I’m at the Cisco AI Summit in Paris this week. This is an opportunity for Cisco, presenting their latest advancements in their AI portfolio. Under Jeetu’s leadership we’re seeing much more streamlined product ranges, simplified messaging (including the AI portfolio), and this isn’t just limited to AI – it also reflects shifts beyond. Some of my thoughts below.

Regarding AI specifically, the move from isolated chatbots towards agentic AI seems undeniable. If you consider that the market was already fragmented during the “cloud native” era a few years ago, it has become even more so in today’s AI age. New tools are being created almost weekly.

This presents a challenge for vendors like Cisco. Attempting to differentiate might focus on performance – but this isn’t likely the factor where they compete against giants like Nvidia. Instead, Cisco’s leadership appears to believe differentiation lies in AI security, which could be a valid proposition. However, Cisco should also consider adding more vertical integration capabilities to their offering and more on AI inferencing.

Let me explain why this might be an opportunity for Cisco. Traditionally, AI builders have relied on public clouds (like AWS or Azure) which provide easy deployment and operation, reducing initial hardware investment needs. These platforms also allow for rapid iteration during the development phase before potentially moving workloads to production. However, when an AI project succeeds and moves towards production deployment, cloud costs frequently get out of control. This forces infrastructure teams to explore “on-premises” alternatives to manage costs.

Moving AI workloads “on premises” isn’t simply about deploying a few server GPUs. It requires the full stack – data, models, inference environment – running on top of robust hardware layers. While public clouds excel here, building a comparable “on-prem” solution is still very challenging.

Focus on inferencing, however, will be crucial. It is great that Cisco is the only networking vendor who provides compatibility with Spectrum X from Nvidia because I understand that training large models is where the significant investment often happens , however companies – especially their enterprise clients – primarily run these models rather than training them and this will only be growing.

There were some interesting announcements around own Cisco models i.e. Deep Network Model. I’m planing to do little bit more testing on this model and see in what tasks will it exactly stand out against models like GPT5 etc. There will be new Time Series Foundational Model being released in November 2025. However, I like the idea that vendors will be in the future building their models which than should allow them building their own more specialised agents - I think.

Moving more into the long term vision Cisco seems determined to play a more active role in Agentic AI, potentially through its AGNTCY project. The same way they were central to defining infrastructure for the Internet in the 90s, perhaps their goal now is to shape “agentic infrastructure.” This involves standardizing the way agents interact, communicating with each other, thereby addressing complexities associated with tools like LangChain, LangGraph, and MCP.

I like how Cisco takes a wider approach and experiments more than some of their competitors with new concepts. The key is now to address underlaying challenges around their compute portfolio and focus more on AI software stack and inferencing, not just training.