#7 - The COVID-19 Forcing Function
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I had a bit more time on my hands this week, so instead of publishing next week, I’ve decided to publish a week early. There’s a small chance I shift to a weekly posting schedule, but we’ll see. I’ve also tried something slightly different with this newsletter, so let me know how this goes.
✏️ Recent blog posts
This was really fun to write and think about — give it a read!
Synthetic Biology and Existential Risk: A COVID-19 Thought Experiment. An interesting topic to mull over while we’re all in quarantine, anyways.
📰 1 topic: The COVID-19 Forcing Function
Over the past few months, new technologies and applications that may otherwise have required longer times for adoption have now been ushered into the market thanks to COVID-19. Below, I list some of these important developments. [Note that I make no distinction on whether these developments are normatively good or bad (most of them have both a positive and negative angle)]. I conclude by asking how these developments might affect VC funding.
Enterprise productivity. One of COVID-19’s big impacts on work is the fact that the vast majority of jobs involve humans interacting with one another, presenting increased risk of transmission. Companies have responded by imposing mandatory work-from-home policies and utilizing enterprise tools like Zoom and Slack to manage the shift to remote work. I expect enterprise productivity tools to continue surging as more companies begin to realize that working from home actually isn’t so bad. In fact, Notion, a collaborative software company, just raised $50m at a whopping $2bn valuation (nearly 70x annual recurring revenue). Matt Mullenweg, founder of Automattic, a company whose workforce is entirely remote, thinks that COVID-19 “pushed remote work forward by five or 10 years than it would have occurred otherwise.”
Robots. Companies have also begun utilizing robots since they (obviously) incur less risk of spreading and contracting the disease. Autonomous vehicles and robots, for instance, have been deployed to deliver groceries and medicine. Zipline, an African drone delivery company, is accelerating its launch plan in the U.S. to begin delivering medical care supplies via drones. Another macro-shift pushing in the direction of more automation is that we’re entering a global recession. Humans are becoming relatively more expensive as firms’ revenues rapidly decline. In these moments, employers shed less-skilled workers and replace them with technology and higher-skilled workers.
Medicine and healthcare. There are a few important sub-developments here:
(1) At-home diagnoses / e-medicine. It’s dangerous for individuals to visit hospitals in person, and hospitals themselves are running out of space. Some hospitals and governments have responded by offering remote office visits. For instance, NHS England, the public healthcare system in England, has mandated a 48-hour turnaround for online primary care consultations. In the U.S., the FCC has authorized $200m for telehealth initiatives. I wonder if the desire for more convenient testing will also make room for other at-home medical solutions, like cheap, convenient blood tests (what Theranos was supposed to be) or toilets that automatically perform urinalyses.
(2) Artificial intelligence diagnoses. Hospitals have been employing AI to screen and identify high-risk patients. The use of AI alleviates the shortage of healthcare workers in the face of ballooning patient demand. It’s also no secret that the U.S. faces an artificial shortage of physicians / massive inefficiencies in the healthcare system. Perhaps COVID-19 will accelerate a re-imagining of the healthcare value chain that relies less on traditionally-trained physicians.
(3) Regulation. In America, President Trump relaxed restrictions on telemedicine services. The FDA has granted emergency approvals for COVID-19 tests and treatment. Perhaps the support today for invoking emergency approvals will translate into an ongoing trajectory towards lighter regulation in healthcare and medicine.Education. Years ago, Clay Christensen said that the Internet was disrupting traditional classrooms and educations. It doesn’t seem like this disruption has played out, or at least not on a large scale. Maybe COVID-19 will accelerate the disruption. The obvious, banal take is the shift to online education. For instance, to help slow the virus' spread, students in Hong Kong started to learning at home, in February, via interactive apps. The more interesting take, however, is whether the shift to online will precipitate more sweeping structural changes. A bunch of American universities, acknowledging the logistical challenge for students, have said they would not require standardized test scores for the upcoming undergraduate admissions application season. Some Stanford undergrads, in the wake of online classes and no reduction in tuition costs, are advocating leaves of absence to take classes at other institutions (for, presumably, much cheaper). Perhaps COVID-19 will be one of the first big dominoes to fall in the continuing disintegration of the > $60k/y four-year university model.
Safety (surveillance?). The US government is reportedly in talks with tech companies like Facebook and Google to use anonymized location data from phones to help track the spread of COVID-19. In China, government-installed CCTV cameras point at the apartment door of those under a 14-day quarantine to ensure they don’t leave. Drones tell people to wear their masks. Digital barcodes on mobile apps highlight the health status of individuals. In South Korea, the government used records such as credit card transactions, smartphone location data and CCTV video, to create a system that tracks individuals and confirmed cases.
A final question is how this forcing function will affect venture capital funding. Granted, VC funding in the short-term will be relatively harder to acquire, given the economic recession. But, perhaps the aforementioned technologies and use cases will constitute strong areas of ongoing investment, depending on how hard the COVID-19 forcing function pushes us. VCs like talking about the S-Curves of innovation (see diagram below), whereby adoption of emerging technologies begins slowly but eventually hits an inflection point and skyrockets. Timing to enter investments in emerging technologies and applications is therefore a crucial question. How far has COVID-19 forced us along the S-Curve for these various technologies / trends? 1 year forward? 5 years forward? Baron Rothschild told us 300 years ago to “buy when there’s blood in the streets.” Today there is, almost quite literally, blood in the streets. Perhaps now’s actually a good time for VCs to buckle down and buy.
📚 5 articles
34 predictions for how COVID-19 will permanently change the world. Fun read.
Washington facial recognition legislation. The State of Washington recently passed a law placing safeguards around the development and use of facial recognition. I personally believe that facial recognition technology will provide a lot of benefits to society, but it also could entrench discrimination and compromise privacy. The Washington law affirms continued investment into facial recognition while ensuring we don’t innovate haphazardly.
Yuval Noah Harari: the world after coronavirus. Harari is one of my favorite writers. This piece is well worth the read and is very relevant to today’s topic.
Softbank reneges on $3bn tender offer for WeWork. Things go from bad to worse for WeWork. I wouldn’t be surprised if WeWork files for bankruptcy sometime in the near future: (1) WeWork was relying on this infusion of cash to make it through an economic crisis; (2) During recessions, property values go down, so WeWork will make less profit on new leases than it otherwise could have; (3) People don’t even want co-working spaces (at least right now) because they’re all working from home.
Stanford proposes a “National Research Cloud.” This is directly relevant to last week’s newsletter. Building sophisticated machine learning models today requires vast amounts of compute power and access to high-quality data. While the dominant tech companies have abundant capital and data, smaller corporations and academic researchers do not. Stanford is advocating for the U.S. government to step in and provide academic researchers with affordable access to high-end computational resources and large-scale government-held datasets. The hope is that such access will further accelerate AI innovation and development in the U.S. I’m a huge fan of this.