Photonic compute (not just interconnect) will achieve commercial viability for AI inference by 2030

The projected 880 TOPS/mm^2 compute density and 5.1 TOPS/W energy efficiency represent 1-3 orders of magnitude improvement over digital. Phase-change materials enabling non-volatile photonic weight storage make in-memory computing viable. The gap between lab and production is large, but the theoretical ceiling justifies the bet.

Confidence: 4/10 Supporting evidence:

• 880 TOPS/mm^2 projected — 1-3 OOM over digital compute Evidence: moderate (Neuromorphic Photonic)
• 5.1 TOPS/W energy efficiency projection Evidence: moderate (Neuromorphic Photonic)
• Phase-change materials enable non-volatile photonic weight storage Evidence: moderate (Neuromorphic Photonic)
• MZI and MRR architectures provide two complementary approaches Evidence: strong (Integrated Platforms)

Challenging evidence:

• All projections, no production results — lab-to-fab gap is historically large for photonics
• Bit precision with analog photonic weights is fundamentally limited
• PCM write endurance (cycle count before degradation) is unknown at scale
• O-E-O conversion for nonlinear activation functions remains a bottleneck
• All-optical nonlinearities using saturable absorbers are immature
• Fabrication yield at wafer scale is unproven
• Hybrid optical-electronic may remain optimal, limiting the "photonic compute" narrative

Evolution:

• Apr 5, 2026 — Initial thesis at 4/10. The theoretical performance is extraordinary but the open problems list is long and fundamental. This is a 2030 thesis because the 2026-2028 window is clearly too early. Commercial viability for inference (not training) is the more achievable target because inference workloads are more predictable.

Depends on: photonic-tensor-cores, photonic-neural-networks Would change if: A photonic compute chip demonstrates production-grade inference performance (not just projections), or if the nonlinearity bottleneck is solved with a practical all-optical approach.