Building the AI Optical Layer: Connectivity, Standards, and the Future of AI Infrastructure

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Building the AI Optical Layer: Connectivity, Standards, and the Future of AI Infrastructure

As AI data centers push past the limits of traditional compute architecture, the industry’s attention is moving deeper into the physical layer. GPUs, accelerators, power systems and cooling platforms still dominate the headlines, but the network fabric that connects those systems is becoming just as critical. A wave of recent announcements points to the same […]

A wave of recent announcements points to the same conclusion: future growth will depend not only on more compute, but on faster, denser, more efficient and more scalable optical connectivity.

A new multi-source agreement is bringing together major technology companies to standardize expanded beam optical connectivity for AI data centers. University of Arizona research is powering a new optical switching technology designed to reduce the energy consumed by data center networks. STL is planning to invest up to $100 million in U.S. manufacturing capacity to support AI data center and telecom customers with optical connectivity products.

Those developments are now being reinforced by a broader series of moves across the optical ecosystem: Corning’s major AI infrastructure partnerships with NVIDIA and Amazon, GlobalFoundries’ push into co-packaged optics, Sivers’ laser-array collaboration with GlobalFoundries, Wiwynn’s co-packaged optics demonstration at Computex, Credo’s acquisition of DustPhotonics, and emerging near-packaged optical interconnect designs from LightSpeed Photonics.

Taken together, these announcements highlight a maturing market around the optical layer of AI infrastructure. The value is not simply faster data movement. It is about reducing deployment complexity, lowering operating overhead, supporting higher-density clusters, improving energy efficiency and strengthening the domestic supply chain behind AI-ready networks. Let’s drill down into what these announcements mean.

Standards for the AI Optical Layer

The launch of a new coalition focused on expanded beam optical, or EBO, connectivity reflects a practical challenge facing AI deployments: as clusters grow larger and more bandwidth-intensive, physical connections become harder to deploy, maintain and scale.

3M announced that it has joined a group of technology leaders to create a multi-source agreement focused on open, interoperable specifications for EBO connectivity in AI infrastructure. The coalition includes a broad mix of companies across the networking, cloud, semiconductor, connector and optical ecosystem which, according to the group’s website, as of June 29, 2026, included 44 member companies, among them many of the industry’s best-known fiber optic and connectivity vendors.

The decision to build to a standard is important; data centers are not built by one vendor or around one component. They are assembled from servers, GPUs, switches, transceivers, fiber assemblies, connectors, cabinets, power distribution systems and software-defined network layers. As the number of optical links increases, the industry needs interoperability across that ecosystem.

Expanded beam optical technology is designed to improve the reliability and maintainability of optical links, particularly in high-density environments. Traditional multi-fiber physical contact connectors can be sensitive to contamination, inspection requirements and handling practices. In large AI clusters, even modest operational friction can multiply into significant deployment and maintenance overhead.

The EBO MSA group (Expanded Beam Optical (EBO) Multi-Source Agreement) is intended to address that issue by creating shared specifications for multiple connector configurations. The goal is not merely to create another connector type, but to establish a common framework that enables multiple vendors to build compatible products for hyperscale, cloud and enterprise AI deployments.

For data center operators, standardized EBO connectivity could reduce complexity in AI network builds, simplify procurement, improve supplier diversity and make optical cabling more resilient in dense environments. For hyperscalers, this can help speed cluster deployment. For enterprises building AI infrastructure, it can reduce the risk of being locked into a narrow ecosystem before the market has fully matured.

Optical Switching Targets Power and Heat

While the EBO coalition focuses on physical connectivity and standardization, the University of Arizona announcement addresses another pressing data center issue: the energy consumed by network switching.

The university said research developed by Pierre-Alexandre Blanche, a research professor at the University of Arizona, is powering Post Quantum Tek’s High-Speed Optical Switch, known as PQT-HOS. The announcement describes the technology as an optical switch that uses light diffraction to keep data in optical format throughout the switching process.

In many conventional data center network architectures, traffic may travel optically across fiber but then be converted into electrical signals for switching before being converted back into optical form. Post Quantum Tek’s approach is described as optical-optical-optical switching, keeping the data in light form throughout the process.

According to Post Quantum Tek and the University of Arizona announcement, the switch is capable of operating up to 1,000 times faster while consuming approximately one-thousandth the energy of conventional switching approaches. Those performance figures remain developer-reported benchmarks that will ultimately need validation through commercial deployment and independent testing, but they point directly at one of AI infrastructure’s defining challenges: ensuring the network does not become the energy bottleneck that limits compute performance.

