The predictable irrationality of Tesla panic

Evolved human psychology and systemic media bias has created false narratives around crashes, fires, and executive turnover

In order to invest wisely, it is necessary to have a correct mental model of the companies that you are betting on — or against. Unfortunately, the human brain evolved in the world of stone tools and hunting-gathering, so we have natural inductive biases that drive us toward incorrect mental models of the modern world. For instance, the availability heuristic: the more often you see something happen, the more often you think it happens. For Paleolithic humans, this made perfect sense. For 21st century humans, this inductive bias is often misleading.

We no longer just see real, first-hand events. Trillions of events occur every day, and we filter them down using technologies like broadcasting and the Internet and social institutions like the news media. Our evolved availability heuristic has been co-opted by an information landscape in which the frequency that we see events happening often has no correlation or even an inverse correlation to the frequency with which they actually occur. If every single one of the 1.3 million annual deaths from car crashes were reported with the same urgency and alarm as plane crashes, I imagine a lot fewer people would be afraid of flying and a lot more people would be afraid of driving. Perversely, plane crashes get more frequent coverage because they are rare, not because they are frequent.

Media coverage and commentary on Tesla leads us to form incorrect mental models of the company because of the availability heuristic. Simply by virtue of the sheer amount of attention the company gets, the more instances of all Tesla-related events we hear about, especially negative events.

Since reporters and editors often see their role as covering problems, risks, and harms (rather than solutions, opportunities, and benefits), all news coverage is slanted toward the negative. So, if you want there to be more negative stories published about a company, just give it more media attention. The negative stories will inevitably follow. If we rely on the availability heuristic, this preponderance of negative stories will give us an impression of the company that is more negative than the reality. This is what I believe is happening with Tesla.

“Glyptodon” by Heinrich Harder, depicting the environment in which our brains evolved.

We find ourselves in a bizarre situation. Porsche executives recently had their offices raided by police to gather evidence on their potential involvement in fraud. Tesla’s main problem is just that it’s fallen behind on its Model 3 production timeline. Yet according to a recent poll by YouGov, there was far more negative media coverage of Tesla than Porsche in a two week period that included a week following the raid. This is not what you would expect on the assumption that news coverage imparts a fair and accurate representation of reality.

My hypothesis — supported by data below — is simply that fewer people in the news media, or on social media, pay attention to Porsche than Tesla, so serious bad news about Porsche gets ignored, while even minutiae about Tesla get close attention. If not a lot of people are paying attention to Porsche-related news, not a lot of people will have heard about the raid. (Even the Ars Technica reporter who wrote about the YouGov poll didn’t mention the Porsche raid in his article, suggesting that he might not have heard about it, either.) Tesla, by contrast, is suffering the perverse effects of its immense ability to capture the world’s interest. Creating excitement and fascination about the future of automotive technology means getting a lot of attention. With that attention comes media coverage, and with media coverage more often than not comes negative stories.

What’s the evidence that Tesla gets more attention than other car companies? First, let’s look at social media. Tesla has the most popular subreddit of any car company, and it’s just slightly ahead of Mercedes-Benz for the most Twitter followers. If you add 40% of CEO Elon Musk’s followers to Tesla’s count (on the conservative assumption that less than half his following is driven by interest in Tesla, given his other ventures), the combined Twitter follower count is 4x higher than for Mercedes-Benz. On Instagram, Tesla has fewer followers than several other companies but a cursory analysis of posts by Galileo Russell shows higher engagement. In March 2018, Galileo found that Tesla’s overall social media following was growing faster than any other car company, and the aforementioned 40% cut of Elon Musk’s social media following was growing even faster than that. However, this included followers from Tesla’s Facebook page, which Tesla pulled down in protest after the revelations about Cambridge Analytica.

Next, let’s look at news coverage. Specifically, let’s look at business news coverage, in order to filter out stuff like car reviews. According to the analytics site mediaQuant, there were 560 mentions of Tesla in business news outlets over the last month. That’s higher than any other auto brand. The runner-up is Volkswagen with 468 mentions, followed by BMW with 453 mentions. Porsche had 109 mentions. GM had 329 mentions, and Ford had 420. I honestly can’t say how accurate mediaQuant’s data is, but it was the only data I could find.

