Epistemic Status: Innovation research and business research is notoriously low quality, and so all the ideas here should be viewed through that lense. What’s impressive about the S-curve and evolution trends literature is how remarkably self-consistent it is using a wide variety of research methods. Whether Simon Wardley analyzing news article about different technologies, Clayton Christensen doing case studies of a specific industry, or Carlotta-Perez taking a historical approach of tracking different technologies, the same S-curve pattern and evolution trends seem to show up. This too should be taken into account when evaluating these ideas.
Basics
This is an S-curve.
The S-curve is a fundamental pattern that exists in many systems that have positive feedback loops and constraints. The curve speeds up due to the positive feedback loop, then slows down due to the constraints.
When the constraint is broken, the positive feedback loop ramps back up, until it hits another constraint.
Recommended Resource: Invisible Asymptotes, which gives a visceral feel for this process of positive feedback and constraints
Common Mistake: Confusing S-Curves With Exponential Growth
Sometimes, people get confused and call S-curves exponential growth. This isn’t necessarily wrong but it can confuse their thinking. They forget that constraints exist and think that there will be exponential growth forever. When slowdowns happen, they think that it’s the end of the growth—instead of considering that it may simply be another constraint and the start of another S-Curve. Knowledge of overlapping S-Curves can help you model these situations in a more sophisticated way.
Diffusion S-Curves
The S-curve pattern is quite common in the spread of ideas, practices, and technologies, although it rarely looks quite as pretty. The example below shows “diffusion s-curves”—How a technology spreads through a population (in this case US households
The positive feedback loop in this case is word of mouth, and the constraints represent fundamental barriers to certain market segments or growth such as simplicity, usability, scalability, price, etc.
This creates smaller s-curves around adoption among specific market segments, and larger s-curves that represent the overall market penetration of the idea, practice, or technology.
In addition to Diffusion S-curves in technology, ideas, and practices, there are Evolution S-Curves. These represent the increase in the traits of these ideas that make them usable in more situations and desirable for more people. When you break through a constraint in one of these properties through innovation, this can often coincide with “unlocking” a new diffusion curve by opening up a new market that wouldn’t previously have used your technology or idea.
In this case the positive feedback loop is the increased understanding and expertise that comes from diffusion of a new innovation in your idea or technology, and the constraint represents fundamental assumptions in the idea, practice, or technology that must be changed through another innovation to make the idea, practice, or technology more desirable.
In the example below the desirable property is hardware speed. Fundamental leaps are made to break through a speed constraint, and then iterated on through the positive feedback loop of information and expertise increasing from adoption. This hits diminishing returns as the new innovation is optimized, and then a new fundamental innovation is needed to overcome the next constraint.
Common Mistake: Confusing Diffusion S-Curves with Evolution S-Curves
Sometimes, I see people make the mistake of assuming that evolution and diffusion s-curves follow the same cycle. Most often, the mistake made here is assuming that when a particular innovation has saturated a certain market, that also means it has “reached its final form” and has no more evolving to do.
There is a related truth—often, an innovation becoming more diffuse will drive innovation as new use cases become apparent. And vice versa, often new innovations will open a new market up by creating use cases that were previously impossible.
However, the two types of curves are driven by two different feedback loops and two different constraints. There’s no reason to expect that they will follow each other, and no reason to expect that one curve leveling off will cause the other curve to level off.
S-Curves Patterns
S-curves become quite useful when paired with an understanding of evolutionary patterns. They can allow you to see in a broad sense what’s coming next for an idea, practice or technology. They can prevent surprises and give you a tool to stay ahead of changes.
There are patterns that exist for both diffusion and evolution S-curves.
Diffusion Patterns
Diffusion patterns describe common themes that happen as trends diffuse through a population. They apply on the micro-level to individual population-segments, and on a macro-level to the overall population.
Diffusion of Innovation
The diffusion of innovation describes 5 separate stages of a diffusion curve: Innovators, Early Adopters ,Early Majority, Late Majority, and Laggards. By understanding the traits of each of these groups, you can get a broad idea of what to expect, and how to slow or speed up adoption.
The Chasm describes a common constraint that occurs in a market segment between “early adopters”—who are willing to put up with a lot, and “early majority”, who expect a lot. There is often a number of evolutionary constraints that must be broken through to bridge this single diffusion constraint and many new ideas, practices, and technologies get stuck in the chasm for that reason.
