Back Testing Tony Seba 3 (Fleshing Out the S Curve)

In my last posts, I have been trying to quantify Tony Seba’s assertion that “essentially no internal combustion engines will be produced after 2030”. Further, we looked at the broad outline of what the required sales trajectory would need be to take electric vehicle (EVs) penetration rates from 1.3% in 2017 to 95% in 2030.

In this post I want to hang some vehicle numbers onto this outline shape. So to start with, we need to determine how many vehicles are being sold today. Various public organisations and private companies put out slightly different numbers, but I have chosen the stats released by the International Organisation of Motor Vehicle Manufacturers (which goes under the abbreviation OICA derived from its name in French).

OICA data show that 97 million vehicles were sold in 2017, consisting of 71 million passenger cars and 26 million commercial vehicles. The recent sales trend looks like this (you can find the chart here):

OICAVehicleSales

These numbers allow us to do a quick fact check with respect to the chart put together by EVvolumes.com at the bottom of my last post. That chart had a total of 1,281,000 EVs sold in 2017 with am EV market share of 1.3%. Put their EV sales number over OICA’s 97 million and we do get 1.3%. Good!

Note we are talking about total vehicle sales. Tony has been full on with his bet, forecasting the demise of the complete ICE vehicle infrastructure. Not for him, wimping out and restricting his argument to passenger cars. So all those trucks, lorries and vans have to go EV too.

In later posts, I will start to slice the data more finely to stress test his forecast, and that will require us to look a vehicle segments, geographical penetration and manufacturer commitment to EV production, but for now let’s just stay with the top line.

Nonetheless, we do need one further tweak before we can attach a number to what 95% sales penetration by EVs in 2030 actually looks like. Obviously, global auto sales are growing, so we are not looking at 95% of 97 million. Accordingly, we need the annual average growth rate in auto sales through to 2030 before we can come up with our EV target.

As a ranging shot, let’s just take the average annual growth rate in global vehicle sales between 2005 and 2017 from the OICA chart above. So I’ve just plugged those numbers into a compound growth rate calculator on the internet to get 3.87% annual growth. Using the 3.87% number, we can then plug that back into a future value calculator and go forward to 2030. A growth rate of 3.87% doesn’t sound much, but the magic of compounding changes 97 million vehicle sales to 159 million vehicle sales by 2030. The bar has been raised for Tony: EV sales now need to go from 1.3 million in 2017 to 159 million in 2030. That’s 122 times!

Nonetheless, we probably need to adjust for a decline in auto sales in China as the market gets more saturated, although India, South America and Africa could start to pick up the growth baton in future.

Moreover, a close reading of Tony’s book “Clean Disruption” suggests that the advent of driverless cars on our roads will dramatically change the pattern of car ownership. Tony is big on “Transport as a Service”, so fewer and fewer people will want to actually own a car when they can tap on a phone app to get the use of one almost instantly.

Even if you don’t believe that autonomous vehicles will pass safety standards for many years to come, app-led transport services like Uber and Lift make car ownership less attractive, particularly for urban dwellers. The decline of driving licence ownership among younger adults in the US and Europe is evidence of this.

In 2016, McKinsey issued a report that incorporated such technological-related disruptions into a macro economic forecast. Their ‘high disruption’ scenario sees 15% of new car sales being autonomous vehicles by 2030. Based on this scenario and other trends in car sharing and so on, they came up with 115 million vehicle sales in 2030 (a 2.45% annual growth rate).

AutoSales2030McKinsey

At this stage, it is worth stressing that the choice of vehicle sales figure for 2030 is a question of subjective judgment. The range of macro economic and technology related variables make a more quantitative approach facile. So let’s split the high vehicle growth scenario of 159 million vehicles and the McKinsey high-disruption scenario of 115 vehicles to arrive at 137 million forecast vehicle sales in 2030. Now Tony is looking for 95% of this number, which is 130 million. So we can plug the 130 million number into the S curve from my previous post and we get this:

ElectricVehicleSalesMillions

It looks like not much is happening until around 2024 in terms of high year-on-year jumps in units sold, but that hides some pretty stunning year-on-year growth rates.

%GrowthRateEVSales

Are those growth rates completely mad? Well let’s compare them with recent year growth rates in a chart from my previous post:

EVVolumes.com

So apart from 2016, the EV sector has been achieving growth rates in and around 60% per annum. So it looks tough, but not completely crazy. Next, let’s look at how much additional capacity needs to come on line each year to support those forecast sales.

