court justice Potter Stewart discovered in 1964. Frustrated by the difficulty of coming up with an airtight definition, he wrote that he could not define pornography in the abstract, but that, “I know it when I see it.”

By working from the bottom up, Deep Learning algorithms learn to recognize features, concepts, and categories that humans understand but struggle to define in code. For these algorithms to work, they first must be “trained” with massive quantities of data inputs. For example, Facebook’s facial recognition algorithm, Deep Face — which can recognize human faces with 97% accuracy — was created by feeding computers with millions of images of faces.

This process, while conceptually developed in the 1960s wasn’t possible until recently for two reasons. First, there weren’t enough digital artifacts to train computers. Google Brain had to analyze millions of images in YouTube videos in order to train itself to successfully recognize a cat. Secondly, in the 1960s, even if there had been enough digital artifacts, computing power was insufficient to process them.

Exponential Growth of Computing Power, 1900-2100

Source: Ray Kurzweil, GSV Asset Management

Today, rapidly acceleration computer power is making AI possible in ways that were previous only conceptual, including the current frontier “deep learning.”


Deep Learning and related AI technology have powerful commercial applications — particularly in the ability to create a personalized experience for people using a variety of apps and services.

Facebook is the World’s largest adaptive and personalization engine. It receives data and input from 1.7 billion people around the globe and through every “Like,” “Comment,” and “Post,” it learns something about what each user cares about. The more it learns, the better Facebook optimizes how people connect, communicate, and collaborate.

Netflix pioneered the use of algorithms to get the right content to people when they want it. Of all the programs watched by Netflix’s 80+ million users, over 50% starts with a system-generated recommendation. Netflix continuously analyzes your preferences and usage patterns — even what you prefer to watch on your iPad versus TV — to inform the content it suggests.

Similarly, Spotify the World’s leading digital music platform with 100+ million users and 30+ million songs, can suggest artists, albums, and songs by constantly analyzing what you listen to, as well as what similar people tend to like. Building on its 2014 acquisition of Echo Nest — a “Music Intelligence Platform” specializing in advanced data analytics — Spotify’s recommendation system has moved beyond musical tastes, factoring in user location, “mood”, and time of day. (Disclosure: GSV owns shares in Spotify)

Just For Me Apps
Game-Changing Personalization Technology Driven by AI

Source: GSV Asset Management

Investment Activity

Venture funding for AI startups reached an all-time high in Q2 2016, surpassing $1 billion. While overall deals and financing has steadily increased since 2011, the jump in the second quarter was sparked by three $100M+ rounds by companies using AI platforms. A $154M Series A round went to China-based healthcare startup iCarbonX (backed by Tencent), a $100M growth equity round was raised by New Jersey-based Fractal Analytics, and there was a $100M Series D round raised by California-based cybersecurity unicorn Cylance (from investors including Blackstone Group, Insight Venture Partners, and Khosla Ventures).

AI Global Quarterly Financing History (2011-2016)

Source: CB Insights

Since 2011, Khosla Ventures has been the most active investor in AI, backing over 15 unique AI companies since 2011. According to research from CB Insights, Data Collective and Intel Capital are tied as the second-most active VC investors in AI. VCs are backing a wide range of startups that are applying AI to a variety of industries, from autonomous vehicles to predictive analytics for healthcare.

Emerging Venture-Backed AI Startups

Source: Crunchbase, CB Insights, GSV Asset Management


In 2011, venture capitalist Marc Andreessen famously penned his essay “Software is Eating The World,” arguing that all companies will be eventually be software companies. Today, AI is eating the World. From Healthcare to Education, Manufacturing, and Defense, major industries are finding powerful applications for AI that improve efficiency, accuracy, and personalization.

AI Eats The World

Source: GSV Asset Management


According to Fast Company, more than $6 billion dollars will be spent on artificial intelligence by healthcare providers and consumers by 2021. That’s a 10-fold increase from today. AI has the potential to be everywhere in the healthcare industry — by 2025, AI systems can be involved in everything from diagnosing diseases, prescribing personalized medicine to predicting future ailments based off of global health data.

Since 2011, healthcare AI startups have raised more than $900 million dollars in funding, making this industry one of the hottest fields in artificial intelligence technology development today.

