Autonomous networking is stalled by an operational gap where changes are implemented without a provable understanding of network behavior, unlike in other fields like pharmaceuticals or software engineering. Current network change processes rely on manual verification and tribal knowledge, leading to significant outages and security incidents that cost over $1 million per hour. While AI is being adopted to accelerate tasks, it exacerbates the problem by scaling unverified changes, highlighting the need for a mathematically accurate digital twin to model network behavior and enable safe autonomous operations. AI
IMPACT Highlights how AI adoption in networking can amplify risks if underlying operational gaps are not addressed.
RANK_REASON The article discusses industry challenges and potential solutions without announcing a new product, research, or funding.
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