Valuing Tesla – A Question for Aswath Damodaran

Professor Aswath Damodaran (https://www.linkedin.com/in/aswathdamodaran/)

has said in his many valuations of Tesla over the last decade: “I know that Tesla does and can sell more than just cars (energy solutions and software), but these are businesses that, at best, can add tens of billions of dollars to the mix, not hundreds” In this he is wrong. By far. By a country terawatt hour.

No-one does company valuation analysis better than Aswath Domodaran. Both modest and learned, he presents valuations with great clarity, providing detailed spreadsheets that allow readers to plug in their own assumptions. His latest Tesla Valuation:

https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:7027067291607449600

I was turned on to Aswath Domodaran by James Stephenson (Twitter: @ICannot_Enough). He did an excellent video critiquing this latest valuation, pointing out that Professor Damodaran omitted, inter alia, the Tesla energy business from his analysis. The video can be found at

http://bit.ly/3jBrmQ0

My intention here is not to criticize Professor Damodaran, but to provide specific emerging facts that may gain his attention and encourage him to review them.

(Many may disagree with the ultimate share of auto market that Tesla will attain, and this will impact the valuation materially. Domodaran allows for this, and the spreadsheets may be appropriately adjusted by those who differ.)

Rather, it is in the dismissal of the Energy market that I, along with James Stephenson, believe Damodaran makes a significant error, assigning to the sidelines a business that will rival the auto business in growth, size and potenitally profitability for Tesla.

Damodaran notes, in his January ’23 update “I know that Tesla does and can sell more than just cars (energy solutions and software), but these are businesses that, at best, can add tens of billions of dollars to the mix, not hundreds.”

In fact, evidence is emerging of a huge market for Tesla industrial and commercial energy storage products. The universal move to renewables in the energy markets is going to need batteries (and a LOT of them) and – even more importantly – software, engineering & productization to implement electrical storage at global scale.

In 2017 Tesla installed what was at that time the world’s largest lithium-ion battery, and the first to exceed the magical 100MWh scale, the 129 MWh Hornsdale battery.  This catalyzed a new industry in grid scale battery storage, opening the floodgates.

In 2019, Sam Korus of Ark Invest argued that that large scale battery storage costs were declining to the point where they would outcompete Natural Gas Peaker Plants, and this would “be…an $800 billion opportunity globally.”  

https://bit.ly/3wYKOZM

However, the real opportunity in energy storage may lie far beyond the replacement of underutilized gas plants – the role, according to Dr. Jeff Dahn stretch to being the back-up to wind and solar energy as these replace ALL fossil fuel power generation sources.

There are few that are as versed in battery and energy technology as Dr. Dahn, Professor in the Department of Physics & Atmospheric Science and the Department of Chemistry at Dalhousie University one of the pioneers of the lithium-ion battery.

Dr. Dahn says that the world will need a fleet of 400 Terawatt Hours of batteries deployed to truly move to sustainable energy. If we assume that the technology gets us to a 50-year battery lifetime, that is a steady state market of 8 TWh, or $4T per year at current prices. This exceeds the size of the Auto market.

Whether you accept Sam Korus’ (now) modest analysis or Jeff Dahn’s more aggressive projections, there is a very, very large market for these giant batteries. Relative costs appear to have declined even more rapidly than Korus’s model, potentially enlarging the market sooner than Korus projected.

Globally the current industrial storage sites in operation, under and awaiting construction, is around 180 projects totaling 146 MWh, 85% of which is in backlog. At historically contracted prices of about $500k per MWh installed with peripheral equipment, this is a backlog of nearly $75b.

http://bit.ly/3x19tgu

Tesla has demonstrated both the size of this market, it’s growth rate, and the current and potential profitability. To date, Tesla have implemented projects totaling more than 10GWh, of which more than 5GWh was delivered in 2022, and 2.5GWh of that in Q4 alone.

The metamorphosis in Q4 was because, within a year of the announcement, Tesla commissioned an industrial modular battery factory to automate and increase the production of its Megapack range of products. It is currently said to be producing at the rate of 12 Megapacks per day, an annual run rate of nearly $5b, and likely to reach its rated production capacity of 40 MWh, or $20b p.a., within this year.

Most recently Tesla announced the expansion of the Gigafactory in Nevada, adding 35GWh of battery cell capacity; this factory also manufactures Megapacks, but the capacity is unknown. Whatever the total capacity of Megapack production, Tesla’s website indicates that Megapacks are sold out for two years.

Tesla is aggressively evolving its battery technology and manufacturing capabilities, to extend its lead in this very rapidly growing market, which has been driven into overdrive by the Inflation Reduction Act of 2022 (IRA). Whatever the Tesla and industry backlog was at the end of last year, this is just the beginning. By way of emphasis, on the date of this writing, Tesla signed agreements for a 1,000 MWh Tesla Megapack system in Southern Ontario. At current market prices, such a project alone exceeds half a billion dollars.

But Lathrop and the new agreements are already being reflected in the Tesla financials. Zach Kirkhorn, at the recent Q4 2022 earning call, said:

“The energy business had its strongest year yet across all metrics, led by steady improvement in both retail and commercial storage. While much work remains to grow this business and improve costs, we believe we are on a good trajectory. As we look toward 2023, we are moving forward aggressively leveraging our strength and cost.”

And,

“…. And so, as other areas of the business become more important, particularly the energy business, which is growing faster than the vehicle business, and as we’re heavily focused on operating leverage here, improving efficiency of our overheads, we think the right metric for us to be focused on is operating margin. And so, I wanted to make sure that I shared that with the investor community as well because that is what we’re primarily managing to now.”

In addition, we had this interchange:

Martin Viecha – Head of Investor Relations at Tesla

“And the last investor question is, with near-infinite global demand for energy storage, where should Tesla build the next Megapack factories? How many are needed on each continent?”

Elon Musk — Chief Executive Officer and Product Architect:

“It’s a good question. It’s not something we — I think we’ll provide an update about that in the future, but it is something we’re thinking about very carefully. But really kind of like what is the fastest path to 1,000 gigawatt-hours a year of production. And you’ll see announcements come out later this year and next that answer that question”

1,000 GWh, at current Megapack prices, is about $500b in revenue, which aligns with our math. It also aligns with the total scale of Tesla’s ambitions, measured by the Tesla Battery Day target: 3 TWh of battery cells in total supply by 2030 from a combination of its own factories, new, and existing suppliers, of which a significant proportion of cells will be dedicated to Tesla energy.

And, of course, a business of this scope utterly changes the valuation of Tesla.

To properly value the impact of a significant energy storage business on the Tesla valuation, we need to look at where Tesla’s energy business has been. To date margins in the Energy business have been – well – marginal. This changed dramatically in the recent 3 quarters as old, fixed price contracts gave way to new, supply-chain indexed contracts and Lathrop spooled up. Growth rates in 2022 averaged above 10% per Quarter, while margins shot up from the first to the second quarter. In the past margins have been negative to barely positive, but for the past three quarters have hovered around the 10% mark. These margins are impacted by the remnants of the old, fixed price energy storage contracts, plus the retail solar business which is still ramping and is probably operating at a negative gross margin. Our assumption is that the operating margin on the storage business is quite significantly north of 15%.

This graph tells the tale.

We see an emerging pattern, not dissimilar to what happened when the Tesla auto business began scaling to the mass market. With Lathrop reaching full productions, and Giga Nevada’s announced expansion, the breadth of Tesla’s energy storage enterprise has become clear, supporting Zach Kirkhorn’s guidance. With their announced ambition of a 3 TWh total battery cell supply by 2030, Tesla are aiming at, and scaling to an industrial energy storage business of many hundreds of billions of dollars.

We believe that Professor it is now time for the good Professor to address the potential of the energy business. Wether we rely on the Korus or the Dahn scenario, and based on current growth, backlogs, and the US Federal Government’s focus on ending US reliance on Fossil Fuels, this is a giant growth area in which Tesla is already a – if not the – leader.