Here’s a major example of how, as best I can tell, the availability heuristic combined with an intense media focus on Tesla has led to a warped understanding of the truth about the company. There is a widely accepted notion in the media and among analysts and commentators that executive turnover at Tesla is abnormally high. Jim Chanos — who is shorting Tesla — claims that “almost all [Tesla’s] senior executives are leaving.” Quartz reported that there is an “exodus of executives” at Tesla. The Financial Post called Tesla’s executive turnover “rapid”. So far, neither Chanos, Quartz, the Financial Post, nor anyone else I’m aware of has offered any actual data to support the claim that executive turnover at Tesla is significantly higher than average.

If the amount of executive turnover at Tesla seems abnormally high, perhaps it is simply because media coverage of Tesla’s executive turnover is abnormally high. As Bloomberg reporter Tom Randall observes, it’s “extraordinary to see news stories about mid-tier VPs and directors.” What makes news for Tesla doesn’t make news for other companies. In Randall’s words: “GM probably has what, 500 people in such roles? How many of those departures get covered?”

A recent example — which is admittedly an outlier — helps illustrate this phenomenon. CBC ran a story about Tesla hiring two Canadian interns on to full-time positions. The story was picked up by Fox Business. (Even Elon Musk tweeting about the Fox Business article was then discussed in an article by CNN Money.) It’s hard to imagine a story about interns at another car company getting international news coverage. If coverage of mid-level VPs and directors is extraordinary, coverage of interns is something beyond that. For comparison, Fox Business’ article on the Tesla interns was 359 words long, whereas its article on the recent ouster of Volkswagen CEO Matthias Mueller ran just 236 words.

Since the volume and granularity of Tesla coverage are so unusually high, we will surely be led astray if we rely on the availability heuristic to get a sense of how frequently Tesla executives leave the company. We should be mistrustful of our gut intuition here, since it is maladapted to this context. Instead, let’s turn to the data and see what it can tell us about Tesla’s rate of executive turnover. The data I’ve been able to find is far from perfect, but it is at least better than a gut feeling so prone to error.

In 2014, a study on executive job search was published in the journal Organizational Science by Peter Cappelli, a professor at the Wharton School, and Monika Hamori, a professor at IE Business School. (Here’s a non-paywalled manuscript.) Cappelli and Hamori found that executives’ average tenure with their current employer was 5 years. The standard deviation was 3.7 years. Unfortunately, the study’s data set was limited to financial companies in the New York area. It may not generalize to other industries and geographies.

Here’s another source of data that may not be as rigorous as an academic study but that draws on a broader data set. Job site ExecuNet conducts regular surveys of executives across industries and throughout the United States. Its survey found that in 2015, executives stayed at a company for an average of 4.1 years.

How does Tesla’s rate of executive turnover compare to these averages? In a March 2017 statement, Tesla claimed that of its “senior leadership team,” 75% had at least been there over 3 years, 60% had been there at least 6 years, and 20% had been there at least 10 years.

So, the average tenure for the 75% of the senior leadership team accounted for in this statement must be at least 6.4 years. Even if the remaining 25% stayed in their jobs for only a single day, the average tenure of the senior leadership team would still be 4.8 years.

That’s 4% shorter than the Cappelli and Hamori study average, and 17% longer than the ExecuNet survey average. Reuters’ running tally of executive departures shows no increase in the rate of departures from 2017 and 2018 year-to-date, so what was true in early 2017 should still be true today.

However, this is not a perfect comparison, since it’s benchmarking the senior leadership team against the base rate for all executives.

Here’s a second data source. Christian Prenzler at Teslarati conducted an analysis of 34 Tesla executives at the VP level and higher. Prenzler’s analysis spans from the beginning of 2017 to May 2018. He found that the average tenure of these execs is 4.5 years. That’s only 10% shorter than the study average of 5.o years, and about 10% longer than the ExecuNet survey average. (Update: Jim Chanos’ spreadsheet tallying Tesla’s executive departures shows an average tenure of 4.6 years among departed executives.)

So, the data I can find indicates that executive tenure at Tesla is somewhere between 10% shorter than average (and well within the standard deviation) and 10% longer than average. That’s between six months shorter and five months longer. This sure doesn’t look like “every executive leaving” or an “exodus of executives”.

Maybe better data can reveal a different story (if you’re aware of any, please let me know), but based on this data Tesla’s executive turnover looks about average. Maybe a bit faster than average, maybe a bit slower than average, but not far from the mean in either direction.

