The digital revolution, which progressively replaced analogue methods with digital circuits, has entered a new phase as AI expands across cloud infrastructure, mobile networks, wearables and physical systems, including drones and robots. Digital computing remains the general-purpose foundation of this expansion: it supports heterogeneous, on-device and decentralised AI through programmable control, mature software and decades of accumulated engineering infrastructure. Yet as energy and data-movement constraints become more significant, that same foundation is increasingly being extended rather than replaced by selected analogue and physical principles that it can host, configure and verify. Photonic, in-memory and neuromorphic architectures offer routes to reducing data movement and accelerating matrix-intensive and event-driven processing, not as alternatives to digital infrastructure but as specialised engines operating within it. This paper argues that hybrid digital--analogue computing represents a credible pathway towards more energy-efficient AI systems: one in which physical substrates earn an expanding role only where they deliver a measurable system-level advantage, under digital orchestration that manages integration, uncertainty and fallback. It examines the architectural principles, workload suitability, energy accounting, software requirements, limitations and open challenges associated with this transition, and argues that future progress should be evaluated through deployed-system metrics rather than isolated peak tera operations per second per watt (TOPS/W) claims.

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