How do we believe this should this affect the valuation? The most important factor is alluded to by Kirkhorn: “we’re heavily focused on operating leverage here, improving efficiency of our overheads, we think the right metric for us to be focused on is operating margin” Functionally, the gross margin on Tesla’s Energy Storage business will fall almost directly to the bottom line. At a guess, it would seem to me that the energy business would almost double the value of Tesla.

Professor Damodaran please pay attention!

The Future of Twitter

TL:DR – Twitter could be a win for Elon and his investors (details provided), but the political risks are clouding the outcome.

we simply attempt to be fearful when others are greedy and to be greedy only when others are fearful


Warren Buffet, Berkshire Chairman’s letter 1986.

The timing of Elon Musk’s Twitter bid may have been an act of greed when others were greedy!

Hence Musk’s efforts to reverse the deal when, just after he made the unconditional offer, fear overcame greed in the markets. This was due to a dramatic reversal of the Feds “free money” policy, the root cause of a decade and a half of “greed”.

The result of Musk’s failure to terminate the deal (or dramatically lower the purchase price) is that he must now, in a recessionary environment, turn around a company that he has significantly encumbered with debt of $13b https://reut.rs/3Vu5OBQ

We examine the financial, operational, organizational, and strategic issues around the Twitter transaction to assess whether Musk will emerge – once again – victorious from his latest tilt at a windmill.

Looking at Twitter’s financial performance to date: it hasn’t been stellar…it’s been downright awful. In every conceivable financial measure, Twitter has performed miserably, and has been getting progressively worse, particularly after the assumption of control by Parag Agrawal.

Since its listing, the financial history of Twitter has been about an out of proportion growth of operating expenses and direct costs of revenue. With a minor exception during the pandemic of 2021, these costs have consistently grown faster than revenue. An unsustainable model

Another, simpler view of expenses vs income, to underscore just how unsustainable was Twitter’s trajectory, particularly as we move beyond a world of ‘free money’. I believe the firm would have had to look for ‘strategic alternatives’ before the end of 2022 – and large lay-offs.

If we compare key financial measures of Twitter with Meta – it’s most direct competitor – the results look dismal; the direct costs at Twitter appear to be twice that of Meta’s, while the operating costs at 30% higher. This has been thematic of Twitter’s financial history.

Even in comparison to a manufacturing company, with the huge direct costs of that industry, Twitter is shamed. The contrast with Tesla highlights the profligacy of Twitter…and the characteristic modesty of the typical Musk operating model.

The problem at Twitter is not just the number of employees – it is also the lack of productivity coupled with the extremely high cost per employee. A comparison to its peers shows a company with extreme costs and a loss per head compared to high profit per head for competitors

These are the sorry, bare financial facts. What follows are the conclusions I believe we should draw from those facts, and the implications for the Musk acquisition.

First: Twitter has struggled since its listing because it has managed its resources ineptly. Despite the popularity of its product, and reasonable revenue growth it has been unable to generate meaningful profits which have shrunk and disappeared despite top line growth.

Second: attempts by the company to expand its TAM by investing in adjacent markets such as video, podcasting has largely failed, including acquisitions such as Vine (video) and Periscope.

The lack of a strategic approach to these acquisitions illustrates how Twitter fails to understand the opportunity the platform affords; therein lies the opportunity that Musk sees in the acquisition.

Twitter claims 217 million monetizable daily active users, and this has grown at about 17% per annum over the last 5 years. It’s used by 23% of US adults with very desirable demographics. It’s also widely used in Japan and India. The sky should have been its limit, it was poised to fly; instead it has plodded along, rooted to the ground. Where does Elon Musk take it?

Before we examine that, we must weigh the impact of his lofty goal for the Twitter purchase: “A trusted digital town square”. This is not a political piece, but the acquisition has become the focus of political passion, and this will have a significant impact on the outcome of acquisition, strategically and financially.

I understand the passion. Elon Musk grew up – as I did – in South Africa, and remembers the repression and censorship of that time and place. Those who emerged from that know the rationalizations that squeeze the freedom of expression from public discourse.

This is what I believe to be the driving force behind his absolutist support for the ACLU position regarding free speech, and his opposition to the concept of “free speech for me but not for thee”.

http://bit.ly/3XwndMp

Making matters worse is the Musk’s habit of tweeting, uncensored and unthought remarks, his blunt talk and childish, often confrontational humor – “I reinvented electric cars and I’m sending people to Mars on a rocket ship, did you think I was going to be a chill, normal dude?”

Adding to that are the events surrounding the first days of Musk’s takeover of Twitter. In an extremely ill-advised, and irresponsible tweet he linked to a highly spurious report about the Paul Pelosi attack in a questionable web site, deleting the tweet after an outcry.

This, together with the turmoil accompanying the immediate lay-offs and mass resignations led to a fraught atmosphere, and seemingly chaotic transition, scaring off corporate advertisers, many of whom announced a suspension of advertising on Twitter.

Shortly after taking control, Musk, working almost around the clock, focused on several key issues, among them bloated staffing, bots, code quality, and revenue sources. His first attempt at reform was to roll out a paid “Blue Check” to indicate a verified account.

This move proved disastrous, perversely permitting a huge spike in bot accounts impersonating real people due to the rushed and poorly structured release. The feature had to be hurriedly withdrawn, adding to the sense that Twitter was out of control.

Musk recently provided a deck he presented in a company talk on November 26. It is in 2 parts:

1, Current State,

2. Future of the Prduct

https://bit.ly/3GQQ4VM

What follows is a brief summary of Musk’s presentation:

Current State: The graphs are light on scale and fuzzy on dates, but they show all-time highs in sign-ups and user active minutes, with peaks in monetizable daily active units. Also, lower hate speech impressions, while reported impersonations after peaking, dropping to new lows.  

Future of the Product: The forthcoming product enhancements describe an “Everything App” including” advertising as entertainment, video, encrypted messaging, longform tweets, a retry at the Blue Verified program, and a payments function.   

Taking all this, and based on the comps I cite from the companies in the graphs above, I have done some scenario testing to determine the financial viability of Musk’s plans. If we take his goals, and given his operating track record of running extraordinarily lean ships, I believe he can achieve industry leading margins along these lines:

Direct Cost: 20%

Operating Margin: 35% (G&A 5%, S&M 15%, R&D 15%)

Pre-tax Income: 45%

With a margin of 45%, we can calculate the target of revenue that Twitter needs to achieve: at the very least, it needs revenues of $2.6b just to cover the interest bill. At revenues of $5b Twitter’s profits would exceed $1b per annum after interest. Beyond these levels, Musk and his investors would begin to reap significant rewards on their investment.

Absent a significant push-back from the market from the political atmosphere Musk has created, it is easy to imagine that, displaying the product marketing, engineering, and operating skills that he has demonstrated, that he will handily exceed the revenues of the legacy Twitter.

There are plausible paths to a win. The platform has very solid foundations, and Musk has an ambitious plan to add signifcant capabilites that could attract an even larger community. He has a very strong record at building very exciting products that customers love, from his earliest days at Zip2, through the X.com and Paypal era, and at Tesla and SpaceX, and he has a clear vision for Twitter.

However, the unknown is the impact that the political battles he is choosing will have on him and on his businesses, but most particularly Twitter. What is certain is that these fights have dramatically raised risks across all his holdings for all shareholders. It would behoove him to get his head down, and do what he does best…drive the company to achieve the seemingly impossible with the implausible.

Road-Tripping with an EV…pictures included

We set off on the maiden voyage of our brand-new Tesla Model X, for which we had waited for almost a year. A 6,000+ mile road trip. Some comments on long-range journeys in EVs in the US today (plus my review of the X). TL/DR – in a Tesla, it’s a snap! X is great – with some caveats.

Our trip as planned in the ABRP.com EV trip planner

Rogie, our aging but willing dog, and I drove out to Bozeman, MT. My wife flew out, and off we set on an ambitious agenda. We had only one month due to commitments but wanted to pack in much that we hadn’t seen in the 40 years we have lived in the US. And to road test the X!