Common Mistake: Assuming a Technology is Irrelevant Because it’s Only Useful for a Small Group
A common mistake that I see is assuming a technology won’t have a broader relevance, and using as evidence that it’s only used by a small group of relatively abnormal people.
Now, what is true is that not all technologies eventually get adopted by everybody, some stay relatively niche. But it’s not very good Bayesian evidence to say that because a technology is used by a small group of weird people, it will not have a broader impact. These diffusion patterns tell us that in fact that MOST technologies that eventually get widespread adoption go through this phase.
Furthermore, they tell us that many of those technologies often get stuck for a while at this early stage because of the Chasm. So even if a technology has staid at this tage for a while (e.g. cryptocurrency), it’s still very little evidence towards that technology not being lifechanging in the future. (In contrast, a technology stalling for a long time at some point past the chasm is better evidence that it may have reached saturation)
Evolution Patterns
Evolution patterns describe common ways that innovations evolve over time to become increasingly desirable. They apply on the micro-level to individual innovations within a trend, and on a macro-level to the evolution of trend as a whole.
Wardley Evolution
Innovations tend to go through four stages—the initial prototype, custom built versions, productized versions that compete, than comoditized versions that are all basically the same. By understanding where you are, you can understand the type of competition likely to happen, the types of processes likely to yield improvements, and large changes that will be needed to stick with the market.
Common Mistake: Not reasoning about likely changes in how the market will be structured.
A common mistake I see when people reason about the future of e.g. Machine Learning, is that they reason as if the current economic style (how people make money from machine learning) will continue the way it has been.
What Wardley Evolution tells us is rather that it’s very frequent for the way a market charges for and makes money with a particular innovation changes, and that change tends to fairly predictable.
For instance, I’ve seen analysis of Machine learning that assumes it will continue to be productized (which leads to very different dynamics in terms of competitive landscape and strategy between different AI vendors), rather than recognizing that it will eventually be commoditized and become a utility.
Simplicity—Complexity—Simplicity
Innovations tend to start out relatively simple as a new approach to a problem. They become increasingly complex to cover more use cases and be more robust, and then become simple again as refinements are made and they’re distilled to their essence.
Common Mistake: Assuming a Particular Innovation is a Dead End Because It’s Gotten Too Complex
One mistake I see pretty frequently is people describing a particular innovation, and saying “well, we’ve added more and more complexity to this and it’s gotten increasingly minimal returns so I expect there too not be too much more innovation in this area.
This can be true, but only if there are other indicators that this is already at the end of the innovation curve. Oftentimes, what’s actually happened is that it’s near the midpoint of it’s innovation curve, and the next innovations will be around compressing/simplifying all the things that have been added. This simplification process then allows the innovation to be used a component to build further innovations off of, as it’s simple enough to be commoditized.
Disruptive Innovation
Sometimes, innovations overshoot the mainstream populations needs on a particular dimension in order to be powerful for a particularly lucrative part of the population. In this case, these innovations or often overtaken by subsequent innovations that lower the performance on that dimension in order to raise it on other dimensions (example: Lower flexibility of a software product but raise the simplicity), these innovations can then “disrupt” the original innovation.
From the perspective a current innovation, the disruptive innovation appears to start below it in the s-curve, but it’s able to gain adoption because the particular performance feature of that innovation is already higher than the market needs, and the new product competes on a different performance feature that is not even a target of.
Common Mistake: Assuming a Particular Player will Win Because They’re Big and Have Lots of Resources
One understandable assumption to make is that big players with more resources will always win. This isn’t necessarily a bad assumption to make—disruptive innovations are much rarer than sustaining innovations.
However, having the disruptive innovation model can help you not make the mistake of just assuming that there’s nothing that can topple the current champ—it gives you a clear model of exactly how this happens, and may even point out industries or areas where you’re more likely to see this disruption take place.
Gartner Hype Cycle
The Gartner Hype Cycle describes a particular way that the media over-inflates people’s expectations of new innovations in comparison to how evolved they actually are for a particular market segment’s needs.