AdditonalAnnualProductionCapacity

This chart allows us to stress test Tony’s from the supply side. Additional production capacity for the EV final product requires production capacity adds right down the supply chain. So to add 0.8 million units of EV capacity in 2018; 1.3 million in 2019; 2.1 million units in 2020, 3.4 million units in 2021 and 5.3 million units in 2022 requires huge ongoing investment in metal mining (lithium, cobalt, graphite, etc), battery cells, battery assemblies and additional vehicle manufacturing lines and factories.

So for my next post we will just assume that the end-user demand is there. If so, are the miners and manufacturers capable of delivering the capacity adds? Let’s see.

Testing Tony Seba 2 (Setting Out the S Curves)

In my last post, I explained Tony Seba’s basic thesis as follows: he forecasts the complete transformation of the world’s entire transport and energy infrastructure by the year 2030. And while, Tony stopped there, I surmised that this disruption, if it takes place, will extend into every aspect of the social sciences. Indeed, for those like me who sometimes despair at the state of the planet, his forecasts could even prove a ray of hope with respect to the wicked problem of climate change. I don’t think it is hyperbole to say that such a transformation would be politically, socially and economically revolutionary.

At the heart of Tony’s thesis is the S curve: the idea that the adoption of technology follows an S curve consisting of three distinct phases: a gradual uptake, explosive growth and then a tapering off. His book “Clean Disruption” doesn’t really touch on the S Curve, but in his presentations this issue is front and centre. I highly recommend you watch the section of the video below from 7:50 through to 9:45 minutes.

This is the heart of his argument:

“No technology in history, successful technology, in history, that I know of have ever been adopted on a linear basis, ever. It gets adopted as an S curve.”

And Tony posits that S curves are getting steeper, with saturation points reached in years not decades as shown by the almost vertical lines for the most recent technology adoptions.

Adoption Rates

Therefore, if Tony is wrong, it will be with respect to whether EV adoption follows an S curve and what shape that S curve will take. OK, let’s start by fitting an S curve to Tony’s following prediction:

There may still be millions of older gasoline cars and trucks on the road. Ten- to twenty year old cars are still on the road today. We may even see niche markets like Cuba where 50-year old cars are the norm. But essentially no internal combustion engines will be produced after 2030.

Now an S curve has four parameters; that is, variables that control is shape. Bear with me: it is actually quite intuitive.

From the chart below, we have the starting point ‘a’: in our case EV sales as a percentage of total global car sales, which in 2017 was 1.3% (I’ll come back to that number). We also have an ending point ‘d’. Now Tony says “essentially no internal combustion engines will be produced in 2030”. I have taken that to mean 95% of new sales in 2030 will be EV.

The inflection point ‘c’ relates to whether the growth will be front-end loaded into the beginning of the forecast period, or more back-end loaded into the end of the forecast period (or somewhere in the middle). Generally, it’s easier to ramp up production at the beginning, since you have fewer resource constraints.

Finally, we have ‘b’ the steepness of the curve. That really tells us whether all the growth is concentrated into a short burst; in the adaption curves at the top of the post, those curves which in effect go vertical, like that for digital cameras, have a high value for ‘b’.

SCurveParameters

Now because we can produce different curves to get from 1.3% penetration in 2017 to 95% penetration in 2030 it may take a little time to prove whether Tony is right or wrong in his projections. But by inspecting the shape of the curves, we can start to discern which of them are completely barking mad and which are mildly ambitious. So I will start with a curve the I have rustled up in Excel as the base-case scenario:

Seba Central Scenario

Under this curve, we start with a penetration rate of 1.3% in 2017 and end with one of 94% in 2030, with 50% penetration reached in 2025. Note that it takes 6 years to go from 20% penetration in 2022 to 80% penetration in 2028. Next, let’s increase parameter ‘b’ and get the curve to stand up.

SebaHyperGrowthSecenario

This is pretty damn aggressive. Tony is doing his victory lap in 2025 and the move from 20% to 80% penetration has taken all of four years. That is a lot of lithium, a lot of battery cells, a lot of battery units and a lot of EVs to bring on stream in short period of time. But note we could take that graph and shift it 5 years to the right. Under that scenario, Tony would still have bragging rights in 2030, but the curve would not go vertical until around 2025.

Now I am going to make the growth period a bit less manic in the middle, with a longer run-up by increasing the value of ‘a’.

