Lasers

 Copilot:

What does the evolution of AI to laser technology instead of copper wiring portend for electricity consumption?

Short answer:  

Switching AI data‑center interconnects from copper wiring to laser/optical links dramatically reduces electricity consumption for data movement — often by 50–80% per bit — but it does not reduce total AI electricity use overall. Instead, it slows the growth curve and prevents power demand from exploding as clusters scale.

🌟 What this evolution actually portends

The shift to photonics (lasers, silicon photonics, co‑packaged optics, microLED optical engines) is not cosmetic — it’s a fundamental change in how AI systems move data. And data movement is now the dominant source of energy consumption in large AI clusters.

1. Copper is hitting a physics wall

Copper interconnects waste enormous energy as heat when pushing multi‑terabit signals between GPUs. In dense AI clusters, up to 50% of total power is spent just moving data, not computing.

2. Optical interconnects slash energy per bit

Laser‑based optical links transmit data with almost no electrical resistance. Reported efficiency gains:

75% reduction in interconnect power with co‑packaged optics vs pluggable optical modules

50–65% reduction in per‑port power vs copper‑based pluggable optics

Up to 80% reduction in interconnect energy per bit when optical chiplets are integrated directly on GPU packages (expected by 2028)

MicroLED optical engines promise sub‑picojoule per bit transmission — far below copper’s limits

3. Why this matters: interconnect energy is now the bottleneck

In modern AI systems:

Moving data between chips consumes more energy than the calculations themselves.

In a 100,000‑GPU cluster, pluggable optics alone burn 800 kW–1.2 MW just for networking.

Replacing copper with photonics directly cuts this overhead.

🔌 So does electricity consumption go down?

Not in absolute terms — but it grows much more slowly.

What improves:

Lower energy per bit → huge savings at scale

Lower cooling requirements (optical links generate less heat)

Higher bandwidth density → fewer repeaters, retimers, amplifiers

More GPUs per rack without exceeding power limits

Example:

A 32,000‑GPU pod can save 400–600 kW by switching to co‑packaged optics — enough to add 40–60 more GPU nodes without increasing the facility’s utility connection.

What does not improve:

Total AI electricity consumption continues rising because:

Clusters keep getting larger

Models keep getting bigger

Demand for AI services keeps increasing

Even with photonics, AI datacenter electricity use is projected to reach 945 TWh by 2030 — roughly Japan’s entire consumption.

Optics slow the growth curve; they don’t reverse it.

🧭 The big-picture implication

Laser‑based interconnects don’t make AI “green” — they make it possible to keep scaling without hitting catastrophic power limits.

In practical terms, this shift portends:

AI clusters will continue growing (100k → 500k GPUs)

Power density per rack will rise, but interconnect power will fall

Electricity demand will increase more slowly than it would with copper

Utilities and governments will face fewer grid‑capacity crises

AI companies can expand without adding 30–50 MW per campus just for networking

Optical interconnects are becoming the only viable path to keep AI scaling without blowing past national‑grid constraints.

🔍 If you want, Louise…

I can break down:

How much electricity you personally save indirectly when AI uses photonics

Whether photonics will make consumer AI cheaper

How Quebec’s grid (Hydro‑Québec) fits into this future

Whether AI usage will ever be metered like electricity

Which direction do you want to explore next?