The technology is not yet described as a fully commercialized data center product. The announcement says the PQT-HOS is patented, bench-proven and ready to be developed into a commercial prototype through the University of Arizona’s Tech Launch Arizona Institute. That places it earlier in the adoption curve than the EBO MSA or STL’s manufacturing investment.

The strategic relevance is substantial. The AI infrastructure market is now aggressively looking for ways to flatten network architectures, reduce electrical conversion points and move more data with less energy. Optical switching is one of the technologies that could reshape how future AI clusters are designed, particularly if it can be manufactured at scale, integrated with existing network architectures and proven under production workloads.

Corning and NVIDIA Put Optical Manufacturing at the Center of AI Factories

The most direct sign that optical connectivity has moved from supporting role to strategic infrastructure may be Corning’s long-term partnership with NVIDIA.

The companies announced a partnership to strengthen U.S. manufacturing for AI infrastructure, with Corning expanding U.S.-based optical connectivity manufacturing capacity by 10x and U.S. fiber production capacity by more than 50%. The expansion includes three new advanced manufacturing facilities in North Carolina and Texas and more than 3,000 new jobs.

NVIDIA has become the central supplier of accelerated computing systems for AI factories, but those systems depend on extraordinary volumes of fiber, connectivity and photonics to move data across clusters. As AI factories grow larger, the network becomes a constraint on how effectively GPUs can be used. Optical connectivity is now part of the scaling equation.

This means the value of the Corning-NVIDIA partnership is threefold:

1. Supply. AI infrastructure buildouts are now so large that a shortage of optical connectivity components can delay deployment just as surely as a shortage of power equipment, transformers or GPUs.

2. Domestic manufacturing. As hyperscale AI campuses become strategic assets, operators and chip companies are placing greater emphasis on resilient U.S.-based supply chains.

3. Optics are becoming more tightly aligned with accelerated computing roadmaps. As NVIDIA systems move toward larger AI clusters and more demanding scale-up and scale-out architectures, the fiber and photonics ecosystem must evolve in parallel.

The announcement also reinforces the idea that AI infrastructure is not just a silicon story. The value of the GPU depends on the ability to connect thousands of GPUs into functioning systems. That makes optical connectivity a foundational component of AI factory design.

Amazon and Corning Reinforce the Data Center Fiber Supply Chain

Corning isn’t limiting its AI infrastructure ambitions to NVIDIA. A separate multiyear, multibillion-dollar agreement with Amazon Web Services (AWS) extends the same strategy into the hyperscale cloud market. Under the agreement, Corning will supply optical fiber, cable and connectivity solutions supporting AWS’s expanding U.S. data center footprint, while the partnership also includes manufacturing expansion, workforce development initiatives and new domestic jobs.

Amazon’s data centers support cloud computing, AI services, enterprise workloads and consumer applications. As those data centers grow denser and more distributed, the need for high-performance optical connectivity rises across multiple layers: inside buildings, between halls, across campuses and between regional facilities. It’s a straightforward fact: cloud data center expansion now depends on fiber capacity at enormous scale.

The workforce component is also important. AI infrastructure is frequently discussed in terms of chips, power and land, but deployment depends on skilled trades and manufacturing labor. Fiber optic production, splicing, testing and installation require trained workers. By tying the agreement to workforce development, Amazon and Corning are addressing one of the less visible constraints on scaling infrastructure: the availability of trained people to build and maintain the optical layer. This issue is one that seems to be hovering over all aspects of future data center development.

Large cloud and AI deployments depend on predictable delivery schedules. Domestic fiber and connectivity supply can help reduce lead times, simplify logistics and improve confidence that network builds will keep pace with compute deployments.

STL Builds U.S. Capacity for AI Data Highways

Sterlite Technologies Ltd. (STL)’s planned investment of up to $100 million in the United States fits directly into the same supply-chain narrative. The company said the investment will strengthen manufacturing capacity for customers, including AI data center and telecom operators, and support connectivity solutions such as terminated optical fiber cables.

The company described its optical solutions as high-density “AI Data Highways” designed to link data center campuses and support massive GPU-driven demand. Which, as we are seeing is a real market need. AI campuses are increasingly being planned at scales that require high-capacity connectivity between buildings and, in some cases, between geographically distributed sites. As power availability, land constraints and grid interconnection timelines shape data center location decisions, operators will need robust optical infrastructure to connect compute resources across larger footprints.

