Ayar Labs CEO Mark Wade’s claim is a forecast about scale, latency and economics—not a rule that every agentic-AI application needs optical links today. In an October 7, 2024 EE Times interview, Wade argued that increasingly interactive AI systems will eventually push large accelerator clusters beyond what copper-based electrical interconnects can deliver economically. Ayar’s own simulation supports that argument under specific assumptions, but it is not an independent benchmark of a production optical-I/O system.
The claim is about economical scaling, not the end of copper
Wade used two deliberately broad phrases: “copper is already broken” and “agentic AI will require optical I/O.” In technical terms, the first is his assessment that electrical links become an inadequate scaling technology for particular high-bandwidth, low-latency AI architectures. It does not mean copper has stopped working or is unsuitable for ordinary servers.
The second is a projection. As models grow, more accelerators cooperate on each request, and agents make several dependent model calls, the cost of moving data can become more important than the cost of performing arithmetic. Wade’s argument is that optical connectivity will be needed in some of those systems to preserve responsiveness and acceptable economics.
The interview and the underlying analysis came from Ayar Labs, a company developing optical-I/O chiplets and light sources. The public evidence therefore establishes a serious engineering thesis and a vendor forecast, not a settled industry requirement.
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Why agentic workloads make interconnect latency visible
A conventional chatbot may invoke a model once and stream an answer. An agentic workflow can create a chain such as:
- The user request is received.
- An agent calls retrieval or another tool.
- A second model checks or critiques the result.
- A planner delegates subtasks to other model instances.
- The system synchronizes those outputs and generates a final response.
Some of these operations can run in parallel, but serial dependencies remain. A delay between model calls, or a long pause while accelerators exchange activations and key-value-cache data, directly affects the user’s experience and the time needed to complete a task.
That is why Ayar’s “interactivity” metric is not simply total tokens per second. As EE Times reported, the metric is tied to token-generation speed and whether a system can support machine-to-machine or agentic applications successfully. The relevant measures include latency, tail latency, predictable token delivery and cost per completed task, alongside aggregate throughput.
Not every agent is interactive in this strict sense. A background research job that can take minutes has a different requirement from a real-time assistant, robotics controller or high-volume API service. Optical I/O is most compelling where communication delay directly limits task completion or revenue.
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The underlying problem is data movement
AI accelerators can perform enormous numbers of matrix and tensor operations, but inference constantly moves weights, activations, KV-cache entries and control messages. When communication is slower than computation, devices wait. Adding GPUs then produces diminishing returns rather than proportional performance.
The pressure increases when a model is partitioned across many accelerators, when memory is pooled or disaggregated, or when several agents exchange intermediate results. A system optimized for maximum offline batch throughput may therefore be a poor design for low-latency, multi-step requests.
Ayar says its optical-I/O architecture is intended to make distributed compute resources behave more like one larger accelerator by increasing bandwidth density and reducing interconnect power and latency. That is the company’s description of the design objective, not proof that every workload will receive those benefits.
What copper limits as systems grow
Electrical SerDes and copper remain the default for many short, inexpensive links. Their constraints become more severe as data rates, distances and connector counts rise:
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- Electrical signals lose quality over traces and cables, requiring equalization, retimers and sometimes forward-error correction.
- Those circuits consume power, add latency and occupy package, board and rack space.
- Reach limits how many accelerators can share a tightly coupled scale-up domain.
- Longer electrical paths make signal integrity and thermal design harder at higher lane rates.
Pluggable optical transceivers solve some reach problems, but they place conversion farther from the compute package and add modules, connectors and power overhead. The choice is not copper versus light in the abstract; it is a system trade-off involving distance, density, serviceability, cost and workload sensitivity.
What Ayar’s optical-I/O architecture does
Optical I/O converts data to light close to the accelerator rather than sending the entire high-speed signal electrically across a board or cable. In Ayar’s design, a silicon-photonics TeraPHY optical-I/O chiplet connects to a host package through a UCIe electrical interface. A separate SuperNova multi-wavelength light source supplies the optical carriers.
Separating the light source from the compute package is intended to improve serviceability: a laser subsystem can be monitored or replaced without replacing the accelerator package. It also creates another active subsystem with its own reliability, fiber-coupling, thermal and maintenance requirements.
This approach is related to co-packaged optics, but the terms are not interchangeable. A meaningful comparison must specify where conversion occurs, whether the link is package-to-package or rack-scale, how the laser is arranged, and which standards and software topologies are supported.
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What Ayar’s simulator actually modeled
According to the interview, Ayar’s tool was a collection of Python modules—more detailed than a spreadsheet, but not an RTL or cycle-accurate simulator. Inputs included workload characteristics, compute, memory capacity and bandwidth, fabric parameters, latency, component costs, power and other economic assumptions.
Its reported outputs were:
- Throughput: users supported at a specified interactivity level.
- Interactivity: token-generation speed or responsiveness.
- Profitability: a comparative systems-economics measure incorporating cost, power and supported service.
That last metric is not a guarantee that an operator or AI company will make money. Results depend on hardware prices, utilization, electricity, software efficiency, model mix and assumed revenue. The public interview does not establish whether the model included every cost of optical packaging, cooling, qualification, maintenance or laser replacement.
