Venture Bytes #51 – Lyft to Lift the Hood

Venture Bytes #51 – Lyft to Lift the Hood

on June 20, 2019

Lyft to Lift the Hood

Lyft plans to start its IPO roadshow on March 18 and start trading on the NASDAQ shortly thereafter. First of a number of unicorns that have announced their intention to exit the private markets, Lyft’s IPO will gauge the appetite for high-profile unicorns against the backdrop of the longest running bull market in recent history and ominous geopolitical and economic cross-currents. All the fears notwithstanding, we believe the odds favor a warm reception for Lyft, not only because it is coming to the public market with good traction in its core ride-hailing market, but the runway ahead is still long and compelling.

The on-demand economy has come of age and few services exemplify the on-demand economy more than the ride-hailing apps. They have not only disrupted the taxi business, they have spawned a new economic order, providing employment to many participants, incremental tax revenue to local economies, and a de-facto “personal chauffeur for hire” for all users. More broadly, on-demand service delivery has reshaped industries across countries.

Whether it’s personal transportation (Uber, Lyft), short term rentals (AirBnB, HomeAway), on-demand delivery services (Postmates) or skilled tasks (TaskRabbit), the app-based services have upended the status quo. These companies transact goods and services through rent rather than sale, allowing people to circulate underused assets. Equally important, people these days carry an advanced, location-aware computing system with them at all times. They largely trust the emerging firms building businesses that serve them and offer much-needed access to a variety of income streams. Future demand drivers include new, autonomous-driven cars and trucks, alternatives to existing public transportation systems (buses, trains, subways), and collaborative partnerships and/or mergers with the major car companies to leverage each other’s strengths.

Millennials, a demographic bulge reshaping the economy and consumer tastes, initially drove the sharing or gig economy. According to the US Census Report, this group numbers 83 million in the US and is more than 25% of the nation’s population – larger as a group than baby boomers. Similarly, there are 1.75 billion millennials worldwide, according to market data from Millennial Week, further underscoring the significance of this group. We see this mobile and digital super generation well on their way to shaping their consumption habits – and all of our habits as well.

Mobile apps are redirecting the flow of people and income in urban cores around the world. In step with this secular trend, a growing user base is moving away from the traditional modes of transportation to on-demand car services that offer seamless, safe, convenient, and always accessible transportation. That said, the ride-hailing market is still in its early stages despite all of its prominence. According to data from IBIS Research, only 15% of adults in the U.S. have used a ride-hailing service such as Uber or Lyft, and only half of all Americans (51%) are familiar with these services but have not actually used them, while one-third (33%) have never heard of these services before.

Furthermore, for most users ride-hailing is a relatively sporadic activity: 26% of ride-hailing users indicate that they utilize these services on a monthly basis, and an additional 56% use them less often than that. However, close to one-in-five users utilize ride-hailing much more frequently: 3% indicate that they use these services on a daily or near-daily basis while 14% indicate that they use them weekly. Put differently, 3% of all American adults use ride-hailing services on a regular (i.e., daily or weekly) basis while 12% use these services once a month or less. The availability of these services could be the limiting factor as they are largely in and around urban areas but that still does not negate the current low penetration rate.

For perspective, Lyft’s core addressable market in the U.S.—the taxi and limousine market—was roughly $19 billion in terms of revenues in 2016 and expected to grow to $23.6 billion by 2021 and $285 billion by 2030 (IBISWorld, Brookings Institute). The US market is highly fragmented, with almost 10% of the revenues coming from NYC. The ride-sharing sub-segment of this market in the U.S. is a small percentage of the total but growing at a much faster rate. As is evident from the chart below, there is still a vast untapped market in the U.S.

Similarly, according to IBIS the global taxi and limousine market stands at around $108 billion today driven primarily by a growing middle class with increasing disposable income in many emerging markets.

