The telecommunications landscape is currently standing on the precipice of its most significant architectural shift in decades, moving away from rigid hardware-centric models toward a fluid, AI-native future. Vladislav Zaimov brings a wealth of experience to this discussion, having spent years navigating the complexities of enterprise telecommunications and managing the inherent risks of vulnerable network infrastructures. As the industry grapples with the transition to 5G-Advanced and looks toward the 6G horizon, the collaboration between traditional network giants and silicon innovators signals a fundamental change in how we think about connectivity. This conversation explores the strategic pivot toward AI-RAN, examining how software-driven upgrades and accelerated computing are set to redefine spectral efficiency and network economics.
The following discussion delves into the upcoming pilot phases for AI-native infrastructure, the staggering projections for spectral efficiency gains by 2028, and the diverse migration paths available for global operators. We also touch upon the shift from traditional hardware replacement cycles to flexible software subscription models and what this means for a market forecasted to reach billions in cumulative revenue.
The shift toward AI-integrated networks seems to be accelerating, but many are curious about the immediate roadmap for these technologies. When can we expect to see the first real-world pilots of the AI-RAN platform, and what will the transition to general availability look like for global operators?
The momentum we are seeing is truly remarkable, and the timeline is much tighter than many industry veterans initially anticipated. We are looking at pilot deployments for the AI-RAN platform starting as early as the end of 2026, which will serve as a critical testing ground for these intelligent architectures. This leads directly into a general release scheduled for 2027, marking a pivotal moment where the theoretical potential of AI meets the practical demands of global traffic. It feels like a generational shift, almost as if we are watching the radio access network transform into a planet-scale AI computer right before our eyes. For operators, this means moving beyond the “uncertainty” that has plagued recent earnings calls and finally placing concrete bets on the next era of infrastructure.
One of the most striking claims regarding this new architecture involves a massive jump in how we utilize our existing airwaves. Could you explain the milestones for spectral efficiency gains and what it means for a carrier to reach a 100 percent improvement by 2028?
The promise of doubling capacity without needing to acquire new, expensive spectrum is the “holy grail” of this industry, and the roadmap to get there is quite aggressive. Currently, we have already seen AI-driven radio innovations deliver more than 20 percent spectral efficiency gains in controlled environments. As we move toward the full launch, that number is expected to climb to 50 percent, eventually surpassing the 100 percent mark by 2028. For a telco, this isn’t just a marginal improvement; it is the equivalent of more-than-doubling the capacity of their existing assets through software intelligence. It creates a sense of breathing room in dense urban environments where spectrum is at a premium, allowing for better returns and a much faster delivery of data-heavy services.
Operators are often hesitant to rip and replace their existing infrastructure due to the massive capital expenditures involved. How does the current strategy provide flexibility through different deployment models, particularly for those already using traditional baseband systems?
The beauty of this transition lies in its modularity, offering three distinct paths that respect the reality of existing investments. For those with a large footprint of AirScale systems, particularly in Europe and Asia, the transition can be as simple as adding an accelerator card or utilizing merchant baseband silicon from partners like Marvell. There is also a standalone AI-RAN node option which handles 4G, 5G, and even future 6G setups, effectively removing the need for a total baseband overhaul. For the more forward-thinking carriers, a cloud-native deployment using commercial off-the-shelf, or COTS, servers is available, which aligns perfectly with the movement toward open and virtualized networks. This variety of choices ensures that whether an operator wants a simple plug-in or a fully “AI-native” public network extension, there is a technical route that doesn’t require a catastrophic financial “rip and replace” scenario.
There is a significant conversation happening around the move from hardware-heavy upgrades to software-defined networks. How will the introduction of subscription-based AI features change the way telcos manage their long-term costs and network evolution?
We are witnessing a fundamental move away from the traditional cycle of expensive, decade-long hardware refreshes toward a more agile, software-driven subscription model. By offering AI features and enhancements as a subscription, vendors are allowing telcos to unlock greater performance from the spectrum they already own without the physical labor of replacing radio units. This creates a much smoother software upgrade path to 6G, effectively future-proofing the network against the next wave of technological change. From a risk management perspective, this reduces the “lumpiness” of capital expenditure and allows for a more predictable, operational-expense-focused budget. It’s an empowering shift for operators who have felt trapped by the rigid hardware limitations of the past, as they can now deploy new services with a few lines of code rather than a fleet of service trucks.
Market analysts have offered a measured take on whether this technology will actually grow the overall RAN market or simply optimize what is already there. With a forecast of $35 billion in cumulative AI-RAN revenues by 2030, how do you view the commercial reality of this transition?
While the $35 billion figure sounds astronomical, the nuanced reality is that AI-RAN is likely to act more as a vital enabler for automation and virtualization rather than a tool for expanding the total market size. Even with the introduction of these new software subscription models, many analysts expect that the technology will generate very little incremental revenue by the end of the forecast period because it is largely replacing or optimizing existing spend. However, the GPU-based RAN segment is a distinct growth area, projected to surpass $1 billion as operators seek more intensive accelerated computing. The real value isn’t necessarily in the “new” dollars, but in the efficiency and the survival of the incumbents; the five largest suppliers accounted for 96 percent of RAN revenue in 2025, and this AI shift is likely to strengthen their hold. It’s a game of network economics where the winner is the one who can squeeze the most out of every hertz of spectrum while keeping operational costs in check.
Given your background in risk management for vulnerable networks, what is your forecast for the role of AI-RAN in securing and stabilizing the next generation of global connectivity?
My forecast for AI-RAN is that it will become the indispensable “immune system” of our global networks, where the radio access layer becomes the first line of defense against both congestion and cyber threats. We are moving toward a period where the network isn’t just a pipe, but a planet-scale AI computer capable of self-healing and real-time optimization. By 2030, I expect that the integration of accelerated GPU computing will allow networks to detect and mitigate anomalies in microseconds, far faster than any human-led operation could dream of. This transition will solidify the dominance of software-defined architectures, making “AI-native” the standard requirement for any carrier hoping to maintain carrier-grade performance in a world of time-sensitive, 6G-enabled workloads. The era of the “dumb pipe” is officially over, replaced by an intelligent, adaptive infrastructure that learns and grows with the demands of the physical world.