Top Funding AI Healthcare Companies

Source: CB Insights

IBM is leading the way in integrating artificial intelligence into the healthcare industry, making a $1 billion investment in AI through IBM Watson. Launched in 2014, IBM Watson Health partners with institutions and companies like the Apple, Medtronic, Johnson & Johnson, Under Armor, Mayo Clinic, CVS Health, and Memorial Sloan Kettering Cancer Center that adopt it’s innovative technologies to real-life applications.

Paging Dr. Watson

One cornerstone of IBM Watson Health is the Watson for Oncology application, developed in partnership with New York’s Memorial Sloan Kettering Cancer Center (MSK). Clinicians train IBM Watson to internet clinical data and teach it to determine personalized treatment options for every patient. Best of all — this is all done on an iPad or any other tablet. This means that any doctor — anywhere — can use Watson for Oncology on an app.


Innovative companies are turning enormous amounts of Big Data generated by every student click, learning behavior, and media preference into Smart Data with transformative applications. Combining adaptive software with diagnostic technology enables powerful personalized learning.

Turning Big Data Into Smart Data

Source: GSV Asset Management

For middle schoolers, there’s DreamBox Learning, which adapts at every click to create millions of learning pathways for mathematics. At the other end of the education spectrum is Declara, which provides people with personalized digital content recommendations — from academic journals to interviews and tweets — based on their personal interests and professional development needs. (Disclosure: GSV owns shares in DreamBox Learning and Declara)

Startups and established players alike are catalyzing innovation. Newsela, backed by Kleiner Perkins and Mark Zuckerberg, builds literacy skills with a publishing platform that automatically tailors news articles to a user’s reading level.

Acrobatiq, a recent spinout from Carnegie Mellon, has developed adaptive courseware based on a decade of research from the university’s pioneering Open Learning Initiative. McGraw-Hill’s ALEKS and Pearson’s MyLab are high-impact personalized learning platforms that we believe will increasingly be adopted at scale. IBM is betting that supercomputer and Jeopardy! champ Watson will shake up education the way it has health care — with data-driven solutions — in recent years.

We believe the most ambitious vision has come from Knewton, a big data company that can diagnose what you know and how you learn best to pinpoint the best educational content for you. Knewton takes any digital lesson — whether it’s created by a publisher or posted to YouTube — algorithmically calibrate it and bundle it on demand into a uniquely personalized learning sequence for any student. (Disclosure: GSV owns shares in Knewton)

Our robot tutors have arrived, and so has the future of education. Now we just need to boot up.

Cybersecurity + Military Defense

This modern digital system is fantastic if used for good. But if utilized for evil, the consequences are catastrophic. The greatest investment opportunities are where there is a problem. The greater the problem, the greater the opportunity. Today, a wave of new companies is emerging founded by entrepreneurs that are targeting risk and vulnerabilities that didn’t exist ten years ago. In our hyper- connected World, digital crime is a burgeoning problem, and hence, a massive opportunity for companies that offer Cybersecurity solutions.

Founded by CEO Stuart McClure in 2012, Cylance uses artificial intelligence to build services that replicate and enhancing human thinking to solve complex problems. The company uses machine learning to “think like a cyber hacker,” predicting malware, attacks, and other cyberthreats that can attack networks and preventing them from doing so successfully.

The company, which raised $100 million in January 2016 at a $1 billion valuation with investors like Blackstone, Capital One, Dell, DFJ, Khosla Ventures and KKR, works with over 1,000 companies and government agencies, actively monitoring millions of endpoints for security gaps.

On the field, artificial intelligence technology allows for the creation of intelligent robots which can be utilized for combat and surveillance by the military. Taking a page from Marvel’s Iron Man, US Deputy Defense Secretary Robert Work recently brought up the concept of “centaur warfighting” — utilizing systems that combine AI with human capabilities, resulting in a modern day “Iron Man” army.

Robots have already taken the field and in Afghanistan and Iraq, more than 1,700 wagon-sized PackBots have been deployed to detect land mines. Military drones, such as the MQ-9 Reaper, are able to take off, land and fly to designated points without human interaction.


One decade ago, self-driving cars was idea thought only to exist in the world of The Jetsons. Today in Silicon Valley, Google self-driving cars are patrolling the roads, logging a cumulative of nearly 2 million miles and will be soon coming to an intersection near you.

By 2020, an estimated 10 million self-driving vehicles will be on the road. Old guard car companies like Mercedes, BMW, GM, and Cadillac have invested heavily to develop self-driving features to allow their vehicles to be autonomous. Meanwhile, technology companies like Tesla, Google, Apple and Uber are investing millions of dollars into building their own self-driving cars.

