Michael Murray, Chairman, President and CEO of Kopin Corporation, inspects a microdisplay
Kopin Corporation
On July 14, New York State Governor Kathy Hochul signed an executive order barring the construction of new “hyperscaler” data centers using 50 megawatts or more of power for up to one year. The move comes at a fraught time for data center construction, which faces widespread opposition – fourteen state legislatures across the country have introduced bills restricting new data center construction, none of which, to this point, have been signed into law. A new wrinkle in this debate was exposed when the New York Times revealed that some social media posts decrying data centers have come from Russia, China, and Iran, and contain misleading claims.
Without continued investment in AI infrastructure, or meaningful improvements in efficiency, the pace of AI deployment could slow significantly. This raises issues of both global competitiveness, particularly against China, and the state of the stock market, which has become increasingly reliant on a handful of big tech companies. But there is an emerging solution that could potentially satisfy both parties – build the data centers, but build them smarter.
One company betting on that future is Kopin. Based in Westborough, Massachusetts, the electronics manufacturer is best known for its microdisplay devices and application specific optical solutions for defense, enterprise, medical and consumer mobile electronics., One of its products is Neural I/o™ (co-developed with Fabric.AI), a MicroLED‑based optical interconnect technology designed to replace traditional copper and laser systems.
Rather than simply adding more GPUs or building larger facilities, Kopin's Neural I/o™ technology redesigns how data flows through AI systems. Instead of relying on conventional electronic connections, Neural I/o™ uses optical interconnects to move data more efficiently between processors and memory. The result is higher bandwidth with significantly lower power consumption, while also reducing the heat generated by AI workloads. Those interested in learning more can read a recently released white paper.
The implications extend well beyond the walls of a single data center. Every percentage point of efficiency compounds across thousands of servers and millions of AI requests each day. Lower energy consumption reduces operating costs, decreases cooling requirements and allows providers to deploy more computing power without proportionally increasing their physical footprint. For cloud providers investing billions of dollars in AI infrastructure, even modest efficiency gains can translate into substantial operational savings. This also addresses one of the central concerns cited by opponents to new data center construction, many of whom have seen their electricity bills rise dramatically.
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“As demand for AI infrastructure accelerates, companies that control critical enabling technologies will define the next era of innovation. Our investment in Neural I/o™, MicroLEDs and AI-enabled architectures positions us to help solve infrastructure challenges that will shape AI for years to come.” says Michael Murray, Chairman, President and CEO, Kopin Corporation.
More efficient data centers won’t just benefit the communities where they are built – they’ll also bring the costs down for the use of AI more broadly. Already, companies that have gone all in on AI are starting to raise concerns about token usage; Uber, for example, announced it had blown through its entire 2026 token budget in only four months and was planning to meter usage going forward. This is not just a pain for companies that have found themselves overspending on AI, but could cause real issues for the upcoming OpenAI IPO, which could potentially be pushed to next year, and the Anthropic IPO, which could happen in the fall. Both companies have thousands of employees and have raised billions in funding, including funding from companies like Microsoft and Nvidia. Either or both of these firms running out of road would have massive implications far beyond the tech sector.
And for all of the AI use cases that are derided – creating “slop” images for a flyer, “writing” a LinkedIn post – there are many more that have real benefit and impact. AI models are being used to analyze chest X-rays to detect early signs of tuberculosis and screen for diabetic retinopathy, for instance, allowing community workers in rural areas to provide preliminary diagnoses without specialized doctors present. Losing that capacity would have a profound negative impact on vulnerable populations.
The debate over data centers is unlikely to be cleanly resolved anytime soon. Communities will continue asking questions about energy use, water consumption and local impacts, while governments grapple with how to support AI innovation without overwhelming existing infrastructure. But the next chapter of AI may be defined not by who builds the largest data centers, but by who builds the smartest ones. Companies that can deliver more intelligence with less energy will not only lower costs; they may also help preserve the public support needed for AI's continued growth.
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