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General Commentary
July 23, 2017

Quantum Leap

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Market Snapshot

Indices Week YTD
GSV 300 1.80% 42.10%
S&P 500 0.70% 9.10%
Dow 0.60% 10.40%
NASDAQ 0.80% 16.40%
Russell 2000 1.40% 1.50%
MSCI P/E Fwd P/E/G
Valuations 25.8x 0.7x
GSV 300 17.6x 2.3x
S&P 500 Now YTD
I-Rates 2.17% -11.40%
10-Year Note 1.03% 102.00%
3-Month Bill - Current
Sentiment - 28.1-38.3
Bull-Bear - 0.97
Put-Call - 11.28
Vix Now YTD
Inflation $1 12.50%
Gold $47.86 -11.10%
Oil - Week
Mutual Funds - -$3.40
Fund Flows (bil) 00-09 09-Now
Growth-Value -34% 218%
Growth 87% 133%
Value

There’s a law about Moore’s law… The number of people predicting the death of Moore’s law doubles every two years.

— Peter Lee, Head of Microsoft’s New Experiences and Technologies organization (NExT)

In 1965, Gordon Moore predicted that the number of transistors on an integrated circuit would double every two years. The effect of “Moore’s Law” is that computing power has doubled — or, costs have been halved — every two years for the past 50 years.

If the automobile industry would have had its own Moore’s Law, a Ford Taurus that cost $20,000 in 1990 would essentially be free and you’d throw it away after you drove it.

Interestingly, while Moore’s Law has been the foundation and catalyst for a global technology revolution, it is not a physical or natural law… it is a conjecture. In other words, the “Law” that has transformed computing and society was really more of a vision of what could become. It was a force of will. And the belief in Moore’s Law has enabled engineers and technologists to imagine a world where you could have a computer in your pocket, self-driving cars, and personalized medicine.

But we’re reaching the end of the curve. According to the latest International Technology Roadmap for Semiconductors (ITRS) — a joint report from chip giants including Intel and Samsung — by 2021 chips will shrink to a point at which it is no longer economically viable to make them smaller. A North Star for the Global Silicon Valley, in other words, is beginning to flicker and fade.

PREDICTIONS FOR THE END OF MOORE’S LAW
Selected Predictions for the End of Moore’s Law by Year Issued and End Date

Source: The Economist, Intel

Currently, the most advanced chips have transistors just 14 nanometers (nm) in diameter, which is thinner than the membrane of a cell wall. If Moore’s Law were to continue at its current clip, transistors would need to shrink to less than 5nm in diameter by 2020. That’s approximately 10 atoms across, or about as thin as the width of a DNA strand. At that scale, the laws of classical physics no longer suffice.

But where the door of classical physics is closing, the door of quantum physics is opening. So-called “Quantum Computing” throws out the assumptions and limitations of “Classical Computing” and is poised to define the next 50-year curve of global innovation.

QUANTUM COMPUTING FUNDAMENTALS

Classical computers encode information in “Bits” — the smallest unit into which all information is broken down. Each bit can take the value of a “1” or a “0.” These ones and zeroes, in turn, act as on/off switches that ultimately drive computer functions. And the computational power of a normal computer is dependent on the number of binary transistors — tiny power switches — that are contained within its microprocessor.

Quantum computers, on the other hand, are based on “Qubits,” which operate according to two key principles of quantum physics: “Superposition” and “Entanglement.” The punchline is that Qubits can be both a one and a zero, and that unlocks a multiple of computing power.

Superposition

In Quantum Physics — the realm of the very, very small — sub-atomic particles can exist in more than one state at the same time. While classical computers depend on Bits taking one of two values, 0 or 1, Qubits, their quantum analogs, can be arranged in “states” that are best thought of as some mixture of both 0 and 1.

Think of Bits and Qubits as spheres. Bits can only exist in two states at either of the two poles of the sphere. Qubits, under Superposition, can be represented as any point on the sphere. As a result, computers using qubits are able to store much more information using less energy and space than classical computers.