STL’s U.S. investment also comes at a time when domestic manufacturing and supply chain resilience are increasingly important considerations for infrastructure buyers. Predictable delivery, qualified suppliers and reduced exposure to supply disruptions are at top of mind for developers and investors. More manufacturing capability closer to U.S. demand will help reduce lead times and support future deployment schedules.

GlobalFoundries Pushes Co-Packaged Optics Toward Production

GlobalFoundries’ SCALE optical module solution adds another layer to the market story: the move from optical innovation to manufacturable photonics platforms.

The company introduced SCALE, a silicon photonics co-packaged optics solution aimed at advanced AI data centers. GF positioned the platform as an OCI MSA-capable (Optical Compute Interconnect Multi-Source Agreement) solution for modern AI scale-up architectures. As shown earlier, the plans for multi-vendor consortium building to a standard are making themselves felt. This platform uses silicon photonics, wavelength-division multiplexing and advanced packaging to improve bandwidth density and scalability compared with traditional copper interconnects.

Co-packaged optics is not just a component substitution. It changes where optical conversion happens. Instead of relying primarily on pluggable optics at the faceplate, CPO brings optical engines closer to switch or compute silicon. As with other optical standard updates, the goal is to reduce electrical trace lengths, improve bandwidth density and lower the energy penalty of moving data.

GF’s value proposition is also about ecosystem readiness. AI infrastructure suppliers need manufacturable, qualified platforms, not just lab demonstrations. Foundry-backed silicon photonics platforms could help move CPO from early adoption into broader commercial deployment by giving optical module and system companies a production path.

Sivers and GlobalFoundries Target the Optical Engine Layer

Just weeks after GF’s SCALE announcement, Sivers Semiconductors and GlobalFoundries announced a collaboration to develop advanced silicon photonics solutions for the AI infrastructure market. Sivers’ laser arrays will be integrated into reference designs built on GF’s silicon photonics platform and made available for GF’s SCALE optical engine solutions.

The collaboration supports multiple optical connectivity architectures, including co-packaged optics, linear pluggable optics and other emerging data center interconnect approaches. That flexibility is important because the industry is not moving toward one single optical architecture. Different workloads, reaches, switch designs and serviceability requirements may favor different solutions.

Wiwynn’s Computex Demonstration Shows CPO Ecosystem Formation

Wiwynn’s Computex 2026 announcement adds a systems-integration perspective. The company said it would showcase co-packaged optics (CPO) interconnect technologies for hyperscale AI data centers, working with ecosystem partners including Ayar Labs, Global Unichip Corp., Browave, Corning, FOCI, Molex, SENKO and TE Connectivity.

Demonstrating partners is the key. CPO cannot scale through isolated component innovation alone. It requires coordination among server designers, silicon photonics suppliers, connector vendors, cable providers, packaging specialists and cloud infrastructure companies.

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The midsummer wave of U.S. data center development is not defined by a single market, developer or technology company. It stretches from the Georgia coast to West Texas, from the industrial Midwest to the Mississippi River. What links the projects announced since early June is not just their scale, it is the realization that scale alone is not enough. Developers are still announcing multibillion-dollar campuses and gigawatt power requirements, but the language surrounding those announcements has changed. Companies are emphasizing who will pay for new generation and transmission, how cooling systems will limit water consumption, what communities will receive beyond temporary construction employment, and when contracted customers will begin occupying capacity. In several cases, the announcement is less about acquiring land than proving that a project has become commercially and electrically credible. As we have seen progressing through the industry, the latest announcements point toward campuses that combine compute, power, financing and community agreements in one development package. OpenAI Goes Direct in Georgia OpenAI, on July 22 disclosed Project Camellia, a long-term data center development in Effingham County, Georgia. OpenAI said it is designing and developing the campus itself and has contracted with Georgia Power for 3.2 gigawatts of electricity, to be delivered in phases from 2028 through 2032. The project has been reported as a roughly $20 billion investment on approximately 1,400 acres, making it one of the largest individual data center proposals currently moving through the U.S. pipeline. Project Camellia is notable not only for its size but for OpenAI’s more direct role. The company has traditionally secured capacity through cloud providers and infrastructure partners. By taking responsibility for designing and developing the Georgia campus, OpenAI is signaling that control over power, schedule and facility design has become strategically important as AI companies compete for increasingly scarce large-scale

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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