Please enjoy ARK's weekly newsletter curated by our thematic analysts and designed to keep you engaged with disruptive innovation. Have a wonderful day!
This week we learned that Waymo’s cars are making Phoenix residents so angry that they are vandalizing and aiming guns at them. Slow-moving and halting, the cars are causing a serious backlash. Their cautious behavior could be the result of a deterministic approach to artificial intelligence and a dependence on high definition maps.
This week, Waymo Research also published a paperdetailing a more probabilistic approach to autonomous driving, much like that associated with Tesla’s Autopilot.In response to its cars’ grandparent-like behavior, Waymo may have plans to alter its driving algorithms and improve performance.
At this time, Tesla seems to be benefitting from the highestcustomer satisfaction ratings of any car brand. One reason may be that individuals around the country own Tesla cars while a corporation, Google, owns Waymo cars in just one city, Phoenix.
That said, Waymo does have a first mover advantage with the most advanced driving features currently being tested on public roads today, a lead it should fight hard to maintain.
Electric Scooters Should Have a Place in Urban Transportation in the Future
While ARK shared its research on the unit economics of dockless electric scooters in July, we are revisiting the topic in light of new information. Scooter companies have enjoyed tremendous growth but have yet to solve the problem of vandalism. This week, one report highlighted that, in October alone, 60 scooters were dumped in and retrieved from a lake in Oakland, California. Another report from The Information concluded that scooters may last only one to two months on the road. As shown in the chart below, given a shorter expected lifespan and higher costs than we anticipated originally, scooter companies will struggle to turn a profit even though, theoretically, they should be able to deliver rides at a lower cost than personally owned vehicles.
Current challenges highlight how different the scooter business is from the Uber/Lyft model. Both Uber and scooter companies benefit from network effects but, unlike Uber, scooter sharing is a capital-intensive business. Uber has to hire managers and provide incentives to drivers, but its drivers use their own vehicles, while companies like Bird have to finance and seed the market continuously with scooters. Not surprisingly, Bird now wants to franchise its business, putting the scooter investment on balance sheets other than its own. Unfortunately, while people love riding scooters, the facts show that they don’t mind putting them at risk.
This week, the highly anticipated and well-funded stablecoin project, Basis, announced it would be shutting down and returning capital to investors. In April 2018, Basis raised $133 million from high-profile investors including Andreessen Horowitz, Bain Capital, Google Ventures, and Lightspeed. While the quality of the team and the promise of its project initially created a buzz and much optimism, regulatory obstacles and concerns have shut it down. According to Basis CEO Nader Al-Naji, regulators seemed likely to deem the basis token a “security" given it would have behaved very similarly to a bond.
Many crypto experts anticipated that Basis would be at the forefront of the competition among the stablecoins that launched during the past year. Because the regulation of cryptoassets other than bitcoin and ether remains unclear, Basis’s algorithmic model would have been impossible to implement. Ironically, because it returned almost all of the capital originally committed, Basis was one of the best performing cryptoassets of 2018.
Is a 3% Return on Checking and Saving Accounts Too Good to Be True?
Robinhood is the latest fintech company to leverage digital wallets and compete with the big banks. The no-fee brokerage app is planning to introduce checking and savings accounts allowing users to pay bills, deposit and mail checks and, most importantly, earn 3% interest, 30 times on average that earned on checking accounts elsewhere.
Robinhood is offering these checking and savings accounts with no minimum balances, no monthly fees, no overdraft and foreign transaction fees, and the daily accrual of interest on cash balances, an enticing proposition for millennials and students. Elsewhere today, college bank branches are charging $49 on average annually in account fees. Robinhood also includes a debit card in partnership with Sutton Bank and has gamified the application process, attracting more than 628,000 signups so far.
This week, however, Robinhood’s promise of SIPC insurance on its checking and savings accounts was challenged by Stephen Harbeck, CEO and President of SIPC, throwing its launch into question.Now we wonder, is Robinhood’s digital wallet too good to be true, or are we witnessing a battle between the old guard - traditional bank branch networks - and the new guard?
Chinese E-Commerce Company, Pinduoduo, is Disrupting Online Retail
China’s online retail market surpassed $1 trillion in 2017, making it the largest in the world and twice the size of the United States’ $455 billion market. Alibaba & JD.com have dominated e-commerce in China but Pinduoduo is making waves with a unique shopping experience.
Pinduoduo (PDD) is the fastest growing e-commerce platform in the world. Primarily with WeChat, TenCent’s messaging app with more than 1 billion users, customers share product offerings with friends and family, hoping to recruit them and earn steep discounts. Unlike other group purchasing platforms, customers using Pinduoduo unlock discounts only when they convince other users to sign up for the same deal: How PDD Works. Thanks to its “Team Shopping” model, Pinduoduo grew its active buyers from 0 to 385.5 million, most of whom live in tier 3/4/5 cities and rural China, in just three years. Colin Huang, Pinduoduo’s founder, has reinvented the online shopping experience by incorporating gaming and social media.
While critics complain that it has turned a blind eye to the sale of fake goods, something Alibaba had to deal with in its early days, Pinduoduo cannot be ignored. It now has more daily active users than JD.com, the best reach into rural China, and the first mover advantage as social-commerce emerges.
Machine Learning Algorithms Can Combat Antimicrobial Resistance
If unaddressed, antimicrobial resistance (“AMR”) has the potential to create a global health crisis as pathogenic microorganisms become immune to medication. In 2016, the World Health Organization (“WHO”) reported roughly 500,000 cases of multi-drug resistant tuberculosis, a form of TB that is immune to the most powerful antibiotics. Such cases are not cost-effective to treat and result in high mortality rates.
AMR is spreading because of the over-prescription of antibiotics and the failure to comply with full treatment regimens. In either case, drug-resistant phenotypes within a microorganismal population increase.
Avoiding an AMR-related pandemic will require novel technological solutions and a concerted global effort to implement them. Genetic variations within microorganisms contribute to their immunity (or susceptibility) to therapy. Generally, the resistance to drugs is inherited through “epistasis”. Epistatic traits are derived from multiple genetic variationsworking in tandem, making bacteria particularly hazardous. Bacteria are capable of the horizontal gene transfer of drug-resistance between members of their population. When a patient discontinues a cycle of antibiotics, a small cohort of remaining bacteria can become immune to the medication and return much more aggressively.
Advances in next-generation DNA sequencing (“NGS”) and artificial intelligence could combine to combat the growth of AMR. More specifically, the epistatic architecture underlying genetic immunity has motivated researchers to use machine learning algorithms to identify and control the spread of antimicrobial drug resistance. A recent study in Natureresulted in the construction of a support vector machine (“SVM”), a type of machine learning algorithm, trained on relationships among bacterial mutations and the impact on antibiotic resistance. The researchers then used a gradient descent technique to reduce the error associated with their predictive model, exposing new variations within microbial DNA that are believed to contribute to immunity and susceptibility. By leveraging neural networks, researchers should be able to decipher the complex, molecular origins of drug-resistance and mitigate, if not eliminate, AMR.
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