BIT VS. QUBIT

Source: Nature, Rigetti Computing, GSV Asset Management

To understand the implications of the power of Superposition, imagine searching for a name in a phonebook. Classical computers will have to scan every name, line-by-line and page-by-page to find a person’s phone number. As a result, classic computers would have to conduct tens of thousands of searches to find one number. Quantum computers will scan the phonebook in its entirety at once and find the number in a single search.

Entanglement

Qubits, on their own, have an immense amount of power. But when they are linked together, in an unique property of Quantum physics called “Entanglement,” their power increases exponentially.

A standard computer depends on the complete isolation of one bit from the next to prevent a computation from going awry. For a quantum computer, the entangling of multiple qubits is paramount. Each Qubit that is linked to another doubles the total processing capabilities. The staggering opportunity for computing power versus traditional Bits becomes clear as you move past 10 Qubits.

EXPONENTIAL SCALING POWERED BY ENTANGLEMENT

Source: Rigetti Computing

According to Rigetti Computing — a quantum computing pioneer backed by Andreessen Horowitz — a quantum computer with 60 Qubits can perform calculations that even today’s most powerful computers cannot. Just how many unique numbers can 100 Qubits store? 1,267,650,600,228,229,401,496,703,205,375. A 300 Qubit computer can perform more computations simultaneously than there are known atoms in the universe. A whopping 10^82 at once.

SILICON VALLEY TACKLES HARD TECH

As quantum computing moves from concept to reality, Silicon Valley is taking notice. Since 2012, there has been approximately $300 million of venture investment across 30 deals to quantum computing startups. The early trend line remains bumpy but financing activity is poised to accelerate.

QUANTUM COMPUTING: GLOBAL VENTURE FUNDING (2012 – 2017 YTD)

Source: Crunchbase, GSV Asset Management

Much of the innovation and investment activity has focused on three core areas: 1) Full-stack Quantum Computing (developing + deploying quantum computers), 2) Software + Implementation Tools, and 3) Cybersecurity + Novel Technology.

D-Wave Systems was the first private company to focus heavily on quantum computing and is the first company in the world to sell quantum computers. Since its founding in 1999, D-Wave has solidified its spot as an early leader in the category and currently counts companies like Alphabet, Lockheed Martin, Volkswagen and NASA as customers.

According to researchers from Google and NASA, D-Wave’s computers are more than 100 million times faster than the best classic computers. Released in January 2017, the company’s latest computer, D-Wave 2000Q, is powered by a chip with 2048 qubits. “What a D-Wave does in a second would take a conventional computer 10,000 years to do,” Hartmut Nevan, director of engineering at Google, observed at the time. D-Wave has raised over $170 million from a syndicate of investors, including Fidelity, Goldman Sachs, DFJ, and In-Q-Tel.

EMERGING QUANTUM COMPUTING STARTUPS

Source: Crunchbase, CB Insights

Rigetti Computing is another early leader developing a “full-stack” quantum computing system — from proprietary integrated circuit hardware to software, and everything in between. This Summer, Rigetti announced the public beta of Forest 1.0, a quantum programming and execution environment.

8-QUBIT QUANTUM PROCESSOR BUILT BY RIGETTI COMPUTING

Source: Wired Magazine, Rigetti Computing

Launched in 2013 by Founder and CEO Chad Rigetti, a physics PhD who led pioneering work on quantum hardware at Yale and IBM, Rigetti Computing is a Y Combinator alum that has raised nearly $70 million from investors including Andreessen Horowitz, Bloomberg Beta, and Data Collective.

BIG TECH GOES QUANTUM

Large technology companies, including Alphabet, Intel, IBM and others have become increasingly focused on understanding quantum computing.

This year, Alphabet pledged to release a commercially viable quantum computer in the next five years. In March, IBM announced plans to offer commercial quantum machines by the end of 2017. While IBM’s 5 qubit quantum computer won’t surpass the performance of classical computers, it will be a huge stepping stone towards a new quantum reality.

