Vladislav Zaimov is a seasoned telecommunications expert who has spent years navigating the complexities of enterprise networks and the high-stakes world of risk management for vulnerable infrastructures. With the telecommunications industry hitting a pivotal moment where artificial intelligence is no longer just a buzzword but a core operational workload, Zaimov offers a unique perspective on how large-scale networks are being redesigned to handle the sheer weight of modern data. This discussion explores the shifting landscape of telco-specific AI models, the critical role of smart orchestration in managing massive token volumes, and the physical reality of fiber infrastructure as it adapts to a world where AI is as essential as electricity.
We are seeing industry leaders process 45 billion inference tokens daily while training models on over a trillion tokens. How does this shift AI from a simple software experiment to a fundamental network workload?
When you look at the sheer scale of processing 45 billion inference tokens every single day, you realize we have moved far beyond the “sand-pit” phase of software development. This isn’t just about a clever chatbot anymore; it is a brand-new type of network workload that requires the same level of reliability as a voice call or a data stream. To support this, organizations are training specialized models on more than one trillion tokens, including focused efforts like the 400 billion tokens used to post-train the OTel 2.0 open-source model. This level of volume transforms the network into a living system where the model itself is a bellwether for how industrial AI will be adopted across every sector. For an engineer, seeing these numbers feels like watching a massive power grid come online for the first time, requiring a level of deterministic performance that only a telco-grade infrastructure can provide.
The concept of an “AI Gateway” suggests that orchestration is becoming more important than the size of the language model itself. How does routing queries to different models based on cost and efficiency change the economic reality for telcos?
The real breakthrough isn’t necessarily building the biggest model on the planet; it’s about the “penny-drop” moment of running AI economically at scale. By using an AI gateway mechanism, a network can intelligently route jobs to the cheapest model capable of handling the task instead of default-firing every query at the most expensive, resource-heavy model available. There is something incredibly efficient about a system that can even switch models halfway through a single conversation if the context changes and a simpler model can finish the job. This approach is reportedly cutting AI costs by up to 90 percent, which translates into saving millions of dollars in operational expenditures. It mirrors how we have always engineered telecoms, focusing on delivering the most efficient performance possible rather than just chasing raw, unbridled power.
Recent bandwidth reports indicate that metro dark-fiber demand has jumped by as much as 20 times in busy AI markets. What does this massive surge tell us about the physical migration of AI inference toward the user?
The physical infrastructure is finally catching up to the digital demand, and the numbers are staggering, with long-haul fiber demand doubling in just a year. Seeing metro dark-fiber demand jump by 20 times in AI-heavy markets tells us that inference is shifting much closer to the end-user to reduce latency and improve responsiveness. It feels like the network is no longer just a pipe that carries applications; it has actually become a part of the AI system itself, functioning as the central nervous system for these workloads. This shift requires us to build networks entirely around AI requirements, ensuring that the high-capacity “wires in the walls” are directly connected to the massive “power stations” of data centers. When you walk through a data center today, you can almost feel the heat and hear the hum of this transition as the infrastructure strains and then expands to meet this 20-fold increase in demand.
What is your forecast for the role of the network as AI becomes as ubiquitous as electricity in our daily lives?
In the coming years, the distinction between the network and the AI system will vanish entirely, and we will view connectivity as the fundamental delivery mechanism for intelligence. Just as we don’t think about the power station when we flip a light switch, users won’t think about the 45 billion tokens being processed in the background; they will simply expect instantaneous, intelligent responses. We will see a more aggressive move toward specialized, smaller models that are orchestrated in real-time, allowing networks to maintain that 90 percent cost efficiency while handling even more complex industrial tasks. The network will cease to be a passive observer of data and will instead become a proactive participant that optimizes itself for AI workloads before the user even initiates a request. Ultimately, the successful telcos will be the ones who treat AI not as an added feature, but as the very fabric of their infrastructure.
