#007: AI, Open Source, and the Future of Embedded Development: How Much Code Will We Actually Write?

In today's Coredump Session, we dive into a wide-ranging conversation about the intersection of AI, open source, and embedded systems with the teams from Memfault and Goliath. From the evolution of AI at the edge to the emerging role of large language models (LLMs) in firmware development, the panel explores where innovation is happening today — and where expectations still outpace reality. Listen in as they untangle the practical, the possible, and the hype shaping the future of IoT devices. Speakers:François Baldassari: CEO & Founder, MemfaultThomas Sarlandie: Field CTO, MemfaultJonathan Beri: CEO & Founder, GoliothDan Mangum: CTO, GoliothKey Takeaways:AI has been quietly powering embedded devices for years, especially in edge applications like voice recognition and computer vision.The biggest gains in IoT today often come from cloud-based AI analytics, not necessarily from AI models running directly on devices.LLMs are reshaping firmware development workflows but are not yet widely adopted for production-grade embedded codebases.Use cases like audio and video processing have seen the fastest real-world adoption of AI at the edge.Caution is warranted when integrating AI into safety-critical systems, where determinism is crucial.Cloud-to-device AI models are becoming the go-to for fleet operations, anomaly detection, and predictive maintenance.Many promising LLM-based consumer products struggle because hardware constraints and cloud dependence create friction.The future of embedded AI may lie in hybrid architectures that balance on-device intelligence with cloud support.Chapters:00:00 Episode Teasers & Welcome01:10 Meet the Panel: Memfault x Golioth02:56 Why AI at the Edge Isn’t Actually New05:33 The Real Use Cases for AI in Embedded Devices08:07 How Much Chaos Are You Willing to Introduce?11:19 Edge AI vs. Cloud AI: Where It’s Working Today13:50 LLMs in Embedded: Promise vs. Reality17:16 Why Hardware Can’t Keep Up with AI’s Pace20:15 Building Unique Models When Public Datasets Fail36:14 Open Source’s Big Moment (and What Comes Next)42:49 Will AI Kill Open Source Contributions?49:30 How AI Could Change Software Supply Chains52:24 How to Stay Relevant as an Engineer in the AI Era⁠⁠Join the Interrupt SlackWatch this episode on YouTube⁠Suggest a Guest⁠Follow Memfault⁠⁠LinkedIn⁠⁠⁠⁠Bluesky⁠⁠⁠⁠Twitter⁠⁠Other ways to listen:⁠⁠Apple PodcastsiHeartRadio⁠⁠⁠⁠Amazon MusicGoodPodsCastbox⁠⁠⁠⁠Visit our website

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