Cisco’s Vimarsh Puneet argues that enterprise AI’s future lies in smaller, specialized models trained on proprietary data rather than larger general-purpose systems. He says sustained domain-specific learning can improve accuracy, strengthen competitive advantage, and support more reliable network automation as fine-tuning becomes cheaper and accessible.
— The last several years of AI progress have followed a simple pattern: bigger models, trained on more data, produce better results. Vimarsh Puneet, Director of Product Management for Enterprise AI at a global enterprise networking company, argues that pattern is running out of room, and that the next phase of enterprise AI belongs to smaller, specialized models trained on narrow, proprietary data instead.
The Ceiling on General-Purpose Models
In a Forbes Technology Council article published March 4, 2026, Puneet described a widening gap between general-purpose AI models and models trained specifically on a narrow domain. He cited a hypothetical comparison in the piece: a company using a general frontier model reaches roughly 80% accuracy on a task and largely stays there, while a company building a specialized model on its own proprietary data starts at a similar point but continues improving, reaching 97% accuracy over several years as the model keeps learning from continued use.
Photo Courtesy of Vimarsh Puneet
Puneet used the 2000 film Memento as an analogy for the limits of feeding a general model large amounts of context at the moment of use, a technique known as context engineering. The film’s protagonist has no long-term memory and relies entirely on notes and physical reminders to function, never actually learning anything cumulatively. “Maybe I’m oversimplifying, but the moat isn’t today’s performance, it’s the learning flywheel that makes the gap wider every month,” Puneet wrote, arguing that a model fed context at each use is functionally similar: it can access information without ever developing the kind of internalized expertise that comes from sustained training on domain-specific data.
Why Proprietary Data Is the New Moat
The argument reframes what counts as a competitive advantage in enterprise AI. When most companies can access the same general-purpose models from the same handful of providers, Puneet’s position is that the resulting output looks similar across competitors, regardless of which company is using it. The differentiator becomes what a company trains on top of that shared starting point: years of accumulated customer interactions, domain-specific operational data, and edge cases that no publicly available dataset would contain.
Puneet’s piece also cited industry figures making similar arguments around the same period, including Hugging Face CEO Clem Delangue’s public comments describing a shift toward what he called a “multiplicity of models that are more customized, specialized” rather than continued reliance on ever-larger general-purpose systems. Puneet’s own framing extended that idea specifically to enterprise infrastructure, arguing that the economics of fine-tuning have changed enough that specialization is now within reach for companies that are not themselves AI research labs.
What This Means for Network AI
The specialization argument connects directly to Puneet’s product work. The network operations AI he leads is built around models trained on an organization’s specific network topology, traffic history, and operational patterns rather than a general-purpose model applied broadly across any network. A specialized model trained on years of a particular network’s behavior can, in Puneet’s framing, develop a kind of operational familiarity that a general model applied fresh to the same network cannot replicate no matter how much context it is given at the moment of use.
That distinction matters for a practical reason beyond accuracy. A general model asked to reason about an unfamiliar network has to infer normal behavior from whatever data it is given at that moment. A model trained specifically on that network’s history already has a baseline for what normal looks like, which changes what kind of automated action can reasonably be trusted to run without a human checking it first.
Puneet’s argument is ultimately a bet about where enterprise AI value accumulates over time: not in access to the largest available model, but in the years of proprietary operational data that a specialized system can learn from and that a general-purpose model, however large, never sees.
The timing of the argument matters as much as its content. Puneet’s piece pointed to falling fine-tuning costs and improved tooling as evidence that specialization, once available only to companies with dedicated AI research teams, has become accessible to any organization with domain expertise and a meaningful amount of its own data. That shift changes the calculation for a company deciding whether to build a specialized model or continue relying on a general-purpose one licensed from an outside provider. Under Puneet’s framing, the decision increasingly comes down to how much proprietary data a company already has sitting unused, and how long it is willing to wait before a competitor with better data discipline pulls ahead on the same task.
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