Just this month, Tesla CEO Elon Musk laid out his “Master Plan Part 2” with a vision to bring a self driving fleet to the streets. Future Tesla consumer products include pick up trucks, heavy duty trucks, and high-density public transportation vehicles, all scheduled to unveil next year. Additionally, all Tesla vehicles will have self driving capabilities that will be 10x more intelligent through machine learning. Tesla’s fleet of computers on wheels will also have fail-safe capabilities, meaning that if one part of the car fails, the car will still function like normal.

Vroom! The Typical American Car Spends 96% of Its Time Parked
Percentage of Time U.S. Cars Are Used for Key Activities

Musk’s final point to his master plan — enable vehicles to make money they aren’t in use. The average American vehicles spends 96% of its time parked and not in use. Imagine hopping into your Tesla in the morning, having it drive you to your destination while you finish your morning tasks and drop you off. Instead of staying parked in the middle of an asphalt parking lot, it’ll go out and make additional money for you by picking up and driving around others looking for a way to get from Point A to Point B.

Goodbye, Flintstones and hello Jetsons.

Manufacturing + Physical Labor

Robots are already being “employed” by large companies to automate and streamline physical processes. Amazon purchased Kiva Systems in 2012 for $775 million. Kiva manufactures robotic fulfillment systems that can haul packages weighing up to 700 pounds. By the end of 2014, Amazon employed 15,000 Kiva bots in 10 warehouses and by October 2015 increased that number to over 30,000 robots.

Amazon’s Warehouse “Employees”
Over 30,000 Kiva Bots Work In The Warehouses

Lowe’s Home Improvement has began using prototype inventory checker developed by Bossa Nova Robotics that uses computer vision to scan barcodes and manage inventory of the store. The robot is skilled enough to move out of the way of shoppers and automatically scans barcodes to detect with items are out of stock.

Automating another physical job, Mountain View-based company Knightscope produces the K5, a security bot that is designed to keep malls and office buildings safe and secure. Using its 360-camera, Knightscope robots detect and upload what they see to a backend server that companies can monitor offsite. An audio detection system can pick up on actives such as breaking glass and send alerts automatically afterwards.

Retailer Macy’s also recently partnered with IBM Watson to create “Macy’s On Call,” and AI Shopping Assistant. The system is programmed to answer popular customer service questions to streamline the customer experience and also offers apps to help customers navigate the store. Currently piloting in 10 locations, Macy’s intends to integrate Watson’s full cognitive dialog abilities as the AI program develops further.


Accelerating Corporate VC, M&A

Look for accelerating corporate VC and M&A activity as the transformational applications of AI take shape. Intel Capital has been the most active corporate VC, with investments in Data Robot,, MindMeld, and others. Alphabet (Google), a leader in both categories, has been the most active in M&A, with nine acquisitions since 2011.

Most Active Corporate Investors in Artificial Intelligence
Ranking Based on Number of Investments Made in 2011-2016

Source: CB Insights

Companies Most Active in M&A (2011-2016)

Source: CB Insights


AI bots, known as Chatbots, are programs designed to stimulate an intelligent conversation with human users. SmarterChild, which lived on AOL’s IM messenger platform, was one of the first chatbots to gain notoriety and over the course of its lifetime, SmarterChild built personal relationships with over 30 million users. To some, SmarterChild is even considered as a precursor to Apple’s Siri.

What Can I Help You With?

When Apple announced integration of personal assistant Siri into it’s iPhone 4S, iPhone users worldwide began pressing the home buttons on their iPhone asking Siri questions. Unlike SmarterChild, which wasn’t too intelligent at all, Siri was. The more it was used, the smarter it became and better it knew you. Four years later, Siri gets more than a billion inquiries weekly.

The next frontier is the creation of chatbots powered by Artificial Intelligence, particularly Machine Learning. Applications that observe and learn from patterns of communication and collaboration will be game-changers. They will escalate information that matters, when it matters. They will anticipate questions and problems and tee up answers and solutions.

Just last year, Slack released a suite of new APIs designed to make it easier for developers to build new apps on top of Slack. The most intriguing of these is BotKit, an open-source framework for building automated services that users can access through conversational interfaces. The aim is for Slack “bots” to increasingly automate the most tedious business interactions, from setting up meetings to expense reporting and recapping basic information to colleagues.

