A Massachusetts startup advances scalable light-control tech for AR, AI and imaging markets
Updated
February 27, 2026 3:59 PM
Myrias Optics' Nanoimprinted All-inorganic Metaoptic. PHOTO: MYRIAS OPTICS
Myrias Optics, a Massachusetts-based optical technology startup, has raised US$2.1 million in a Seed 1 financing round to accelerate the commercialization of its advanced light-control technology. The round was led by MassVentures, with participation from existing investors Hoss Investment Inc., Maroon Venture Partners and Tenon Venture Partners, as well as new investors Mill Town Capital, TiE Boston Angels and Doug Crane. This new round follows a US$3.3 million seed financing completed in December 2023, led by Asia Optical, and a US$1.5 million Direct-to-Phase II award from the National Science Foundation. In total, Myrias has secured US$6.9 million to date, positioning it to move from development to scaled production.
The company builds ultra-thin, nano-patterned surfaces that precisely control how light moves through a device. These structures replace or enhance traditional lenses and optical parts inside products such as augmented reality headsets, AI data center hardware, consumer electronics, industrial systems and medical imaging devices. The goal is straightforward: to deliver high optical performance while making the parts easier and more cost-effective to manufacture in large quantities.
Across industries such as augmented reality and AI infrastructure, manufacturers face a common challenge. They need highly precise light-guiding components that can withstand heat and long-term use. At the same time, those components must be produced consistently and at scale. Traditional semiconductor-style fabrication can be costly, while polymer-based optical manufacturing can face limits in durability and thermal stability.
Myrias addresses this gap by using inorganic materials and a nanoimprint manufacturing process to create stable, repeatable optical layers on wafers. This approach is designed to combine performance with manufacturability. In augmented reality systems, for example, the company’s technology enables higher viewing angles while remaining suitable for volume production. In AI data centers, the same material and process advantages support improved light transfer and stronger performance under demanding thermal conditions. These benefits also extend to advanced imaging systems in consumer, industrial and medical markets.
The new Seed 1 funding is intended to expand manufacturing capacity and scale pilot production lines. The company will also continue executing active customer programs. Myrias is already working with strategic partners and Tier 1 supply chain participants to integrate its waveguide and light-shaping solutions into commercial AR platforms, AI photonics systems and advanced imaging products. The capital, therefore, supports a clear next step: moving from validated prototypes to a steady commercial supply.
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The hidden cost of scaling AI: infrastructure, energy, and the push for liquid cooling.
Updated
January 8, 2026 6:31 PM
The inside of a data centre, with rows of server racks. PHOTO: FREEPIK
As artificial intelligence models grow larger and more demanding, the quiet pressure point isn’t the algorithms themselves—it’s the AI infrastructure that has to run them. Training and deploying modern AI models now requires enormous amounts of computing power, which creates a different kind of challenge: heat, energy use and space inside data centers. This is the context in which Supermicro and NVIDIA’s collaboration on AI infrastructure begins to matter.
Supermicro designs and builds large-scale computing systems for data centers. It has now expanded its support for NVIDIA’s Blackwell generation of AI chips with new liquid-cooled server platforms built around the NVIDIA HGX B300. The announcement isn’t just about faster hardware. It reflects a broader effort to rethink how AI data center infrastructure is built as facilities strain under rising power and cooling demands.
At a basic level, the systems are designed to pack more AI chips into less space while using less energy to keep them running. Instead of relying mainly on air cooling—fans, chillers and large amounts of electricity, these liquid-cooled AI servers circulate liquid directly across critical components. That approach removes heat more efficiently, allowing servers to run denser AI workloads without overheating or wasting energy.
Why does that matter outside a data center? Because AI doesn’t scale in isolation. As models become more complex, the cost of running them rises quickly, not just in hardware budgets, but in electricity use, water consumption and physical footprint. Traditional air-cooling methods are increasingly becoming a bottleneck, limiting how far AI systems can grow before energy and infrastructure costs spiral.
This is where the Supermicro–NVIDIA partnership fits in. NVIDIA supplies the computing engines—the Blackwell-based GPUs designed to handle massive AI workloads. Supermicro focuses on how those chips are deployed in the real world: how many GPUs can fit in a rack, how they are cooled, how quickly systems can be assembled and how reliably they can operate at scale in modern data centers. Together, the goal is to make high-density AI computing more practical, not just more powerful.
The new liquid-cooled designs are aimed at hyperscale data centers and so-called AI factories—facilities built specifically to train and run large AI models continuously. By increasing GPU density per rack and removing most of the heat through liquid cooling, these systems aim to ease a growing tension in the AI boom: the need for more computers without an equally dramatic rise in energy waste.
Just as important is speed. Large organizations don’t want to spend months stitching together custom AI infrastructure. Supermicro’s approach packages compute, networking and cooling into pre-validated data center building blocks that can be deployed faster. In a world where AI capabilities are advancing rapidly, time to deployment can matter as much as raw performance.
Stepping back, this development says less about one product launch and more about a shift in priorities across the AI industry. The next phase of AI growth isn’t only about smarter models—it’s about whether the physical infrastructure powering AI can scale responsibly. Efficiency, power use and sustainability are becoming as critical as speed.