  Foundational models have caused a paradigm shift in the way artificial
intelligence (AI) systems are built. They have had a major impact in natural
language processing (NLP), and several other domains, not only reducing the
amount of required labeled data or even eliminating the need for it, but also
significantly improving performance on a wide range of tasks. We argue
foundation models can have a similar profound impact on network traffic
analysis, and management. More specifically, we show that network data shares
several of the properties that are behind the success of foundational models in
linguistics. For example, network data contains rich semantic content, and
several of the networking tasks (e.g., traffic classification, generation of
protocol implementations from specification text, anomaly detection) can find
similar counterparts in NLP (e.g., sentiment analysis, translation from natural
language to code, out-of-distribution). However, network settings also present
unique characteristics and challenges that must be overcome. Our contribution
is in highlighting the opportunities and challenges at the intersection of
foundation models and networking.
