There will be 50 billion IoT devices by 2030.

That's 50 billion things generating data that needs intelligence.

AI + IoT is the convergence that doesn't get enough attention because it's not as photogenic as chatbots. But it's arguably more impactful.

Smart factories with predictive maintenance that catches equipment failure days before it happens. Agricultural sensors combined with weather ML models that tell farmers exactly when and how much to irrigate. Health monitors that detect cardiac anomalies and alert doctors before patients feel symptoms.

The technical stack is surprisingly accessible: edge devices (Raspberry Pi, Jetson Nano), MQTT or Kafka for data streaming, TensorFlow Lite or ONNX for on-device inference, time series models for prediction, and cloud for periodic retraining.

What makes this field interesting is the constraint engineering. You're deploying models on devices with limited memory, limited compute, limited battery. You can't just throw a bigger GPU at the problem. You have to be genuinely clever about efficiency.

If you enjoy the puzzle of making powerful models run in resource-constrained environments, AIoT is deeply satisfying work. And the demand is growing faster than any other AI subdomain I track.