For startup founders, Bill Gross’s TED Talk “The Single Biggest Reason Why Startups Succeed” highlights that timing your market entry is the most crucial factor for success, surpassing even funding, team, or the idea itself. His research indicates that startups with excellent timing have a significantly higher chance of success. By understanding market timing, founders can better strategize their entry and increase their chances of success.

This post will explore three models for understand market timing. The aim is to help founders and Investors better understand market timing so they can make more informed decisions.

Let’s take Artificial Intelligence as an example. It represents both a promising opportunity for startups and a potential threat to established internet incumbents. Throughout this post, we will pull on that AI market timing thread to determine where the AI market is today.

Why care about understanding market timing?

Emerging opportunities like AI have the power to displace existing technology. History has often taught us that new and emerging technology often destroys existing products and markets. Large enterprise incumbents have often ignored or dismissed emerging tech to their detriment.

For example, in his book “Losing the Signal,” Sean Silcroft documents BlackBerry’s spectacular rise and fall. The team at Blackberry famously scoffed at the iPhone, citing its lack of a physical keyboard and inefficiency with bandwidth as a reason consumers wouldn’t adopt the new product. Kodak ignored the digital camera until it was too late. Netflix’s move to online distribution sparked a fierce battle with Blockbuster. We know how that ended. Those who ignore history are often doomed to repeat it.

However, AI represents the next wave of transformative technology poised to change industries and markets in ways that are hard to imagine. That said, while it’s essential to heed the lessons of Kodak and Blockbuster, there will be new and emerging opportunities for those who seize the day. AI will be the next technology wave, transforming markets and industries as the Internet did. Let’s dig deeper and use some models to gauge AI’s timing.

Two models for understanding market timing

Measuring market timing poses many challenges, but two models stand out for characterizing different stages of this timing: the Gartner Hype Cycle (GHC) and the Technology Adoption Life Cycle (TALC). Gartner most effectively describes the pre-revenue hype phase of market timing. The Technology Adoption Cycle helps us understand how new technology is adopted and translates into revenue growth. The challenge lies in the two models differing y-axis, representing hype and revenue. However, combining these models can provide a comprehensive view of the entire market lifecycle, from innovation trigger to decline.

Gartner Hype Cycle

The Gartner Hype Cycle model, developed in the mid-1990s, has evolved to become a prominent model for understanding market timing and emerging technologies. The Hype Cycle provides valuable insights into new technologies’ stages and eventual productivity across various industries. The Gartner Hype cycle is at its best early in the market life cycle.

Gartner Hype Cycle
Image by Gartner the Gartner Hype Cycle
According to Gartner, there are five phases in the hype cycle:

  • The innovation trigger marks the introduction of a new technology, sparking initial interest and excitement.
  • The peak of inflated expectations represents the period of overhyped anticipation and unrealistic expectations surrounding the technology.
  • The trough of disillusionment indicates a decline in enthusiasm and belief in the technology’s potential as the initial hype wanes and practical challenges emerge.
  • Slope of Enlightenment – This phase corresponds to the transition from a period of understanding, adjustment, and maturation as the technology’s value becomes more accepted.
  • Plateau of Productivity—This phase aligns with the technology’s widespread adoption, maturity, and integration into mainstream market applications.

The Technology Adoption Life Cycle

The Technology Adoption Lifecycle (TALC) has a history that dates back close to 75 years. Frank Bass’s Iowa corn seed study is the basis for the first iteration of this model. The next major iteration of the model was called a Diffusion of Innovation by Everett Rogers in the 1960s, which categorized the adoption into five distinct stages. Finally, there is the pinnacle of the TALC model, which Geoffrey Moore documented in this three-book series: Crossing the Chasm, Inside the Tornado, and Dealing with Darwin. There is ample evidence to support the TALC model and customer categories.
Gartner Hype Cycle
Image by Wikipedia

  • Innovator: Innovators are tech enthusiasts and risk-takers who eagerly embrace new technologies, often seeking novelty and creativity. They are the first to adopt innovations, driving early market feedback and experimentation.
  • Early Adopter: Early adopters are visionaries and opinion leaders who carefully select and adopt new technologies based on their potential to provide a competitive edge. They serve as influencers and advocates, helping innovations gain traction in the market.
  • Early Majority: The early majority consists of pragmatists who adopt technologies upon proof of their value and practical benefits. Technology acceptance by pragmatists is crucial in propelling widespread adoption.
  • Late Majority: The late majority represents skeptics and traditionalists who adopt technologies once they have become a standard or a necessity, often due to peer pressure or competitive forces.
  • Laggard: Laggards are cautious and resistant to change, often being the last to adopt new technologies. They prefer established and familiar solutions and are hesitant to embrace innovation.

Let’s stroll down the TALC history lane with a brief overview of these developments.

Graph of the S-curve and adoption distribution from the Iowa Corn Seed Study
1950 Iowa Corn Seed Study: The technology adoption concept originated in a seminal Iowa corn seed study in the 1950s. The Iowa corn seed study, conducted by Frank Bass and other researchers at Iowa State University, was officially titled “A New Product Growth for Model Consumer Durables.” This influential study laid the groundwork for understanding the dynamics of product adoption and the product life cycle concept. It identified the characteristic bell-shaped curve of product adoption and provided a framework for understanding the product life cycle stages, from introduction to eventual decline.