Dog (Rogie) was my copilot

In summary our agenda was: Yellowstone, Salt Lake City, Zion National Park, Lake Powell, Grand Canyon (North + South Rims), Sedona, Flagstaff, Albuquerque, Amarillo, OK City, Bentonville AR, Memphis, Nashville, Gatlinburg TN, then home.

At the North RIm of the Grand Canyon. It was pretty chilly! On balance it was a great time of the year to do this trip, and the weather was pretty temperate everywhere we traveled.

About the state of EV travel in the US. If you are driving a Tesla, it’s almost as convenient as traveling in a gas guzzler, but does require more planning, preferably in advance of the trip; with any other make of EV it wouldn’t have been as easy, if at all feasible.

Tesla has, in the last 10 years single-handedly created an EV infrastructure. The world, at least the US has now awakended to the necessity of building a universal charging infrastructure.

The secret is not only Tesla’s incredible network of Superchargers (SC), and the seamless integration into the vehicles’ Nav facilities, but the amazing job that Tesla has done in promoting destination chargers to hotels nationally (and I would guess, internationally).

At the Horseshoe Ben of the Colorado River Gorge. Very difficult to put into words the majesty of the views we encountered on the trip.

Having a destination charger allows you start the day with a (free) full charge, with first stop for lunch! Had we realized the opportunity, we could have – and should have booked 100% of our stays in charger-equipped hotels. But the SC network is the real ace up the sleeve.

Looking down on Horeshoe Bend. The Colorado is at a critically low level because of the prevailing drought

Every destination was in reach of multiple choices of SCs on the route, most of which are conveniently located near services for a bite and a rest stop. On the way out I travelled between 500 and 700 miles each day, so maintained 2-hour stages, and 20–30-minute charges.

The presence of the Aspens in the vast Kaibab National Forest approching the North Rim of the Grand Canyon made for a stunning sight

Once my wife joined, we began touring and site seeing, stopping for 2 or 3 days in each location, and the trip legs reduced to a few hundred miles. Generally, we set off mid-morning, stopped to charge for 30-40 minutes in the lunch hour, and arrived at about check-in time.

With Rogie and a sadly depleted Lake Powell in the background.

If the hotel had a destination charger, I would hook up, and we could just take off in the morning. Failing that I would take the car to the local supercharger before or after dinner, or we would charge up in the morning on our way out. Quite convenient.

Upper Yellowstone Falls

The great Tesla value-add is not just the SC+destination network; it’s the integration of the car+network, showing options, and number of charging points free at each location, recommending stops. I can’t imagine planning/executing a trip without that.

Zion National Park provided us with stunning views

I tried abetterrouteplanner.com to plan stops & hotels along the way for my outbound trip, and a guide for hotels to book, but found the UI clunky and awkward, with far too many gotchas. After struggling with it, I gave up and stuck to the Tesla website and in-car nav.

Ansel Adams captured the extraordinary patterns that we saw amongst the tree stumps dotting the active geyser fields

Improvement ideas: need to ensure that there are garbage cans convenient to the SC locations, and ensure they are serviced. Some stops are serviced by the local merchants and are spotless…other not so much. One had a transparent tip-box for local volunteers – it was pristine!

The Chapel of the Holy Cross in Sedona. This small Chapel sits atop a small Mesa in dramatic fashion, in a town that is one of the many jewels of the Southwest.

About the X: fantastic tourer, best I have yet experienced. Single major complaint: glare from early/late sun pouring into the vast windshield and driver/passenger window, not dealt with efficiently by the ingenious yet insufficient visor. It’s a design flaw in my estimation.

A little bit of whimsy on Route 66….Cadillac Ranch

I had misgivings about the Yoke before driving the car but loved it from the first. I prefer the Model 3/Y single screen to the S & X split screen. I don’t believe the split screen adds much info, but it distracts from the single focus on information implemented in the 3/Y.

A 60+ year old Conoco gas station in Shamrock TX. Virtually the entire town of Shamrock is preserved in the 60s. One of the greatest treat along Route 66.

I am not sure the stalkless driving solution as in the X & S is yet fully solved. Not even after a month and 6,000 miles of driving did I develop an instinctual grasp of the controls, and I sounded the horn, set off the wipers, and initiated a call, each on more than one occasion!

My wife with boyfriend. Elvis prevades Nashville and Memphis. Graceland was a wonderful surprise…not tacky as I expected, in fact – if anything – somewhat understated.

Wish I had time to have racked up sufficient Safety Score miles to have FSD added (as I have on my daily driver Model 3). But Autopilot (NOA) proved very helpful (mostly freeway miles) and made the driving incredibly easy. I did all the driving, and never felt tired at any stage.

One of Elvis’ Rolls. Panel gaps compared poorly with my new X. Of course that may have something to do with the car being about 70 years old!

Also wish I had had time to get the car wrapped before taking off on the trip. We ended up getting two tiny chips on the nose paintwork (a minor miracle given the freeway miles), not apparent to the casual review. Hopefully when I get it wrapped, they’ll repair the damage.

We had some great meals on the trip, but the most wonderful surprise was breakfast at Monell’s in Nashville. I am not one for Southern breakfasts, but this was a great culinary and social experience. I voted this the best meal of the journey…and of the last year.

We bought the X for my wife, a replacement for an 8-year-old Hyundai Santa Fe. There’s going to be a fight before I hand it over to her. I dearly love that car now.

The Valuers Dilemma: Understanding the Tesla Stock Valuation

This morning I watched a fascinating debate on the YouTube channel “Tesla Daily” (https://www.youtube.com/watch?v=C0Fl6JBQgrc) between Rob Maurer and David Trainer. Rob is a self-confessed Tesla Bull, a columnist on The Street who hosts this excellent and influential YouTube Channel. (full disclosure – I am a subscriber and Patreon supporter of his). David is a respected, long time Wall Street analyst, the founder and CEO of New Constructs, an independent financial analyst, and a self-confessed fan of the Tesla car and its founder Elon Musk.  However, in contrast to Rob, David has a very sober view of the current stock price, considering it highly speculative. His firm has authored a note warning that Tesla is “the most dangerous” stock for the investors with fiduciary responsibility.

What struck me as I listened to the debate was that both Rob and David appear to talk past what I consider to be the root cause of the current Tesla stock price, and its probable future value. Elon Musk is famous for insisting on looking at every problem from first principles – and even more famous for being so successful in the application of those principals. Let’s follow Elon’s dictum here to learn why Tesla’s stock has reached such stratospheric levels and evaluate whether it is likely to remain as high or even grow further.

In the matter of the Tesla valuation, I believe the first principle that governs is the Innovators Dilemma, which for brevity we will refer to as InDi. It underpins the entire value proposition represented by Tesla, not just in the gigantic global automotive market, in transportation in general, and perhaps – ultimately even more importantly – in the global energy markets.

InDi is not well understood by the analysts, and is seldom accorded significant value, which is perplexing as it is an extremely well understood process. It was formulated by Clayton M. Christensen while Professor of Business Administration at the Harvard Business School and popularized by him in many writings. Christensen writes:

When disruptive technologies emerge, dominant, well-run companies often stumble. These companies tend to use the same sound business judgment that has guided them through previous changes, including:

  • Listening to what current customers want
  • Providing more and improved versions of what customers want
  • Investing in projects that promise the highest returns

However, in the face of disruptive innovations, these strategies don’t produce the same results. This is the innovator’s dilemma: The approaches that lead to success in adopting most innovations lead to failure when confronting disruptive innovations.

