AI Will Change The Internet Forever: Why Networks Must Evolve For The AI Supercycle

The internet is about to go through one of its biggest architectural shifts since the rise of video streaming.

After spending three days at Mobile World Congress in Barcelona, in my role as Futurist in Residence for Nokia, one message came through with real clarity: AI is not simply adding more traffic to the internet, it is changing the shape, timing, direction and purpose of that traffic. The networks that powered the last era of digital growth were designed for very different workloads.

For years, internet growth was defined by a relatively clear pattern. People consumed content. They streamed video, downloaded files, browsed websites and joined video calls. Traffic was growing fast, yet much of it was predictable. AI changes that. It introduces far more interaction, more uplink demand, more traffic between data centers and a rising volume of machine-to-machine communication that happens without a person initiating it.

Nokia’s latest Global Network Traffic Report, produced with Bell Labs Consulting, makes the point very clearly: “Global network traffic is changing in character, not just in volume.” That single observation may be one of the most important takeaways from MWC this year.

The Internet Is Entering The AI Supercycle

In his keynote at MWC, Nokia CEO Justin Hotard framed this shift in a way that I found especially useful. He said, “Every network that has been built has been optimized around its primary workflow, the dominant workload, starting with voice, then we had data, then video and rich media, and now we’re in the AI super cycle, and the dominant workload is AI, and the network needs to evolve ultimately.”

Each phase of the internet has had its defining traffic pattern. Voice networks were built for calls. Broadband and mobile data networks were built for websites, apps and then streaming. AI brings a new dominant workload, and it places very different demands on connectivity.

According to Nokia’s report, total global Wide Area Network, or WAN, traffic, meaning the data moving across long-distance networks that connect cities, countries, data centers, businesses and cloud systems, is projected to grow roughly three to seven times by 2034, reaching between 2,277 and 4,878 exabytes per month depending on the scenario. In the moderate scenario, AI traffic reaches 921 exabytes per month by 2034 and accounts for about 30% of total global WAN traffic. AI-related traffic is also forecast to grow faster than traditional traffic, with a 23% compound annual growth rate in the moderate scenario.

Those numbers are huge. The more important point, however, is what is inside them.

AI Changes The Direction Of Traffic

One of the core insights from Nokia’s research is that the old download-heavy internet model is starting to give way to a much more balanced and dynamic system. Historically, downlink traffic dominated because people were mainly consuming content. In the AI era, users are also sending far more information back into the network.

That includes prompts, images, video streams, sensor data, context from smart glasses, voice interactions and real-time signals from industrial systems. In some cases, people are engaging directly with AI assistants. In others, AI systems are working behind the scenes, calling other models, retrieving context, checking safety constraints and coordinating actions.

This is already starting to show up in everyday consumer use. Nokia’s report says that as people use AI tools to create content and rely more on AI assistants, they are sending more data back into the network, not just receiving it. At the same time, interactive AI services depend more on fast and stable connections, because even small delays can make them feel slow or unreliable. It is also becoming visible in enterprise environments, where machine vision, robotics, industrial telemetry and AI copilots are pushing more operational traffic over wide-area networks.

That means the internet of the near future will be less about passive consumption and more about continuous exchange.

Three Forces Are Reshaping Network Traffic

What I found especially helpful in Nokia’s analysis is that it breaks the traffic shift into three broad forces.

The first is the rise of more immersive and interactive digital experiences. As AI makes online services more responsive, network quality becomes more important. It is not only about speed, it is also about consistency, because these experiences depend on the network reacting quickly and smoothly.

The second is the movement of enterprise and industrial operations toward the edge. Digital twins, robotics, AI copilots, remote support and industrial automation all rely on flows of data between on-site infrastructure and distant compute resources. That makes traffic more distributed and more variable, especially when workloads move between edge and cloud.

The third is that AI is beginning to generate traffic autonomously. This may prove to be the most disruptive change of all. Nokia estimates that by 2034, 37% of total network AI traffic will be machine-generated. In other words, a growing share of internet traffic will come from systems talking to systems.

That is a major departure from the human-centered internet we are used to thinking about.

Generative, Agentic And Physical AI Will Stress Networks In Different Ways

One of the strongest themes on Nokia’s booth was that different categories of AI create different networking demands.

Generative AI tends to be uplink-heavy because users increasingly submit rich, multimodal inputs for inference. That means text, images, audio and video are moving into the network, not just results moving back out. Agentic AI introduces bursty patterns because autonomous systems can trigger waves of searches, model calls, database queries and action loops in short periods of time. Physical AI, which powers machines operating in the real world, makes low latency far more important because delays can affect how a robot, device or system senses and responds. Co-ordination and control of autonomous vehicles and drones requires real-time updates across a wide area on the ground and in the air.

This is where Hotard’s keynote added an important layer. He explained that AI traffic is “bursty, it’s dynamic,” and increasingly “token driven and not stream driven.” Traditional networks have been built around flows that are comparatively linear and predictable. AI workloads are less tidy. They are spiky and far more dependent on timing.

Hotard also argued that future networks will need to deliver “token certainty,” meaning the right token arrives on time, with the required latency, quality, security and trust.

The Inter-Data-Center Internet Becomes Far More Important

Another big lesson from Nokia’s report is that AI traffic does not simply travel from one place to another and stop there. A single AI request can move between local networks, central networks, edge systems, cloud platforms and several data centers before the final answer comes back. So one AI interaction can trigger several separate movements of data across the internet. Nokia estimates that by 2034, user-generated AI traffic could total 921 exabytes per month, while the related traffic moving between data centers could reach 3,260 exabytes per month.

That means the AI internet will depend heavily on data center interconnects, optical infrastructure, routing and the ability to move workloads efficiently between hyperscalers, telecom providers, enterprise environments and the edge. That is why Nokia is placing such emphasis on the shift toward what Hotard described as AI factories and new network topologies that connect them.

Why AI-Native Networks Will Matter

The big strategic implication is that adding AI tools on top of legacy networks will not be enough. Hotard made that case when he said that layering intelligence onto existing networks is “the start of the journey,” while the destination is “an architecture that’s fully AI native.” He also argued that the old siloed model of network domains has to give way to a more unified, software-defined, dynamic cross-domain approach.

That fits with Nokia’s report, which shows that future networks will need to handle more symmetric traffic, tighter latency requirements and massive growth in interconnect demand.

For telecom operators, cloud providers, enterprises and policymakers, this has immediate implications. Capacity planning has to change. Edge strategy becomes more important. Deterministic performance matters more. Security and trust need to be built into the flow of AI traffic itself. The network becomes a strategic enabler of AI adoption, not a passive utility sitting in the background.

The Next Internet Will Be Built For Intelligence

The most important lesson I took away from MWC is that AI will change the internet from the inside out.

Yes, traffic volumes will rise. Yes, AI apps will become mainstream. Yet the real shift is deeper. The internet is moving from a network built mainly to distribute information to one increasingly designed to support connected intelligence, where humans, machines, models and systems are continuously exchanging context and coordinating action.

We are still early enough to shape the architecture of the AI era. The choices being made now about connectivity, interconnect, edge computing, software-defined networking and AI-native infrastructure will determine which organizations are ready for what comes next.

The AI era will not run on yesterday’s internet. It will require networks designed for a world where intelligence is distributed, traffic is dynamic and performance has to be far more predictable than before. After what I saw and heard at MWC, I am convinced that this shift is already underway. The question is whether our networks will evolve fast enough to support the AI Supercycle.

You can read the full Nokia report here: https://www.nokia.com/asset/213660/

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