Vladislav Zaimov is a seasoned telecommunications specialist whose career has centered on the resilience and strategic management of enterprise networks. With the explosive rise of AI-driven demand, his insights into the physical and logical layers of connectivity have become essential for understanding how we build the infrastructure of the digital age. In this conversation, we explore the radical transition from localized data centers to the “scale-across” era, where optical networks are no longer just pipes but the very fabric of the compute environment itself. We discuss the looming physical limits of fiber optics, the geographical shifts forced by power shortages, and the innovations allowing us to link hundreds of thousands of GPUs across vast distances to ensure distributed resources function as one.
As AI clusters reach unprecedented sizes, we are seeing a shift where compute power is no longer confined to a single building. How is this scale-across architecture fundamentally changing the way you approach network design and reliability?
The transition we are witnessing is nothing short of a paradigm shift because the network is no longer just a peripheral service; it is now an integrated component of the AI compute fabric. As we push toward clusters that house 1 million GPUs by the end of 2026, the sheer physical and energy footprint makes it impossible to contain everything within a single facility. We are moving from a scale-up mentality to a scale-across reality where GPUs distributed over hundreds or even thousands of kilometers must perform as if they were sitting on the same rack. This requires us to rethink latency and synchronization, treating the long-haul optical transport as a direct extension of the internal data center architecture. It is a high-stakes environment where any hiccup in the wide area network can stall a massive training job, making the reliability of these sprawling architectures the top priority for any enterprise operator.
We are reaching the theoretical limits of what a single fiber pair can carry. In an era of insatiable bandwidth demand, what strategies are becoming most effective for scaling capacity without simply laying an infinite amount of new glass?
We are indeed staring down the barrel of the laws of physics, specifically the Shannon limit, which constrains how much data we can cram into a single strand of fiber. Because of this, the industry is pivoting away from the obsession with single-fiber records and focusing instead on multi-fiber architectures and operational efficiency. We are deploying technologies like coherent pluggables and sophisticated multi-fiber amplifiers that allow us to scale capacity horizontally while keeping a tight lid on power and space requirements. There is a lot of excitement around hollow-core fiber, which could potentially expand the transmission spectrum and capacity per fiber, but it is still far from wide-scale commercial deployment. For the moment, the goal is to make the management of multi-fiber networks so simple and automated that the complexity of the physical layer does not slow down the growth of the AI infrastructure.
It seems that the search for power is now the primary driver for where we build infrastructure, rather than where the fiber already exists. How does this geographical disruption impact the strategy for long-distance optical connectivity?
Power availability is now the ultimate gatekeeper for AI expansion, dictating the map of our industry more than any other factor. We are seeing a trend where new data centers are being constructed in geographically remote areas simply because they have the necessary electrical capacity, even if they are far from population centers or existing fiber backbones. This forces us into a massive buildout cycle where we have to design and deploy long-distance optical connectivity to these power-first locations from scratch. It is a double-edged sword; while it creates a significant logistical challenge, it also gives us a blank canvas to build modern, optimized networks using the latest generation of energy-efficient amplifiers and fiber types. We are not just patching old networks anymore; we are mapping a new digital geography defined by the proximity to the power grid rather than historical infrastructure.
When we talk about clusters exceeding 500,000 GPUs, the networking requirements spill out from the data center into the wide area network. What does this convergence mean for the collaboration between cloud providers and network infrastructure suppliers?
The lines between the data center team and the carrier team have completely blurred in this new era. When you have 500,000 GPUs working on a single problem, the connectivity that was once considered external is now a core part of the compute architecture required to operate the environment. This has forced a much closer, almost symbiotic cooperation between AI providers and infrastructure suppliers to ensure that the optical layer can handle the bursty, high-intensity traffic patterns typical of AI training. We are designing systems where the optical transport is effectively application-aware, capable of adapting to the specific needs of the distributed GPU cluster it serves. This convergence ensures that the geographically dispersed resources function as a single, coherent machine, which is the only way to meet the processing demands of our current year.
What is your forecast for the evolution of distributed AI networks?
Looking ahead through the end of 2026 and beyond, I expect we will see the total normalization of the network-as-a-backplane concept, where distance becomes almost irrelevant to AI performance. We will see 1 million-GPU clusters become the standard for foundational model training, and these will be supported by highly automated, multi-fiber optical meshes that self-heal and reconfigure in real-time. The traditional concept of a standalone data center will fade, replaced by compute regions where the fiber connectivity is just as dense and integrated as the copper traces on a motherboard. Power will remain the primary constraint, leading to even more creative distribution of hardware across continents, but our optical technologies will bridge those gaps so seamlessly that the end-user will never know their AI was trained across three different time zones. We are building a global computer, and the optical network has officially become its nervous system.