Elucidating on those key finding, Wikipedia adds the following woes to the incumbent’s situation, (each of which readily identifiable with the OEMs!):

  • Small markets struggle to impact an incumbent’s large market
  • Disruptive technologies have fluid futures, as in, it is impossible to know what they will disrupt once matured
  • Incumbent Organizations’ value is more than simply their workers, it includes their processes and core capabilities which drive their efforts
  • Technology supply may not equal market demand. The attributes that make disruptive technologies unattractive in established markets are often the ones that have the greatest value in emerging markets

On the other hand, consider the position of the challenger:

  • They develop the disruptive technology with the ‘right’ customers. Not necessarily their current customer set
  • They place the disruptive technology into an autonomous organization that can be rewarded with small wins and small customer sets
  • They fail early and often to find the correct disruptive technology
  • They allow the disruption organization to utilize all of the company’s resources when needed but are careful to make sure the processes and values were not those of the company

It is easy to recognize the Tesla in these qualities. Finally, Wikipedia points out

  • Disruption is a process, not a product or service, that occurs from the fringe to mainstream
  • Originate in low-end (less demanding customers) or new market (where none existed) footholds
  • New firms don’t catch on with mainstream customers until quality catches up with their standards
  • Success is not a requirement and some business can be disruptive but fail
  • New firm’s business model differs significantly from incumbent

Success, in simple terms, Christensen says, is “correlated with a business model that is unattractive to its competitor”. This is brilliantly true in the case of Tesla vs. OEMs.

A telling example of the business model problem of incumbents becomes apparent in the YouTube debate when Rob Maurer points to the direct sales model of Tesla, capturing the profit margin that would otherwise go to the dealer. David Trainer argues that the dealership network of the OEMs is a strength, allowing the OEMS “concentrate on their main business”, providing them with broad distribution. What eludes him is that this same dealer network becomes uneconomic in an EV world, as the service and spare parts business on EVs are not 10% of that of an ICE vehicle. In simple terms, the dealership can only be run at a loss, and Tesla’s online sales are a significant advantage. Early evidence is that OEMs have been unable to persuade their dealership network to sell EVs, contributing to the woeful sales of legacy OEM sales of their electric vehicles. The dealership network is at the core of the OEM business model that would be legally, culturally and financially impossible to voluntarily sever; yet with it, the OEM EV future is probably doomed.

Another example of the business model problem is the “deep supply chain” mentioned by Trainer as a significant advantage to OEMs. Unfortunately for them, this may be the most serious problem for the OEM business model. It is this supply chain that inhibits the development of a vehicle, with an integrated battery/drivetrain/HVAC/computer system, a vitally necessary step to creating a competitive EV offering (and the reason for the failure of so many “Tesla Killers” to date). The key intellectual property of OEMs retain is their internal combustion engine designs, possibly the only component – apart from the sheet metal – that they develop and manufacture in-house. Ironically this is the technology that is of least value – in fact no value – in this new market.

To compound the problem, the challenge is not just a matter of replacing an internal combustion engine with an electric motor, and simply adding a battery in the stead of a gas tank. Instead it is a highly complex problem of redesigning the drivetrain and vehicle into a single, comprehensive whole. Depending on a supply-chain network to provide this does not permit the iterative design/development necessary to rapidly evolve successful solutions to this very difficult problem. With well evolved, century old technology, depending on supply chains for R&D of everything but the engine made sense: but the situation has changed, and dramatically so; disruption is now occurring. OEMs development cycles traditionally stretch to years. Tesla iterates its designs from month to month to month.

Large OEMs are not given to iterative design/development. This is a longer discussion, and perhaps key in differentiating Tesla from the incumbents. It is sufficient to point to the continuing and growing technological leadership of the company’s vehicles over the incumbents. It is instructive that the industry has not yet been able to manufacture a car to compete with the Tesla Model S, first sold seven year ago.

One could cite many other startlingly clear examples of InDi in the Tesla versus all the others debate. This is also true of Tesla’s work in the energy markets, but I won’t do that here, it is all well documented, and Rob is probably more of an expert than I am in this field. Instead I want to return to the question raised at the outset: the problem of valuation. How does one value stocks of InDi companies, stocks that David Trainer is sure to label, as he has with Tesla, as the “most dangerous” of all stocks.

The answer, of course, is “with great difficulty.” Aswath Damodaran, the “Dean of Valuation”, NYU professor famous for valuation methodology, talks about his struggle – and consistent failure – to value Amazon, that well-known bookseller (http://aswathdamodaran.blogspot.com/2018/04/amazon-glimpses-of-shoeless-joe.html)

Oh yes, of course I know Amazon is not a bookseller. But in the early days we were told – by professors of valuation – that Amazon would have to sell all the books sold in the world to justify its valuation. Amazon taught us several important points. It proved the Christensen principles “Disruptive technologies have fluid futures”, and “Disruption is a process, not a product or service, that occurs from the fringe to mainstream”, and probably a few more besides. What is more important about the Amazon example, is the admission – in the above cited article – made by Damodaran of the extreme importance of this fact:

“Bezos …telling his stockholders that if Amazon built it (revenues), they (the profits and cash flows) would come. In all my years looking at companies, I have never seen a CEO stay so true to a narrative and act as consistently as Amazon has, and it is, in my view, the biggest reason for its market success.”

And further:

“I have consistently under estimated not only the innovative genius of this company, but also its (and its investors’) patience.”

So here we arrive (finally) as my thesis. Like Jeff Bezos, Elon Musk is an innovative genius, who has defined clearly his objectives, methods and objectives. He has created an innovative, rapid learning machine to create products with enormous market appeal and success, in gigantic global markets, and delivered by a highly productive business model. The firm has found its stride in producing and delivering in mass and is in the process of demonstrating its ability to scale. It has built and successfully brought to production in a record time (at several times the speed of the legacy OEMs) a massive factory in China. It is in the process of – simultaneously – building three gigantic factories across the world and is demonstrating a confident touch in those buildouts. And, to top it all off, Tesla is finding ways to drastically reduce costs of manufacture (https://electrek.co/2020/08/25/tesla-start-operations-worlds-largest-casting-machine/).

All Good. Rob aced these points. Here is the miss. Tesla DOES NOT INTEND TO MAKE PROFITS in the foreseeable future. Tesla has said in many fora that is intends to minimize profits. Elon said in the last conference call that the objective to show no more than 1% or so of profits. He, like Jeff Bezos in the quote above, understands that the InDi is focused on market share, not profitability. Tesla is focused on and is on a clear track toward dominance of the Automobile market. And I confidently predict that Tesla’s shareholders, like Amazon’s before them (many of them, after all, are the same people and institutions) will be quite content with that.

Tesla already has a huge price advantage over its competition. Critics – David Trainer amongst them – do not yet realize that Tesla will shortly no longer be a “premium” priced vehicle – Tesla is persistently driving down the price of its vehicles, and their cars are rapidly approaching price parity with Toyota, even while their automotive gross margins are trending higher than the OEMs. Tesla has demonstrated significantly better price/value against their competition.

Why? Because of Wrights Law (The cost of a unit decreases as a function of the cumulative production – https://spectrum.ieee.org/tech-talk/at-work/test-and-measurement/wrights-law-edges-out-moores-law-in-predicting-technology-development) is on Tesla’s side. They have 1 million EVs behind them. No other OEM approaches them, and they will deliver half a million vehicles this year, 5 times more than the nearest competitor, over a million next year, and on and on. VW hope to reach 1.5 million vehicles by 2025. At that point, Tesla are targeting to have delivered over 8 million vehicles (and have plants already built or in building stages) to enable them to deliver those numbers. Tesla’s production cost should be dramatically lower than that of other OEMs. (ARKinvest, a fund manager dedicated to disruptive innovation brought Wright’s Law to my attention – https://ark-invest.com/wrights-law/)

Tesla could translate this advantage into profitability, but the point the point – according to Elon and the company – Tesla won’t. Tesla will drive the cost of the car down inexorably, while at the same time dramatically increasing the efficiency of their capital expenditure (https://cleantechnica.com/2019/10/26/capital-efficiency-teslas-obsession/)

So, throw away your spreadsheets. All those CPAs and valuation specialists carefully compounding profitability and cash flow – its not going to happen. Prices are going to be driven down, free cash is going to be aggressively invested into plant, charging infrastructure, service centers, AI chip development for Autonomy, supercomputers for AI training, Powerwalls, Solar Roof, Autobidder energy trading platform, and on and on. But no profits, and no free cash flow.     