SlowRampUpScenario

Now Tony gets to 95% one year late (I think we should be generous enough to give him that). Further, the EVs take over the world period (from 20% to 80%) now takes place between 2024 to 2029.

OK, time for some real numbers. Here are global EV sales and penetration rates from EVvolumes.com (as you can see, this is where I get my 1.3% starting penetration rate from in 2017).

EVVolumes.com

This adds a couple of new dimensions to our analysis: unit sales of EVs per year and year-on-year percentage growth rates. Keeping unit sales and growth rates in mind, we can take the theoretical underpinnings and parameters of Tony Seba’s EV S-curves, and attach just such real-world numbers onto the curves and see if they look sane. That will be the topic of my next post.

Testing Tony Seba 1 (Spring Is Coming)

I think you should take a view on Tony Seba. For some he is a seer, for others a charlatan.  Regardless, he is someone not to be ignored. Indeed, if he is right, everything is about to change. So if you aren’t aware of his take on where the world is going, I highly recommend you watch one of his videos (for example here).

Tony’s PowerPoint presentation and pitch hasn’t actually changed much over the last four years. Indeed, if you click through his YouTube presentations from a variety of events, you get a distinct feeling of deja vu. But perhaps, like a modern-day Messiah, Tony feels the need to deliver just one central message time and time again:

Get ready for a technological disruption that will dwarf all those that have gone before.

His first book “Winner Takes all” is a typical airport self-improvement shopping list for the wannabe tech CEO, but then came “Clean Disruption” in 2014.  In this, Tony broadened his range to encompass, well, everything. The tag line on the front cover says it all:

How Silicon Valley will make oil, nuclear, natural gas, coal and conventional cars obsolete by 2030.

While this industrial disruption is epic, the follow-on implications for urbanisation, climate change, geopolitics, development, growth, wealth and inequality are just as mind-blowing. Thankfully, Tony didn’t run with those themes too, but it doesn’t take much of an imagination to extrapolate out from his conclusions into a broad variety of social and political domains.

So this is what he says will happen by 2030:

  • Solar will become the dominant form of energy production
  • Centralised electric utility companies will be in retreat and the price of electricity will plummet
  • Electric vehicles will replace internal combustion engine vehicles
  • Cars will become self-driving and individual car ownership will collapse
  • Nuclear is dead
  • Oil is dead
  • Natural gas is dead
  • Biofuels are dead
  • Coal is dead

Wow! Moreover, this book was written in 2014, and given it’s now 2018, that means we only have 12 more years to go!

You have to respect Tony for one thing: he has given us a testable hypothesis. In times gone by, futurologists were careful to either give a specific numerical forecast, or a specific date for a vague non-numerical forecast — but never a specific numeral forecast with a specific date attached.

If such strategists were “doing a Tony”, they would have said everything will change in 2030, but not specify how much it will change; or, things will change by X amount, but not when such change would take place. By so doing, an erroneous forecast could always be dumped without too much reputational risk.

No such intellectual cowardice for Tony. Here is just one example of his forthright approach to forecasting, from page 127:

There may still be millions of older gasoline cars and trucks on the road. Ten- to twenty-year-old cars are still on the road today. We may even see niche markets like Cuba where 50-year old cars are the norm. But essentially no internal combustion engines will be produced after 2030. Oil will also be obsolete by then.

To make things manageable, I am going to pick off his forecasts in bite-sized chunks and see how they are doing from when they were first floated back in 2014. To put that in perspective, we are already 25% through Tony’s original forecast horizon.

Let’s  start with the above quote surrounding electric vehicles, not least because Tony set out a variety of milestones back in 2014 that should show us whether we are on our way to his EV nirvana. The first of these relates to S curves, which will be the topic of my next post.

I’m Back with Bad News and Good News

Well, the emergence from hibernation took some time (it’s a long story). So since I’ve been away, let’s firstly start by depressing everyone with four charts from the excellent and scary NASA’s “Vital Signs of the Planet”:

GlobalTemp

Not forgetting this:

CO2\

Plus this:

SeaLevel

And of course this:

LandIceAntarctica

Also for Arctic sea ice extent, below is how we have started the current melt season (hint: not good), from National Snow and Ice Data Center):

ArcticSeaIceExtent

So am I totally depressed (it seems an occupational hazard for anyone paying attention to climate change these days, see here)? While the above charts do not fill me with joy, I will finish this new post (after a very long time away) with something a bit more upbeat: batteries.

Now in prior posts, I was a little bit skeptical about the battery revolution. But that was in 2015 and it is now 2018.