A few large tech companies are focusing their efforts on quantum computing by partnering with leading academic institutions and research centers. Intel, whose microchip innovations led to the Silicon Valley we see today, committed $50 million towards quantum computing research in The Netherlands at QuTech in 2015.

Microsoft has created Station Q, a consortium of the world’s leading computer scientists, mathematicians, and physicists — all dedicated to building an universal quantum computer. As part of this initiative, Microsoft launched QuArC (Quantum Architectures and Computation Group) to focus on developing the enabling software to power quantum systems.

“BIG TECH” GOES QUANTUM
Major Corporate Quantum Computing Initiatives

Source: CB Insights, Company Disclosures, GSV Asset Management

Other companies such as Lockheed Martin and Google have turned to partnering with startups, particularly D-Wave, to understand and accelerate the development of commercial quantum computing applications. Google is D-Wave’s largest partner and the company has access to the latest technology and updates coming out of the startup for up to seven years. Google currently operates a quantum computer from D-Wave in NASA’s Quantum Artificial Intelligence Lab (QuAIL), where it is exploring applications in areas such as search, pattern recognition, logistics and management, and Space.

WHAT’S NEXT

Just as Moore’s Law unlocked a wave of innovation across multiple industries, quantum computing is fundamentally an enabling technology. Already, there have been a crop of startups utilizing quantum computing technology in the fields of big data, artificial intelligence, and biology, to name a few. As more commercially viable quantum computers begin to hit the market, we expect to see the technology applied more broadly.

KEY APPLICATIONS: QUANTUM COMPUTING

Source: GSV Asset Management

Bubblin'

by Luben Pampoulov

Supplying the Sharing Economy

Last week I wrote about the Ride Sharing economy and its “Musical Chairs game” competitive landscape. This week I will focus on the supply side of the Sharing Economy — the drivers, the hosts, the service providers.

The booming success of the Sharing Economy is happening because of both, the demand- and supply-side disruption. On the demand side, the power comes from the ease of selection, ease of payment, and the high transparency. Whether Airbnb, or Lyft, or Rover, the user will find what she is looking for very quickly. The payment will happen automatically in the background, and the satisfaction will most likely be high. That’s why all of the above services have thrived to become leaders and disruptors in their respective industries. (Disclosure: GSV owns shares in Lyft, Rover).

Dog Sitting as a Side Job

Source: Rover

On the supply side, the Sharing Economy offers an attractive ROI to the service providers; whether a host, a driver, or dog-sitter, all those providers make extra money from offering something they already own or do. In most cases, service providers offer the services as a second job, and only few specialize in being a Lyft driver full time, or in renting out on Airbnb as a primary profession.

In some case, competition on the supply side is intense. The best example is the ride sharing service in the U.S.; rivals Uber and Lyft are not only battling for passengers, but also for drivers. Over the last years, the two leaders have fought for drivers by offering sign up incentives, or premium incentives for a minimum number of rides.

Every-time I get into an Uber or Lyft, I ask the driver which service they prefer to drive for. A few years ago, the responses were mixed, with 6 out of 10 preferring Lyft because of the tipping feature. Over the last year however, the ratio has shifted dramatically to Lyft’s favor, at about 9-to-1. The obvious reasons include Uber’s bad image, its bad internal culture against women, the aggressiveness of its management, etc. To the contrary, Lyft’s driver-friendly attitude combined with its hip and cool culture have pushed it up in that race. Clearly, Lyft is winning on the supply side, and is starting to to gain strong traction on the demand side as well.

I recently read an interesting article in Forbes, looking at the supply side of the Sharing Economy. The article compared different Sharing Economy service providers and how they fare in terms of distribution of income. Airbnb hosts are those who make most money, on average, obviously as the price tag of housing exceeds that of a ride or that of a day service. The average Airbnb hosts in the U.S. makes $924 per month on average, while the average Lyft driver makes $377, and the average Uber driver makes $364.

