COREDUMP #014: Why Your IoT Project Still Hasn't Taken Off - And How to Fix It

Memfault

Aug 5, 2025

Memfault

00:00 Introduction to IoT Challenges

01:54 Afzal’s Journey in IoT & Early Expectations

04:11 The 500K SIM Card Mirage

08:10 Why IoT Projects Stall: Internal Resistance & Misaligned Incentives

14:25 Rethinking the “IoT” Label: Lessons from Pebble

17:28 When Good Tech Still Fails: The Organizational Blindspot

20:43 Field Reality Check: Why Real-World Feedback is Critical

26:00 Who Owns It? Accountability After Launch

29:07 Designing for Fault Tolerance in Connected Devices

32:52 Fragmentation in IoT: Meeting Diverse Customer Needs

37:27 Niche Focus as a Winning Strategy

39:57 What IoT Can Learn from AI’s Go-to-Market Playbook

44:42 Drivers for Success i the IoT Space

47:14 The Future of IoT: Regulation, Trust & E-Waste

50:32 Final Reflections on Long-Term Ownership & Customer Impact

In today’s Coredump Session, the team takes a hard look at why some IoT projects stall before they ever hit scale. From organizational missteps to product-market fit challenges, they explore the hidden forces that derail even technically sound products. You’ll hear candid insights on why being “connected” isn’t enough—and what it really takes to succeed in IoT today.

Key Takeaways

The biggest blockers for IoT teams aren’t always technical—they’re organizational, strategic, and systemic.
Shipping a connected device doesn't guarantee product-market fit, especially if it's missing the right workflows or visibility.
Engineers often build what's technically possible instead of what's valuable for the business.
Collecting data from devices is table stakes—but what matters is how you *use* that data to drive action.
Many teams lack a clear owner for post-deployment success, leading to blind spots in field performance.
“Observability” should go beyond crash logs and include signals that help prioritize engineering work.
Product-market fit isn’t static—it has to be reevaluated and maintained across the device lifecycle.
Tools that automate crash deduplication, root cause analysis, and regression detection are game-changers for scaling support without burning out your team.

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