S-curve is a sigmoid curve in mathematical terms, but we are not here to talk about mathematics. I have come across the s-curve while studying various domains including marketing, strategy, mathematics, and philosophy. This is a concept I have been intrigued by for a very long time.
If life and business could fit on a linear model, it would be an easy task to predict anything; everything would be so simple. Unfortunately this is not how it works. Cause and effect is not as simple as a light switch where the system reacts immediately.
Let’s first introduce the concept and then we can get into how it is applicable to personal life. Here is the generic form of the curve:
In the fermentation stage there is very little output for a large input. i.e: huge effort may yield only a little in the near future. This is the investment phase of the curve. In the Take-off phase, there is a large amount of output for very little incremental input. This is also known as the pay-off period. In the Stagnation phase, you have reached saturation and incremental output is relatively low compared to the of input.
This concept can be applied in many different fields. In the marketing domain, it explains the lifecycle of adoption of new technology or diffusion of disruptive innovations as shown below:
Diffusion or adoption is relatively slow at the outset but then enters the stage of hyper growth, which typically reaches saturation above 90 of addressable market.
In the innovation strategy domain it explains the performance improvement over time of given technology improvement initiative. It highlights that as you invest on improving the performance of a system, the initial gains are very low compared to the overall investment in R&D. However as time passes, it starts to payoff and the results are steep returns. This image below clearly highlights the time delay aspect of returns in a complex system:
An individual acquiring a new skill can be plotted on the s-curve as well. As show in the diagram below by Juan Mendez, moving up the personal learning curve can be slow at first as you attempt to gain expertise in your new domain. But with time it accelerates to a sudden mastery, this eventually slows down as you stop learning new things and stop enjoying the domain or as Juan Mendez puts it, when the thrill ride is over.
Similarly the s-curve can be plotted for individuals pursuing new goals. Eventually the thrill ride will be over. As you hit the saturation in your pursuits of new goals, you should jump on to a new s-curve. If you do not you still might be okay financially, but you might be less happy and your confidence along with your general well-being will take a hit. In my opinion this is what leads to mediocre existence. It is sad to see so many talented engineers in Silicon Valley fall quarry to this and end up working smaller jobs at large companies with only incremental growth in their careers.
As Saul Kaplan shares: “My life has been about searching for the steep learning curve because that’s where I do my best work. When I do my best work, money and stature have always followed.”
In summary, it is easy to plan life when things are linear which they could be if you want to lead an ordinary career. However the path to an extraordinary career and therefore, existence, is not-linear and our brain eventually requires the dopamine of the unpredictability. This picture makes a compelling case for how to navigate your career and personal growth. In closing, as Juan puts it: “Don’t be afraid. It takes courage to jump from one curve to the next. Staying in the comfort zone is easy, but greatness happens when you escape from it”.
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