Common Mistake: Discounting a Particular Technology Because it Was Overhyped in the Past
I’ve frequently seen arguments of the form—“Oh, you think this technology will have a massive impact? That’s what they were saying a couple years ago and they massively overpromised.
Like other patterns, this is not saying that there aren’t technologies that are massively overhyped and don’t pan out. However, knowledge of the Gartner Hype cycle an show you that almost all popular technologies were once overhyped, so the argument of “this technology was overhyped in the past” isn’t very good evidence of how transformative it will be. Rather, you’ll want to map it against an evolution S-curve to see how overhyped you expect it to be relative to it’s current level of evolution.
Windermere Buying Hierarchy
The Windermere Buying Hierarchy describes four different improvement focuses that an innovation optimizes over time. First, it’s trying to solve for functionality, then reliability, then convenience, and finally price. This loosely maps to the stages of Wardley Evolution.
Recommended Resource: Haven’t found a good one, learned about it through Clayton Christensen’s work.
Common Mistake: Using Reliability, Convenience or Price as a Reason an Innovation Won’t be Successful
You know the drill by now… it’s not that reliability, convenience, or price are never reasons that a technology fails. But you’ll want to map these against the evolution S-curves. It’s common to see arguments about a technology not being viable because it’s too expensive, when the S-curve is still WAYY at the early stage and we wouldn’t even have expected the market to start thinknig about price optimization yet.
Only if the market has already reached that point in the S-curve and optimized that trait as much as it could, should you use this as a viable reason why you don’t expect the technology to spread further.
Conclusion
S-curves and s-curve patterns are a useful tool for quickly analyzing systems, particularly when looking at diffusion of trends and evolution of innovations. They can heuristically identify solutions and probabilities that would otherwise be quite time consuming to figure out using something like a full system or functional analysis.
Hopefully you find this tool useful in your quest to understand all the things.
S-Curves for Trend Forecasting
Epistemic Status: Innovation research and business research is notoriously low quality, and so all the ideas here should be viewed through that lense. What’s impressive about the S-curve and evolution trends literature is how remarkably self-consistent it is using a wide variety of research methods. Whether Simon Wardley analyzing news article about different technologies, Clayton Christensen doing case studies of a specific industry, or Carlotta-Perez taking a historical approach of tracking different technologies, the same S-curve pattern and evolution trends seem to show up. This too should be taken into account when evaluating these ideas.
Basics
This is an S-curve.
The S-curve is a fundamental pattern that exists in many systems that have positive feedback loops and constraints. The curve speeds up due to the positive feedback loop, then slows down due to the constraints.
When the constraint is broken, the positive feedback loop ramps back up, until it hits another constraint.
Recommended Resource: Invisible Asymptotes, which gives a visceral feel for this process of positive feedback and constraints
Common Mistake: Confusing S-Curves With Exponential Growth
Sometimes, people get confused and call S-curves exponential growth. This isn’t necessarily wrong but it can confuse their thinking. They forget that constraints exist and think that there will be exponential growth forever. When slowdowns happen, they think that it’s the end of the growth—instead of considering that it may simply be another constraint and the start of another S-Curve. Knowledge of overlapping S-Curves can help you model these situations in a more sophisticated way.
Diffusion S-Curves
The S-curve pattern is quite common in the spread of ideas, practices, and technologies, although it rarely looks quite as pretty. The example below shows “diffusion s-curves”—How a technology spreads through a population (in this case US households
The positive feedback loop in this case is word of mouth, and the constraints represent fundamental barriers to certain market segments or growth such as simplicity, usability, scalability, price, etc.
This creates smaller s-curves around adoption among specific market segments, and larger s-curves that represent the overall market penetration of the idea, practice, or technology.
Recommended Resource: Wikipedia on Diffusion of Innovation
Evolution S-Curves
In addition to Diffusion S-curves in technology, ideas, and practices, there are Evolution S-Curves. These represent the increase in the traits of these ideas that make them usable in more situations and desirable for more people. When you break through a constraint in one of these properties through innovation, this can often coincide with “unlocking” a new diffusion curve by opening up a new market that wouldn’t previously have used your technology or idea.
In this case the positive feedback loop is the increased understanding and expertise that comes from diffusion of a new innovation in your idea or technology, and the constraint represents fundamental assumptions in the idea, practice, or technology that must be changed through another innovation to make the idea, practice, or technology more desirable.