1960 Technology Adoption Life Cycle: In the 1960s, the Iowa corn seed study underwent refinements that led to the development of the technology adoption life cycle. This evolution, notably advanced by Everett Rogers, introduced the concept of diffusion of innovations and identified distinct adopter categories. The subsequent developments yielded valuable insights into the factors influencing technology adoption, the dynamics of market acceptance, and the importance of early adopters in driving widespread adoption of innovations.

1991 Geoffrey Moore’s series of books, including “Crossing the Chasm,” “Inside the Tornado,” and “Dealing with Darwin,” offer compelling evidence supporting the existence and significance of the life cycle concept across diverse industries and technologies.

Technology Adoption Lifecycle with Chasm
Image by Smith House Designs
“Crossing the Chasm” addresses the critical transition from early adopters to the broader market, emphasizing the challenges of “crossing the chasm” between early adoption and mainstream market acceptance. Moore introduces the technology adoption stages, such as innovators, early adopters, early majority, late majority, and laggards, highlighting each group’s distinct needs and behaviors.

“Inside the Tornado” delves into the intense growth phase of product sales, when they start “Scaling up.” Moore discusses the rapid market acceptance and the challenges companies face in managing this explosive growth, including scaling operations, meeting demand, and fending off competition.

“Dealing with Darwin” extends the life cycle concept to the broader context of corporate evolution and the impact of disruptive innovation. Moore explores how companies can adapt and evolve to maintain relevance in an ever-changing market landscape.

Comparing TALC to GHC

I am reasonably sure that the Gartner Hype Cycle and the Technology Adoption Life Cycle are good models for understanding market timing. While the GHC and TALC have many similarities, they also have one key difference. The critical difference is hype; GHC has it, and TALC does not. There are two different types of technology adoption cycles. There is the rational technology adoption cycle, characterized by TALC, and an irrational cycle, which GHC characterizes. The next couple of sections will explore the topic of rational/irrational technology adoption in more detail.

There are a number of blog posts and papers comparing GHC and TALC. There is a good by Udayan Banerjee which shows how new tech goes through three phases: Hype, Struggle, and Success. However, Udayn speculates that not all products go through these three phases. Many new technologies fail altogether, and some go directly from the innovation trigger to the slope of enlightenment. The conclusion is that not all technology follows the Gartner Hype Cycle. I agree. TLAC follows a rational product introduction with little or no hype. Only technology is subject to hype that creates an irrational introduction. Based on these conclusions, technology adoption can be either rational or irrational market timing.

Adoption lifecycle illuminated by Hype Cycle
Image by Michael Herman at Hyperonomy Digital Identity Lab

TALC a rational technology introduction model

TLAC follows a rational product adoption cycle. Typically, a new technology or product will pass through the five stages of the TALC. Innovators and early adopters are the first to try a new product or technology. Once vendors have navigated the value proposition so that the pragmatists of the early majority are satisfied, the new product achieves wider market acceptance and enters the growth phase. The last to adopt a new product are laggards. For example, the superior corn seed of the Iowa Corn seed study took ten years from initial introduction to market saturation. The normal TALC distribution and s-curve are signs of a rational product introduction.

GHC an irrational technology adoption model

I hypothesize that irrational and overhyped technologies go through the hype cycle. There are two distinct hype signals: sentiment or speculative revenue. Hype sentiment is your garden-variety Gartner Hype Cycle and indicator of new technology with overinflated expectations. This type of sentiment hype follows the path of the traditional Gartner Hype cycle. However, significant speculative revenue early in the adoption cycle may indicate an emerging transformative technology at a scope and scale much larger than the traditional hype cycle.

Speculative revenue indicates transformational technology

Assuming there are two distinct hype signals: sentiment or speculative revenue. Significant irrational revenue spent on infrastructure early in the adoption cycle may indicate speculation. Excessive speculation indicates an emerging transformative technology at a scope and scale much larger than the traditional hype cycle.

For example, there was much hype and irrational speculation around the emergence of the Internet at the turn of the millennium. Many stocks rocketed to great heights only to crash back to earth after sustainable revenue reality failed to match the overinflated market expectations. The introduction of AI has the same irrational air as the introduction of the Internet. The introduction of the Internet also digitally transformed most industry. In much the same way, I suspect that AI will have a similar transformative effect on industry.

Nvidia and Cisco comparison

Image by Seeking Alpha – “What If Nvidia Of Today Is The Cisco Of 2000?

One Data Point to support the speculative revenue position is a graph by Seeking Alpha comparing Cisco and Nvidia … it is almost like Deja vu all over again. That said, the seeds of speculation are the potential indicators of the birth of a new industry. Speculation is often an indicator of great potential. Many of the current BIG tech companies (FaceBook, Amazon, Netflix, Google) emerged from the ashes of the speculation around the Internet Tech Bubble.

Rational, Irrational & speculative market timing

There are potentially three mental models to gauge the timing of a new product or technology:

    • Rational Technology Introduction – Technology Adoption Lifecycle,
    • Irrational Technology Introduction – Gartner Hype Cycle
    • Speculative Transformative Technology Introduction – Next Post

Knowing the difference … priceless!

Ian Paul Graham
I help startup founders and investors with startup market research for emerging opportunities.