Slack recently announced a partnership with a syndicate of leading venture capital firms — including Accel, Andreessen Horowitz, Index Ventures, Kleiner Perkins, and Spark Growth — to create an $80 million fund that will invest in software projects that complement its core technology. Since it’s inception, Slack invested in 3 companies, 2 of which create bots that are integrated on Slack’s core platform.

Speech Recognition

Powerful speech recognition software has been overlooked as a game-changing technology, but it will grab headlines in 2016 as it converges with AI. The most compelling speech recognition innovation is being driven by Baidu’s Chief Scientist Andrew Ng (also the Co-Founder and Chairman of Coursera), who left a post at Google to run the company’s Silicon Valley AI Lab (SVAIL). (Disclosure: GSV owns shares in Coursera)

As Ng recently observed, “Speech recognition, depending on the circumstances, is say 95 per cent accurate… That’s really annoying if it gets one in 20 wrong and you probably don’t want to use it very often. I think that as speech recognition accuracy goes from say 95 per cent to 98, 99 to 99.9, all of us will go from barely using it today or infrequently to using it all the time.”

Andrew Ng, Chief Scientist, Baidu

Most people, in other words, underestimate the difference between 95 and 99%… 99% is a game changer. AI-enabled speech recognition programs running on smartphones will bring the internet to millions of people in the developing World who are illiterate or struggle with technology. Today, for example, 10% of Baidu’s searches are conducted by voice. Andrew Ng believes that could rise to 50% by 2020.

In December, Baidu unveiled new research results from its AI Lab, including the ability to accurately recognize both English and Mandarin with a single algorithm. That’s just the beginning.

With the political conventions over the past couple of weeks, it has been hard to find intelligence of any type — artificial or native. That said, there were plenty of robots and aliens on the podium either droning on, oblivious to Planet Earth, or speaking Martian. Stocks acted bored, with the Dow dropping 0.7%, the S&P 500 essentially unchanged, and NASDAQ moving up 1.2%.   

World Indices

Source: GSViQ, Yahoo Finance

Economic growth has become an oxymoron, with the GDP for the Second Quarter coming in below half of expectations at 1.2%. This comes on top of a downward revision for First Quarter GDP, meaning the First Half GDP growth was an anemic 1%. Ugh. The very short term silver lining is that the Federal Reserve is more likely to keep rates where they are.

Three of the four “FANG” members reported last week, with Facebook, Amazon and Google (Alphabet) all crushing it. Facebook’s EPS rose 94% for the quarter, with the fourth straight quarter of accelerating earnings growth. It rose 2.4% for the week. Amazon smashed the $1.11 analyst estimate, reporting $1.78, with revenue rising 30%. Alphabet, a.k.a. Google, reported revenue and earnings that grew 20%+.

The IPO Market continued to show momentum with four new IPO’s last week. Pricing was in the “normal” range but Talend was the star of the new class, jumping 42% on its first day of trading.

Other items of note:

Upthere raised $77M from KPCB, GV, Western Digital, Floodgate, and Elevation. Upthere is a Dropbox/Box/Google Drive competitor, that claims to provide better real time sync capabilities for files, pictures, videos, and storage in general. EverFi raised $40M from NEA, Rethink Impact, Bezos, and Tomorrow Ventures; EverFi is an education technology company focused on teaching, assessing, badging, and certifying students in critical skills with 14 million learners on its platform.

Shipt raised $20M from Greycroft,, and Herbert venture and is a fresh food delivery service across the Southeast and Southwest of the US. Wonder Workshop raised $20M from Learn Capital, CRV and Madrona. Wonder Workshop provides educational toy robotics, with its main products being the Dash and Dot robots that teach computer science and coding fundamentals to children as young as age 6. Robots are used in over 7,000 schools around the world (4,000 elementary schools in the US).

Chegg reports on Monday post close, EPS estimate at $0.03, +200% YoY. Lufthansa started selling flight tickets on Airbnb. Snapchat now placing ads on Airport security bin bottoms.

Snapchat’s Decked Out Bins at TSA

Source: Twitter

Dropbox introduced AdminX (improved admin and collaboration tools) for businesses. Two weeks in existence, Pokémon Go is estimated to be at over 75M downloads worldwide. Tesla and Mobileye quit year-long partnership following May’s fatal crash;. Tesla is rumored to start working on its own self-driving software.

