Of N-Curves And S-Curves

Most innovators are equipped with hammers and tend to see all new markets as nails. Except they don’t have hammers, they have S-Curve models…

N-Curves

S-Curves are one of the most common oversimplifications of how new businesses appear and grow. You might not know the name coined by Everett Rogers in 1962 or that it refers to sigmoid functions, but you do know how it goes.

At the beginning of a new venture things are slow. Then things take momentum, accelerate, and ramp up pretty much vertically (A), before smoothing out at a plateau (B) marking the cruise speed of the business.

This is a typical S-Curve situation:

S-Curve

Any entrepreneur would only be concerned by two things: how quickly do we get to ramping up the business and how high will be the plateau? Of course in reality it’s much messier. There are starts and stops, pitfalls, false accelerations, etc.

This is a useful oversimplification.

It’s also absolutely wrong in A LOT of cases.

Optimism and Entrepreneurship

The first assumption that S-Curves models might get wrong is that even if we smooth out things, not all businesses just move forward.

Consider how we project autonomous cars penetration in the market: not a single model is not a S-Curve. They might be discordant on when the plateau might be reached, they might even adjust expectations with a 500% error margin…

EV Forecast

But no model bets that for instance ten million self-driving vehicles will be sold in 2020 and back to 5 million in 2021. There is right there an inherent form of optimism.

And many cultures like the US or China are built on such form of pervasive optimism —or, as HOFSTEDE would say « low uncertainty avoidance. » The future might be slow, it might be less than expected, but the only way is up. You could stagnate but not regress. At the very worst there is no take off and an unsatisfying plateau is reached before any profits are made. Enabling further this cultural bias, there is also the underlying belief in « progress » that we acquired during the XIXth century: technology is the great social enabler that always helps us move forward.

But of course, in many cases markets are not so compliant…

Innovation Is Not Invention

I already discussed dozens of times on the fundamental mistake we make when forgetting that innovation is not about inventing technology, but about changing the social order. Whether you use technology or not to do so.

The core assumption that « If cars start to have enough detection capabilities and computing power, then the market will follow because it’s going to be a) safer b) cheaper and of course c) easier. » is just that: an assumption. While this gives no room for anything else than pure market rationality, aren’t you just a bit worried that markets might not be perfectly rational? I know I am.

 The Emotional Markets

Following that logic, I would offer that we keep S-Curves as useful oversimplification of rational markets, or simply markets that are just stopped from taking off because technology capabilities are not met yet. Cheap energy, curing cancer, better education, are all probably such markets.

Better food? Probably not so simple because of powerful cultural and social forces at play. Better mobility? Dreadfully complex given the egotistical relations with cars as status symbol, or placeholders for our need for freedom.

When we study such markets where emotions predate rationality something amazing happens. We realize that there is push back.

There is no S-Curve anymore. If at first such markets accept innovation (and even sometimes reach for it quite faster than expected) there is a moment where the burgeoning innovation starts to take enough room to not just displace, but antagonize the core market (1). And when core market pushes back indeed, it is forcefully to regain ground over the brand new thing. An uncanny valley of sort is then created (2), where the early adopters start to disappear and revert to the previous market paradigm.

Eventually, innovation is accepted and displace the old paradigm (3). And there is then very often a strong acceleration that doesn’t make up for the time lost, but that cements a deeper penetration of the new paradigm.

This is what we call a N-Curve.

If you’re looking for such examples of innovation slowing down, falling in a hole and getting back in market much later, you’d might want to go back on how the 20-foot shipping container finally found its way to supremacy. Or more recently, on how new systems of payment like mobile in EU or PIN cards in US, aren’t taking hold.

Teslas and Roombas

So, if you’re trying to represent how autonomous vehicles will reach full market penetration you should ask yourself if we are talking about mp3 displacing CDs. Is it about a globally rational market needing to get used to a « better way », or is it more complicated?

As previously hinted, I’m betting on a N-Curve market. Let me illustrate with two questions:

  1. Is it more acceptable to have 10,000 killed by human drivers or « only » 1,000 killed by robot cars?
  2. How long and how much pain will be involved to retrofit cities to fully automated traffic?

I could have asked many more, but these two are quite essential.

Question 1. is about market acceptability. It’s purely a question of empathy, social response and culture. Homo economicus is not invited to answer the question. It’s you, me and our neighbors weighing in with all of our personal values, with our messy understanding of things… and probably most of all our tendency to be risk-adverse. If you’re in China or Singapore this question will be difficult, but the government might be enough to impose the innovation. If you’re in Helsinki or London, this is another ball game altogether.

Question 2. on the contrary seems very much rational. It is only on the surface… Say that making London fully compatible for automated traffic would require £20bn of infrastructure investments (for reference London olympics budget was £9bn in 2012) and three years of profits from tourism and commerce falling 30%. Who will be ready to pull the trigger? Remember that we believe that autonomous cars will change cities for the better. We don’t know. We don’t even have yet a real-life example of 10,000 habitants within a few square kilometers perimeter of fully automated mobility during a year. So this is again about optimism, emotions and fuzzy parts of our brain dealing with the problem.

