For years, copper has been the unseen backbone of AI's explosive growth, carrying data between and within the chips that power large language models and other compute-intensive workloads. But as AI models balloon in size and complexity, the limits of copper-based interconnects are becoming impossible to ignore—and by 2026, its dominance is starting to fray.
The Scaling Wall
AI scaling has historically followed a predictable path: more data, larger models, and exponentially more compute. But this trajectory has collided head-on with the physics of copper. In traditional data centers, copper cables—used for both power and data transmission—are hitting fundamental constraints: signal degradation over distance, energy losses, and electromagnetic interference. Above 50-100 Gbps per lane, copper's effectiveness plummets, forcing system designers to adopt active cables or shorter reach links, both of which add cost and complexity.
In advanced packaging, copper interconnects within a chip or between chiplets face a similar wall. As transistor densities increase and dies shrink, copper vias and traces become parasitic—they introduce resistance and capacitance that slow signals and generate heat. At the 2nm node and beyond, copper's resistivity rises sharply due to grain boundary and surface scattering effects, making it less attractive for critical signal paths.
The Shift to Optical and New Materials
By 2026, the industry is actively pivoting to alternatives. In data centers, optical interconnects—using silicon photonics and co-packaged optics—are moving from niche to mainstream. Optical links can carry data at Terabit speeds over distances where copper simply cannot compete, and they consume less power per bit, a critical advantage in facilities already struggling with energy budgets. Hyperscalers and AI cloud providers are now deploying optical transceivers at the rack level, and some are even experimenting with optical backplanes to replace copper-based PCBs.
At the chip level, the search for copper replacements is accelerating. Ruthenium, cobalt, and molybdenum are being explored as interconnect metals for the most demanding layers, offering better electromigration resistance and lower resistivity at nanoscale dimensions. Meanwhile, carbon nanotubes (CNTs) and graphene are being researched for their exceptional conductivity and thermal properties, though manufacturability remains a challenge.
Chiplets and Advanced Packaging
The rise of chiplets—small, modular dies assembled into larger packages—is another force loosening copper's grip. In multi-die systems, the interconnect between chiplets is the performance bottleneck. While copper-based interposers and bridges (like Intel's EMIB or TSMC's CoWoS) remain prevalent, they are increasingly paired with optical I/O to handle the most demanding traffic. In 2026, we're seeing the first hybrid packages that use both copper for power delivery and optical for data transfer, marking a significant architectural shift.
The Investment and Competitive Landscape
This transition is not happening in a vacuum. Major semiconductor and networking companies—including AMD, Intel, NVIDIA, and Marvell—are investing heavily in optical I/O and advanced packaging R&D. Startups like Ayar Labs and Lightmatter have garnered significant funding for their optical interconnect technologies. At the same time, the copper supply chain is being jolted by price volatility and geopolitical concerns over mining, further nudging the industry toward alternatives.
But copper is far from obsolete. It remains the most cost-effective solution for many short-reach, low-speed applications, and its embedded infrastructure is vast. The shift is evolutionary, not revolutionary—copper will coexist with optics and novel metals for at least the next decade. However, the momentum is unmistakable: copper's grip on AI scaling is slipping, and the industry is racing to build the interconnect stack for the next era of computing.
Looking Ahead
As AI models continue to scale toward trillion-parameter frameworks and real-time inference becomes a priority, the interconnect bottleneck will only become more critical. The winners of the AI race will be those who can master the new physics of data movement—whether through optical, novel conductors, or hybrid approaches. Copper's legacy is secure, but its future role is narrowing. The next chapter of AI scaling will be written not in copper, but in light and new materials.