The Gig Economy’s Death Cross

Financial Charts

Financial markets have some predetermined signals that are supposed to alert investors that the bottom is getting ready to fall out of the market. One of the more popular indicators is the “Death Cross,” which happens when the short term average value (usually 50 days) of a stock falls below the long term average value (usually 200 days). Some financial advisors argue this means a stock has peaked and you can expect a long term downward trend to begin, and we’ll theorize a similar indicator exists for the gig economy.

Market Share Over Profitability

Burning Money

The gig economy is Silicon Valley’s most extreme version of the Software as a Service (SaaS) business model. A business model that, as noted in a previous post, we think offers no long term value to the economy or the consumers that are using it. We’ve concluded this based on the fact the those who participate in this type of business model are offering services well below fair market value to gain market share, without concern over how the business will eventually reach profitability.

Because these businesses are offering services at an unprofitable level, they are easily identifiable by their high cash burn rates and the fact their operations are usually subsidized through private investment capital or early-stage Initial Public Offerings (IPOs). The list of notable companies we consider to be a part of this group is Uber, Lyft, Instacart, DoorDash, MoviePass, etc., and a variety of scandals have emerged over how some of these companies are paying their “contractors.”

Each one of these companies facilitates a connection between an end-user and service provider with the hopes of receiving a piece of the action – the very definition of a middle man. The problem is a middle man can’t earn any money in a non-profitable endeavor, and the results are beginning to show, something that should worry anyone associated with these companies.

It Can’t be that Bad

Borrowing Money

Take Uber for example, which has a well-publicized issue with driver wages falling below, or being just above minimum wage. With labor rates so low (a goal of so many organizations), many would imagine Uber must be a highly profitable business, but according to self-reported earnings, Uber’s losses are widening, and growth is slowing. These reports are leaving some early investors wondering if they will ever see a return on their investment, with the only possible fix to the situation being a significant increase in service rates.

In other news, MoviePass’s recent spiral has also been high-publicized as it continues to restructure pricing and access for its users to shave their massive losses. The most recent changes resulting in a lower number of users on the platform and higher prices for those customers that remain, although the only metric anyone cares about is the 126 million dollars in losses.

Even Lyft, which seems to attract fewer negative public relations mentions than the previous companies, has been forced to make some changes. Since Lyft also doesn’t have a profitable structure, recent changes in minimum wage laws have forced them to raise their rates to guarantee their drivers earn a living wage.

How to Create a Death Cross

In case you haven’t noticed the trend, time vets all business models, and the fix for all of these businesses is the same – they have to raise prices. The problem is most of their users have been acquired based on the price point of the service, not on its quality; therefore, the inevitable result of raising prices is a decrease in users.

It’s quite simple when you boil it down to most simple mathematical elements. On the hand, you have the price of the service, and on the other, you have the number of user on the platform. As the price goes up, the number of users comes down, and at some point, these two lines will overlap creating the “Gig Economy Death Cross.”

Death Cross

The Gig Economy Death Cross represents a sobering reality for these business models. At its core, it’s the maximum rate they can charge for their service before the user loss becomes unreasonable, and the resulting evaluation will determine if the company is dead on arrival.

Take the dollar amount at the point of intersection, multiply it by the number of users to estimate the revenue earned, and then subtract the company’s expenses. If the resulting number is in the red, I suggest you start polishing up that resume.

It’s always convenient for believers in these businesses to argue that an evaluation of this kind is too simplistic, we would counter by illustrating most of the business community’s longest standing principles are along these lines. Beliefs like, supply and demand, or word-of-mouth is the best advertising, have long stood the test of time, so it’s hard for us to comprehend how something like needing to generate more money than you spend could have slipped through the cracks.

Please leave any comments or questions about this post in the section below.

 

 

The Ugly Truth About Self-Driving Cars

Driving

Great Expectations

Recently, I’ve been reading a lot of articles that are trying to temper the expectations related to autonomous vehicles, and with great satisfaction, I would like to say…it’s about time. If you bothered to read my article about VR being overhyped and underdelivered, you probably noticed I mentioned some other technologies that fall into that same category, and autonomous vehicles are one of them. It’s not that I’m skeptical about the benefits of the technology, I just understand that achieving those benefits are significantly further down the road than anyone wants to admit. If you don’t believe me, keep reading…

According to IHS Automotive, a leader in automobile industry statistics, at the beginning of 2016, “the average age of all light vehicles on the road in the U.S. had climbed slightly to 11.5 years.” Even if fully autonomous cars were available today, America wouldn’t see any significant market penetration for at least a decade, and most of it would be limited to higher socioeconomic areas. To everyone who thought self-driving cars were going to be bobbing and weaving down the streets of their local cities by 2020, you should probably prepare to be disappointed.