If we can expect that implementing vacuum robots as the standard in the market will be a S-Curve situation, it’s because there is a transparency to it at first. You don’t see or feel a direct impact in your life when a first neighbour is using a Roomba. You still don’t see anything when twenty of them have switched. You might be realizing that the new paradigm has set foot in the core market during a dinner party at some friend’s house that are not particularly young or tech-savvy, but who have different models of Roombas cruising in the house. And you might push against the paradigm for so many different reasons. But at some point, you’ll be the one not having a smartphone and you’ll cave in.

In a N-Curve situation the matter is different.

Say that we’re not talking Roombas anymore, but Blade Runner’s replicants. Human-like robots with some level of AI capable of vacuuming and walking the dog around the neighbourhood are a whole different matter to deal with. Even in a city of several million people commuting to work every day, a few dozens of self-driving cars can create a sentiment of unease that could explode to frontal opposition when the number grows… or that a first fatal accident happens.

Beachheads and Uncanny Valleys

Most of the usual S-Curve ideas of innovation are about creating a first beachhead in the market with innovators, then early adopters. We know there will be a chasm to reach the early core market and that things will be complicated with volume… but what matters first is to create a critical mass of adoption. Things will unfold from there.

In a N-Curve market, the critical mass is not a beachhead because it will manifest huge push back and lead the innovation in what I called an uncanny valley.

This notion of uncanny valley came from to the way movies trying to render humans in CGI got better and better, to a point where we rejected them. With more polygons, finer animations and shadings the models started to be good enough but not perfectly real.

Uncanny Valley

This is too close to be a cartoon and feels uncomfortable. Although the technology has dramatically improved, it becomes cringe-worthy or plain frightening.

With technology or innovation we can have this very same mechanism. While we are all keen on paper to get more and more positive externalities with technological progress, we can face a Rubicon that we’ll refuse to cross because the other bank will be a deep and irremediable change of social order.

Uber is kinda cool when it’s about on-demand taxi at a decent fare in a clean car. When we understand that the logical next step is on-demand jobs for a large part of the workforce delivering food, building shelves, or nursing patients, then we rebuke. The S-curve dives, progress is deemed evil and the N-curves appear.

The 5 Levels Are Not Linear

The fundamental objective is that autonomous vehicles gets to the point where they blend seamlessly in normal traffic. No one would know, except that they’d be safer, more cost efficient —and yes, in their spare computational time, write poetry.

My first bet is that several more vehicles but cars, will reach full autonomy very rapidly (the now famous Level 5):

Yeah, this is a S-Curve and probably very wrong…

At first within secured perimeters, such as an oil refinery, or a manufacturing facility. Well, we are probably there in many cases. This is easy, it does involve professionals that already deal with specific rules and regulations in their workplace.

Level 2 is connecting autonomous vehicles to a public environment. They won’t circulate in it though. Think automated airport shuttles connecting terminals to parking lots, or subway lines. Yes, we’re also already there. Yet many other use case will be implemented extending the reach and the familiarity we will have with transportation systems that are not human operated.

Level 3 is where we start to get into the proximal future: autonomous vehicles in the middle of public transportation and logistics systems, but keeping themselves in check on virtual or physical lanes. Convoy of trucks fully autonomous on highways while the driver sleeps and take back the wheel in cities. Container boats crossing oceans and pilots boarding them to get them in or out a port.

Level 4 is pushing the limit to now actually mixing autonomous vehicles maybe on specific duties, but with no precise boundaries to where they could go. I’m thinking ambulances: we’re trained to give way to emergency vehicles, it’s not a stretch to think they could drive around with only a medical staff. But it will be probably more about postal services, yes taxis, and fresh good delivery.

And, this is where society will rebuke.

Technology will be ready, societal acceptance will have been raised for several years, but also the future of « we don’t need drivers anymore » will be massively palpable in our everyday life as the last step.

This is not going to be pretty… for a while (maybe a long while).

What About You?

To wrap up things, the discussion is important whether you are in the mobility market or not. As many of us, you’ve been fed some standard models about innovation and S-Curves are pretty much the only model we refer to. What is pernicious is that they work so well on average that we don’t challenge them anymore or try to remember the core hypothesis that manifests them.

Innovation is always a race against time with the market and competitors. And being under such stress requires taking shortcuts, or as said initially, oversimplifying reality. It’s only fair. And you might be in a market where the emotional response of customers and stakeholders is much more important than sheer rationality… I’m thinking banking, luxury, food at the moment, but it’s far from limitative.

Then it’s also a matter of taking the right shortcuts. Shortcuts that may seem very counter-intuitive to partners and investors used to standard innovation playbook.

This is often where great successes are delivered.

Author: Philippe

Philippe has been training 200-300 startups a year since 2007, consulted for dozens of multinationals on rupture innovation or corporate incubation, and was directly involved in more than 150 startups building their market fit and scaling up their business. He also teaches business model innovation in key MBA programs whether they are in Paris or Shanghai. And since 2017, Philippe is now living in Amsterdam, one of the best European business hub around.