Another interesting observation is the distribution of income; across most services, the majority of the providers (drivers, hosts, etc.) are in the lowest income bracket of the service, meaning the majority of providers do the job rarely and infrequently. But in Airbnb’s and Lyft’s case, the majority of hosts and drivers are in the next higher income bracket, indicating these services have better stickiness and more power providers.

Source: Fortune

Source: Fortune

The ultimate winners in the Sharing Economy will be those with the best marketplace for both — the users and the providers. We continue to be very bullish on marketplace leaders Lyft, Airbnb and Rover. 

Pioneer Notes

by Li Jiang

Investing Organically

As our cohort of Pioneer Accelerator startups settled into their program, I started reflecting on the timeless question of what characterizes great startups. I came up with two more simple ideas:

Organic team + organic product/idea = baseline attributes needed for a successful startup

That is not to say that having those two attributes will guarantee more success, but those are baseline requirements for me to get excited about a company.

1. Organic team

An organic team is easy to identify — have the founders worked together in some meaningful capacity before?

The reason you want to back an organic team is because starting a company is so difficult. Teams who haven’t worked together before won’t know how to handle the most difficult periods.

Inorganic teams are those that may seem totally qualified and have the right experience but are hired together just to address an opportunity. Without the right team chemistry, however, it is very difficult to survive the tough moments in a startup’s lifecycle.

2. Organic product/idea

An organic idea is either a personal problem that the founders want to solve or a problem that the founders have been very close to.

Inorganic ideas are the ones that people sit around thinking about by asking themselves “what can I make a lot of money on?”

I run in fear of inorganic teams who got together because they saw a big financial opportunity — inorganic idea. You can spot those from a mile away and if it’s not totally authentic, the company is likely to fail to attract investors and more employees down the road.

I only invest in organic teams working on organic ideas. This might seem like a simple and limiting heuristic, but the core thesis stems from the fact that startups are so hard that unless the team is organic and the idea is authentic, people will give up on the team or product sooner or later as they hit the rocky spots on the rollercoaster that we call a startup.

Market Update

Week ending July 23, 2017

World Indices

America Index 10/16/2017 YTD Week
U.S. GSV 300 107.4 42.1% 1.8%
NYSE 11812.0 6.8% 1.0%
Dow 21813.7 10.4% 0.6%
NASDAQ 6265.6 16.4% 0.8%
NASDAQ-100 5822.5 19.7% 0.5%
Russell 2000 1377.5 1.5% 1.4%
S&P 500 2443.1 9.1% 0.7%
Brazil Bovespa 71073.7 18.0% 3.4%
Mexico IPC 51373.2 12.6% 0.6%
Canada S&P TSX 15056.0 (1.5%) 0.7%
Euro-Asia Index 10/16/2017 YTD Week
China SSE 3331.5 7.3% 1.9%
Heng Seng 27848.2 26.6% 3.0%
Singapore Straits Times 3259.6 13.1% 0.2%
Indonesia JKSE 5915.4 11.7% 0.4%
Japan Nikkei 225 19452.6 1.8% (0.1%)
India Sensex 31596.1 18.7% 0.2%
Russia RTS 1979.1 (11.4%) 2.5%
France CAC 40 5104.3 5.0% (0.2%)
Germany DAX 12167.9 6.0% 0.0%
U.K. FTSE 100 7401.5 3.6% 1.1%



U.S. Indices Snapshot

Valuation P/E Est. P/E/G Price/Sales
LTM NTM Growth LTM NTM LTM NTM
S&P 500 24.2x 17.6x 7.60% 3.2x 2.3x 2.4x 2.1x
NASDAQ 25.2x 17.5x 7.90% 3.2x 2.2x 2.6x 2.2x
Russell 2000 24.7x 17.6x 6.30% 3.9x 2.8x 1.9x 1.6x
GSV 300 50.6x 25.8x 38.40% 1.3x 0.7x 5.5x 4.0x

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