I think it’s also worth mentioning that average CEO tenure at large-cap S&P 500 companies is 7.2 years. Elon Musk has been CEO for 10 years and recently agreed to a compensation plan that requires him to remain as CEO or in a similar leadership role (where he would be the CEO’s boss) for another 10 years.

It looks like with executive turnover the availability heuristic generated an intuition, and most people haven’t bothered to check that intuition against data. This is how easy it is for an unchecked intuition to spread and even get reported as fact.

The availability heuristic appears to be responsible for other Tesla narratives as well. Recalls have long gotten intense scrutiny, despite the fact that Tesla has until recently had one of the lowest recall rates in the industry. Some writers scour the Internet for reports of quality issues with the Model S, and then treat these issues as representative. Yet Consumer Reports rates Model S reliability as above average. (Although it is true that Model X reliability is rated very low, and Model S reliability used to be worse.)

Crashes that occur while Autopilot is active are met with panicked proclamations that the technology is unsafe, despite NHTSA finding that Tesla airbag deployments dropped 40% following the initial release of Autopilot. The IIHS also found that cars equipped with Autopilot hardware have 13% fewer collisions overall (which includes minor collisions where airbags don’t deploy). It’s worth also mentioning that Tesla drivers have a relatively low overall rate of serious injuries.

The NHTSA and IIHS statistics pertain to the Hardware 1 version of Autopilot. New Teslas are equipped with the Hardware 2 computing and sensor suite and use a new version of the software called Enhanced Autopilot. Enhanced Autopilot has not yet been independently evaluated in the same way as Hardware 1 Autopilot. Tesla has promised to release quarterly safety statistics on Enhanced Autopilot. My recommendation is that Tesla give the raw data to an academic group that can independently evaluate it. If Enhanced Autopilot really does improve safety, allowing a trustworthy outside group to audit the data would bolster the credibility of Tesla’s safety claims. (Similarly, I think it might be a wise move for Tesla to commission an outside organization to audit its factory safety. An independent safety audit can help Tesla identify any real problems and evaluate whether media reports on its factory safety are fair and accurate.)

Harvard cognitive scientist Steven Pinker — who critiques systemic biases in news coverage — agrees that coverage of crashes where Autopilot is active is misleading:

If Autopilot does indeed increase safety, and alarmist stories about Autopilot prompts Tesla owners to turn the software off (as Tesla has claimed), then these stories may cause preventable injuries or deaths. News outlets should be mindful of how their reporting might lead the public to misunderstand risk and inflict unnecessary harm as a result.

Fires that ignite after Teslas crash get a similar alarmist treatment, even though gasoline cars are far more likely to catch fire. Estimates on gas cars’ flammability vary from 4 times to 11 times to 20 times more likely to catch fire than a Tesla. Unless a Tesla is involved, little attention is paid to the hundreds of deaths caused by vehicle fires that occur on U.S. highways every year. If it happens with a Tesla, it’s a headline. If it happens with a Ford, GM, Volkswagen, Toyota, or Nissan, it isn’t.

The theme in all these cases is a failure to benchmark against averages. This happens a lot, and it leads to misleading reporting. When Tesla fired 2% of its workforce after an annual performance review, the story was misleadingly reported by CNBC as “mass firings”. By comparison, the average annual rate of firings and layoffs at American manufacturing companies is 10%.

Besides the availability heuristic, what also appears to be at work in these cases is confirmation bias: the human tendency to pay attention to facts that support one’s prior beliefs, and discount or ignore facts that don’t. This bias may affect an individual news or opinion writer, or a group of writers and editors at a media outlet. Once a media narrative is established across multiple outlets, confirmation bias becomes a collective (rather than individual) phenomenon. Aaron Zamost calls this collective phenomenon “narrative gravity”: a prevailing narrative exerts an influence on the reporting of all new information, bending it to fit the previously perceived pattern.

This is how false conventional wisdom gets formed. We think we know something we really don’t because we’re misled by unrepresentative reporting and the inductive biases hard-wired in our brains. Our impulse toward herding — basing what we think on what other people think — exacerbates the phenomenon and makes false conventional wisdom harder to shake.

