Is being a IoT Engineer
at risk from AI?

Not in the foreseeable future. While AI accelerates firmware coding, protocol setup, and data analysis, IoT engineering is fundamentally a hardware-software hybrid discipline. Physical sensor integration, field deployment, debugging connectivity in real-world environments, and navigating power/latency trade-offs require hands-on expertise and contextual judgment AI cannot replicate remotely. The role will shift toward higher-level architecture and edge AI, but demand remains strong.

Repetitive coding tasks—generating device drivers, MQTT clients, REST API wrappers—are already 50-70% automatable with LLM assistants. Telemetry analysis and anomaly detection are increasingly handled by AutoML pipelines. Configuration management for common protocols is also being streamlined by AI tooling. However, these are table-stakes tasks. The differentiating work—hardware integration, security architecture, field troubleshooting, and cross-functional design—remains firmly in human hands.

Focus on edge AI and on-device ML inference (TinyML, model quantization, ONNX Runtime). Deepen your security skills—device identity, secure boot, cryptographic protocols—especially in regulated industries like healthcare and automotive. Learn power optimization and energy-harvesting techniques for battery-free IoT. Finally, cultivate systems thinking: the ability to architect end-to-end IoT solutions spanning hardware, firmware, cloud, and ops is what separates senior engineers from junior ones, and it is not automatable.

Salaries are likely to polarize. Junior engineers who primarily write boilerplate firmware may see wage pressure as AI tooling raises productivity expectations. Senior engineers with security, edge AI, or regulated-industry expertise will command premiums, as their judgment and cross-disciplinary skills are scarce and hard to replace. Geographic arbitrage may also narrow as remote tooling improves, but physical deployment work—especially in industrial and remote settings—retains local wage advantages.

Yes, modestly. IoT engineering's physical component—sensor calibration, hardware debugging, field deployment—creates a natural moat against full automation. Pure software roles (e.g., web development, data analysis) are more exposed because their entire workflow is digital and accessible to AI agents. That said, the software portions of IoT work (firmware, cloud integration) are automating quickly, so resilience depends on maintaining hardware and systems expertise, not just coding skills.

Entry-level roles are under pressure but not disappearing. AI tooling raises the bar: new hires are expected to ship faster and handle more complexity with assistant help. Internships and junior positions increasingly emphasize hands-on hardware work, security fundamentals, and cross-functional collaboration rather than pure coding. If you are entering the field, prioritize projects that involve physical devices, real-world constraints, and regulated domains—these signal skills AI cannot yet replicate.

Yes, significantly. Industrial IoT (manufacturing, energy, agriculture) involves longer device lifecycles, stricter safety and compliance requirements, and more physical integration—all of which slow AI adoption and favor experienced engineers. Consumer IoT (smart home, wearables) moves faster, tolerates more automation, and has thinner margins, making junior roles more vulnerable. If resilience is your priority, target industrial, medical, or automotive IoT where regulatory and reliability demands create defensible expertise.