Tag Archives: Clean Disruption

Testing Tony Seba’s EV Predictions 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):


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).


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:


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.


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


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.


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.

For those of you coming to this series of posts midway, here is a link to the beginning of the series.

Testing Tony Seba’s EV Predictions 2 (Setting Out the S Curve)

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. More formally, an S curve is produced by a logistic function, which you can see examples of here).

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’.


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 that 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.


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’.


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).


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.

For those of you coming to this series of posts midway, here is a link to the beginning of the series.

Testing Tony Seba’s EV Predictions 1 (Spring Is Coming)

You should have an opinion on Tony Seba,  He is someone not to be ignored because the upcoming technological change he predicts is so important. 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:

“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! That sounds ridiculously ambitious, if not ludicrous. Nonetheless, Tony always includes a pair of photos to tackle such doubts. Here is a picture of New York in 1900:

Where Is the Car? jpeg

And here is a picture of the same street in 1913, 12 years later:

Where Is the Horse? jpeg

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.