Systemic irrationality is not a rare aberration, but an enduring part of the human experience. We are the same species that until the mid-1700s criminally prosecuted animals and sentenced them to imprisonment or death. Contemporary education and institutions only constrain the instincts of our Paleolithic brains so much. Without a feedback loop that checks our intuitions against data, our beliefs become unmoored from reality, and there is no limit to how far they can drift.

In general, I think we are not suspicious enough of crowd psychology, and not radical or adventurous enough in our attempts to apply epistemological first principles rigorously. It is easy to smirk at the absurdity of a 14th century judge sentencing a pig to death by hanging for eating a communion wafer. It is harder to wade through the mental fog and bias of historical perspective when we think about the needless carnage we might be perpetuating today. For instance, by making people afraid of things that are actually safer, like planes, electric cars, and Autopilot.

Tesla’s Gigafactory 1 in Sparks, Nevada. Photo by Planet Labs.

Not every case is nearly so clear-cut, but my hunch is that other pieces of conventional wisdom about Tesla have also been accepted with insufficient scrutiny. We just believe them because a critical mass of other people believe them.

For instance, there is a widely held assumption that each incremental sale of a new electric vehicle model will result in the loss of a sale for another electric vehicle model. The result is zero-sum competition for a fixed number of electric vehicle sales. This is the assumption behind every worried mention of “oncoming competition” for Tesla, as if only electric cars are competition and gasoline cars aren’t. If that were the case, why would incumbent automakers feel compelled to invest tens of billions of dollars in electric cars? If electric cars don’t compete with gasoline cars, their existing sales are safe and they don’t need to worry about new technology.

I would argue — based on both empirical data and theoretical precepts of economics — that the opposite is true: the sale of new electric vehicle models will increase aggregate electric vehicle sales by taking market share from gasoline vehicles. Electric vehicles appear to be crossing the threshold where their unsubsidized total cost of ownership falls below that of gasoline counterparts. The more models that launch, the faster the industry-wide transition to electric propulsion will happen.

Another example of conventional wisdom: lots of observers argue it is foolish for Tesla to try to fully automate its car factories because GM tried to do that in the 80s and it was an abysmal failure. This ignores the fact that technology evolves. GM also tried making an electric car in the 90s, and it too was a failure. Yet here we are. This criticism overlooks the paradigm shift in AI that occurred around 2012: deep learning. Deep learning is the same breakthrough that is enabling recent progress on self-driving cars. For instance, Waymo, writes that when it first used deep learning in its self-driving cars, “within a matter of months, we were able to reduce the error rate for pedestrian detection by 100x.” Those same dramatic improvements can, in theory, be applied to factory robots.

My hunch is that in the coming quarters and years, lots of people are going to be surprised by what happens with Tesla because it runs contrary to the conventional wisdom. There are the makings of a dramatic turnaround story, even if it’s just public perception that turns around, not the underlying reality.

Whether it’s executive turnover, recalls, reliability, Autopilot, vehicle fires, or employee firings, negative sentiment and even key tenets of some investors’ short theses are based on arguably immaterial anecdotal evidence, rather than hard, quantitative evidence. Similarly, widespread assumptions like zero-sum competition for electric vehicle sales or the futility of factory automation overlook crucial pieces of data like the cost of ownership tipping point or 100x performance increases from deep learning. With Tesla, so much of the conventional wisdom is wrong. The data shows the truth.

My mission in writing about Tesla is to bring the truth to light. If I’m successful, my work will be helpful to everyone who has any kind of stake in the matter, regardless of whether you happen to agree with all my views. It is flabbergasting and disturbing to see the level of tribalism in investment discourse on Tesla. To me, it seems crazy and yet all too human to build an identity, an in-group, and out-group out of… your opinion on a stock. I have to think it distorts the rationality of everyone involved when people self-label as “Tesla bulls” or “Tesla bears” and entrench into opposing camps. The ferocity is almost on the level of Democrats vs. Republicans. Why? What purpose does that serve?

My hope is that investment analysis can be approached the same way that topics of debate in science and philosophy are approached: disagreement without acrimony, based on evidence and reasoning, with the shared aspiration that the push and pull of respectful, open-minded debate will inch us closer to truth over time. A tribal commitment to a belief, or a grudge accumulated from the disrespect of those who disagree with you, get in the way of forming correct mental models of companies just as much as faulty inductive biases like the availability heuristic. The scientific method and norms of scientific discourse show us an effective way to discover the truth together – the most effective way in all of human history so far.