Dominic Jainy is a seasoned IT professional whose career has been defined by the intersection of telecommunications and emerging intelligence. With a deep background in machine learning and blockchain, he has spent years analyzing how decentralized technologies can transform traditional infrastructure. As the industry stands on the precipice of the AI-RAN revolution, Jainy provides a critical perspective on how global vendors like Nokia and Ericsson are navigating a landscape that demands both extreme fiscal discipline and radical innovation.
In this conversation, we explore the metamorphosis of telecommunications companies into integral components of the AI infrastructure stack and the divergent strategies of global vendors. We also discuss why the private 5G market currently serves as the most successful blueprint for large-scale innovation and address the financial and structural contradictions that keep major operators moving at a measured, cautious pace.
The concept of AI-RAN suggests a significant shift in identity for traditional carriers. How do you see the role of telcos evolving as they integrate more deeply into the AI infrastructure stack?
We are witnessing a profound transformation where telcos are no longer just the “pipes” of the digital world; they are becoming essential components of the AI infrastructure stack itself. Nokia’s recent roadmap, which points toward a 2027 commercial launch for its AI-RAN platform, highlights a vision where the radio access network becomes a compute powerhouse. By aiming for specific efficiency targets by 2028, these companies are positioning themselves to offer what I call “connectivity plus” outcomes, such as integrated analytics and local cloud compute. It is a high-stakes evolution that turns a simple 5G radio into a digital change platform capable of supporting agentic AI and automated tools. This shift requires a massive leap in how operators view their hardware, moving from static boxes to dynamic, GPU-accelerated environments that can handle real-time processing at the edge.
The private 5G sector is often described as the most successful and clear version of modern telecoms. What lessons should major public operators take from the triumphs seen in these smaller, specialized deployments?
Private 5G acts as a brilliant, complicated miniature laboratory where the industry’s best ideas—like 5G-Advanced and network APIs—are finally being put to work in practical, mission-critical ways. In these environments, we see the true value of connecting workers, sensors, and robots to a system that prioritizes uptime, safety, and innovation above all else. Unlike the broader consumer market, these specialized networks are already delivering on the promise of connectivity plus control and connectivity plus automation. They prove that when you focus on one industry or one enterprise at a time, you can build a sustainable business case that isn’t just about speed, but about critical gains in productivity. This sector shows the rest of the industry exactly what it wants to be: a digital change platform for the industrial economy.
With major vendors like Ericsson and Nokia pursuing different starting points for the AI era, how are current economic pressures and supply chain constraints shaping the competition?
There is a fascinating tension between the strategic “corners” of the industry right now, with Ericsson showing incredible discipline through strong margins and careful cost control. However, they are feeling the heat as a short-term run on AI componentry is putting pressure on their supply chain costs, while they wait for the investment cycle to catch up to the mobile network. In the other corner, Nokia is betting on a brand new vision of GPU-accelerated AI-RAN to hoover up early experiments and establish a foothold in the infrastructure stack. The challenge for everyone is that mobile revenues remain constrained by drawn-out 5G Standalone investments, making it difficult to fund the next leap. It creates a bottleneck where the hardware is ready to evolve, but the financial timing must be perfect to avoid overextending in a low-return environment.
Considering the notoriously slow nature of the telecom industry, what are the primary obstacles preventing large-scale operators from moving as fast as the private 5G sector?
Large operators carry a massive weight of responsibility that smaller enterprise networks simply do not have, which naturally breeds a sense of caution toward every 5G-era promise. Whether it is network slicing, edge computing, or the current buzz around APIs, these firms have to ensure that any investment translates into a business case they can actually afford to build. We have seen that the returns on major infrastructure investments can be sluggish, which makes them justifiably wary of jumping headfirst into the next cycle of AI-driven radio upgrades. There is a deep contradiction at the heart of the business: they have never had a clearer vision of the future, yet they must move at the pace of a mission-critical utility. This slow timing is a defensive mechanism against the high costs of infrastructure that must serve millions of people with near-perfect reliability.
What is your forecast for the AI-RAN market as we approach the end of the decade?
By the time we hit the 2027 and 2028 milestones set by the major players, I expect the RAN market to be defined less by hardware volume and more by the depth of software and compute integration. We will see the radio access network become a direct extension of the global AI cloud, where low-latency inference happens right at the tower to support vehicles and machines. While the market might not swell in terms of pure radio sales, the value will shift toward those “connectivity plus” services like ISAC and agentic AI that deliver tangible industrial gains. The industry will eventually align with the model set by the private 5G sector, succeeding one radio and one application at a time. The winners will be the operators who stop selling simple data connectivity and start selling guaranteed safety, innovation, and automation.