Against this backdrop, Lyft is expected to make its public market debut in April. Having started operation in 2012, Lyft is the second largest ride-hailing company in the U.S. It has generally been a distant rival of Uber, but since 2017, it has started to close the gap. As a relatively late mover in the ridesharing market, Lyft benefited from certain advantages, including the “free rider effect.”

The company was able to focus more time and resources on marketing its specific brand whilst Uber had to bear the responsibility of building consumer awareness around ridesharing and establishing a new service category.

Lyft has also benefitted from the aggressiveness Uber displayed in pushing for a legal framework for ridesharing applications. This meant lower risk levels for Lyft while entering a new city that already had Uber. Lyft directly or indirectly benefited from Uber’s efforts to rapidly increase the ride-hailing market while bearing little incremental costs itself. The company exited 2017 with $4.7 billion in bookings (+141% Y/Y) and a loss of $669 million, according to our Manhattan Venture Research (MVR) estimates. For 2018, MVR expects the company to record $7.7 billion in gross bookings (+50% Y/Y).

The parade of high-profile consumer tech IPOs is about to begin. The public markets are ready to receive them after a lull in IPOs since the first half of 2018 and the never-ending hunger for growth stories.


A Hardware Speed Bump for Neural Networks

Do we have the hardware for Artificial Intelligence (AI)? That is a pivotal question that needs to be addressed before we talk about the wonders technology will that AI provide.

It is generally believed that the next leg up in technological innovations is going to be powered by AI. But one factor that is being ignored or not talked about enough is the fact that the speed of adoption of AI is going to be a function of computer processing speeds. At this point, we don’t have fast-enough computers to derive the full potential of AI.

We are at a point in time, as in the 1960s, where we know the full potential of certain technologies, but don’t have the processing power to realize it. The most advanced category of AI is deep learning, which requires deep neural networks to process the given inputs. Neural networks require more processing power than regular algorithms, and the current state of computing technology is expected to reach its limit soon.

The current AI optimization process is slow, limiting the full development of the technology. Training a deep neural network is mathematically rigorous and time-consuming which prevents deep learning from achieving its true potential. According to Digital Catapult, a UK-government funded agency, a single training run for a deep neural network can cost up to $10,000, which can be prohibitively expensive for startups. The promises of many AI startups are theoretically attainable in the near-term, though in practice this may take longer than the industry would indicate given the money (cost) intensive, time and labor intensive nature of training neural networks.

The current technology is not yet ready for wide deployment, as it draws too much power and has extended calculation times. According to a paper published by Nvidia’s AV (Autonomous Vehicles) team in 2016, a typical neural network has about 27 million connections and 250,000 parameters, so it requires multi-billion calculations for a single run, and with algorithmic complexity increasing year-over-year, considerable advances in technology are required to increase efficiency. A computer with more semiconductors would operate at unsustainable temperatures and transistors are still relatively too big to stack up in a computer core. For this reason, the training of highly complex neural networks, like AV, takes years to accomplish.

AI does not have the computing power to be fully implemented rapidly until a new processor technology is developed. Many startups, like Wave Computing and Graphcore, are taking innovative approaches to develop new processing units specific to deep learning that would overcome the weaknesses of the current hardware. These AI semiconductor startups will be able to compete with large companies like Nvidia, which are more focused on improving the current processing units. Recently, Intel launched a project to develop an AI processor to catch up with AI demands, but the CTO of AI, Amir Khosrowashi, advises that it will take 10 years to get to the market. According to Gartner, the AI chip industry could grow up to $34 billion in the next 4 years making it a lucrative investment opportunity. With the new developments that these AI-chip startups are already bringing, the whole AI industry would be able to accelerate developments and permeate even more of every-day life than it currently does.

We are headed for a speed bump on the implementation of AI that only a new semiconductor technology is going to be able to overcome. Deep learning needs to mimic the powerful human brain’s decision making, and it requires numerous calculations. Our current computers do not have enough processing power to catch up with increasingly demanding AI computations. The era where AI becomes the norm in every technological area needs faster computing power, and these new AI startups stand as the necessary catalyst for change.

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