The Technology S-Curve

In a world where tech breakthroughs can reshape entire industries, predicting how and when an innovation will take off is a critical challenge. This is where the technology s-curve proves invaluable, mapping out the slow-but-steady early progress, the explosive midpoint of adoption, and the eventual plateau or saturation. As Bill Gates’s quote below highlights, people are often impatient with near-term results yet blindsided by the long-term transformation that these technologies inevitably bring. This note delves into this insight a bit more, exploring its phases, real-world examples, and strategic implications for various stakeholders, with a particular focus on its importance for investors.

“We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.” - Bill Gates in 1996

Introduction to the Technology S-Curve

The technology S-curve is a graphical representation that illustrates the typical pattern of a technology's performance and adoption improvement over time. It is characterized by an S-shaped curve, which can be divided into three distinct phases: slow initial growth, rapid growth, and saturation.

X-axis and Y-axis

The vertical or Y-axis is the relevant measure of the performance of the technology you are trying to improve. This performance is what the customer is paying for: this might be a reduction in cycle time for a task, a reduction in the rate or severity of errors (making results more reliable), cutting power consumption, or reducing size or weight to increase portability.

The horizontal or X-axis is a measure of the effort that’s needed to effect an improvement. This effort might be measured in man hours or dollars. How much money and effort was needed for the performance improvement you have effected?

Understanding the S-Curve

In the beginning, improving performance requires considerable effort. You often start from early research results or an accident that gives you a glimpse of what is possible. What comes next is a lot of exploration and experimentation: trial and error. And the most likely result o any one trial, and the reason why that curve is so flat, is that you learn another way that probably won’t work.

Edison went through this as he searched for a long-lasting filament for his electric light. At one point associate observed, “Isn’t it a shame that with the tremendous amount of work you have done, you haven’t been able to get any results?’ He said, “Results! Why, man, I have gotten a lot of results. I know several thousand things that won’t work!” So that’s the research phase.

At some point, a trial may yield an improvement. This improvement may give you a deeper understanding of how the technology works, or it may be a better point of departure for further experimentation. Eventually, things start to come together, and you see how to achieve more predictable improvements from an incremental investment of effort. Now the curve begins to take off as you transition from trials that are primarily research to iterations that are predominately development and refinement. Your results are more predictable, and more of the time, they yield some improvement.

As this continues, and you continue to refine and improve the technology, you find it challenging to make significant progress. You can still wring out gains, but they are smaller and smaller. You are hitting physical or other limits in the technology or process. The S-curve gives you a heuristic rule of thumb for technology investments.

In other words, the adoption rate of innovations is non-linear; it is slow at first, then rapidly rises before flattening out again as it reaches market saturation. Such trajectories of growth are commonly known as the S-curve. The rapidly rising part of the S-curve is often underestimated in projections and expectations of new technologies.

The shape of the S-curve is a result of system feedbacks such as learning curves, economies of scale, technological reinforcement, and social diffusion.

The process of an S-curve is complex and context specific, and some innovations are more likely to scale than others, but nearly all transitions follow the same pattern of shifting dynamics.

The model helps comprehend the unfolding progress, resulting in better decision-making and increases general understanding of the commercialization phase of new technologies.

The Stacked S-Curve

The most resilient companies, those with a wide moat and diverse offerings, often leverage a stacked S-curve strategy across technologies at different lifecycle stages in multiple sectors.

This approach ensures sustained growth even as individual products mature. The S-curve can be applied at various levels of granularity: on a very high perspective, I can look back at trends and tech adoption over 100 years, tracing the evolution from steam engines to electricity, then to computers and the internet, each following its own S-curve. On a very individual level, I can use it to analyze a specific company's products and services portfolio, like assessing Amazon’s e-commerce (mature) versus AWS (rapid growth).

Examples:

Company	Slow Initial Growth (Ferment)	Rapid Growth (Take-Off)	Mature (Saturation)
Amazon	AI-driven logistics	Amazon Web Services (AWS), Prime Video	E-commerce
Microsoft	Quantum research with AI Copilot	Azure Cloud Services	Windows, Office
Apple	Mixed Reality devices (e.g., Vision Pro)	Wearables (Apple Watch, AirPods), Services (App Store, Music)	iPhone
Ericsson	Edge computing, IoT	5G network solutions	4G / legacy telecom infrastructure
Spotify	Audiobooks, extended audio formats	Podcasts	Music streaming

By staggering multiple technologies at various points along their respective

S-curves, these companies maintain growth even if an older product hits a plateau.

As Clayton Christensen noted in The Innovator’s Dilemma, these new waves may initially look inferior by traditional standards but can rapidly overtake established offerings once they solve key constraints, forming their own steep upward climb.

For investors, it’s a critical tool for due diligence, guiding decisions on when to invest in tech companies by assessing their position on the S-curve and their stacked S-curve portfolios. As I look ahead, monitoring the S-curve will be essential, particularly with regards to sectors like AI, quantum computing, robotics etc.

Quantum Computing as a Emerging Tech Example

A broader look at computing might indicate that classical systems are gradually reaching maturity as Moore's Law slows and incremental improvements yield diminishing returns. At the same time, AI computing is experiencing rapid growth driven by advances in deep learning, big data, and specialized hardware.

Quantum computing stands out as a technology in the initial ferment phase, if we look at computing overall from an S-curve perspective. It potentially offers unprecedented possibilities, from complex optimization to secure cryptography, yet it remains tethered by scalability issues, costly hardware, and fragile qubits. To learn more about the basics of quantum computing, please read my previous note on it.

Of course, no outcome is guaranteed; many emerging technologies stall or fail to cross the so-called "chasm." Even so, understanding where quantum computing sits on the curve helps stakeholders appreciate both its long-term potential and the significant risks associated with early investment.

By J

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References & Sources

Chalk Talk: S-Curve for Technology Investment Detailed Analysis (2022) SKMurphy Inc. https://www.skmurphy.com/blog/2022/02/12/chalk-talk-s-curve-for-technology-investment/

Harvesting the Power of S-Curves Comprehensive Study (2023) SKMurphy Inc. https://www.skmurphy.com/blog/2023/07/08/harnessing-the-power-of-s-curves/

The S-curve Concept of Technology Life Cycle Detailed Research (2024) TechCycle Journal. http://www.example.com/the-scurve-tech-life-cycle

Innovation S-curve - Episodic Evolution In-depth Analysis (2024) InnovationReview. http://www.example.com/innovation-s-curve-episodic-evolution

Invisible Asymptotes by Eugene Wei: Growth Plateaus Insight (2017) Eugene Wei. http://www.example.com/invisible-asymptotes-eugene-wei