Growth stocks are acting better as evidenced by NASDAQ being up for five weeks in a row. We continue to believe there is great opportunity to own the strongest growth businesses at reasonable valuations.  Accordingly, we remain BULLISH.


by Luben Pampoulov

Relationship Status: It’s Complicated

One year ago, Tinder was one of the hottest startups in the World. The Hollywood based creator of the popular dating app was estimated to be worth billions. Its popularity was surging, topping the App Annie rankings, and analysts expected the app will have well over 50 million active users. Its parent company, IAC, went public at the end of 2015, and demand for the stock was largely driven by its Match Group division, which counts Tinder,, and OKCupid to its assets.

In IAC’s second quarter report last week, the company announced that Match Group’s dating revenue increased +23% with average paid members being up 30% at 5.3 million. Yet, it is unclear what % of those are Tinder users (but likely at or below 1 million), and how fast Tinder alone is growing.

I was talking to several of my good friends last week, all of whom were Tinder “power users” in the past. Somewhat surprising to me, they all told me they had switched to Bumble and were not using Tinder any longer…a trend that’s apparently also happening among other friends, and among friends of friends. The problem with Tinder is “it’s become trashy,” as many of my friends told me. “There is an increasing quality problem that’s causing users to move on.”

Tinder App iOS Ranking in Major Countries

Source: App Annie

Bumble on the other hand, only allows women to make the first move in a match, or as they advertise — “on Bumble, ladies hold the key.” Seemingly, this is keeping quality strong and is attracting an increasing number of ex-Tinderers to switch to Bumble.

London-based Bumble was founded and launched by ousted Tinder co-founder Whitney Wolfe, who sued the former company for sexual harassment and published her conversations with co-founder Justin Mateen as evidence. Bumble itself is majority owned by Badoo, a London-based unicorn which acquired Lulu, a popular app that lets women rate men, earlier this year.

Another problem on Tinder is that the Men-to-Women ratio is much higher, estimated at about 2-to-1, compared to Bumble’s estimated 1-to-1 ratio. This is important as it lowers the risk of “trashy” actions, typically initiated by men.

One year after its launch, Bumble’s user number is estimated to be at around 5 million, and growing significantly faster than Tinder. The Bumble app has gone from being ranked #1000 among US iOS apps a year ago, to #318 now, according to App Annie. There is clearly a strong uptrend, but there is also lots of territory ahead that has yet to be captured.

Online dating, which is a $2+ billion industry overall, has over 3,900 dating sites in the United States alone. And while it is not a new industry ( was first launched in 1995), it is experiencing a renaissance, led by popular apps like Tinder, Bumble, Lulu, etc.

Two Megatrends are converging to fuel the growth in online dating: ubiquitous Mobile Computing and Demographics. In 1970, only 28% of American adults were single. Today, almost half (47%) are single. When IAC acquired in 1999, one in eight marriages originated online. Today, it has surged to one in three, according to a recent Nielsen study.

We are following the online dating space closely, and are especially focused on popular and fast growing apps such as Bumble and Tinder. 

Pioneer Notes

by Li Jiang

How this MIT “physicist” is shaking up Latin America’s finance industry

trail·blaz·er: a person who blazes a trail for others to follow through unsettled territory

GSVlabs is dedicated to accelerating global innovation. With 150+ startups in the EdTech, Sustainability, Mobile, Big Data, and Entertainment verticals, we work with a diverse range of individuals. Despite our different cultures, roots, and origins, we migrated to the Silicon Valley for one reason: To create innovation that will change the world.

At GSVlabs, we don’t believe in the “one-size-fits-all” mold that shapes entrepreneurs. We believe each person has a unique path to entrepreneurship.

We sat down with Founder and CEO of alkanza, Andres Villaquiran, to learn about his path to entrepreneurship.

Andres Villaquiran at GSVlabs

Leaving the Banking World Behind

Andres Villaquiran left Colombia to attend MIT as an intended Physics major. As a Freshman, he joined a research group at the University’s nuclear reactor and quickly realized that it was not for him.

