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  • Introduction
    • Machine-to-Machine Learning White Paper Introduction
    • What is M2ML?
    • 3 Waves - Overcoming Limitations of Human Performance
  • Wave 1
    • Wave 1 – Vision & Identification
    • Wave 1 – Archetypes
    • Wave 1 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
  • Wave 2
    • Wave 2 – Building an MVP
    • Wave 2 – Archetypes
    • Wave 2 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
    • Wave 2 - The North Star: Building for the J-Curve
  • Wave 3
    • Wave 3 – Building a Platform
    • Wave 3 – Archetypes
    • Wave 3 – Opportunity Areas
      • Automating & Roboticizing Stodgy Industries
      • Accelerating Serendipitous Discovery
  • Insights
    • Identifying Winners Across Waves
    • Case Study: Rapid Robotics
    • Go-to-Market Playbook
      • Product
      • Sales
      • Platform
    • Waves in Motion
  • Conclusion
    • Why Now?
    • Get in Touch
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  1. Wave 2

Wave 2 – Building an MVP

Prototype to Test Hypotheses

Build device plumbing to establish an ROI-positive prototype.

W1 produces use-case specific hypotheses; W2 involves prototyping a product to test those hypotheses, and break an idea as early as possible.

This is when fundamental infrastructure is designed to compete for performance specifications, developer adoption, desirable unit economics and deployment logistics, and customer love.

Many solutions “can be built” - W2 answers the question of whether they should be built.

This inevitably points toward a recalibration of ROI: not just whether the hypothetical mechanics (W1), but also the practical logistics of the solution work.

For startups, the danger is high in getting stuck in W2 - because of the lengthy iteration timelines paired with an often unclear picture of success. Additionally, sunk cost psychology may drive deployment of “solutions” that don’t pass the bar of both mechanical and economic proof (favorable ROI).

To avoid this trap, it is crucial to first identify the customer’s required performance (decoupled from the full potential of the technology), in order to then define the necessary device plumbing.

From there, we suggest relaxing that standard to build quick prototypes that provide enough performance data to justify a jump to W3 (go-to-market, and then scale deployment), or on the other hand a retraction to the prior state (if the effort does not portend positive ROI / economics).

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Last updated 3 years ago

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