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As AI clusters scale, copper hits physical limits and the next bottleneck becomes optical infrastructure. Lasers, transceivers, optical engines, specialty glass, and the test/burn-in capacity behind it.
Owns the laser + optical switching side of the trade. One of the cleanest pure plays on AI optical demand.
Wins from lasers, modules, and networking hardware that power hyperscale AI infrastructure and cloud expansion.
One of the best ways to play AI optical transceivers — major hyperscaler demand for 800G and 1.6T connectivity.
Benefits from the push toward faster semiconductor-to-optical integration as AI infrastructure scales.
Controls a huge part of the DSP + interconnect story with optical networking chips and high-speed connectivity.
Sits at the center of AI networking through switching, custom silicon, and optical interconnect demand.
The Ethernet backbone moving massive AI workloads across hyperscale clusters and data centers.
Supplies the specialty glass + fiber needed for the optical transport layer behind AI infrastructure.
Benefits from building and scaling the actual hardware behind networking systems and optical modules.
Wins from burn-in + testing demand as AI ASICs and high-power optical hardware move into production.
Focused on lower-cost optical engines designed to improve efficiency inside AI data centers.
Pushing next-gen polymer photonics that could make optical communication faster and more efficient.
Exposure to advanced laser systems supporting precision photonics and next-gen optical demand.
Specialty optics and photonic components tied to industrial, defense, and AI-driven systems.
Connectivity + infrastructure side helping AI clusters scale faster.
AAOI | AEHR | ALAB | ANET | AVGO | COHR | GLW | JBL | LITE | LPTH | LWLG | MRVL | POET | QCLS | SIVE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AAOI | · | 0.40 | 0.39 | 0.38 | 0.40 | 0.56 | 0.42 | 0.41 | 0.53 | 0.29 | 0.21 | 0.32 | 0.29 | 0.00 | — |
| AEHR | 0.40 | · | 0.25 | 0.31 | 0.32 | 0.46 | 0.46 | 0.43 | 0.38 | 0.31 | 0.35 | 0.35 | 0.33 | 0.08 | — |
| ALAB | 0.39 | 0.25 | · | 0.54 | 0.48 | 0.41 | 0.31 | 0.42 | 0.34 | 0.22 | 0.21 | 0.37 | 0.30 | 0.15 | — |
| ANET | 0.38 | 0.31 | 0.54 | · | 0.50 | 0.42 | 0.38 | 0.38 | 0.31 | 0.14 | 0.18 | 0.32 | 0.19 | 0.04 | — |
| AVGO | 0.40 | 0.32 | 0.48 | 0.50 | · | 0.48 | 0.47 | 0.49 | 0.49 | 0.21 | 0.18 | 0.42 | 0.28 | 0.12 | — |
| COHR | 0.56 | 0.46 | 0.41 | 0.42 | 0.48 | · | 0.64 | 0.55 | 0.71 | 0.28 | 0.28 | 0.40 | 0.29 | 0.09 | — |
| GLW | 0.42 | 0.46 | 0.31 | 0.38 | 0.47 | 0.64 | · | 0.56 | 0.60 | 0.26 | 0.26 | 0.33 | 0.30 | 0.07 | — |
| JBL | 0.41 | 0.43 | 0.42 | 0.38 | 0.49 | 0.55 | 0.56 | · | 0.52 | 0.26 | 0.26 | 0.35 | 0.29 | 0.08 | — |
| LITE | 0.53 | 0.38 | 0.34 | 0.31 | 0.49 | 0.71 | 0.60 | 0.52 | · | 0.23 | 0.15 | 0.28 | 0.18 | 0.04 | — |
| LPTH | 0.29 | 0.31 | 0.22 | 0.14 | 0.21 | 0.28 | 0.26 | 0.26 | 0.23 | · | 0.23 | 0.16 | 0.34 | 0.07 | — |
| LWLG | 0.21 | 0.35 | 0.21 | 0.18 | 0.18 | 0.28 | 0.26 | 0.26 | 0.15 | 0.23 | · | 0.21 | 0.28 | 0.11 | — |
| MRVL | 0.32 | 0.35 | 0.37 | 0.32 | 0.42 | 0.40 | 0.33 | 0.35 | 0.28 | 0.16 | 0.21 | · | 0.25 | 0.22 | — |
| POET | 0.29 | 0.33 | 0.30 | 0.19 | 0.28 | 0.29 | 0.30 | 0.29 | 0.18 | 0.34 | 0.28 | 0.25 | · | 0.18 | — |
| QCLS | 0.00 | 0.08 | 0.15 | 0.04 | 0.12 | 0.09 | 0.07 | 0.08 | 0.04 | 0.07 | 0.11 | 0.22 | 0.18 | · | — |
| SIVE | — | — | — | — | — | — | — | — | — | — | — | — | — | — | · |
Themes are editorial groupings — narrative buckets used for technical screens. They exist alongside bubbles, which are validated empirically by capital-flow co-movement.
When a theme's avg correlation is high, the editorial story is also tradeable as a bloc. When it's low, the narrative groups stocks that don't actually move together — useful for thinking, less useful for trading.