Rob and David both rely on the spreadsheet method and apply a predetermined formula to arrive at a discount of some variation of combination of the profits/cash flow of a firm for a given (presumed) scenario. In this they are joined by the Dean of Valuation, Aswath Damodaran.

But Damodaran should have learned from his Amazon experience. In the case of Amazon, he regrets selling his shares in 2012, and missing the huge run that stock has enjoyed since. But in January of this year he once again applied his spreadsheet formulas, this time to his investment in Tesla, resulting in him sellin his stock at $640. He said:

 “The momentum is strong, and the mood is delirious, implying that Tesla’s stock price could continue to go up. That said, I am not tempted to stay longer, though, because I came to play the investing game, not the trading game, and gauging momentum is not a skill set that I possess. I will miss the excitement of having Tesla in my portfolio, but I have a feeling that this is more a separation than a permanent parting, and that at the right price, Tesla will return to my portfolio in the future.”

In this, I believe he is wrong. I don’t believe this is a “story” stock, nor is it a “momentum” stock. It will certainly fluctuate very widely over time, given the emotions of its supporters and detractors. But, now that Tesla has demonstrated its ability to execute, it is highly unlikely that it will return to the “right price” according to his formulations.

Tesla today, like Amazon in the early 2000s, is a stock that has proven its ability to both innovate at the extreme, and to execute. It truly deserves the Innovators title. It is on a clear path to dominance – not just in the EV market – but in the Auto market, and beyond that in the energy market. What is missing in Rob, David, and Aswath’s calculus, is the formula that values an Innovator that can disrupt large, established markets. We must re-examine the arc of Amazon, Google, Microsoft (in the early Gates years), Apple (in the Jobs years) and the sparse number of firms that have truly demonstrated the characteristics of Innovators. These characteristics are not ephemeral – they take some years to evidence themselves – and they have clear markers. They do not occur frequently, certainly not as frequently as Venture Capital sponsors would have us believe, given the number and rate at which they berth their “Unicorns”. But for legitimate disruptors I believe the appropriate valuation is a function of their Total Addressable Market (TAM). In the case of Amazon it is a function of the retail and technology markets in the geographies in which they operate. In the case of Tesla, it is in the global automotive and energy markets in which they operate.

Just Not FEELing it

In a recent DecisionCAMP conference, in the “ask a vendor” free-for-all, I – once again – voiced my concerns about the inclusion of FEEL (Fairly Easy Expression Language) in the DMN (Decision Model and Notation) specification. This is an issue that I raised at the outset of my involvement with the working group, and continue to raise, as I do now.

To the extent that an expression language is a necessity – and of course it is – FEEL is not the solution. FEEL creates a barrier to entry by requiring the initiate to learn yet another language, when there is at our disposal a perfectly acceptable expression language, originated in Excel, now used by many spreadsheet clones, and clearly a global standard as it is used by hundreds of millions of practitioners. It just so happens that the most effective users of Excel are the key users of decision management tools – business analysts.

The resort to FEEL in many cases is an attempt to overcome what is a lack of capability in the model notation. Some practitioners even regard the modeling notation as just a way to “sketch” the models by the business analysts, so that they, the practitioners, may then “perfect” the models in FEEL language! In other cases, FEEL is used to implement procedural functions, negating the declarative intent of the model.

These examples of the use of FEEL defeat what I consider to be the principal reasons for DMN – to enable businesspeople to express AND MANAGE business logic. The abstraction of that logic out of language formalism into a visually descriptive but accurate and rigorous notation is key to achieving that goal.

I am not alone in this. Carlos Serrano-Morales of Sparkling Logic opined in the discussion that in practice the use of FEEL leads to models that defeat the purpose of interoperability. Called on to defend FEEL, Gary Hallmark of Oracle – one of the original – and principal advocate – of FEEL in the original DMN working group – made the acute observation that while he believed there was still a place for organizations like OMG, that in large measure it was the Open-Source community that was propagating modern day “standards”, suggesting to me that Gary’s views on FEEL have evolved. The glacial rate of evolution of DMN, compared to the dynamic demands of the decision modeling community, lead to the vendor work arounds, and constantly create customized evolutions of DMN/FEEL to meet client needs.

Over the last year or so, I have become acutely aware of the Babel of different scripting, expression and programming languages in the marketplace. This is due to my work on the ALE (Automated Language Extraction) product we, Sapiens Decision, are introducing to market. ALE uses Machine Learning and other methods to extract business logic from programming languages – and potentially natural language – and render that logic into normalized decision models, to be managed in a decision management tool.

We believed that the hundreds of billions of lines of code in COBOL legacy solutions were obvious targets for ALE to extract the business logic, and then manage as decision models in Sapiens Decision. Turns out that the real problem is the logic embedded in any/all computer language(s), even in the most modern of systems!

One representative, but striking, use case I will cite is a super-regional insurance company that recently implemented Sapiens Decision. This company is five years into a strategic re-organization that involved the implementation of a new, enterprise-wide, Policy Administration System (PAS), a classic step in modernization of a legacy insurance company. The CIO recently made the statement to a user group forum at Sapiens Decision that had they implemented decision management at the outset of their journey, they would have “saved tens of millions of dollars.” The reasons for the savings are multi-fold, a contributing factor being the proprietary language used by the PAS in implementing customized business logic. This leads to a combination of issues:

  • The need for a group of specialized (read: highly paid) developers with the knowledge of the unique language used by the PAS to perform customizations (of which there are – of necessity – a great many.
  • A classic combination of business analyst working with developers to implement the solution, leading to lengthy (and costly) implementation and change process.
  • Lengthy – and expensive processes to upgrade the PAS system as it progresses through its life cycle of version following version.

By removing the custom logic (and even a significant portion of the native logic) from the PAS, and having it rendered in decision models, the PAS is made “lightweight”. The decisions are exposed to the PAS as an API. The PAS becomes capable of being upgraded in compressed cycles, and able to be supported by a significantly smaller developer team. Business analysts become more effective, able to author and test their requirements directly; even more importantly they can manage the full scope of the business logic, and isolate change opportunities and process improvements without the deep archeological dives into the code previously necessary.

Given this great use case for decision management, the problem remains that the client has the very large body of existing code to be converted to decision models. Of course, we could manually extract the logic, but cost and time to value are dramatically collapsed using ALE.

This is one example of the use of ALE. To date we have faced client requirements to extract the business logic from an extraordinary variety of rule platforms and languages. The list (not exhaustive) includes COBOL, of course, but also Java, Python, and proprietary languages in several different enterprise systems, ETL tools, and business rule languages (an aside – yes, rule languages are about as bad as programming languages in terms of their proprietary nature, highly specialized technical staffs, the difficulty of traceability, and the entropy in the structure of rules in the rule repository over time.)

Clearly, the last thing the client seeks to do is re-embed any part of the logic, once it Is ultimately freed of language dependency, into yet another language, most particularly one supported by a relative handful of practitioners.

If anything, in this second decade of decision management, I am even more passionate in the belief that a declarative, visual representation of even the most complex business logic is the holy grail. I would be the first to admit that our current tools are far from sufficient to achieve this objective. In the next post I will provide – in the spirit of Open Source – my thoughts on the problems to be tackled together with ideas for solutions.

Elon Time? Part 1

Elon Musk has developed a reputation for so-called “Elon Time”, projecting new products or services in seemingly impossible to deliver timescales. While good humouredly accepting the charge, he responds “I may be late, but I always deliver!”

Despite this reputation, Musk consistently projects the financial performance of Tesla with amazing accuracy; in fact, one may say that he has been fantastically prescient, significantly besting even the most farsighted of fellow CEOs.