You may be asking yourself, why is the timeline is so important? It’s important because of one of the most significant benefit promised through the evolution of autonomous vehicles is related to safety, and achieving it can’t be accomplished until autonomous vehicles comprise ~90% of all cars on the road. Keep in mind that number is my personal calculation, but until self-driving cars make-up a significant portion of vehicles on the road, cities won’t see any significant decrease in the number of automotive accidents that occur every year.

Human Error

Do you know the most common cause of accidents for self-driving vehicles? It’s human error, the same thing it’s always been. Accidents have been happening for the same reason for as long as I can remember. Someone makes a wrong decision, puts other people’s lives at risk, and placing a computer at the helm of one of the vehicles won’t change this fact as long as there are humans on the road with them. Waymo, autonomous vehicle spinoff from Google, stopped reporting their accidents at the beginning of 2018, making it harder for interested parties to keep up with their efforts to remove human error from the roadways, but the good news is, California has archived all of the previous reports on their site if you’re interested in reading through them.

The most common accident type reported were humans rear-ending self-driving cars. Because computers don’t make decisions, they make calculations; autonomous vehicles will ALWAYS run the risk of being plowed into at a yellow light when there are humans in the cars surrounding them. If a computer controlled vehicle can safely stop before the intersection, it will do so, while its human counterparts can be expected to merely say the light was “pink” when they hit the intersection. Human behavior of this type is precisely why autonomous vehicles face such an uphill battle when it comes to public acceptance.

Humans expect the vehicles around them to make decisions in the manner they do, and that means running into the back of a lot of computer-driven vehicles. Running a yellow light is one of the riskiest human driving behaviors on the road, one that we take for granted as we’re driving with other humans, but it’s also one that computers won’t tolerate. Other behaviors, like coming to a complete stop at a stop sign (never happens in California…) and when turning right on a red light, will all lead to accidents between the computer and human-driven vehicles.

Winner Take All

Computers will always strive to provide an element of society that humans can never achieve, perfection, and their achievement of it will only further highlight human imperfections (more accidents). Ultimately, it will be a human that forces a self-driving car to have to choose between saving the lives of its passengers or taking the lives of other drivers. Right now, some engineer is sitting in a room evaluating a Kobayashi Maru scenario that forces a self-driving car to choose lesser of two evils in an unwinnable situation.

For example, a human driver falls asleep and crosses over into oncoming traffic, and someone has to die. Will your self-driving car decide to save its passengers or the passengers in another vehicle? You won’t know the answer to the question until it’s too late. Simply knowing that an engineer has to program a predetermined outcome into a computer for this scenario is already a scary enough thought. What I’m more afraid of is the method that needs to be employed to significantly decrease the chances of any unnecessary carnage happening as a result of these kinds of scenarios.

In a situation where a catastrophic event is inevitable, and death is an assured outcome, the best way to minimize the damage is to make sure all autonomous vehicles react to the situation in the same way. I’ll give you a second to digest that…

“To prevent additional cars from being involved in accidents, all autonomous vehicles on the road should be running the same system so they can anticipate the calculations of other vehicles in their proximity. Think of it as hive mind.”

If a car suddenly blows a tire on the freeway, every autonomous vehicle should avoid the car, in unison, at the same speed, in the same direction, to prevent any unnecessary collisions. If all the cars are running the same system, the other self-driving vehicles on the road won’t need to guess the calculations of the other vehicles involved; they’ll already know what’s going to happen. Instead of having a ten car pile up, the result is a two-car accident, saving more lives in the process.

This aspect of the technology isn’t frequently discussed, but we all know what it means, someone needs to have a monopoly on self-driving vehicle technology. Even though the United States has antitrust laws in place, to truly reach the pinnacle of efficiently concerning autonomous vehicles, only one technology should be implemented. So I’m putting everyone on notice…the self-driving car market is playing a winner take all game, and they should all know that winning is everything.