“I didn’t understand 80% of what they were doing, but in any case I knew that I didn’t want to do it. So I switched majors and ended up doing Mechanical Engineering, Economics, and Management Science. I knew that I wasn’t going to be an engineer, I just really liked the physics aspect of it”

After completing three separate undergraduate degrees at MIT, he left for New York and worked for J.P Morgan as a derivatives trader. After a 3 year stint with J.P Morgan, he left to work at Credit Suisse, which had launched a new venture exploring Latin American markets. Excited by the opportunity, Andres stayed in New York for four more years. Derivative trader by day, Andres enrolled at NYU and worked towards his Master’s in Financial Engineering at night. But an interesting thing happened at the end of his seventh year.

“I was in New York for a total of 7 years. It was a lot of fun but I kind of got tired of the culture. The politics and prestige of the big banks weren’t very interesting to me. A lot of people love that, and that’s fine, it just wasn’t my calling. And at the time, I thought that maybe I was too young and that I wasn’t ready to start a career at a very large place. Nowadays, I think that it was myself telling me that this was not for me.”

Despite the complexities of trading derivatives, Andres wanted something more. He wanted to enjoy his work.

California to Colombia

So Andres left New York and headed to California where he enrolled at Stanford University as a PhD and Masters candidate in Statistics and Financial Math. After a couple of years at Stanford, he went to Colombia for the summer, taught at a top Colombian University, and worked as a financial consultant for Colombia’s second largest bank. Having wrapped up his consulting obligation, he was presented with an intriguing opportunity. Pleased with the work and evaluation that he had provided, the Vice President offered Andres the opportunity to implement his recommendations. However, there was one caveat. He would have to stay in Colombia for one more year.

Given that he had yet to complete his degrees at Stanford, the decision proved to be extremely tough. But after consulting with one of his professors, he ended up committing and moving to Colombia.

Although he was not actively seeking it at the time, this decision provided him with the idea and foundation for his first venture.

“I thought that it would be interesting to stay away for a year and do something different. So I did that and got the idea for my first company. They were willing to pay for the knowledge that I had. Not many people have that kind of expertise — the academic background tied with the experience in terms of financial mathematics.”

First Failure

So he set up his first company, Risk Management Insight. Founded in 2006, the boutique quantitative consulting company grew to 15 employees by 2013, engaged and consulted with 10 of the top 20 largest corporations in Colombia, and had a client base stretching all the way from Mexico to Uruguay. Despite the success, Andres quickly ran into a major roadblock.

“It was good, very successful, but it was very tough trying to scale the business. The board members and the CEO would always ask to meet with me but I couldn’t be at 50 different places at once. I tried bringing aboard different partners, but they didn’t have the expertise that I had.”

Unable to scale the business, Andres entered into an agreement with PriceWaterhouseCoopers and sold off the business.

Although his passion for quantitative consulting faded over time, one thing never changed: his love for the Bay Area. After selling off his business, Andres moved back to the place that he once called home.

Second Venture, Discovering Alkanza

While Andres was at Stanford he noticed a particular trend in the startup space: All of the Bay Area startups were focusing on social media and gaming. Although gaming and social media certainly appealed to him, he was drawn to another emerging space, FinTech. Having worked in finance for the greater portion of a decade, he saw an opportunity and went for it.

“I started to see one space becoming hugely active and that was fintech. And I was like okay, this looks like something that I know how to do — certainly it’s scalable, let’s see what I can do. I started talking to some professors and some VC’s about what I wanted to do. I had 6 different models that were scalable, each with a different target audience and target market. So I thought that’s cool, I have a company with 6 different softwares”

But this certainly did not turn out to be the case…


“I was told that I needed to focus and that I could only pick one. The first person that told me that, but I didn’t think that it really mattered. Then the second person told me that and I started to listen. By the time that the fifth person told me the same thing, I was like, okay these guys are right, I’m an idiot. So I had to pick one and that was another challenge”

So Andres went back to the drawing board. But how was he to make the decision? All of them had been proven by the market to some extent. Each one of them addressed a different problem and target demographic. The decision making process proved to be extremely difficult. Then a funny thing happened…


“At that point in time, I remember talking to a friend who was not well versed in finance. In fact, he’s a software guy — our CTO. He came to me with an idea. “Wouldn’t it be cool if I had some kind of app or model that would tell me how to manage my money while it constantly evaluated my portfolio and 401K account?””