The following slide, taken from a February 2014 Tesla deck introducing the “Gigafactory” for the first time, projected 500,000 vehicle sales in 2020, six years in the future.

https://www.tesla.com/sites/default/files/blog_attachments/gigafactory.pdf

At the time this slide was produced in February of 2014, Tesla had never produced even 7,000 cars in a quarter. They had but a single vehicle model, at a price point that could not conceivably reach volume annual sales, and they were faced at having to invent and develop a range of technologies to enable and justify the 2020 projected 500,000 vehicle sales per year, only 6 years hence. Amongst the many technologies that would have to be evolved to make that possible were batteries, at volumes and specifications that were not considered practical or economic at the time.

https://www.tesla.com/sites/default/files/blog_attachments/gigafactory.pdf

An illustration of the scope of the challenge is that Tesla were planning to build more batteries in a single U.S. based plant as the entire global cell production at that time, almost all of which based in Asia (and all focused on delivering computer/phone batteries).

So, Tesla had to (1) Persuade a leading, Asian based battery supplier to the computer industry to (2) invest a very significant amount of money in an unprecedented manufacturing plant in the US, to (3) manufacture batteries based on a new design and evolving technology for a (4) a newly minted car manufacturer that claimed it was going to 15x its sales (5) with a car that was not yet designed, but would contain (6) not only the novel batteries, but a whole new drive train (not to mention computer system) (7) and the success of the car would depend on a greater than 30% reduction in cell cost!

What could possibly go wrong?

It is quite stunning that Tesla will hit, and probably best these delivery goals set in early 2014, despite the broad range of unknowns on that date. (As we sit here at the end of Q3 2020, the best estimates for deliveries for the year 2020 are a shade over the 500,000 targeted in 2014!).

Given the pressure of constant, rapid innovation, Tesla’s guidance has been quite reliable, at least in it’s broad brushstrokes, and despite some understandable lapses.

The company has had to learn to introduce products, and production lines. In the past, they faltered in the introduction of both the Model X and the Model 3, and in managing global delivery logistics for the Model 3, all of which had repercussions on Tesla’s financial expectations. However, that past negative must be balanced with the most recent quarters: the extraordinary speed of the Shanghai factory build out, the smoothness of Model Y launch execution, the launch of the Model Y in Shanghai, and the apparent high speed of the build out of Giga Berlin and Giga Texas.

Given the gigantic scope of their 2014 ambition, achieving it has been truly astonishing, giving the expression “Elon Time” a completely different complexion.

In Elon Time, Part Deux we will explore what the future holds for Tesla, per Elon.

Placing a Value on Tesla

This morning I watched a fascinating debate on the YouTube channel “Tesla Daily” between Rob Maurer and David Trainer. Rob is a self-confessed Tesla Bull, a columnist on The Street who hosts this excellent and influential YouTube Channel. (full disclosure – I am a subscriber and Patreon supporter of his). David is a respected, long time Wall Street analyst, the founder and CEO of New Constructs, an independent financial analyst, and a self-confessed fan of the Tesla car and its founder Elon Musk; However, in contrast to Rob, David has a very sober view of the current stock price, considering it highly speculative. His firm has authored a note warning that Tesla is “the most dangerous” stock for the investors with fiduciary responsibility.

What struck me as I listened to the debate was that both Rob and David appear to talk past what I consider to be the root cause of the current Tesla stock price, and its probable future value. Elon Musk is famous for insisting on looking at every problem from first principles – and even more famous for being so successful in the application of those principals. Let’s follow Elon’s dictum here to learn why Tesla’s stock has reached such stratospheric levels and evaluate whether it is likely to remain as high or even grow further.

In the matter of the Tesla valuation, I believe the first principle that governs is the Innovator’s Dilemma, which for brevity we will refer to as InDi. It underpins the entire value proposition represented by Tesla, not just in the gigantic global automotive market, in transportation in general, and perhaps – ultimately even more importantly – in the global energy markets.

InDi is not well understood by the analysts, and is seldom accorded significant value, which is perplexing as it is an extremely well understood process. It was formulated by Clayton M. Christensen while Professor Business Administration at Harvard Business School and popularized by him in many writings. Christensen writes:

When disruptive technologies emerge, dominant, well-run companies often stumble. These companies tend to use the same sound business judgment that has guided them through previous changes, including:

  • Listening to what current customers want
  • Providing more and improved versions of what customers want
  • Investing in projects that promise the highest returns

However, in the face of disruptive innovations, these strategies don’t produce the same results. This is the innovator’s dilemma: The approaches that lead to success in adopting most innovations lead to failure when confronting disruptive innovations.

Elucidating on those key finding, Wikipedia adds the following woes to the incumbent’s situation, (each of which readily identifiable with the OEMs!):

  • Small markets struggle to impact an incumbent’s large market
  • Disruptive technologies have fluid futures, as in, it is impossible to know what they will disrupt once matured
  • Incumbent Organizations’ value is more than simply their workers, it includes their processes and core capabilities which drive their efforts
  • Technology supply may not equal market demand. The attributes that make disruptive technologies unattractive in established markets are often the ones that have the greatest value in emerging markets

On the other hand, consider the position of the challenger:

  • They develop the disruptive technology with the ‘right’ customers. Not necessarily their current customer set
  • They place the disruptive technology into an autonomous organization that can be rewarded with small wins and small customer sets
  • They fail early and often to find the correct disruptive technology
  • They allow the disruption organization to utilize all of the company’s resources when needed but are careful to make sure the processes and values were not those of the company

It is easy to recognize the role of Tesla in these qualities. Finally, Wikipedia points out:

  • Disruption is a process, not a product or service, that occurs from the fringe to mainstream
  • Originate in low-end (less demanding customers) or new market (where none existed) footholds
  • New firms don’t catch on with mainstream customers until quality catches up with their standards
  • Success is not a requirement and some business can be disruptive but fail
  • New firm’s business model differs significantly from incumbent

Success, in simple terms, Christensen says, is “correlated with a business model that is unattractive to its competitor”. This is brilliantly true in the case of Tesla vs. OEMs.

Rob provides a telling example of this when he points to the direct sales model of Tesla, capturing the profit margin that would otherwise go to the dealer. David Trainer argues that the dealership network of the OEMs is a strength, allowing the OEMS “concentrate on their main business”, providing them with broad distribution. What eludes David is that this same dealer network becomes uneconomic in an EV world, as the service and spare parts business on EVs are not 10% of that of an ICE vehicle. In simple terms, the dealership can only be run at a loss, and Tesla’s online sales are a significant advantage. Early evidence is that OEMs have been unable to persuade their dealership network to sell EVs, contributing to the woeful sales of legacy OEM sales of their electric vehicles. The dealership network is at the core of the OEM business model that would be legally, culturally and financially impossible to voluntarily sever; yet with it, the OEM EV future is probably doomed.

Another example of the business model problem is the “deep supply chain” mentioned by Trainer as a significant advantage to OEMs. Unfortunately for them, this may be the most serious problem for the OEM business model. It is this supply chain that inhibits the development of a vehicle, with an integrated battery/drivetrain/HVAC/computer system, a vitally necessary step to creating a competitive EV offering (and the reason for the failure of so many “Tesla Killers” to date). The key intellectual property of OEMs retain is their internal combustion engine designs, possibly the only component – apart from the sheet metal – that they develop and manufacture in-house. Ironically this is the technology that is of least value – in fact no value – in this new market.

To compound the problem, the challenge is not just a matter of replacing an internal combustion engine with an electric motor, and simply adding a battery in the stead of a gas tank. Instead it is a highly complex problem of redesigning the drivetrain and vehicle into a single, comprehensive whole. Depending on a supply-chain network to provide this does not permit the iterative design/development necessary to rapidly evolve successful solutions to this very difficult problem. With well evolved, century old technology, depending on supply chains for R+D of everything but the engine made sense: but the situation has changed, and dramatically so; disruption is now occurring. OEMs development cycles traditionally stretch to years. Tesla iterates its designs from month to month to month.

Large OEMs are not given to iterative design/development. This is a longer discussion, and perhaps key in differentiating Tesla from the incumbents. It is sufficient to point to the continuing and growing technological leadership of the company’s vehicles over the incumbents. It is instructive that the industry has not yet been able to manufacture a car to compete with the Tesla Model S, first sold seven year ago.