In the example below the desirable property is hardware speed. Fundamental leaps are made to break through a speed constraint, and then iterated on through the positive feedback loop of information and expertise increasing from adoption. This hits diminishing returns as the new innovation is optimized, and then a new fundamental innovation is needed to overcome the next constraint.
Recommended Resource: Open University on Evolution S-Curves
Common Mistake: Confusing Diffusion S-Curves with Evolution S-Curves
Sometimes, I see people make the mistake of assuming that evolution and diffusion s-curves follow the same cycle. Most often, the mistake made here is assuming that when a particular innovation has saturated a certain market, that also means it has “reached its final form” and has no more evolving to do.
There is a related truth—often, an innovation becoming more diffuse will drive innovation as new use cases become apparent. And vice versa, often new innovations will open a new market up by creating use cases that were previously impossible.
However, the two types of curves are driven by two different feedback loops and two different constraints. There’s no reason to expect that they will follow each other, and no reason to expect that one curve leveling off will cause the other curve to level off.
S-Curves Patterns
S-curves become quite useful when paired with an understanding of evolutionary patterns. They can allow you to see in a broad sense what’s coming next for an idea, practice or technology. They can prevent surprises and give you a tool to stay ahead of changes.
There are patterns that exist for both diffusion and evolution S-curves.
Diffusion Patterns
Diffusion patterns describe common themes that happen as trends diffuse through a population. They apply on the micro-level to individual population-segments, and on a macro-level to the overall population.
Diffusion of Innovation
The diffusion of innovation describes 5 separate stages of a diffusion curve: Innovators, Early Adopters ,Early Majority, Late Majority, and Laggards. By understanding the traits of each of these groups, you can get a broad idea of what to expect, and how to slow or speed up adoption.
Recommended Resource: Diffusion of Innovations book by Everett Rogers
The Chasm
The Chasm describes a common constraint that occurs in a market segment between “early adopters”—who are willing to put up with a lot, and “early majority”, who expect a lot. There is often a number of evolutionary constraints that must be broken through to bridge this single diffusion constraint and many new ideas, practices, and technologies get stuck in the chasm for that reason.
Recommended Resource: Crossing the Chasm book by Geoffrey Moore
Common Mistake: Assuming a Technology is Irrelevant Because it’s Only Useful for a Small Group
A common mistake that I see is assuming a technology won’t have a broader relevance, and using as evidence that it’s only used by a small group of relatively abnormal people.
Now, what is true is that not all technologies eventually get adopted by everybody, some stay relatively niche. But it’s not very good Bayesian evidence to say that because a technology is used by a small group of weird people, it will not have a broader impact. These diffusion patterns tell us that in fact that MOST technologies that eventually get widespread adoption go through this phase.
Furthermore, they tell us that many of those technologies often get stuck for a while at this early stage because of the Chasm. So even if a technology has staid at this tage for a while (e.g. cryptocurrency), it’s still very little evidence towards that technology not being lifechanging in the future. (In contrast, a technology stalling for a long time at some point past the chasm is better evidence that it may have reached saturation)
Evolution Patterns
Evolution patterns describe common ways that innovations evolve over time to become increasingly desirable. They apply on the micro-level to individual innovations within a trend, and on a macro-level to the evolution of trend as a whole.
Wardley Evolution
Innovations tend to go through four stages—the initial prototype, custom built versions, productized versions that compete, than comoditized versions that are all basically the same. By understanding where you are, you can understand the type of competition likely to happen, the types of processes likely to yield improvements, and large changes that will be needed to stick with the market.
Recommended Resource: Learn Wardley Mapping- Free Resource from Ben Mosior
Common Mistake: Not reasoning about likely changes in how the market will be structured.
A common mistake I see when people reason about the future of e.g. Machine Learning, is that they reason as if the current economic style (how people make money from machine learning) will continue the way it has been.
What Wardley Evolution tells us is rather that it’s very frequent for the way a market charges for and makes money with a particular innovation changes, and that change tends to fairly predictable.
For instance, I’ve seen analysis of Machine learning that assumes it will continue to be productized (which leads to very different dynamics in terms of competitive landscape and strategy between different AI vendors), rather than recognizing that it will eventually be commoditized and become a utility.
