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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 – Archetypes

Compelling Primary Use Cases to Speed through Wave 2 and Develop an MVP

PreviousWave 2 – Building an MVPNextWave 2 – Opportunity Areas

Last updated 3 years ago

1) Startups building optimized infrastructure to extract value from emerging and maturing data sources

Companies are using novel data sources (e.g. biosensors, miniaturized sensors, LIDAR), and applying them to augment system performance.

2) There is an explosion in data but solutions that ensure reliability are needed

Teams tackling use cases where consistently-labeled training data does not exist or introduces unwanted effects, like bias, drift, lack of interpretability, and low predictive power are compelling.

3) Companies balancing off-the-shelf and custom built solutions, innovating in speed to implementation

In this approach, companies abstract the hardware into software objects, and continually expand capabilities via software updates while the hardware remains (mostly) unchanged. Off-the-shelf equipment is great for W2 demonstration, early validation and initial adoption, while circling back to custom built enables W3 performance improvements and platform capabilities.

4) Startups creating breakthroughs in scaling existing platforms or services

Containers, popularized by , helped scale software services in the cloud. However, container management was cumbersome and unreliable, until bridged that gap.

5) Biology teams transcending science to harness computer science via DNA synthesis, lab automation, and machine learning

These teams are focused on designing bio-products to solve unique requirements like sustainability, taste or low unit cost. Companies in this space are typically focused on engineering a bio-process based on an already-proven scientific breakthrough, and are pushing the limits of engineering to get to market.

Adoption cycles in hardware are long and building for interoperability is capital and time intensive. This slows down go-to-market velocity for startups in hardware or component-focused businesses like System-on-Chip (SoC), networking devices or battery makers. For this reason, we shy away from these plays as part of our investment thesis.

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