Applications & Use Cases · Study deck
IoT History: Adoption and Cost
A useful device can stay niche until cost, trust, and supporting networks cross a threshold.
Blueprint Bina is your guide for this deck.
After studying this chapter
Learning objectives
You will be able to:
- Explain: This reading constrains iot segments sit at different points on the adoption curve; integration, policy, and trust can slow even cheap technologies and supplies the visual evidence for the iot adoption s-curve.
- Explain: Phase 2: Apple Watch Launch and Skepticism (2015): Even after Apple entered the market, critics focused on what smart watches did worse than existing products rather than what they uniquely enabled.
- Explain: IoT parallel: Smart meters were deployed for billing accuracy, then became grid optimization tools, then enabled demand-response programs worth billions.
- Explain: IoT is enabling predictive maintenance, precision agriculture, remote healthcare.
Major section
The Anatomy of Paradigm Blindness
Anchoring to Existing Behavior: Forecasters assume people will continue behaving as they currently do.
- McKinsey asked "Who among current telephone users would pay $4,000 for a worse phone?" instead of asking "What would 100 million NEW users do with portable communication?".
- Linear Extrapolation of Exponential Change: Technology costs drop exponentially (Moore's Law), but human minds think linearly.
- The revenue shift is a useful warning for IoT forecasts.
Major section
Historical Lesson Pitfalls
Learning from history is essential, but misapplying these lessons can be just as dangerous as ignoring them.
- Pitfall 1: "Everything is the next telephone" fallacy.: Not every new technology is a paradigm shift.
- Some IoT products genuinely are solutions looking for a problem.
- Acknowledging these real challenges is not paradigm blindness; it is prudent engineering.
Major section
Innovator's Dilemma in IoT
Clayton Christensen's "Innovator's Dilemma" (1997) explains why successful companies fail to adopt disruptive technologies.
- Expertise Becomes a Liability.
- IoT parallel: Manufacturing companies optimized for product reliability may dismiss sensor data as "unnecessary complexity".
- In 1983, no AT&T customer was asking for a mobile phone.
- New Use Cases Emerge Unexpectedly.
Major section
Innovator's Dilemma in IoT (continued)
Incumbents correctly calculate that the new technology is inferior -- for existing use cases.
- IoT is enabling predictive maintenance, precision agriculture, remote healthcare.
- IoT parallel: Smart meters were deployed for billing accuracy, then became grid optimization tools, then enabled demand-response programs worth billions.
- The Lesson for IoT Professionals:: When evaluating IoT applications, ask not "Does this solve existing problems better?" but rather "What new problems can this solve that were previously impossible?".
Major section
The IoT Adoption S-Curve
Technology adoption follows a predictable S-curve pattern, but the timing and steepness of the curve are consistently underestimated.
- This reading constrains iot segments sit at different points on the adoption curve; integration, policy, and trust can slow even cheap technologies and supplies the visual evidence for the iot adoption s-curve.
Major section
Smart Watch Journey
Phase 3: The Unexpected Killer App (2018-2020): None of these use cases were in the original product pitch.
- The smart watch story perfectly illustrates how paradigm blindness works -- and how it eventually gets overcome.
- Swiss watch executives declared Apple Watch would not affect their market.
- Hospitals integrate smart watch data into patient records.
Major section
Smart Watch Journey (continued)
Phase 2: Apple Watch Launch and Skepticism (2015): Even after Apple entered the market, critics focused on what smart watches did worse than existing products rather than what they uniquely enabled.
- They emerged from the combination of sensors + connectivity + processing that only a smart watch on a wrist could provide.
- Phase 4: Essential Health Infrastructure (2022-2026): By 2026, smart watches have become medical devices.
- It comes from enabling entirely new capabilities that were impossible before connectivity.
Deck summary
Key takeaways
Anchoring to Existing Behavior: Forecasters assume people will continue behaving as they currently do.
- Learning from history is essential, but misapplying these lessons can be just as dangerous as ignoring them.
- Clayton Christensen's "Innovator's Dilemma" (1997) explains why successful companies fail to adopt disruptive technologies.
- Incumbents correctly calculate that the new technology is inferior -- for existing use cases.
- Technology adoption follows a predictable S-curve pattern, but the timing and steepness of the curve are consistently underestimated.
Retrieval practice
Recall check 1 of 2
Blueprint Bina says: answer from memory, then check your reasoning.
Q1A widely reported 1980s forecast put the year-2000 mobile market at 900,000 units, while ITU recorded 738 million subscriptions. What best explains this roughly 820x actual-to-forecast ratio?
Show answer
Answer: B Correct!
Retrieval practice
Recall check 2 of 2
Blueprint Bina says: answer from memory, then check your reasoning.
Q2A startup proposes putting IoT sensors in all concrete structures to monitor curing and structural health. A construction industry veteran dismisses it: 'Concrete has been fine for 2,000 years without sensors. This is a solution looking for a problem.' Which historical parallel BEST describes this response?
Show answer
Answer: B Correct!
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Answers
Answer key.
- B · Correct!
- B · Correct!