What the modeled scenarios showed
| Scenario or figure | What was reported | Evidence status |
|---|---|---|
| Baseline | Nvidia GB200 system | Simulation input reported by Ayar |
| Theoretical next-generation accelerator | About 2.4× compute, 1.5× memory capacity, 1.25× memory bandwidth and 2× scale-up I/O versus the baseline | Modeled configuration |
| Same system scale | Roughly 30%–40% higher throughput, but no modeled profitability improvement | Simulation output |
| Current-generation scale | Up to 64 accelerators, described as approximately one rack under the assumptions | Model boundary, not a universal rack limit |
| GPT-4 scenario | Higher agentic-interactivity levels required larger systems and optical I/O in the model | Company projection |
| Hypothetical 14-trillion-parameter model | A 64-GPU system could not reach the modeled interactivity threshold | Projection, not a measurement of a deployed model |
The simulation also indicated diminishing returns beyond one rack for single-user inference speed when copper interconnect limitations constrained communication. None of these results independently proves that an optical system will deliver the same performance or economics in production.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the public product material says
Ayar currently lists TeraPHY specifications as preliminary and subject to change. The product page states up to 8 Tbps bidirectional bandwidth, 10 ns latency per chiplet excluding optical time of flight, a bit-error rate below 10−12, reach from millimeters to kilometers, eight full-duplex optical ports and up to 512 Gbps per port. It describes 16 WDM transceiver slices per port, a UCIe electrical interface and an NRZ implementation without forward-error correction: Ayar TeraPHY specifications.
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SuperNova is described as an external light source with up to 16 wavelengths, 16 ports, 256 optical data channels and up to 16 Tbps bidirectional bandwidth, with CW-WDM MSA compliance and a field-replaceable positioning: Ayar SuperNova specifications. Ayar also publishes vendor comparisons claiming 5×–10× higher bandwidth, 10× lower latency and 4×–8× better power efficiency than traditional pluggable-optics-plus-electrical-SerDes links. Those are company claims, not independent results across all systems.
At SC24, Ayar reported a demonstration of 4 Tbps bidirectional optical I/O, latency below 10 ns and below 5 pJ/bit, described as roughly 10 W, with error-free transfer without forward-error correction: SC24 demonstration details. The 4 Tbps demonstration, 8 Tbps TeraPHY figure and 16 Tbps SuperNova capacity refer to different products or configurations and should not be combined into one universal link specification.
In March 2025, Ayar announced what it called the first UCIe optical interconnect chiplet for AI scale-up and repeated an 8 Tbps TeraPHY claim: announcement. Its 2026 OFC material describes a rack-scale demonstration with Wiwynn, while the company’s homepage discusses designs involving thousands of GPU connections. Demonstrations and partner announcements show technical progress; they do not establish broad, high-volume commercial deployment: OFC material.
Where optical I/O fits among the alternatives
| Approach | Strength | Limitation | Best fit |
|---|---|---|---|
| Electrical SerDes and copper | Established supply and software ecosystems | Power, reach and signal-integrity limits | Short links and current platforms |
| Pluggable optics | Long reach and field replacement | Conversion and module power overhead | Data-center networking |
| Optical-I/O chiplets | Dense bandwidth close to the package | Advanced packaging and ecosystem maturity | AI scale-up and disaggregated memory |
| Co-packaged optics | Short electrical paths and dense optical connectivity | Thermal and service complexity | Switches, accelerators and rack fabrics |
| Model and software optimization | Can reduce communication demand without new optics | Cannot remove physical limits entirely | Every architecture |
Designers can also delay the need for optical links with larger local memory, higher-bandwidth HBM, quantization, sparsity, KV-cache compression, better collective-communication libraries, hierarchical fabrics and improved model partitioning. Optical bandwidth cannot fix poor placement, inefficient synchronization or a workload dominated by serial computation.
When optical I/O is—and is not—the sensible choice
Cases that strengthen the argument
- Many accelerators must exchange data at high bandwidth and low latency.
- Links must extend across a package, board, rack or multiple racks.
- Power per transmitted bit and rack density are hard constraints.
- Memory is pooled or disaggregated.
- Expensive accelerators need high utilization and predictable tail latency.
- The architecture is expected to expand beyond one tightly coupled rack.
Cases where electrical links may remain preferable
- The system is small, short-reach or bandwidth-moderate.
- The workload is offline or tolerant of seconds or minutes of delay.
- Existing electrical scale-up already meets the service target.
- Cost, supply-chain simplicity and platform maturity outweigh ultimate density.
- The organization cannot yet absorb advanced packaging, laser or service requirements.
What remains unproven
No public independent reproduction of Ayar’s economic simulation establishes that every agentic system needs optical links. The conclusion is sensitive to model size, interactivity targets, utilization, GPU and optical-component prices, electricity, software efficiency, HBM improvements and future electrical-link advances. “Agentic” also covers workloads with very different latency requirements.
Optical I/O shifts rather than eliminates engineering problems. Drivers, modulators, detectors, lasers, cooling, packaging, control electronics, link training, reliability and software topology still matter. Standards such as UCIe can help interoperability, but they do not make packaging, firmware and thermal designs plug-and-play.
Bottom line for infrastructure planners
Optical I/O is likely to become increasingly important when AI systems must connect large numbers of accelerators with high bandwidth, low latency and strict power limits. Wade’s forecast is plausible as a conditional statement about those architectures, and Ayar’s modeling illustrates the pressure. It is not proof that all agentic AI requires optics now, nor that optical links guarantee profitability. The right decision depends on the model, interactivity target, topology, utilization and complete system cost.
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