One could cite many other startlingly clear examples of InDi in the Tesla versus all the others debate. This is also true of Tesla’s work in the energy markets, but I won’t do that here, it is all well documented, and Rob is probably more of an expert than I am in this field. Instead I want to return to the question raised at the outset: the problem of valuation. How does one value stocks of InDi companies, stocks that David Trainer is sure to label, as he has with Tesla, as the “most dangerous” of all stocks.

The answer, of course, is “with great difficulty.” Aswath Damodaran, the “Dean of Valuation”, NYU professor famous for valuation methodology, talks about his struggle – and consistent failure – to value Amazon, that well-known bookseller

Oh yes, of course I know Amazon is not a bookseller. But in the early days we were told – by professors of valuation – that Amazon would have to sell all the books sold in the world to justify its valuation. Amazon taught us several important points. It proved the Christensen principles “Disruptive technologies have fluid futures”, and “Disruption is a process, not a product or service, that occurs from the fringe to mainstream”, and probably a few more besides. What is more important about the Amazon example, is the admission made by Damodaran in the article cited above, of the extreme importance of this fact:

“Bezos …telling his stockholders that if Amazon built it (revenues), they (the profits and cash flows) would come. In all my years looking at companies, I have never seen a CEO stay so true to a narrative and act as consistently as Amazon has, and it is, in my view, the biggest reason for its market success.”

“I have consistently under estimated not only the innovative genius of this company, but also its (and its investors’) patience.”

So here we arrive (finally) at my thesis. Like Jeff Bezos, Elon Musk has defined clearly his objectives, methods and objectives. He wrote them down – they are available on the Tesla web site in articles entitled “Secret Master Plan” and “Master Plan (Part Deux)” written 14 and 4 years ago respectively. Tesla has followed the strategic path set out with remarkable fidelity.

Tesla has created an innovative, rapid learning machine to create products with enormous market appeal and success, in gigantic global markets, and delivered using novel but efficient business models.

The cars started out as expensive, appealing to a coterie of ecologically aware, well-off customers, but have moved significantly down the cost scale, and dramatically widened the appeal of the product. In the past two years the firm has found its stride in producing and delivering in mass and is in the process of demonstrating its ability to scale, and have consistently driven down the price – and cost – of the mass production vehicles. It has built and successfully brought to production in a record time (at several times the speed of the legacy OEMs) a massive factory in China. It is in the process of – simultaneously – building three gigantic factories across the world and is demonstrating a confident touch in those buildouts. Also Tesla is finding ways to drastically reduce costs of manufacture and evolving new and emerging products into related, but huge markets (as prescribed by Christensen.)

All Good. Rob aced these points. Here is the miss: Tesla DOES NOT INTEND TO MAKE PROFITS in the foreseeable future. Tesla has said in many forums that it intends to minimize profits. It’s in the published Master Plan. Elon said in a recent conference call that the objective was to show no more than 1% or so of profits. He, like Jeff Bezos, understands that the disrupter is focused on market share, not profitability. Tesla intends to, and is on a clear track toward dominance of the Automobile market. NOT THE EV SECTOR – THE ENTIRE AUTO MARKET. Calculating the EV sector as a percentage of the auto market, then calculating Tesla’s market share is missing the point. Tesla intends to ensure that the entire auto market becomes an EV market. In this I have little doubt they will succeed, in a leadership role, and in less than ten years. ICE cars are the flip phones of 2030. Value that.

A Tale of Two Countries…Part Deux

All data downloaded from worldometers.info 8/15/2020 09:00 EST

On May 9 we wrote about the great divide between the “hot” cities/states, and the rest of the country, remarking on the significant divide between those states with high rates, versus the majority of states with low COVID-19 infections (https://www.linkedin.com/pulse/tale-two-countries-larry-goldberg/?trackingId=KoRoENT6ZX6AqnV%2BX%2Fe44Q%3D%3D)

Now that the infection rates have risen significantly in many states and localities, it is time to revisit those infection rates.

Nothing more clearly illustrates the disparity across the divide than the graph above. It shows deaths per day to the commencement of the Pandemic in early March, until this week. It compares, in scale, the course of the pandemic in New York State and California from the first onset in early/mid march, to current time.

We see the early explosion of infections in New York State (principally New York City area) to a peak of 1,000 death per day, followed by a rapid decline to deaths in the single digits, less than a month after the onset of the first infections. In California, a state twice as large as New York, on the other hand, there was a gradual rise to double digits over a period of a month, which was maintained over three months of lockdown, followed by a modest rise to a peak of 200 deaths a day by early August after a degree of loosening of the lockdown. Since California is twice as populous as New York, it can be seen that there is a huge disparity between the states. To date, New York has suffered a state-wide mortality rate of about 1,700 per 1 million of population, versus California at less than 300 per million.

The disease appears to have run its course in New York, and there is an effective community immunity (given continued public precautions); this is not the case in California, when infections, and deaths, continue. But using IHME projections, the likely mortality count is unlikely to double. Thus, at best, New York will end up with about 3 times the mortality rate of California.

All data downloaded from worldometers.info 8/15/2020 09:00 EST

The table above shows the disparity between four of the principal population centers on the US North-East corridor (New York, New Jersey, Connecticut and Massachusetts) compared to the rest of the USA. These three states alone, with a population of about 8% of the US, are enough to have a huge impact on the Infection Fatality Rate (IFR) of the entire USA. If we were to discount the impact of these states, the US would have a IFR of better similar to those of the European countries, none of which evidenced levels of concentration remotely approaching those of the N/E Seaboard.

What caused this spike? In the absence of detailed evidence, we can only guess. It appears that the concentration of international Airports in the corridor, feeding directly into heavily trafficked mass-transit systems connecting dense population centres had roles to play. Couple these factors with a lack of early warning, of preparedness, and of awareness, and the locating of COVID-19 overflow patients into elder care facilities; all this contributed heavily to the disaster.

Each week new ideas and theories about COVID-19, its science, treatments emerge, not to mention ongoing debate about the politics of the pandemic. We try to provide the emergent data, and allow people to form their own conclusions.

Weekly Graphs

Each week, with some exceptions, we update our graphs tracking the disease. We do so with no comment this week.

All data downloaded from worldometers.info 8/15/2020 09:00 EST USA

This graph summarizes, in 7-day moving average trend lines the state of the pandemic in the USA. The secondary peak of infections is shown to have occured on 7/26. This chart indicates a 26 day lag time between infection rise and proportionate death. Given this, we may see a peak in the mortality rate occur around 8/21, with rates in excess of 2,000 deaths per day.

All data downloaded from worldometers.info 8/15/2020 09:00 EST USA

The states that are emblematic and comprise of the major component of the late stage bloom in the US are California, Texas and Florida. We follow them as they are a strong indicator of the likely course of this stage of the pandemic. As in the total US numbers, mortality rates reflect the rise in the disease rate from 26 days prior, even if the mortality rate is departing somewhat from this correlation in the most recent weeks. In the above graph we have shifted the infection rates back by 26 days to more clearly show this correlation. It is not clear that there is yet a peak in the infection rate for these states, unlike that which we see for the US as a whole. This is of concern, and we will be watching this statistic closely over the coming period.

Uploaded from https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm 08/15/2020 0900 EST USA

The rise in excess deaths in recent weeks reflects the forthcoming second, but lower, peak of infections in the US.

Uploaded from https://coronavirus.jhu.edu/testing/testing-positivity 8/15/2020 0900 EST USA

The above graph indicates those states (shown in green) that are indicating positive results on 5% or less for all test cases they have conducted over the prior 10 days. This is the WHO goal that indicates a sufficient level of testing to enable a re-opening. As of this week only 17 states have met this goal, while most have re-opened their economies to some degree or another. The testing situation continues to not show any material improvements.