The evangelist of the great battery nirvana is Tony Seba, who sees EVs plus battery storage as the next great disruption: a disruption so massive that it will eclipse the computer and compete with the coming of the railways and the spread of electrification.

Being a grumpy old Brit, I was somewhat cynical about Tony and his PowerPoint battery slide deck. But, to repeat, that was 2015 and this is 2018. And after doing a deep dive into battery material miners over the last few month, I feel I may have been a tiny bit wrong and Tony a tiny bit right. Quite how wrong I was and how right he is I have yet to fathom. So for my return to blogging, we are going to go ‘full on’ battery nerd for a while. So it is Battery Banter Redux folks.

For those of you who think this is all tech ‘blah’, I actually disagree. In fact, I think batteries hold the key as to whether we can constrain climate change to around two degree of warming (give or take a bit), or the dystopian three and up. So I will finish this post with what I think is a very upbeat chart from a company called Nemaska Lithium. And in future posts I am going to explain why I don’t think this chart is barking mad (in fact it could be conservative) and why, if true, it will change everything.

LithiumMarket

 

Back from Hibernation

Well, it’s two years since I have posted on this blog. Since then,  I have dealt with death, divorce and disease on the personal front. Apologies for the silence: when you are being emotionally clubbed over the head, then you lack the bandwidth to write.

Meanwhile, the replacement of man by machine has continued apace, Arctic sea ice extent is flirting with new lows (so climate change remains the spectre at the feast) and commodities have demonstrated abundance not scarcity. Politics and economics, the two loves of my life, are all over the place. The science of well-being evolves, but the political establishment generally looks back for truths instead of forward.

Individuals aware of the existential threats to the existing order wrestle over whether to retreat into a narrow, local world (look inward) or challenge the existing orthodoxies (look outward).

So, yes, we are living though interesting times. Technology as Diamandis and Kotler’s biblical ‘abundance’ or Kunstler’s technology as false magic. We may be sitting on an exponential curve of technology, but we are certainly sitting on a similar curve of climate change. And all this is taking place against a backdrop of political, economic, legal and social institutions designed for the 1900s. A lot to talk about.

Data Watch: UAH Global Mean Temperature, June 2015 Release

On July 6th, Dr Roy Spencer released the University of Alabama-Huntsville (UAH) global average lower tropospheric temperature anomaly as measured by satellite for June 2015 (here). The anomaly refers to the difference between the current temperature reading and the average reading for the period 1981 to 2010 as per satellite measurements.

June 2015: Anomaly +0.33 degrees Celsius This is the 3rd warmest June temperature recorded since the satellite record was started in December 1978 (36 June observations). The warmest June to date over this period was in 1998, with an anomaly of +0.56 degrees Celsius. Full data set available here (click for larger image).

UAH Global Temp July 2015 jpeg

The El Nino Southern Oscillation (ENSO) cycle is the main determinant of new temperature records over the medium term (up to 30 years) . The U.S. government’s Climate Prediction Centre currently has an El Nino advisory in effect and is forecasting that the current El Nino event is set to continue through into 2016 (update 9 July 2015 here):

Overall, there is a greater than 90% chance that El Niño will continue through Northern Hemisphere winter 2015-16, and around an 80% chance it will last into early spring 2016.

Given this background, I would expect the UAH anomalies to remain elevated for some time.

As background, five major global temperature time series are collated by different international agencies: three land-based and two satellite-based. The terrestrial readings are from NASA GISS (Goddard Institute for Space Studies), HadCRU (Hadley Centre/Climate Research Unit in the U.K.), and NCDC (National Climate Data Center). The lower-troposphere temperature satellite readings are from RSS (Remote Sensing Systems, data not released to the general public) and UAH (Univ. of Alabama at Huntsville).

The most high profile satellite-based series is put together by UAH and covers the period from December 1978 to the present. Like all these time series, the data is presented as an anomaly (difference) from the average, with the average in this case being the 30-year period from 1981 to 2010. UAH data is the earliest to be released each month.

One of the initial reasons for publicising this satellite-based data series was due to concerns over the accuracy of terrestrial-based measurements (worries over the urban heat island effect and other factors). The satellite data series have now been going long enough to compare the output directly with the surface-based measurements. All the time series are now accepted as telling the same story (for a fuller mathematical treatment of this, see Tamino’s post at the Open Mind blog here). Note that the anomalies produced by different organisations are not directly comparable since they have different base periods. Accordingly, to compare them directly, you need to normalise each one by adjustment to a common base period.