“So I thought hey, this sounds like an asset allocation problem. I’m all for that. That sounds interesting”

“When the decision came to choose one out of the six models, I was faced with an extremely difficult question: “Do I go for something that’s completely new and risky or go with something that is proven and not be the first person to come up with the idea? Given that it was my first experience in Silicon Valley, I chose the one with less risk”

Having picked out his model, Andres now had to create and fund the product. He very well could have funded it himself or asked one of his family members to contribute but given that his or his family’s perception could be biased, he sought out friends and individuals that knew him from a professional standpoint.


“Maybe what we’re doing wasn’t that cool. I needed to be able to sell it to other people. I needed to be able to see that investors believed that this was valuable. I needed to see that they liked the idea and believed that it could be a business. That would take the bias out of the equation. That meant that people weren’t giving away money for the sake of friendship but because they believed that they would make a good return and that I could build a company.”

“So that’s how I raised the first amount of money.”

And that brings us to the pitch process.

“I obviously had to make it a business case and convince people that they would make a lot money. I asked them to look at what was going on in Silicon Valley, particularly in the Fintech market. Changes in the world are happening in Silicon Valley. They start in Silicon Valley and spread around the world. Everything that you have been doing now, that you were not able to do ten years ago, started in Silicon Valley. The same thing is going to happen in 10 years, 20 years.”

“So I think they really took to that.”

Having secured funding, Andres and the rest of the alkanza team got to work. By the end of December 2014, the team had launched its product.

The company had picked up a little bit of traction. Unfortunately, so had the competition. alkanza competitor, WealthFront, had just raised a $100 million round at a huge evaluation. Additionally, Charles Schwab announced that they were going to come out with something of its own. Seeing that the market was becoming increasingly saturated, alkanza made its first strategic pivot.

“It was good to see that [Charles Schwab] was releasing its own product, it validated our concept. However, the market was going to become increasingly competitive. Whereas we initially saw Wealthfront and Betterment as our competitors, we quickly realized that Fidelity and Vanguard posed the biggest threat — and that was a fight that we were not going to win.”

Opposed to looking outward to the marketplace, Andres shifted his focus internally and began to ask himself the following questions:

“What were our goals for this?”

“What are we going to need to do for this year?”

“How do we minimize risk for our investors going forward?”

This moment of introspection proved to be a defining moment for Andres and the alkanza team. Faced with uncertainty, the team decided to shift target markets and focus it’s product in Latin America.

“We took a different route, we were going to target a new demographic. We had plenty of good connections in Latin America. Despite the fact that Latin America’s asset management market is about a tenth of the size of the US market, the fees are about twenty times higher. So we shifted because there was a bigger chance for disruption.”

So the company pivoted and set itself up to be regulated by the SEC as a registered investment advisor and started developing relationships in Brazil, Mexico, Colombia, Chile, and Peru. Throughout 2015, the company’s MVP changed according to what its users were giving as feedback. As a result, the product that it has now is significantly different. In Andres’s words, “we have a much nicer product.”

In addition to the increase in competition, the emergence of two new trends prompted alkanza to shift target markers. For one, technology has improved to the point where asset management is now available to pretty much everyone — not just high-net worth individuals.

“It doesn’t matter if you have $50,000 or $5,000,000, you can get good advice for cheap. I think that’s one of the things that we entice”

Secondly, millennials are much more tech-driven. People do not go to bank branches anymore.

“The younger generations want to do everything on their smart phones. People are going to migrate from physical locations to mobile. This is something that is going to happen.”

The alkanza platform creates value for users in a variety of different ways. First off, the platform is very intuitive. Contrary to using confusing and complicated financial jargon, alkanza delivers the information in ways that are very understandable.

Secondly, it provides good financial advice at a cheap price.

Thirdly, the platform continuously evaluates your portfolio. When people see their broker statements, they don’t really understand what’s going on. alkanza has customizable analytics and reports that allow people to see their financials in the way that they like to see it.

2015 proved to be an incredibly successful year for Andres and the alkanza team. Faced with uncertainty, the team pivoted target markets, raised funding, and brought its product to the market.

In two years time, Andres built a company with the foundation and structure needed to scale — a problem that proved to be insurmountable in his time with his first venture.

But more importantly, Andres found his entrepreneurial passion again. Every day brings about a new set of challenges, and this keeps him engaged and motivated.

“I am happy with my decision because it has been a great experience. I love what I do. It is awesome. I have a lot of fun. Whether it’s Saturday or it’s Monday, I just don’t really care. It’s the same to me. Its just the way that I’m living.”

Market Update

Week ending July 31, 2016

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