Downloaded from rt.live 8/2/2020 0900 EST USA

This graphic is based on analysis compiled by rt.live, and indicates states that have an Rt less than 1.0 (green), and those above (red). Rt is a key measure of how fast the virus is growing. It’s the average number of people who become infected by an infectious person. If Rt is above 1.0, the virus will spread quickly. When Rt is below 1.0, the virus will stop spreading. Rt for any given state should be considered against the total number of infections in a given state. Rt over 1 in a state with a minimal number of infections may be less serious than a state with a large number of infections having an Rt that may be under 1. Perhaps also of consequence is the uncertainty bars: these are the bars above and below the bubbles, and they have expanded considerably in the last two weeks, indicating that there should be some concern about the accuracy of the numbers.

All data downloaded from worldometers.info 8/15/2020 0900 EST USA

We end with our compilation of US pandemic statistics vs a set of European states to contrast the experience of the two continents. Unless there is a significant new wave of infections, it is clear that the mortality rate in the US is going to outpace that of Europe as a whole. However, absent Germany, or the 4 N/E corridor US states, and the two continents would be balanced. The experience of Germany needs much deeper examination. We will be delving into that in the coming weeks.

Weekly Statistical Survey of COVID-19 in the US

This week we provide a brief survey of an update to the statistics we have followed over the past five months.

All data downloaded from worldometers.info 8/2/2020 09:00 EST USA

This graph summarizes, in 7-day moving average trend lines the state of the pandemic in the USA. The secondary peak of infections is shown to have occured on 7/26. This chart indicates a 26 day lag time between infection rise and proportionate death. Given this, we may see a peak in the mortality rate occur around 8/21, with rates in excess of 2,000 deaths per day.

All data downloaded from worldometers.info 8/2/2020 09:00 EST USA

The states that are emblematic and comprise of the major component of the late stage bloom in the US are California, Texas and Florida. We follow them as they are a strong indicator of the likely course of this stage of the pandemic. As in the total US numbers, mortality rates reflect the rise in the disease rate from 26 days prior, even if the mortality rate is departing somewhat from this correlation in the most recent weeks. In the above graph we have shifted the infection rates back by 26 days to more clearly show this correlation. It is not clear that there is yet a peak in the infection rate for these states, unlike that which we see for the US as a whole. This is of concern, and we will be watching this statistic closely over the coming period.

downloaded from https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm 8/2/2020 09:00 EST USA

The modest rise in excess deaths in recent weeks is beginning to reflect the forthcoming second peak of infections in the US.

downloaded from https://coronavirus.jhu.edu/testing/testing-positivity 8/2/2020 09:00 EST USA

The above graph indicates those states (shown in green) that are indicating a positive results on 5% or less for all test cases they have conducted over the prior 10 days. This is the WHO goal that indicates a sufficient level of testing to enable a re-opening. As of this week only 17 states have met this goal, while most have re-opened their economies to some degree or another.

downloaded from rt.live 8/2/2020 09:00 EST USA

This graphic is based on analysis compiled by rt.live, and indicates states that have an Rt less than 1.0 (green), and those above (red). Rt is a key measure of how fast the virus is growing. It’s the average number of people who become infected by an infectious person. If Rt is above 1.0, the virus will spread quickly. When Rt is below 1.0, the virus will stop spreading. Rt for any given state should be considered against the total number of infections in a given state. Rt over 1 in a state with a minimal number of infections may be less serious than a state with a large number of infections having an Rt that may be under 1. Perhaps also of consequence is the uncertainty bars: these are the bars above and below the bubbles.

All data downloaded from worldometers.info 8/2/2020 09:00 EST USA

We end with our compilation of US pandemic statistics vs a set of European states to contrast the experience of the two continents. Unless there is a significant new wave of infections, it is clear that the mortality rate in the US is going to outpace that of Europe as a whole. However, absent Germany, and that would change the balance. The experience of Germany needs much deeper examination. We will be delving into these numbers in the coming weeks.

Testing, testing, testing…

Readers of these posts know that we are extremely disappointed in how the whole testing story unfolded throughout the entire COVID-19 pandemic in the US.

On the 7th May we wrote in these columns:

“If there is any single major failure of policy and implementation of the science at the CDC, and at every level of government, it is in the area of testing.”

Things have not improved much since then. Below is the chart we showed yesterday, updated to current date, which shows States with a positivity above 5%, which is the level that indicates that only those that seek medical attention are being tested. This indicates that those states may not be able to understand whether the disease is spreading, and whether opening is recommended.

Uploaded from https://coronavirus.jhu.edu/testing/testing-positivity on 7/27/2010 at 13:00 EDT USA

There are other, profound problems with our testing. It’s too slow, taking days and sometimes weeks to return a result. And it is too difficult…requiring PPE clad, trained professionals to administer. This means that it is also too expensive and too ponderous to be used either universally, or frequently. And, to cap it all off, it takes to long to return, sometimes days, even weeks, by which time it is too late.

The Fix

We believe there is a real solution to the problem on the horizon. A shout out to Daniel Gerson, whose relentless researching, turned up some exciting developments that offer great promise if their advocates can overcome huge bureaucratic and regulatory hurdles endemic in our regulatory and healthcare systems today.

Daniel pointed us to https://www.youtube.com/watch?v=h7Sv_pS8MgQ&fbclid=IwAR21wiBpz4aI9hfp_BpiYlSGDcBkG4eiH7m_vfuxPHcXfEvhyWXSajL1ulM, a video on the MedCram YouTube channel. Apart from highly recommending the channel, and in particular Dr. Seheult the host of MedCram, Daniel was excited to have me learn about the work of Dr, Michael Mina of Harvard.

We highly recommend watching the video, but if you wish the Cliffs Notes, we will summarize the key takeaways below.

First, it is necessary to talk about Dr. Mina’s credentials, as he comes as an expert. He is an Assistant Professor of Epidemiology at Harvard T. H. Chan School of Public Health and a core member of the Center for Communicable Disease Dynamics (CCDD). He is additionally an Assistant Professor in Immunology and Infectious Diseases at HSPH and Associate Medical Director in Clinical Microbiology (molecular diagnostics) in the Department of Pathology at Brigham and Women’s Hospital, Harvard Medical School. His professional background and published work may be found here: https://ccdd.hsph.harvard.edu/people/michael-mina/

The summary of the video: Dr. Mina shows that the COVID-19 tests currently being conducted are – apart from being too costly and taking too long – too accurate!

Yes, you read that correctly, too accurate. His thesis is simple: the accuracy makes the test highly susceptible to finding positive results from people who are no longer infectious, and to make the test that sensitive, we have sacrificed speed, ease of use of the test, and economy. The video provides clarity by going into great detail on this issue.

Dr. Mina explains that we could produce, today, an extremely inexpensive, paper-strip/saliva test that could be self-administered, comfortably, at home that would immediately – in a matter of minutes – indicate whether a person is infected with COVID-19 and is infectious, and with an appropriate level of accuracy. In scientific terms it means printing monoclonal antibodies onto paper strips. The cost? At most, he says, a couple of dollars a strip.

Think about it. If every household in the US had a set of strips that they are able to use daily, then each and every one of us would be able to test ourselves on, test our children, every day.

Each of us could daily determine whether we were clear of disease, and therefore free to attend school, work and recreation without fear of infecting anyone.

Sound too good to be true?

The Science and the Politics of Testing

Without going into the science in excruciating detail, we can say that we have done a great deal of due diligence, and to our understanding, Dr. Mina’s findings are solid. We have the means to deliver at scale the tests he mentions, and these tests are capable of being used by the lay person, at home, in about 10 minutes or so, with no discomfort or difficulty. It the simplest possible terms, all that is required is spitting on a paper strip.

In short, it is a reliable test that determines whether a person is infected with COVID-19, and is thus infectious.

The test is not as sensitive as the current clinical tests. However, the sensitivity and accuracy of the clinical tests are not an advantage – and in some respects may be a disadvantage. The reason is well explained in the video, but essentially the virus, below certain levels of viral load, is not communicable. So the highly sensitive tests that we give could well quarantine people who are not longer infectious.

But this is the rub: our bureaucrats are fixated on sensitivity and accuracy, and will have a hard time accepting and investing in a test that they consider less than perfect. So in this sad case, the perfect may drive out the good, when ironically the good is better than the perfect.