The Fog of Technology

The Fog of Technology

Technology cycles impact strategy, and in turn investment and talent decisions. The “Fog of Technology" explains the challenge technologists face when assessing new technologies as they move from the initial trigger to adoption. ?

While the market is enthusiastic about technologies at the peak of inflated expectations, very few technologies ever reach mainstream adoption. The number of new technologies, the high failure rate, the lack of visibility and the long duration for winners to emerge makes it difficult to act and react.

To identify technologies likely to reach mainstream adoption, decision makers can get ahead of the game by continuously monitoring developments and tailor investment behavior to each stage of the technology cycle.

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Technology cycles impact strategy, and in turn investment and talent decisions.

Strategy:

Technology evolves in cycles, with new ideas emerging and older ones fading out. Businesses need to understand these cycles to stay competitive. For example, early-stage technologies might require exploration, while mature ones call for scaling. Knowing where a technology sits in its lifecycle helps companies plan their long-term strategies effectively.

Investments:

When and how much to invest in a technology depends on where it is in its cycle. Early on, the risks are high but the rewards could be significant. Later, as the technology matures, the focus shifts to larger, more stable investments. Companies that understand these stages can decide when to invest heavily, hold back, or exit.

Talent:

Every new technology needs people who can work with it. Businesses must hire new talent or train existing employees to keep up. By tracking technology cycles, companies can plan their workforce strategy, ensuring they have the right skills at the right time.


The sheer number of new technologies, the high failure rate, the lack of visibility and the long horizon for winners to emerge makes it difficult to act and to react

New technologies are exciting but unpredictable. They often follow a common pattern called the Hype Cycle:

  1. Trigger Stage: The idea emerges.
  2. Peak of Expectations: Buzz and inflated promises.
  3. Trough of Disillusionment: Reality sets in, and many lose interest.
  4. Adoption Phase: Practical uses are found, and the technology becomes widely used.

Take Generative AI as an example:

  • In 2017, Google published the Transformer model “Attention is all you need”, sparking interest.
  • By 2020, Generative AI entered the trigger stage.
  • By 2022, it reached the "Peak of Expectations," with widespread hype.

However, most technologies don’t reach the adoption phase. Research shows:

  • Up to 47 technologies are tracked each year.
  • Only 18% of these technologies reach their adoption phase.
  • A typical technology spends just 1.5 years being closely tracked before fading.
  • For those that succeed, it can take 11 years or more from inception to adoption.

This high failure rate and long time horizon make it challenging for businesses to pick winners early.


Decision makers can get ahead of the game by continuously monitoring developments and tailor investment behavior to each stage of the technology cycle

To succeed in a world of rapidly evolving technology, leaders need a clear strategy for managing technology investments:

Steps for Success:

  1. Continuously Monitor: Keep a close eye on technologies as they move through the Hype Cycle. Regularly assess their relevance to your business goals.
  2. Diversify Investments: Spread your bets. Invest in technologies at different stages of their cycle to balance risks and opportunities.
  3. Exit Quickly: If a technology isn’t delivering results, move on. Reallocating resources to better opportunities prevents wasting time and money.

Investment Behavior by Stage:

  • Trigger Phase: Make small, exploratory investments. Most technologies in this stage won’t succeed, but the upside is high.
  • Peak Phase: Limit investments to pilots and trials. Hype is at its peak, but the risk of failure remains high.
  • Trough Phase: Look for undervalued opportunities but proceed cautiously; many technologies fail here too.
  • Adoption Phase: Invest significantly in proven technologies, focusing on scaling operations and hiring the right talent.

By tailoring strategies to each stage of the technology lifecycle, decision-makers can cut through the Fog of Technology and mitigate risks, capitalize on opportunities, and stay ahead of competitors.


To see the full analysis with all the data ??

https://www.slideshare.net/slideshow/the-fog-of-technology-technology-hype-cycles/273591200






Wayne Pau

Development Architect at SAP

2 个月

Christian Dahlen - interesting read. I feel like the #hypercycle is just very dated concept. Even certain technologies have "facets" (or different versions of themselves). Takes "Machine Learning". Over 20 years I took a 4th year University of Waterloo SYS411 (I think) which was called "Machine Intelligence" because the prof refused to call "Artificial". I stayed up multiple nights running training for soft version of "Soccer Bots" to teach them to play soccer on each other (see: https://en.wikipedia.org/wiki/RoboCup). 10 years later I worked with some java-based neural net that could answer natural language text messages. Did the curve really stretch that far back? Or is the hyper with ChatGPT version of AI a totally different curve than either of those before it? If there is a #fogoftechnology' (which honestly believe there is one)... it is that often we group like things together (Gestalt Psychology or Mere-effect?) and over-simply things. I think the quicker companies can get deep understanding of technologies, the better off they will be. Move from marketing to understanding (and possibly application). Those are the companies that will win.

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