For most of the past decade, the standard framing of silicon photonics for data center interconnects has been "promising technology, commercialization perpetually deferred." The list of capabilities promised and underdelivered is long: silicon-native light sources, monolithic integration of modulator and detector arrays, cost parity with VCSEL-based copper solutions at scale. Each generation of silicon photonics modules has closed some gaps while exposing new ones. The result has been a long plateau of early adoption in high-end coherent telecom links and limited penetration into the hyperscale data center market that matters for AI infrastructure.
The situation is changing, and the change is driven by a specific external pressure: the bandwidth requirements of large-scale AI training clusters have grown faster than copper interconnect technology can track. The relevant constraint is not the peak throughput of a single link — copper SerDes continues to improve — it is the aggregate bandwidth density achievable in a multi-rack AI pod when power and physical cabling are factored in. Silicon photonics addresses a combination of constraints that copper cannot, and several companies in the Coexin portfolio are in production with interconnect products that prove the commercialization thesis is no longer aspirational.
Why AI Bandwidth Requirements Are Different
A large AI training cluster — say, 4,000 accelerators — has a very different interconnect requirement from a general-purpose data center of comparable compute. The communication pattern is dominated by the all-reduce and all-to-all operations that synchronize gradient updates across the entire cluster at every training step. For a cluster with a communication collective throughput requirement of 400 Gb/s per accelerator, 4,000 nodes implies approximately 1.6 Pb/s of total cluster-internal bandwidth — much of it crossing rack-to-rack distances of 5 to 30 meters.
At those distances and that density, high-speed electrical SerDes faces two compounding problems. The first is signal integrity: at 112 Gb/s per lane on a PCIe-length trace, equalizer power consumption grows rapidly, and at 224 Gb/s the front-end receiver power is approaching the same order of magnitude as the digital processing behind it. The second is physical density: a high-radix electrical switch requires copper cables or PCIe traces between every port, and the cable mass and bend radius of 1,600 ports of 4×100G copper in a single rack becomes a thermal and physical management problem before it becomes an electrical one.
Optical fiber has essentially zero distance-dependent loss at relevant cluster-internal distances, and photonic transmitters at 400G per lane consume substantially less power than the high-gain electrical equalizers they replace at those reach targets. The power and density argument for optical interconnects in large AI clusters is not theoretical — it is observable in current cluster designs, where the fraction of total rack power consumed by networking has become a meaningful fraction of the total.
What Silicon Photonics Adds Over Conventional Optical
VCSEL-based multimode optical modules have served short-reach data center interconnects effectively for two decades. The question is not whether optics are better than copper at data center distances — they are, and have been for some time — but whether silicon photonics specifically offers advantages over conventional III-V-based optical modules.
The silicon photonics case rests on three differentiators. The first is co-packaging potential: silicon photonic transceivers can in principle be manufactured on the same foundry lines as silicon CMOS, enabling co-packaged optics (CPO) architectures in which the optical transceiver is mounted directly on the same package as the switch ASIC. This eliminates the electrical SerDes channel between ASIC and pluggable module, which is where signal integrity loss accumulates at 224G+ speeds. The thermal, power, and signal quality benefits of CPO are well-documented; the manufacturing challenge is integrating photonics and CMOS in a single package at the yield economics that make volume production viable.
The second differentiator is wavelength-division multiplexing (WDM) density. Silicon photonics platforms can support dense WDM on a single fiber using ring resonators or arrayed waveguide gratings fabricated in the silicon layer, multiplexing many wavelengths onto a single waveguide. This enables a physical layer with many times the bandwidth of a single-wavelength transceiver in the same footprint — relevant for the high-radix switch ports where physical connector density constrains total bandwidth.
The third is cost trajectory. Silicon photonics benefits from CMOS foundry economies of scale in a way that discrete III-V components do not. As wafer volumes increase, the per-unit cost of silicon photonic components should track the semiconductor cost learning curve. The current price premium of silicon photonics over conventional optical is a function of early-stage manufacturing volume, not a fundamental material cost constraint.
Lightmatter's Passage interconnect fabric, a Coexin Fund II portfolio investment, is the clearest current demonstration that the CPO path is commercially executable. Passage uses silicon photonic waveguides to connect chiplets on a photonic interposer, moving data between compute tiles at optical speeds without the energy penalty of repeated electrical signal regeneration.
The Remaining Engineering Gaps
The narrative that silicon photonics for AI interconnects has "arrived" is accurate at the high end of the market — flagship AI training clusters with aggressive power and density constraints. It overstates the maturity for the broad market. The engineering gaps that remain are well-understood; the question is timeline and capital intensity to close them.
The most significant remaining gap is the silicon light source problem. Silicon is an indirect bandgap semiconductor and is an inefficient light emitter. Every practical silicon photonics transceiver today uses an external III-V laser (typically InP-based) that is edge-coupled or evanescently coupled to the silicon waveguide layer. This external laser adds cost, creates a coupling loss budget, and introduces a thermal management problem — the laser is typically the highest-temperature component and must be temperature-controlled to maintain wavelength stability. The long-term solution — a silicon-compatible gain medium, whether via germanium-tin, wafer-bonded III-V, or quantum dot epitaxy — is actively researched but not yet manufacturable at commercial scale.
The second gap is packaging yield for co-packaged optics. The flip-chip bonding process that attaches a silicon photonic die to a switch ASIC package must achieve optical alignment tolerances of roughly 0.5 micrometers over the full array of edge couplers or grating couplers on the photonic die. This is achievable with active alignment, but active alignment is slow and expensive. Passive alignment solutions that meet the tolerance budget at production throughput rates are still being refined. Until packaging yield for CPO reaches the same level as conventional pluggable transceiver assembly, the cost model for CPO remains unfavorable for all but the highest-performance applications.
The third gap is reliability and qualification. Data center operators have strict reliability requirements for installed hardware: multi-year mean time between failures, humidity and vibration tolerance, accelerated aging validation. Silicon photonics transceivers have a shorter qualification history than conventional optical modules, and the failure modes of ring resonators, grating couplers, and thermo-optic phase shifters under long-term thermal cycling are less well-characterized than for mature III-V components. Qualification cycles for new module designs run 18 to 36 months, creating a structural lag between engineering readiness and volume deployment.
The Investment Implication
The gap between demo and data center is not a physics gap — the physics of silicon photonics interconnects for AI clusters is well understood. It is a manufacturing and qualification gap, and those gaps close with capital and time in ways that physics gaps do not. The companies that have survived to this point in the silicon photonics ecosystem — having navigated a decade of early-adoption cycles and near-death experiences as successive waves of copper improvement pushed out the commercial crossover — have accumulated process knowledge and customer relationships that represent a genuine moat.
The pattern that Coexin looks for in photonic interconnect investments is not a novel device architecture but a credible path to production economics: a process integration team with foundry relationships at the right nodes, a packaging roadmap that addresses the alignment tolerance problem, and a customer pipeline that provides the volume to drive down unit costs. The technology is mature enough that the risk is now industrial, not scientific. That is exactly the stage at which deep-tech infrastructure investments tend to compound most reliably.
This memo represents the views of the author. It does not constitute investment advice or a recommendation to buy or sell any security. References to portfolio companies are for illustrative purposes only.