Current Temp:
Super Hot
AI innovation continues to accelerate, driving fundamental changes in how data is processed, moved, and accessed across global networks.
Google introduced AI Mode, an experimental version of search powered by its Gemini 2.0 model, a preview of what Google search may evolve into over time. AI Mode can handle complex, multi-step queries, provide follow-up options, and pull live local and product information – all in a conversational, chatbot-like experience. Unlike traditional search, AI Mode synthesizes data from multiple related sources at once, aiming to reduce the need for users to visit individual websites. Read more.
The problem? Today’s networks weren’t built for AI at this scale. Legacy infrastructure is struggling to keep up with the sheer amount of data AI requires. If the infrastructure can’t keep up, businesses face downtime, rising costs, and missed opportunities
More servers and more power aren’t enough to support the AI data center boom. To sustain AI’s rapid growth, and ensure enterprises can fully leverage it, we need to build more modern long-haul networks – faster, denser, and more resilient than ever before. These networks are the key to seamlessly transporting massive AI workloads between hyperscale data centers. As data centers expand and rack densities soar, networks must scale with them.
AI Number of the Week:
This is nearly double the adoption rate from just ten months ago, according to McKinsey’s latest Global Survey on AI.
This surge in AI adoption isn’t just experimentation, companies are already seeing real business impact, with gen AI delivering both cost savings and revenue growth. And the momentum is only building.
Read more.
This dramatic shift highlights the critical role of scalable, high-performance networks in powering the next generation of AI innovation. But it also exposes a potential bandwidth gap in the U.S., where current infrastructure risks falling short.
The solution? Doubling down on building long-haul fiber networks at scale. It’s the only way to ensure data centers have the connectivity needed to deliver the speed, efficiency, and reliability that organizations demand.
Hot Take:
Shawn Edwards, Chief Security Officer, Zayo
AI is transforming network security, both as a powerful defense mechanism and an evolving threat vector.
AI-driven attacks, from automated phishing campaigns to deepfake-powered social engineering, are evolving at machine speed, exploiting gaps in traditional network defenses.
At the same time, AI models themselves have become new attack surfaces – vulnerable to data poisoning, adversarial inputs, and manipulation that can corrupt AI-driven decision-making.
Here’s what too many organizations overlook: AI doesn’t just change what threats look like; it changes what your network needs to do to stop them. In an AI-first world, performance and security are inseparable. AI workloads demand high-speed, low-latency connectivity, but that performance can’t come at the cost of security.
Your network must scale for bandwidth and compute, while simultaneously offering:
- Encrypted traffic inspection
- AI-enhanced DDoS Protection
- Autonomous, real-time threat mitigation
Without these capabilities, you aren’t securing their AI investments – you’re exposing them.
Bottom Line: If your network security can’t match the speed and intelligence of AI-powered threats, you’ve already lost. Rushing to adopt AI without upgrading how your network detects, defends, and governs means you’re handing attackers the advantage.
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Infrastructure
AI innovation won’t slow down: How multi-model AI is powering speed to marketDigital Realty
Future Forecast:
What Experts See Coming Next (and Why It Matters)
We are in the midst of a significant inflection point in the growth of optical networks. According to lan Redpath, Research Dir., SP Networks at OMDIA, “Al is catalyzing a generational optical-network build cycle. The largest cloud service providers are constructing gigawatt Al training centers, and CSPs are responding with connectivity at Pbps scale.”
Generative AI is driving foundational demand for bandwidth, and that demand will only grow as applications evolve, requiring ever-faster connectivity between GPU resources and enterprise applications.
Here’s how bandwidth requirements scale as AI capabilities progress: Read more.