Greedy Greeks?

The shock referendum announcement by Alexis Tsipras over the Troika’s austerity demands has radically increased the chance that Greece will fall out of the eurozone.

I am surprised that the markets, and indeed the Greek people themselves, did not give more credence to this outcome over the last few weeks. The received wisdom of most market pundits is that an 11th hour agreement would be reached.

Meanwhile, Greeks have been pulling money out their banks, but at a very leisurely pace. To me, this nonchalance appears bizarre. The chart below shows Greek bank private-sector deposits falling from 160 billion euro prior to Syriza’s election victory to around 130 billion euro at end May. The chart is made to look more spectacular by having the y-axis commence at 100 billion euro.

Even if last week you had only assigned a 5% probability to a return to the drachma, such an outcome would result in a 30-50% decline in the value of your savings when denominated in euro. Risk equals probability times effect. The probability might have been assessed—wrongly as it turns out—as small, but the impact should have been deemed as large. The prudent man or woman would have parked their money abroad until a deal was sealed and then repatriated the money once confidence was restored. And for small accounts that couldn’t justify the hassle and fees of an inter-country transfer, you could always stash cash under the bed. Yet relatively few have followed such a simple risk control strategy (Chart from Bloomberg here).

Greek Bank Private Sector Deposits jpeg

At this point, it appears improbable that the banks will open on Monday, and the Greek authorities will have to introduce capital controls and bank deposit withdrawal limits. If this is indeed the case, the likelihood of avoiding a return to the drachma looks remote.

Very soon the blame game will begin. However, from my perspective there is a certain inevitability about the outcome, which rests on long-term economic and political factors that are rarely raised by most commentators. After a political tour to Greece two years ago, I blogged about these issues here, here and here.

Front and centre of the factors driving Greece toward its current predicament is the country’s terrible demographics. Let’s look at its current and projected old-age dependency rate, which I took from Eurostat. Currently, the ratio of the elderly (65+) to working age (15-64) is 1:3. However, this ratio is rapidly moving toward 1:2 (click for larger image).

Greek Dependency Ratio Comparative jpeg

Not surprisingly, such demographics are putting a huge burden on the state with respect to pensions. Even the right-of-centre Wall Street Journal goes beyond the stereotype of greedy Greeks in recognising this fact (source: here). So while the aggregate Greek pension burden is very high in a European context when compared with GDP, it is not so high when we put pension spending on a per person basis.

Greek Pensions % of GDP jpeg

Greek Pension Spending per 65+ jpeg

With demographics like this, the only way a country can maintain living standards is through securing high productivity growth. And to do that, in a global economy, a country needs a comparative advantage in industries that exhibit high productivity growth.

Unfortunately, since entering the euro at what proved to be the wrong rate, Greek growth has been concentrated on just a few industries such as tourism, real estate, shipping services and infrastructure projects benefitting from EU regional development funding. Many of these industries got savaged in the wake of 2008/09 financial crisis, and those that have remained reasonably robust, such as tourism, are not great engines of productivity growth.

As Japan amply demonstrates, when a country enters a steep demographic transition, it is very difficult to secure high rates of economic growth. But that doesn’t mean that you can’t maintain full employment, social  cohesion and well-being. Japan has partially done this through accepting declines in real wages and a depreciation of its currency. Indeed, the Japanese middle class tourist, once king of Bloomingdales and Harrods in the 1980s, is now relegated to factory outlets.

For the IMF, Greece has been pushed toward reformimg its soft infrastructure: land registry, tax collection, business licensing system, closed shops and so on and so forth. These are all noble causes—and in the course of time should bring some productivity improvements. But the IMF‘s second critical goal, internal devaluation, has proved a disaster. Adjusting wages and prices downwards without producing an economic slump is an almost impossible task. Moreover, the key demographic segment that is critical to future productivity gains—highly educated young adults—have reacted to austerity by flocking to the UK and Germany in droves. The Guardian reported on this depressing brain-drain in January this year (here)

If you are struggling with adverse demographics and poor competitiveness, the last thing a country needs is for its actual economic output to be substantially below its potential output. But this is what you get if you implement a vicious policy of austerity within the context of a lack of effective demand and a fixed exchange rate. Far better is to adjust prices through maintaining a flexible exchange rate and allowing a modicum of inflation. And the only way for this to occur is for Greece to leave the euro and return to a freely floating drachma.