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’.

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 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.

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.

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

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