How Will Fiber Optic Networks Keep up With AI?

As AI capabilities continue advancing, the need for robust fiber optic networks is becoming increasingly pressing.

Written by Michael Underdown
Published on Apr. 22, 2024
How Will Fiber Optic Networks Keep up With AI?
Image: Shutterstock / Built In
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The technological landscape is evolving rapidly, with artificial intelligence and machine learning workloads driving unprecedented demand for connectivity infrastructure.

The AI era promises to transform industries, restructuring how businesses operate and interact with data. Technological progress has highlighted the importance of fiber optic networks, known for their exceptional bandwidth capabilities and low latency.

Fiber optic networks have become the heart of modern communication systems, supporting the massive data requirements of AI applications.

Inference AI Vs. Learning AI

Inference AI

  • Makes decisions in real-time using pre-trained models.
  • Requires low computational power.
  • Needs fast data transmission for rapid decision-making.
  • Fiber optic networks ensure swift data transfer speeds.
  • Benefits from edge computing facilities by metro fiber networks.

Learning AI

  • Trains models over time using data.
  • Requires high computational power.
  • Needs high bandwidth for transferring large data sets.
  • Fiber optic networks provide high bandwidth for data transfer.
  • Location agnostic.

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Benefits of Converging AI and Fiber Networks

The relationship between AI and fiber optic networks is mutually beneficial, with each driving advancements in the other. As AI applications become more sophisticated and data intensive, the need for robust fiber optic infrastructure continues to grow.

Conversely, the speed and efficiency of fiber optic networks empowers AI systems to process and analyze data at unprecedented rates. This unlocks new possibilities for innovation across various sectors.


How AI Puts Pressure on Fiber Networks

The rapid adoption of AI across key industries such as healthcare, smart cities and cloud computing is placing immense pressure on existing fiber optic networks. As AI applications continue to rapidly increase, fiber providers must proactively expand and upgrade their infrastructure to meet the surging bandwidth demands.

For this reason, demand on metro networks, or metropolitan area networks, will surge due to their strategic positioning to support low-latency AI inference use cases by bringing connectivity closer to the edge. This ensures seamless and rapid data transport for real-time decision-making.

Across industries, numerous AI use cases such as autonomous vehicles, remote healthcare and predictive maintenance highlight the critical role of high-speed fiber connectivity in AI’s future growth. These real-time AI applications rely heavily on low-latency data transmission facilitated by robust fiber networks.

The convergence of AI and fiber optic networks represents the integration of cutting-edge technologies that are reshaping the digital landscape. Fiber optic leaders must keep pushing innovation, using these advancements to drive efficiency, reliability and scalability in network infrastructure.


Use Case: Smart City Surveillance System

An example of using inference AI is a smart city surveillance system deployed by a local government to improve public safety and security.

In this scenario, the system uses a network of high-definition cameras strategically placed across the city to monitor traffic flow, detect suspicious activities and respond to emergencies in real time.

To maximize the surveillance system’s effectiveness, the local government integrates inference AI algorithms directly into the network infrastructure. These AI algorithms analyze the video feeds from the cameras in real time, automatically identifying and flagging potential security threats, traffic anomalies and other actionable events.

The smart city surveillance system heavily relies on real-time analysis and decision-making. The inference AI algorithms generate a significant volume of data that needs to be processed and transmitted without delay.

This puts immense pressure on the metro network provider to design and manage the local network infrastructure.

How Would the Network Handle the Extra Pressure?

For the inference AI algorithms to work effectively, they require high bandwidth and low latency.

The constant stream of video data from the surveillance cameras must be transmitted quickly and efficiently to the central AI processing units, or edge data centers, for analysis. Any delays or holdups in the network could compromise the system’s ability to detect and promptly respond to security threats.

Metro network operators face several challenges in optimizing their infrastructure to support the demands of inference AI. They must invest in upgrading their network capacity to handle the increased data traffic generated by the smart city surveillance system.

Additionally, they need to ensure that the network latency remains minimal to enable real-time analysis and decision-making.

Innovative Networking Solutions and Collaboration

Inference AI applications such as the smart city surveillance systems present both opportunities and challenges for metro network operators. Understanding the specific bandwidth and latency requirements of AI workloads is crucial.

Investing in innovative networking solutions enables operators to effectively support the growing demand for real-time analytics and decision-making in smart city environments.

Collaboration between network operators, AI technology providers and local governments are essential to ensuring the seamless integration of AI into urban infrastructure while maintaining the reliability and security of the metro network.

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What’s to Come for Fiber Optic Networks?

Looking forward, the predicted surge in bandwidth needs for AI emphasizes the urgent need for fiber optic providers to plan for massive growth.

Companies with existing fiber infrastructure have different challenges than those that are building new networks. Identifying challenges that can hinder accessibility are crucial.

As a result, companies may need to advocate for policies that incentivize AI/fiber co-development through public-private partnerships. They could also explore emerging fiber technologies such as hollow-core and integrated photonics to solve large-consumption bandwidth demand challenges.

Understanding how customers are using AI is important to designing a solution that meets the needs of the specific applications required. Network operators that understand the nuanced needs of AI-demands on fiber optic networks have stood the test of time. For instance, since inference AI requires proximity-based access, it’ll require high-capacity, low-latency metro networks.

Staying ahead by understanding technology shifts, innovative solutions, investment strategies and service expectations will make a difference every step of the way.

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