Simplicity—Complexity—Simplicity
Innovations tend to start out relatively simple as a new approach to a problem. They become increasingly complex to cover more use cases and be more robust, and then become simple again as refinements are made and they’re distilled to their essence.
Recommended Resource: TRIZ for Dummies book by Lilly Haines-Gadd
Common Mistake: Assuming a Particular Innovation is a Dead End Because It’s Gotten Too Complex
One mistake I see pretty frequently is people describing a particular innovation, and saying “well, we’ve added more and more complexity to this and it’s gotten increasingly minimal returns so I expect there too not be too much more innovation in this area.
This can be true, but only if there are other indicators that this is already at the end of the innovation curve. Oftentimes, what’s actually happened is that it’s near the midpoint of it’s innovation curve, and the next innovations will be around compressing/simplifying all the things that have been added. This simplification process then allows the innovation to be used a component to build further innovations off of, as it’s simple enough to be commoditized.
Disruptive Innovation
Sometimes, innovations overshoot the mainstream populations needs on a particular dimension in order to be powerful for a particularly lucrative part of the population. In this case, these innovations or often overtaken by subsequent innovations that lower the performance on that dimension in order to raise it on other dimensions (example: Lower flexibility of a software product but raise the simplicity), these innovations can then “disrupt” the original innovation.
From the perspective a current innovation, the disruptive innovation appears to start below it in the s-curve, but it’s able to gain adoption because the particular performance feature of that innovation is already higher than the market needs, and the new product competes on a different performance feature that is not even a target of.
Recommended Resource: The Innovator’s Dillema—Book by Clayton Christensen
Common Mistake: Assuming a Particular Player will Win Because They’re Big and Have Lots of Resources
One understandable assumption to make is that big players with more resources will always win. This isn’t necessarily a bad assumption to make—disruptive innovations are much rarer than sustaining innovations.
However, having the disruptive innovation model can help you not make the mistake of just assuming that there’s nothing that can topple the current champ—it gives you a clear model of exactly how this happens, and may even point out industries or areas where you’re more likely to see this disruption take place.
Gartner Hype Cycle
The Gartner Hype Cycle describes a particular way that the media over-inflates people’s expectations of new innovations in comparison to how evolved they actually are for a particular market segment’s needs.
Recommended Resource: Mastering the Hype Cycle—Book by Jackie Fenn (Disclaimer: Haven’t read this one, only aware of the Gartner Hype Cycle in passing)
Common Mistake: Discounting a Particular Technology Because it Was Overhyped in the Past
I’ve frequently seen arguments of the form—“Oh, you think this technology will have a massive impact? That’s what they were saying a couple years ago and they massively overpromised.
Like other patterns, this is not saying that there aren’t technologies that are massively overhyped and don’t pan out. However, knowledge of the Gartner Hype cycle an show you that almost all popular technologies were once overhyped, so the argument of “this technology was overhyped in the past” isn’t very good evidence of how transformative it will be. Rather, you’ll want to map it against an evolution S-curve to see how overhyped you expect it to be relative to it’s current level of evolution.
Windermere Buying Hierarchy
The Windermere Buying Hierarchy describes four different improvement focuses that an innovation optimizes over time. First, it’s trying to solve for functionality, then reliability, then convenience, and finally price. This loosely maps to the stages of Wardley Evolution.
Recommended Resource: Haven’t found a good one, learned about it through Clayton Christensen’s work.
Common Mistake: Using Reliability, Convenience or Price as a Reason an Innovation Won’t be Successful
You know the drill by now… it’s not that reliability, convenience, or price are never reasons that a technology fails. But you’ll want to map these against the evolution S-curves. It’s common to see arguments about a technology not being viable because it’s too expensive, when the S-curve is still WAYY at the early stage and we wouldn’t even have expected the market to start thinknig about price optimization yet.
Only if the market has already reached that point in the S-curve and optimized that trait as much as it could, should you use this as a viable reason why you don’t expect the technology to spread further.
Conclusion
S-curves and s-curve patterns are a useful tool for quickly analyzing systems, particularly when looking at diffusion of trends and evolution of innovations. They can heuristically identify solutions and probabilities that would otherwise be quite time consuming to figure out using something like a full system or functional analysis.
Hopefully you find this tool useful in your quest to understand all the things.