Please enjoy ARK Disrupt Issue 111. This blog series is based on ARK Brainstorming, a weekly discussion between our CEO, Director of Research, thematic analysts, ARK’s theme developers, thought leaders, and investors. It is designed to present you with the most recent innovation takeaways and to keep you engaged in an ongoing discussion on investing in disruptive innovation. To read the previous issue, click here.
1. Will Phantom Auto Enable Autonomous Driving Before The Wireless Transition to 5G
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This week ARK visited Phantom Auto, a startup that is providing teleoperation-as-a-service to companies testing and deploying autonomous vehicles. ARK envisions that autonomous taxis will not have drivers but will require human assistance on rare occasions. As with air traffic control, professionals probably will instruct troubled or disengaged cars from remote locations.
Phantom Auto has responded to this opportunity, setting up a remote center to serve companies developing autonomous driving technology. In a car operated remotely by a Phantom Auto engineer, we enjoyed an uneventful ride. Equipped with a modem and cameras, the Phantom-operated car transmitted video feeds over a cellular network to its tele-operators.
Phantom has developed its proprietary edge in “bonding”, working with several cellular providers to transmit video footage seamlessly, with minimal latency. While we do not have the specifics, Phantom asserts that its system’s latency will be well below the 150 milliseconds that experts consider critical for autonomous driving.
Consequently, Phantom should be able to accelerate the deployment of autonomous taxis before the debut and proliferation of 5G, the next generation of wireless technology. While 5G may debut in 2019, it is unlikely to proliferate sufficiently to support autonomous taxi networks for another few years.
ARK estimates that an autonomous taxi ride will cost $0.35 per mile, roughly half the cost of driving a personal car in the US, at commercial release in the next year or two. That cost discrepancy should trigger a massive transition in point to point mobility from personal cars to autonomous taxi networks. Of that $0.35 cost per mile, labor costs could account for 20-40% as each remote operator supports a limited number of taxis initially, but should decline over time as machine and deep learning increase their productivity. As autonomous taxi networks disrupt professional ridesharing, Uber, Lyft, Didi, and Ola drivers should be able to transition to tele-operator opportunities. In fact, a former Uber driver was one of Phantom’s first tele-operators.
A number of commercial autonomous taxi networks should launch this year, including those from AlphabetGOOGL, GMGM, and possibly TeslaTSLA. Tele-operator networks will be a crucial enabler.
2. Deep Learning Will Detect Early-Stage Disease, Saving the Health Care System Billions
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Cardiogram, a private company based in San Francisco, is at the forefront of adopting wireless and wearable technology to identifying health abnormalities at their earliest stages. This week, Cardiogram and UCSF published a study that “validated the accuracy of DeepHeart, a deep neural network distinguishing between people with and without diabetes, achieving 85% accuracy on a large data set which included 200 million heart rate and step count measurements.” The study set out to prove that wearables and artificial intelligence could detect health conditions like diabetes, hypertension, sleep apnea and atrial fibrillation accurately and early. According to the CEO of Cardiogram, “twenty-four percent of people with diabetes, and 88.4 percent with pre-diabetes, don’t realize they have it.”
In our view, AppleAAPL could be a prime beneficiary of the movement toward wireless wearables as they improve the quality of health care and lower its cost. Specifically, insurers should be willing to subsidize – if not fund – wearable devices like the Apple Watch, adding incentives like lower deductibles or co-pays for consumers willing to let them monitor signals associated with early stage disease. Apple’s closed ecosystem and trusted brand should give it an edge in negotiating partnerships with insurance companies. In fact, as an entree to its suite of products, the Apple Watch could move the needle for Apple’s revenues and earnings during the next few years.
Deep learning will play an important role in potential wearables-insurance ecosystems. While critics of deep learning focus on the algorithms and data that evolve in a “black box”, advocates are highlighting the discovery of patterns and relationships among variables that human researchers never imagined or considered. As noted in the study, Alphabet’s DeepMind work is a “first step in showing how health conditions can be detected using techniques first developed in natural language processing and computer vision.”
3. Lab-Grown Meat Could Disrupt an $800 Billion Industry
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Lab-grown meat could be a new source of disruptive innovation impacting $800 billion in economic activity. Seemingly far-fetched now, lab-grown meat will offer enormous benefits to society, particularly if its costs continue to decline dramatically.
Very little has changed in meat production since the domestication of animals more than ten thousand years ago. Converting animals into meat not only requires the vast use of land, water, and feedstock but also throws off significant waste and methane, a highly potent greenhouse gas. The meat production process is among the least energy inefficient in the food industry, requiring 23 calories of feed to generate 1 calorie of meat.
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